Data Management

Data Governance Best Practices and Implementation Strategies

Actian Corporation

September 9, 2024

Summary

This blog outlines essential practices for establishing a robust data governance framework, emphasizing the importance of clear objectives, defined roles, and continuous monitoring to ensure data quality, security, and compliance across the organization.

Establish a Clear Governance Framework: Define objectives, roles, and responsibilities to ensure accountability and alignment with business goals.
Implement Robust Data Quality Management: Regularly assess and cleanse data to maintain accuracy and reliability, supporting informed decision-making.
Ensure Continuous Monitoring and Adaptability: Regularly review and update governance policies to adapt to evolving business needs and regulatory requirements.

No matter what industry you work in, you know how important it is to collect data. Retail workers rely on customer data to inform their buying decisions, healthcare workers need comprehensive and accessible data on their patients to provide treatments, and financial professionals analyze large sets of market data to make predictions for their clients. But collecting data for your organization isn’t enough — it needs to be reliable, secure, accessible, and easy for the members of your company to use. That’s where data governance comes in.

Data governance is a term for an organization’s practices and processes that help it optimize its data usage. Why is data governance important? It includes plans to protect data systems against cybersecurity threats, streamline data storage solutions, set up data democratization rules, and implement products and data platforms that support greater data efficiency throughout an organization. The specific data governance policies professionals use greatly depend on their industry, the type of data they collect, how much data they use, and other factors. However, some data governance best practices can help professionals — whether they have data roles or not — create policies that optimize and simplify their data usage.

Data Governance vs. Data Compliance

Depending on your industry, you may hear the term data compliance commonly used alongside data governance. Data compliance refers to the policies and procedures that help you meet external legal requirements surrounding your data, and data governance has more to do with optimizing and securing your data for internal use. Data compliance doesn’t include industry standards or the requirements of partner companies, just what’s legally required. Data compliance laws may influence what data governance policies you implement, but you’ll mostly work with legal teams to ensure you meet these requirements.

For example, if your organization does business in countries that belong to the European Economic Area, you must abide by the General Data Protection Regulation. This law dictates how companies collect, process, and dispose of personal data. It has a huge impact on sharing data outside of your organization, data retention timelines, and data democratization and destruction policies.

Going Beyond the Data Governance Framework

A solid data governance program requires a well-structured data governance framework that addresses data quality, collection, management, privacy and security. Organizations manage these critical components by creating company-wide policies and forming departments of data professionals who work together to support the larger data governance framework. Some of the departments that contribute to overall data stewardship include:

Data
Analytics
Engineering
IT
Legal
Compliance
Executive Management

Data stewards consistently work with these departments to create and improve their policies and strategies. A governance program with high data trust never stays stagnant, so they learn about the ever-changing needs and habits of these teams to make sure data remains the fuel of a well-oiled business.

While there may be some policies that are tailored to specific departments that use data, effective data governance requires cooperation from every team in the company. If a sales team creates a lead database outside of data governance policies, which isn’t accessible to the rest of the company, that data isn’t being used effectively. If there’s a team storing metadata in unprotected spreadsheets instead of utilizing an already-established data catalog used by the rest of the organization, it weakens the governance framework.

Data Governance Best Practices

Effective data governance is mandatory. Once you assess the needs of the department stakeholders and create a framework based on them, it’s time to create your data governance program. Here are some widely-held best practices in data governance to help you begin a new program or refine one that’s fallen behind the times.

Establish Clear Roles

For any data compliance program to succeed, data stewards must make sure that the major stakeholders know their individual and collective responsibilities. This includes who’s ultimately in charge of the data, who’s responsible for maintaining data quality, who takes charge of the data management strategy, and who’s responsible for protecting it from cyber threats. This organizational chart can get a little complex at larger organizations, but ensuring there are no gaps in responsibility is one of the most critical best practices in data governance.

Develop and Enforce Data Quality Policies

Collecting as much data as possible and sorting it out after isn’t always a good strategy for data governance. Effectively utilizing data in your industry only works if the data is accurate, reliable, and relevant. If data isn’t collected often enough or doesn’t include information that your organization relies on, then it’s not meeting its true potential.

Establishing a standard for data quality begins with learning the needs of stakeholders across your organization; collecting data that no one needs is a waste of valuable resources. Then, you must create your data quality dimensions, or what defines the data you use as high-quality. The most common data quality dimensions are:

Relevance
Completeness
Accuracy
Validity
Consistency
Uniqueness
Timeliness

Ensure Data Compliance & Security

High-quality data is a valuable commodity, and there’s no end to the bad actors and black hats developing new ways to steal it. IT and cybersecurity professionals are invaluable and should impact many of the data security best practices in your data governance plan. For example, they can make the most informed decision about what access control model to use for your data systems, which will affect how permissions to data are given. If they feel that data masking is appropriate for your data systems, they can walk you through the benefits of blurring vs. tokenization.

Plan Data Audits & Policy Checks

As we mentioned, a quality data governance program is constantly evolving and adapting to meet the changing needs of an organization — even when that feedback isn’t given directly to you. Performing regular data audits can provide insights into how well your data governance program bolsters data trust, whether there are any gaps in your procedures, who isn’t getting with the program, and more. If you notice that your strategy isn’t meeting the needs of your data governance framework, don’t worry — data governance policies should be streamlined and updated every so often, and it just means you’ve identified solid ways to improve data trust.

Strategy for Implementing Data Governance

Once you’ve developed your framework, spoken to stakeholders to assess their needs, developed strategic policies and processes based on data governance best practices, and received approval from the higher-ups, it’s time to put your plan into action. Here’s a step-by-step guide to help you get your data governance program off the ground.

1. Document Your Policies and Processes

Before you can expect members of your organization to follow your plan, they need to be made aware. Creating detailed documents that define your plan makes it easier to notify coworkers of the upcoming changes to their regular routines and creates a source of truth that everyone can refer to. Having these responsibilities outlined in a document ensures there’s no confusion and can keep you from having to frequently re-explain the finer points of your plan to critical stakeholders.

2. Discuss Roles & Responsibilities

You’ve likely talked to key members of your data governance plan about their role and responsibilities to make sure they’re able to perform their duties. However, explaining these things in-depth ensures that there’s no confusion or gaps in the plan. Encourage these members to ask questions so that they fully understand what’s required of them. It’s possible that they’ve agreed to what you’ve asked without fully understanding the processes or considering how their data governance role would conflict with other responsibilities.

3. Set Up Your Data Governance Tools

Your bold new data governance plan may require new tools — or reconfiguring existing solutions — to succeed. Suppose the level of data analysis your organization requires can only be achieved with a NoSQL database, or your plan hinges on integrating multiple data sources. Once you’ve received buy-in from management, you’ll want to implement and configure these tools to your specific needs before allowing wider access to them.

Performing this step early can help ensure that these solutions are working the way you’ve intended and that your coworkers aren’t using tools that are only partially working. Using tools yourself also provides an opportunity to streamline and automate any processes that you weren’t very familiar with before.

4. Train Your Employees

Maintaining a data governance plan doesn’t just require buy-in from managers and executives — it takes effort from every member of the organization. Training employees about their role in the company’s data governance goes beyond how to use things like a new data archiving solution that you’ve implemented. Everyone needs to be aware of their role and how they fit into the larger framework of data governance to ensure that there are no gaps in your strategy.

5. Promote a Data-Driven Culture

Regularly reminding members of your organization of how crucial data is — as well as following the data governance plan — helps ensure that they don’t lapse in their responsibilities and your program runs smoothly. For example, it’s said that the biggest cybersecurity threat these days is a company’s least-informed employee. Sending company-wide updates each time a new threat or scam becomes known to the larger cybersecurity community helps keep data governance top-of-mind and ensures that the components of your plan function properly.

While data governance plans should be fairly rigid for other members of your organization, you should think of yours as fluid and flexible to meet changing needs. Company growth and evolving organizational needs are good things, and one can’t over-appreciate the link between sustainable growth and data governance growing and adapting alongside it. You can use these best practices in data governance to adapt or create new plans that make your organization more efficient, productive, and secure, no matter what changes come its way.

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Actian data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data division of HCLSoftware, at actian.com.

Data Platform

TCP-H Report Showcases Actian’s Price-Performance Dominance

Phil Ostroff

September 9, 2024

Actian Shines in TPC-H Benchmark Report, Outperforming Major Competitors

In August of this year, Actian conducted a TPC-H benchmark test utilizing the services of McKnight Consulting Group. While some companies perform and publish their own benchmarks, Actian prefers to utilize the services of a third party for true, reliable and unbiased testing. Based in Plano, Texas, the McKnight Consulting Group has helped over 100 companies with analytics, big data, master data management strategies and implementations, including benchmarking.

Actian conducted a similar TPC-H benchmark test last year, validating that it indeed was faster than some of its key competitors such as Google BigQuery and Snowflake, with a performance of 11 times and three times faster than each vendor, respectively. Since then, the Actian engineering team has continued to enhance the performance capabilities of the Actian Data Platform with the understanding that it needs to meet the requirements of its existing and prospective customer base.

This is especially important given the growth in business use cases and the sources of data used in day-to-day operations. Actian is always striving to keep ahead of the curve for its customers, and its ability to provide both rapid data processing capabilities and, in turn, unparalleled price-performance, have been key factors in its product roadmap.

In this recent TPC-H benchmark test, Actian decisively outperformed its competitors Snowflake, Databricks, and Google BigQuery.

Key Benchmark Findings

Raw Performance: Actian Data Platform‘s execution speed was significantly faster than all three competitors tested in the benchmark. It achieved nearly eight times the performance of Databricks, over six times that of Snowflake, and an impressive 12 times the performance of BigQuery.
Concurrency: Even with five concurrent users, Actian Data Platform maintained its performance advantage, outperforming Databricks by three times, Snowflake by over seven times, and BigQuery by 9.6 times.
Price-Performance: Actian Data Platform’s combination of speed and affordability was unmatched. It offered a price-performance ratio that was over eight times better than both Snowflake and BigQuery.

This is a significant improvement over last year’s fantastic results and is a testament to Actian’s commitment to database performance and price performance. Actian, with over 50 years of experience in data and database models, continues to show its prowess in the market.

What Does This Mean for Actian’s Current and Future Customers?

For businesses seeking a high-performance, cost-effective data warehouse or analytics platform, the benchmark results are a compelling reason to consider the Actian Data Platform. Here’s why:

Faster Insights: Actian’s superior performance means that businesses can get answers to their most critical questions faster. Actian has always aimed to provide REAL real-time analytics, and these results prove that we can get customers there. This can lead to improved decision-making, increased operational efficiency, and better customer experiences.
Lower Costs: Actian Data Platform’s favorable price-performance ratio translates into significant cost savings for businesses. By choosing Actian, organizations can avoid the high and sometimes unpredictable costs associated with other data platforms while still achieving exceptional results. This leads to long-term total cost of ownership benefits that other vendors cannot provide.
Scalability: Actian Data Platform’s ability to handle concurrent users and large datasets demonstrates its scalability. This is essential for businesses that need to support growing data volumes and user demands – two business needs that every organization is facing today.

Price Performance is Top of Mind

Today, CFOs and technical users alike are trying to find ways to get the best price performance possible from their database management systems (DBMS). Not only are CFOs interested in up-front acquisition and implementation costs, but also all costs downstream that are associated with utilization and maintenance of whichever system they choose.

Technical users of DBMS offerings are also looking for alternative ways to utilize their systems to save costs. In the back alleys of the internet (places like Reddit and other forums) users of various DBMS platforms are talking with others about how to effectively “game” their DBMS platforms to get the best price performance possible, sometimes leading to the development of shadow database solutions to try to save costs.

With the latest TPC-H benchmark results showing that the Actian Data Platform performs over eight times better than both Snowflake and BigQuery, companies looking for outstanding price performance in their future and, indeed, current DBMS systems need to consider Actian.

Take the Next Step

Actian Data Platform’s dominance in the TPC-H benchmark is a clear indication of its exceptional capabilities. By delivering superior performance, affordability, and scalability, Actian offers a compelling solution for businesses seeking a powerful and cost-effective data platform. If organizations are looking to unlock the full potential of their data with confidence, Actian is worth a closer look.

To download the complete TPC-H report from McKnight, click here.

About Phil Ostroff

Phil Ostroff is Director of Competitive Intelligence at Actian, leveraging 30+ years of experience across automotive, healthcare, IT security, and more. Phil identifies market gaps to ensure Actian's data solutions meet real-world business demands, even in niche scenarios. He has led cross-industry initiatives that streamlined data strategies for diverse enterprises. Phil's Actian blog contributions offer insights into competitive trends, customer pain points, and product roadmaps. Check out his articles to stay informed on market dynamics.

Data Quality

How Integrated, Quality Data Can Make Your Business Unstoppable

Derek Comingore

September 3, 2024

integrated quality data can make your business unstoppable

Successful organizations use data in different ways for different purposes, but they have one thing in common: data is the cornerstone of their business. They use it to uncover hidden opportunities, streamline operations, and predict trends with remarkable accuracy. In other words, these companies realize the transformative potential of their data.

As noted in a recent article by KPMG, a data-driven culture differentiates companies. “For one, it enables organizations to make informed decisions, improve productivity, enhance customer experiences, and confidently respond to challenges with a factual basis,” according to the article.

That’s because the more people throughout your organization with access to timely, accurate, and trusted data, the more it improves everything from decision-making to innovation to hyper-personalized marketing. Successful organizations ensure their data is integrated, governed, and meets their high-quality standards for analytical use cases, including GenAI.

Data is the Catalyst for Incremental Success

Data is regularly likened to something of high value, from gold that can be mined for insights to the new oil—an invaluable resource that, when refined and properly utilized, drives unprecedented growth and innovation. However, unlike oil, data’s value doesn’t diminish with usage or time. Instead, it can be used repeatedly for continuous insights and ongoing improvements.

When integrated effectively with the proper preparation and quality, data becomes an unstoppable force within your organization. It enables you to make strategic decisions with confidence, giving you a competitive edge in the market.

Organizations that invest in modern data analytics and data management capabilities position themselves to identify trends, predict market shifts, and better understand every aspect of their business. Moreover, the ability to leverage data in real-time enables you to be agile, responding swiftly to emerging opportunities, and identify business, customer, and partner needs.

In addition, making data readily accessible to everyone who benefits from it amplifies the potential. Empowering employees at all skill levels with barrier-free access to relevant data and easy-to-use tools actively promotes a data-driven culture.

Solve the Challenge: Overcome Fragmented and Poor-Quality Data

Despite the clear benefits of trusted, well-managed data, many organizations continue to struggle to get the data quality needed for their use cases. Data silos, resulting from lack of data integration across systems, create barriers to delivering meaningful insights.

Likewise, poor data governance erodes trust in data and can result in decision-making based on incomplete or inaccurate information. To solve the poor data quality challenge, you must first prioritize robust data integration practices that break down silos and unify data from disparate sources. Leveraging a modern data platform that facilitates seamless integration and data flows across systems is crucial.

A unified platform helps ensure data consistency by connecting data, transforming it into a reliable asset, then making it available across the entire organization. The data can then be leveraged for timely reports, informed decision making, automated processes, and other business uses.

Implementing a strong data governance framework that enforces data quality standards will give you confidence that your data is reliable, accurate, and complete. The right framework continuously monitors your data to identify and address issues proactively. Investing in both data integration and governance removes the limitations caused by fragmented and poor-quality data, ensuring you have trusted insights to propel your business forward.

5 Surprising Wins From Modern Data Integration and Data Quality

The true value of data becomes evident when it leads to tangible business outcomes. When you have data integrated from all relevant sources and have the quality you need, every aspect of your business becomes unstoppable.

Here are five surprising wins you can gain from your data:

Hyper-Personalized Customer Experiences

Integrating customer data from multiple touchpoints gives you the elusive 360-degree view of your customers. This comprehensive understanding of each individual’s preferences, buying habits, spending levels, and more enables you to hyper personalize marketing. The result? Improved customer service, tailored product recommendations, increased sales, and loyal customers.

Connecting customer data on a single platform often reveals unexpected insights that can drive additional value. For example, analysis might reveal emerging trends in customer behaviors that lead to new product innovations or identify previously untapped customer segments with high growth potential. These surprise benefits can provide a competitive edge, allowing you to anticipate customer needs, optimize your inventory, and continually refine targeted marketing strategies to be more effective.

Ensure Ongoing Operational Efficiency

Data integration and quality management can make operations increasingly efficient by providing real-time insights into supply chain performance, inventory levels, and production processes. For instance, a manufacturer can use its data to predict potential supply chain delays or equipment breakdowns with enough time to take action, making operations more efficient and mitigating interruptions.

Plus, performing comprehensive analytics on operational data can uncover opportunities to save costs and improve efficiency. For instance, you might discover patterns that demonstrate the most optimal times for maintenance, reducing downtime even further. Likewise, you could find new ways to streamline procurement, minimize waste, or better align production schedules and forecasting with actual demand, leading to leaner operations and more agile responses to changing market conditions.

Mitigate Current and Emerging Risk With Precision

All businesses face some degree of risk, which must be minimized to ensure compliance, avoid penalties, and protect your business reputation. Quality data is essential to effectively identify and mitigate risk. In the financial industry, for example, integrated data can expose fraudulent activities or non-compliance with regulatory requirements.

By leveraging predictive analytics, you can anticipate potential risks and implement preventive measures, safeguarding your assets and reputation. This includes detecting subtle patterns or anomalies that could indicate emerging threats, allowing you to address them before they escalate. The surprise benefit? A more comprehensive, forward-looking risk management strategy that protects your business while positioning you to thrive in an increasingly complex business and regulatory landscape.

Align Innovation and Product Development With Demand

Data-driven insights can accelerate innovation by highlighting unmet customer needs and understanding emerging market trends. For example, an eCommerce company can analyze user feedback and usage patterns to develop new website features or entirely new products to meet changing demand. This iterative, data-driven approach to product development can significantly enhance competitiveness.

Aligning product development with demand is an opportunity to accelerate growth and sales. One way to do this is to closely monitor customer feedback and shifts in buying patterns to identify new or niche markets. You can also use data to create tailored products or services that resonate with target audiences. One surprise benefit is a more agile and responsive product development process that predicts and meets customer demand.

Get Trusted Outcomes From GenAI

Generative AI (GenAI) offers cutting-edge use cases, amplifying your company’s capabilities and delivering ultra-fast outcomes. With the right approach, technology, and data, you can achieve innovative breakthroughs in everything from engineering to marketing to research and development, and more.

Getting trusted results from GenAI requires quality data. It also requires a modern data strategy that realizes the importance of using data that meets your quality standard in order to fuel the GenAI engine, enabling it to produce reliable, actionable insights. When your data strategy aligns with your GenAI initiatives, the potential for growth and innovation is endless.

Have Confidence That Data is Working for You

In our era where data is a critical asset, excelling in data management and analytics can deliver remarkable outcomes—if you have the right platform. Actian Data Platform is our modern and easy-to-use data management solution for data-driven organizations. It provides a powerful solution for connecting, managing, and analyzing data, making it easier than you probably thought possible to get trusted insights quickly.

Investing in robust data management practices and utilizing a modern platform with proven price performance is not just a strategic move. It’s a necessity for staying competitive in today’s fast-paced, data-driven world. With the right tools and a commitment to data quality, your company can become unstoppable. Get a custom demo of the Actian Data Platform to experience how easy data can be.

About Derek Comingore

Derek Comingore has over two decades of experience in database and advanced analytics, including leading startups and Fortune 500 initiatives. He successfully founded and exited a systems integrator business focused on Massively Parallel Processing (MPP) technology, helping early adopters harness large-scale data. Derek holds an MBA in Data Science and regularly speaks at analytics conferences. On the Actian blog, Derek covers cutting-edge topics like distributed analytics and data lakes. Read his posts to gain insights on building scalable data pipelines.

Data Platform

Using a Data Platform to Power Your Data Strategy

Traci Curran

September 3, 2024

using a data platform to power your data strategy

In today’s fast-paced digital landscape, organizations are increasingly recognizing the critical role that data plays in driving business success. The ability to harness data effectively can lead to significant competitive advantages, making it essential for businesses to adopt robust data management strategies.

Understanding the Importance Data Strategy for Data Management

Data management involves collecting, storing, organizing, and analyzing data to inform business decisions. As the volume and complexity of data continue to grow, traditional data management methods are becoming inadequate. Organizations often find themselves dealing with data silos, where information is trapped in isolated systems, making it difficult to access and analyze. According to the McKinsey Global Institute, data-driven organizations are 23 times more likely to acquire customers, six times more likely to retain them, and 19 times more likely to be profitable than their less data-savvy counterparts. This statistic underscores the necessity for businesses to implement effective data management practices.

The Evolution of Data Platforms

Historically, data management relied heavily on on-premises solutions, often requiring significant infrastructure investment and specialized personnel. However, the advent of cloud computing has transformed the data landscape. Modern data platforms offer a unified approach that integrates various data management solutions, enabling organizations to manage their operational and analytical needs efficiently. A data platform is a comprehensive solution combining data ingestion, transformation, and analytics. It allows users across the organization to access and visualize data easily, fostering a data-driven culture.

Key Features of a Modern Data Platform

When selecting a data platform, organizations should consider several critical features:

Unified Architecture

A data platform should provide a centralized data warehouse that integrates various data sources, facilitating easier access and analysis.

Data Integration Capabilities

The ability to connect and transform data from disparate sources is essential for creating a single source of truth.

Real-Time Analytics

Modern platforms support streaming data, enabling organizations to analyze information as it arrives, which is crucial for timely decision-making.

Data Quality Management

Features that ensure data accuracy and consistency are vital to maintain trust in the insights derived from the data.

User-Friendly Analytics Tools

Built-in visualization and reporting tools allow users to generate insights without extensive technical expertise.

Overcoming Modern Data Challenges

Despite the advantages of modern data platforms, organizations still face challenges such as:

Data Overload: The exponential growth of data can overwhelm traditional systems, making it difficult to extract meaningful insights.
Cost Management: As organizations move to the cloud, managing operating costs becomes a top concern.
Skill Shortages: The demand for data professionals often exceeds supply, hindering organizations’ ability to leverage their data effectively.

To address these challenges, businesses must adopt innovative technologies that facilitate rapid insights and scalability while ensuring data quality. If you’re looking to advance your use of data to improve your competitive advantage and operational efficiency, we invite you to read our new Gorilla Guide® To… Using a Data Platform to Power Your Data Strategy for a deep dive into the benefits of a unified data platform.

About Traci Curran

Traci Curran is Director of Product Marketing at Actian, focusing on the Actian Data Platform. With 20+ years in tech marketing, Traci has led launches at startups and established enterprises like CloudBolt Software. She specializes in communicating how digital transformation and cloud technologies drive competitive advantage. Traci's articles on the Actian blog demonstrate how to leverage the Data Platform for agile innovation. Explore her posts to accelerate your data initiatives.

Databases

GenAI at the Edge: The Power of TinyML and Embedded Databases

Kunal Shah

August 28, 2024

brain and computer to show AI and tinyml and embedded databases

The convergence of artificial intelligence (AI) and edge computing is ushering in a new era of intelligent applications. At the heart of this transformation lies GenAI (Generative AI), which is rapidly evolving to meet the demands of real-time decision-making and data privacy. TinyML, a subset of machine learning that focuses on running models on microcontrollers and embedded databases, which store data locally on devices, are key enablers of GenAI at the edge.

This blog delves into the potential of combining TinyML and embedded databases to create intelligent edge applications. We will explore the challenges and opportunities, as well as the potential impact on various industries.

Understanding GenAI, TinyML, and Embedded Databases

GenAI is a branch of AI that involves creating new content, such as text, images, or code. Unlike traditional AI models that analyze data, GenAI models generate new data based on the patterns they have learned.

TinyML is the process of optimizing machine learning models to run on resource-constrained devices like microcontrollers. These models are typically small, efficient, and capable of performing tasks like image classification, speech recognition, and sensor data analysis.

Embedded databases are databases designed to run on resource-constrained devices, such as microcontrollers and embedded systems. They are optimized for low power consumption, fast access times, and small memory footprints.

The Power of GenAI at the Edge

The integration of GenAI with TinyML and embedded databases presents a compelling value proposition:

Real-Time Processing: By running large language models (LLMs) at the edge, data can be processed locally, reducing latency and enabling real-time decision-making.
Enhanced Privacy: Sensitive data can be processed and analyzed on-device, minimizing the risk of data breaches and ensuring compliance with privacy regulations.
Reduced Bandwidth Consumption: Offloading data processing to the edge can significantly reduce network traffic, leading to cost savings and improved network performance.

Technical Considerations

To successfully implement GenAI at the edge, several technical challenges must be addressed:

Model Optimization: LLMs are often computationally intensive and require significant resources. Techniques such as quantization, pruning, and knowledge distillation can be used to optimize models for deployment on resource-constrained devices.
Embedded Database Selection: The choice of embedded database is crucial for efficient data storage and retrieval. Factors to consider include database footprint, performance, and capabilities such as multi-model support.
Power Management: Optimize power consumption to prolong battery life and ensure reliable operation in battery-powered devices.
Security: Implement robust security measures to protect sensitive data and prevent unauthorized access to the machine learning models and embedded database

A Case Study: Edge-Based Predictive Maintenance

Consider a manufacturing facility equipped with sensors that monitor the health of critical equipment. By deploying GenAI models and embedded databases at the edge, the facility can:

Collect Sensor Data: Sensors continuously monitor equipment parameters such as temperature, vibration, and power consumption.
Process Data Locally: GenAI models analyze the sensor data in real-time to identify patterns and anomalies that indicate potential equipment failures.
Trigger Alerts: When anomalies are detected, the system can trigger alerts to notify maintenance personnel.
Optimize Maintenance Schedules: By predicting equipment failures, maintenance can be scheduled proactively, reducing downtime and improving overall efficiency.

The Future of GenAI at the Edge

As technology continues to evolve, we can expect to see even more innovative applications of GenAI at the edge. Advances in hardware, software, and algorithms will enable smaller, more powerful devices to run increasingly complex GenAI models. This will unlock new possibilities for edge-based AI, from personalized experiences to autonomous systems.

In conclusion, the integration of GenAI, TinyML, and embedded databases represents a significant step forward in the field of edge computing. By leveraging the power of AI at the edge, we can create intelligent, autonomous, and privacy-preserving applications.

At Actian, we help organizations run faster, smarter applications on edge devices with our lightweight, embedded database – Actian Zen. Optimized for embedded systems and edge computing, Actian Zen boasts small-footprint with fast read and write access, making it ideal for resource-constrained environments.

Additional Resources:

About Kunal Shah

Kunal Shah is a product marketer with 15+ years in data and digital growth, leading marketing for Actian Zen Edge and NoSQL products. He has consulted on data modernization for global enterprises, drawing on past roles at SAS. Kunal holds an MBA from Duke University. Kunal regularly shares market insights at data and tech conferences, focusing on embedded database innovations. On the Actian blog, Kunal covers product growth strategy, go-to-market motions, and real-world commercial execution. Explore his latest posts to discover how edge data solutions can transform your business.

Data Management

Sync Your Data From Edge-to-Cloud With Actian Zen EasySync

Johnson Varughese

August 28, 2024

Sync Your Data From Edge-to-Cloud with Actian Zen EasySync

Welcome back to the world of Actian Zen, a versatile and powerful edge data management solution designed to help you build low-latency embedded apps. This is Part 3 of the quickstart blog series that focuses on helping embedded app developers get started with Actian Zen.

Establishing consistency and consolidating data across different devices and servers is essential for most edge-to-cloud solutions. Syncing data is necessary for almost every mobile, edge, or IoT application, and developers are familiar with the basic concepts and challenges. That’s why many experienced developers value efficient solutions. The Actian Zen EasySync tool is a new utility specifically designed for this purpose.

This blog will guide you through the steps for setting up and running EasySync.

What is EasySync?

Zen EasySync is a versatile data synchronization tool that automates the synchronization of newly created or updated records from one Zen database server to another. This tool transfers data across multiple servers, whether you’re working on the edge or within a centralized network. Key features of EasySync include:

Flexible Syncing Schedule: Sync data can be scheduled to poll for changes on a defined interval or can be used as a batch transfer tool, depending on your needs.
Logging: Monitor general activity, detect errors, and troubleshoot unexpected results with logging capabilities.

Prerequisites

Before using EasySync, ensure the following in your Zen installation:

System Data: The files must have system data v2 enabled, with file format version 13 or version 16.
ZEN 16.0 installed.
Unique Key: Both source and destination files must have a user-defined unique key.

EasySync Usage Scenarios

EasySync supports various data synchronization scenarios, making it a flexible tool for different use cases. Here are some common usage scenarios depicted in the diagram below:

Push to Remote: Synchronize data from a local database to a remote database.
Pull from Remote: Synchronize data from a remote database to a local database.
Pull and Push to Remotes: Synchronize data between multiple remote databases.
Aggregate Data From Edge: Collect data from multiple edge databases and synchronize it to a central database.
Disseminate Data to Edge: Distribute data from a central database to multiple edge databases.

actian edge easysync chart

Getting Started With EasySync

To demonstrate how to use EasySync, we will create a Python application that simulates sensor data and synchronizes it using EasySync. This application will create a sensor table on your edge device and remote server, insert random sensor data, and sync the data with a remote database. The remote database can contain various sets of data from several edge devices.

Step 1: Create the Configuration File

First, we need to create a JSON configuration file (config.json). This file will define the synchronization settings and the files to be synchronized, where files are stored in a source (demodata) and destination (demodata) folders.

Here is an example of what the configuration file might look like:

{
  "version": 1,
  "settings": {
    "polling_interval_sec": 10,
    "log_file": " C:/ProgramData/Actian/Zen/logs/datasync.log",
    "record_err_log": " C:/ProgramData/Actian/Zen/logs/recorderrors.log",
    "resume_on_error": true
  },
  "files": [
    {
      "id": 1,
      "source_file": "btrv://localhost/demodata?dbfile= sensors.mkd",
      "source_username": "",
      "source_password": "",
      "destination_file": "btrv://<Destination Server>/demodata?dbfile= sensors.mkd",
      "destination_username": "",
      "destination_password": "",
      "unique_key": 0
    },
    {
      "id": 2,
      "source_file": "btrv://localhost/demodata?dbfile=bookstore.mkd",
      "destination_file": "btrv://<Destination Server>/demodata?dbfile=bookstore.mkd",
      "create_destination": true,
      "unique_key": 1
    }
  ]
}

Step 2: Write the Python Script

Next, we create a Python script that simulates sensor data, creates the necessary database table, and inserts records into the database.

Save the following Python code in a file named run_easysync.py. Run the script to create the sensors table on your local edge device and server, and to insert data on your edge device.

import pyodbc
import random
import time
from time import sleep
random.seed()
def CreateSensorTable(server, database):
    try:
db_connection_string = f"Driver={{Pervasive ODBC Interface}};
ServerName={server};
DBQ={database};"
        conn = pyodbc.connect(db_connection_string, autocommit=True)
        cursor = conn.cursor()
       # cursor.execute("DROP TABLE IF EXISTS sensors;")
        cursor.execute("""
            CREATE TABLE sensors SYSDATA_KEY_2(
                id IDENTITY,
                ts DATETIME NOT NULL,
                temperature INT NOT NULL,
                pressure FLOAT NOT NULL,
                humidity INT NOT NULL
            );
        """)
        print(f"Table 'sensors' created successfully on {server}")
     except pyodbc.DatabaseError as err:
         print(f"Failed to create table on {server} with error: {err}")
def GetTemperature():
     return random.randint(70, 98)
def GetPressure():
     return round(random.uniform(29.80, 30.20), 3)
def GetHumidity():
     return random.randint(40, 55)
def InsertSensorRecord(server, database):
     temp = GetTemperature()
     press = GetPressure()
     hum = GetHumidity()
     try:
      insert = 'INSERT INTO sensors (id, ts, temperature, pressure, humidity) VALUES (0, NOW(), ?, ?, ?)'
        db_connection_string = f"Driver={{Pervasive ODBC Interface}};ServerName={server};DBQ={database};"
        conn = pyodbc.connect(db_connection_string, autocommit=True)
        cursor = conn.cursor()
        cursor.execute(insert, temp, press, hum)
        print(f"Inserted record [Temperature {temp}, Pressure {press}, Humidity {hum}] on {server}")
    except pyodbc.DatabaseError as err:
        print(f"Failed to insert record on {server} with error: {err}")
# Main
local_server = "localhost"
local_database = "Demodata"
remote_server = "remote-server_name"
remote_database = "demodata"

# Create sensor table on both local and remote servers
CreateSensorTable(local_server, local_database)
CreateSensorTable(remote_server, remote_database)

while True:
    InsertSensorRecord(local_server, local_database)
    sleep(0.5)

Syncing Data from IoT Device to Remote Server

Now, let’s incorporate the data synchronization process using the EasySync tool to ensure the sensor data from the IoT device is replicated to a remote server.

Step 3: Run EasySync

To synchronize the data using EasySync, follow these steps:

Ensure the easysync utility is installed and accessible from your command line.
Run the Python script to start generating and inserting sensor data.
Execute the EasySync command to start the synchronization process.

Open your command line and navigate to the directory containing your configuration file and Python script. Then, run the following command:

easysync -o config.json

This command runs the EasySync utility with the specified configuration file and ensures that the synchronization process begins.

Conclusion

Actian Zen EasySync is a simple but effective tool for automating data synchronization across Zen database servers. By following the steps outlined in this blog, you can easily set up and run EasySync. EasySync provides the flexibility and reliability you need to manage your data on the edge. Remember to ensure your files are in the correct format, have system data v2 enabled, and possess a user-defined unique key for seamless synchronization. With EasySync, you can confidently manage data from IoT devices and synchronize it to remote servers efficiently.

For further details and visual guides, refer to the Actian Academy and the comprehensive documentation. Happy coding!

About Johnson Varughese

Johnson Varughese manages Support Engineering at Actian, assisting developers leveraging ZEN interfaces (Btrieve, ODBC, JDBC, ADO.NET, etc.). He provides technical guidance and troubleshooting expertise to ensure robust application performance across different programming environments. Johnson's wealth of knowledge in data access interfaces has streamlined numerous development projects. His Actian blog entries detail best practices for integrating Btrieve and other interfaces. Explore his articles to optimize your database-driven applications.

Data Platform

How Data is Revolutionizing Transportation and Logistics

Kasey Nolan

August 28, 2024

blue traffic lines showing data transportation and logistics

In today’s fast-paced world, the transportation and logistics industry is the backbone that keeps the global economy moving. Logistics is expected to be the fastest-growing industry by 2030. As demand for faster, more efficient, and cost-effective services grows, you’ll need to be able to connect, manage, and analyze data from all parts of your business to make fast, efficient decisions that improve your supply chain, logistics, and other critical areas.

Siloed data, poor data quality, and a lack of integration across systems can hinder you from optimizing your operations, forecasting demand accurately, and providing top-tier customer service. By leveraging advanced data integration, management, and analytics, you can transform these challenges into opportunities, driving efficiency, reliability, and customer satisfaction.

The Challenges: Harnessing Data in Transportation and Logistics

One of the most significant hurdles in the transportation and logistics sector is accessing quality data across departments. Data is often scattered across multiple systems—such as customer relationship management (CRM), enterprise resource planning (ERP), telematics systems, and even spreadsheets—without a unified access point. This fragmentation creates data silos, where crucial information is isolated across individuals and business units, making it difficult for different departments to access the data they need. For instance, the logistics team might not have access to customer data stored in the CRM, which can hinder their ability to accurately plan deliveries, personalize service, proactively address potential issues, and improve overall communication.

Furthermore, the lack of integration across these systems exacerbates the problem of fragmented data. Different data sources often store information in varied and incompatible formats, making it challenging to compare or combine data across systems. This leads to inefficiencies in several critical areas, including demand forecasting, route optimization, predictive maintenance, and risk management. Without a unified view of operations, companies struggle to leverage customer behavior insights from CRM data to improve service quality or optimize delivery schedules and face other limitations.

The Impact: Inefficiencies and Operational Risks

The consequences of these data challenges are far-reaching. Inaccurate demand forecasts can lead to stockouts, overstock, and poor resource allocation, all of which directly impact your bottom line. Without cohesive predictive maintenance, operational downtime increases, negatively impacting delivery schedules and customer satisfaction. Inefficient routing, caused by disparate data sources, results in higher fuel costs and delayed deliveries, further eroding profitability and customer trust.

Additionally, the lack of a unified customer view can hinder your ability to provide personalized services, reducing customer satisfaction and loyalty. In the absence of integrated data, risk management becomes reactive rather than proactive, with delayed data processing increasing exposure to risks and limiting your ability to respond quickly to emerging threats.

The Solution: A Unified Data Platform

Imagine a scenario where your transportation and logistics operations are no longer bogged down by data fragmentation and poor integration. With a unified view across your entire organization, you can access accurate, real-time insights across the end-to-end supply chain, enabling you to make data-driven decisions that reduce delays and improve overall efficiency.

A unified data platform integrates fragmented data from multiple sources into a single, accessible system. This integration eliminates data silos, ensuring that all relevant information—whether from CRM, ERP, telematics, or GPS tracking systems—is available in real-time to decision-makers across your organization.

For example, predictive maintenance becomes significantly more effective when historical data, sensor data, and telematics are integrated and analyzed consistently. This approach minimizes unplanned downtime, extends the lifespan of assets, and ensures that vehicles and equipment are always operating at peak efficiency, leading to substantial cost savings.

Similarly, advanced route optimization algorithms that utilize real-time traffic data, weather conditions, and historical delivery performance can dynamically adjust routes for drivers. The result is consistently on-time deliveries, reduced fuel costs, and enhanced customer satisfaction through reliable and efficient service.

A unified data platform also enables the creation of a 360-degree customer view by consolidating customer data from various touchpoints—such as transactions, behaviors, and support interactions—into a comprehensive and up-to-date profile. This holistic view allows you to offer personalized services and targeted marketing, leading to higher customer satisfaction, increased loyalty, and more successful sales strategies.

Proactive risk management is another critical benefit of a unified data platform. By analyzing real-time data from multiple sources, you can identify potential risks before they escalate into critical issues. Whether you’re experiencing supply chain disruptions, regulatory compliance challenges, or logistical issues, the ability to respond swiftly to emerging risks reduces potential losses and ensures smooth operations, even in the face of unforeseen challenges.

Face the Future of Transportation and Logistics With Confidence

As the transportation and logistics industry continues to evolve, the role of data will only become more critical. The Actian Data Platform can help you overcome the current challenges of data fragmentation, poor quality, and lack of integration in addition to helping you position yourself at the forefront of innovation in the industry. By leveraging data to optimize operations, improve customer service, and proactively manage risks, you will achieve greater efficiency, cost-effectiveness, and customer satisfaction—driving greater success in a competitive and dynamic market.

About Kasey Nolan

Kasey Nolan is Solutions Product Marketing Manager at Actian, aligning sales and marketing in IaaS and edge compute technologies. With a decade of experience bridging cloud services and enterprise needs, Kasey drives messaging around core use cases and solutions. She has authored solution briefs and contributed to events focused on cloud transformation. Her Actian blog posts explore how to map customer challenges to product offerings, highlighting real-world deployments. Read her articles for guidance on matching technology to business goals.

Data Platform

5 Misconceptions About Data Quality and Governance

Dee Radh

August 27, 2024

misconceptions-about-data-quality-and-governance

The quality and governance of data have never been more critical than it is today.

In the rapidly evolving landscape of business technology, advanced analytics and generative AI have emerged as game-changers, promising unprecedented insights and efficiencies. However, as these technologies become more sophisticated, the adage GIGO, or “garbage in, garbage out,” has never been more relevant. For data and IT professionals, understanding the critical role of data quality in these applications is not just important—it’s imperative for success.

Going Beyond Data Processing

Advanced analytics and Generative AI don’t just process data; they amplify its value. This amplification can be a double-edged sword:

Insight Magnification

High-quality data leads to sharper insights, more accurate predictions, and more reliable AI-generated content.

Error Propagation

Poor quality data can lead to compounded errors, misleading insights, and potentially harmful AI outputs.

These technologies act as powerful lenses, magnifying both the strengths and weaknesses of your data. As the complexity of models increases, so does their sensitivity to data quality issues.

Effective Data Governance is Mandatory

Implementing robust data governance practices is equally important. Governance today is not just a regulatory checkbox—it’s a fundamental requirement for harnessing the full potential of these advanced technologies while mitigating associated risks.

As organizations rush to adopt advanced analytics and Generative AI, there’s a growing realization that effective data governance is not a hindrance to innovation, but rather an enabler.

Data Reliability at Scale: Advanced analytics and AI models require vast amounts of data. Without proper governance, the reliability of these datasets becomes questionable, potentially leading to flawed insights.

Ethical AI Deployment: Generative AI in particular raises significant ethical concerns. Strong governance frameworks are essential for ensuring that AI systems are developed and deployed responsibly, with proper oversight and accountability.

Regulatory Compliance: As regulations like GDPR, CCPA, and industry-specific mandates evolve to address AI and advanced analytics, robust data governance becomes crucial for maintaining compliance and avoiding hefty penalties.

But despite the vast mines of information, many organizations still struggle with misconceptions that hinder their ability to harness the full potential of their data assets.

As data and technology leaders navigate the complex landscape of data management, it’s crucial to dispel these myths and focus on strategies that truly drive value.

For example, Gartner offers insights into the governance practices organizations typically follow, versus what they actually need:

why modern digital organizations need adaptive data governance

Source: Gartner

5 Data Myths Impacting Data’s Value

Here are five common misconceptions about data quality and governance, and why addressing them is essential.

Misconception 1: The ‘Set It and Forget It’ Fallacy

Many leaders believe that implementing a data governance framework is a one-time effort. They invest heavily in initial setup but fail to recognize that data governance is an ongoing process that requires continuous attention and refinement mapped to data and analytics outcomes.

In reality, effective data governance is dynamic. As business needs evolve and new data sources emerge, governance practices must adapt. Successful organizations treat data governance as a living system, regularly reviewing and updating policies, procedures, and technologies to ensure they remain relevant and effective for all stakeholders.

Action: Establish a quarterly review process for your data governance framework, involving key stakeholders from across the organization to ensure it remains aligned with business objectives and technological advancements.

Misconception 2: The ‘Technology Will Save Us’ Trap

There’s a pervasive belief that investing in the latest data quality tools and technologies will automatically solve all data-related problems. While technology is undoubtedly crucial, it’s not a silver bullet.

The truth is, technology is only as good as the people and processes behind it. Without a strong data culture and well-defined processes, even the most advanced tools will fall short. Successful data quality and governance initiatives require a holistic approach that balances technology with human expertise and organizational alignment.

Action: Before investing in new data quality and governance tools, conduct a comprehensive assessment of your organization’s data culture and processes. Identify areas where technology can enhance existing strengths rather than trying to use it as a universal fix.

Misconception 3:. The ‘Perfect Data’ Mirage

Some leaders strive for perfect data quality across all datasets, believing that anything less is unacceptable. This pursuit of perfection can lead to analysis paralysis and a significant resource drain.

In practice, not all data needs to be perfect. The key is to identify which data elements are critical for decision-making and business operations, and focus quality efforts there. For less critical data, “good enough” quality that meets specific use case requirements may suffice.

Action: Conduct a data criticality assessment to prioritize your data assets. Develop tiered quality standards based on the importance and impact of different data elements on your business objectives.

Misconception 4: The ‘Compliance is Enough’ Complacency

With increasing regulatory pressures, some organizations view data governance primarily through the lens of compliance. They believe that meeting regulatory requirements is sufficient for good data governance.

However, true data governance goes beyond compliance. While meeting regulatory standards is crucial, effective governance should also focus on unlocking business value, improving decision-making, and fostering innovation. Compliance should be seen as a baseline, not the end goal.

Action: Expand your data governance objectives beyond compliance. Identify specific business outcomes that improved data quality and governance can drive, such as enhanced customer experienced or more accurate financial forecasting.

Misconception 5: The ‘IT Department’s Problem’ Delusion

There’s a common misconception that data quality and governance are solely the responsibility of the IT department or application owners. This siloed approach often leads to disconnects between data management efforts and business needs.

Effective data quality and governance require organization-wide commitment and collaboration. While IT plays a crucial role, business units must be actively involved in defining data quality standards, identifying critical data elements, and ensuring that governance practices align with business objectives.

Action: Establish a cross-functional data governance committee that includes representatives from IT, business units, and executive leadership. This committee should meet regularly to align data initiatives with business strategy and ensure shared responsibility for data quality.

Move From Data Myths to Data Outcomes

As we approach the complexities of data management in 2025, it’s crucial for data and technology leaders to move beyond these misconceptions. By recognizing that data quality and governance are ongoing, collaborative efforts that require a balance of technology, process, and culture, organizations can unlock the true value of their data assets.

The goal isn’t data perfection, but rather continuous improvement and alignment with business objectives. By addressing these misconceptions head-on, data and technology leaders can position their organizations for success in an increasingly competitive world.

About Dee Radh

As Senior Director of Product Marketing, Dee Radh heads product marketing for Actian. Prior to that, she held senior PMM roles at Talend and Formstack. Dee has spent 100% of her career bringing technology products to market. Her expertise lies in developing strategic narratives and differentiated positioning for GTM effectiveness. In addition to a post-graduate diploma from the University of Toronto, Dee has obtained certifications from Pragmatic Institute, Product Marketing Alliance, and Reforge. Dee is based out of Toronto, Canada.

Data Governance

Understanding the Role of Data Quality in Data Governance

Traci Curran

August 26, 2024

abstract depiction of data quality in data governance

Summary

This blog explains why strong data quality is essential within a data governance framework, detailing how establishing standards and processes for accuracy, consistency, and monitoring ensures reliable, compliant, and actionable data across the organization.

Core dimensions define trusted data – Data quality relies on metrics like accuracy, completeness, consistency, timeliness, conformance, uniqueness, and usability, each requiring governance policies and validation processes to maintain trust.
Governance tools and automation streamline quality – Automated profiling, validation, and cleansing integrate with governance frameworks to proactively surface anomalies, reduce manual rework, and free up teams for strategic initiatives.
Governance + quality = AI-ready, compliant data – Combining clear standards, metadata management, and continuous monitoring ensures data is reliable, compliant, and fit for advanced analytics like AI and ML.

The ability to make informed decisions hinges on the quality and reliability of the underlying data. As organizations strive to extract maximum value from their data assets, the critical interplay between data quality and data governance has emerged as a fundamental imperative. The symbiotic relationship between these two pillars of data management can unlock unprecedented insights, drive operational efficiency, and, ultimately, position enterprises for sustained success.

Understanding Data Quality

At the heart of any data-driven initiative lies the fundamental need for accurate, complete, and timely information. Data quality encompasses a multifaceted set of attributes that determine the trustworthiness and fitness-for-purpose of data. From ensuring data integrity and consistency to minimizing errors and inconsistencies, a robust data quality framework is essential for unlocking the true potential of an organization’s data assets.

Organizations can automate data profiling, validation, and standardization by leveraging advanced data quality tools. This improves the overall quality of the information and streamlines data management processes, freeing up valuable resources for strategic initiatives.

How Data Quality Relates to Data Governance

Data quality is a fundamental pillar of data governance, ensuring that data is accurate, complete, consistent, and reliable for business use. A strong data governance framework establishes policies, processes, and accountability to maintain high data quality across an organization. This includes defining data standards, validation rules, monitoring processes, and data cleansing techniques to prevent errors, redundancies, and inconsistencies.

Without proper governance, data quality issues such as inaccuracies, duplicates, and inconsistencies can lead to poor decision-making, compliance risks, and inefficiencies. By integrating data quality management into data governance, organizations can ensure that their data remains trustworthy, well-structured, and optimized for analytics, reporting, and operational success.

The Key Dimensions of Data Quality in Data Governance

Effective data governance hinges on understanding and addressing the critical dimensions of data quality. These dimensions guide how organizations define, manage, and maintain data to ensure it is useful, accurate, and accessible. Below are the essential aspects of data quality that should be considered when creating a data governance strategy:

Accuracy: Data must accurately reflect the real-world entities it represents. Inaccurate data leads to faulty conclusions, making it crucial for governance policies to verify and maintain correctness throughout the data lifecycle.
Completeness: Data should capture all necessary attributes required for decision-making. Missing or incomplete information can compromise insights and analyses, so governance practices should ensure comprehensive data coverage across all relevant systems and processes.
Consistency: Data needs to be presented in a uniform way across various platforms and departments. Inconsistent data can create confusion and hinder integration, which is why governance should enforce standards for formatting and data structures.
Timeliness: The value of data diminishes over time, so it’s essential that data is up-to-date and relevant for current analysis. Governance efforts should ensure real-time updates and schedules for periodic data refreshes to maintain data’s usefulness.
Conformance: Data should comply with predefined syntax rules and meet specific business logic requirements. Without conformance, data could lead to process errors, so governance should focus on maintaining compliance with validation rules and predefined formats.
Uniqueness: To avoid redundancies, data should be free from duplicate entries or redundant records. A strong data governance framework helps establish processes to ensure data integrity and prevents unnecessary duplication that could skew analytics.
Usability: Data must be easily accessible, understandable, and actionable for users. Governance frameworks should prioritize user-friendly interfaces, clear documentation, and efficient data retrieval systems to ensure that data is not only accurate but also usable for business needs.

Addressing these key dimensions through a comprehensive data governance framework helps organizations maintain high-quality data that is reliable, consistent, and actionable, ensuring that data becomes a strategic asset for informed decision-making.

How to Achieve Data Quality in Data Governance

Achieving high data quality within a data governance framework is essential for making informed, reliable decisions and maintaining compliance. It involves implementing structured processes, tools, and roles to ensure that data is accurate, consistent, and accessible across the organization.

Let’s explore key strategies for ensuring data quality, such as defining standards, using data profiling techniques, and setting up monitoring and validation processes.

Define Clear Standards

One of the most effective strategies for ensuring data quality is to define clear standards for how data should be structured, processed, and maintained. Data standards establish consistent rules and guidelines that govern everything from data formats and definitions to data collection and entry processes. These standards help eliminate discrepancies and ensure that data across the organization is uniform and can be easily integrated for analysis.

For instance, organizations can set standards for data accuracy, defining acceptable levels of error, or for data completeness, specifying which fields must always be populated. Additionally, creating data dictionaries or data catalogs allows teams to agree on terminology and definitions, ensuring everyone uses the same language when working with data. By defining these standards early in the data governance process, organizations create a solid foundation for maintaining high-quality, consistent data that can be relied upon for decision-making and reporting.

Profile Data With Precision

The first step in achieving data quality is understanding the underlying data structures and patterns. Automated data profiling tools, such as those offered by Actian, empower organizations to quickly and easily analyze their data, uncovering potential quality issues and identifying areas for improvement. By leveraging advanced algorithms and intelligent pattern recognition, these solutions enable businesses to tailor data quality rules to their specific requirements, ensuring that data meets the necessary standards.

Validate and Standardize Data

With a clear understanding of data quality, the next step is implementing robust data validation and standardization processes. Data quality solutions provide a comprehensive suite of tools to cleanse, standardize, and deduplicate data, ensuring that information is consistent, accurate, and ready for analysis. Organizations can improve data insights and make more informed, data-driven decisions by integrating these capabilities.

The Importance of Data Governance

While data quality is the foundation for reliable and trustworthy information, data governance provides the overarching framework to ensure that data is effectively managed, secured, and leveraged across the enterprise. Data governance encompasses a range of policies, processes, and technologies that enable organizations to define data ownership, establish data-related roles and responsibilities, and enforce data-related controls and compliance.

Unlocking the Power of Metadata Management

Metadata management is central to effective data governance. Solutions like the Actian Data Intelligence Platform provide a centralized hub for cataloging, organizing, and managing metadata across an organization’s data ecosystem. These platforms enable enterprises to create a comprehensive, 360-degree view of their data assets and associated relationships by connecting to a wide range of data sources and leveraging advanced knowledge graph technologies.

Driving Compliance and Risk Mitigation

In today’s increasingly regulated business landscape, data governance is critical in ensuring compliance with industry standards and data privacy regulations. Robust data governance frameworks, underpinned by powerful metadata management capabilities, empower organizations to implement effective data controls, monitor data usage, and mitigate the risk of data breaches and/or non-compliance.

The Synergistic Relationship Between Data Quality and Data Governance

While data quality and data governance are distinct disciplines, they are inextricably linked and interdependent. Robust data quality underpins the effectiveness of data governance, ensuring that the policies, processes, and controls are applied to data to extract reliable, trustworthy information. Conversely, a strong data governance framework helps to maintain and continuously improve data quality, creating a virtuous cycle of data-driven excellence.

Organizations can streamline the data discovery and access process by integrating data quality and governance. Coupled with data quality assurance, this approach ensures that users can access trusted data, and use it to make informed decisions and drive business success.

Why Data Quality Matters in Data Governance

As organizations embrace transformative technologies like artificial intelligence (AI) and machine learning (ML), the need for reliable, high-quality data becomes even more pronounced. Data governance and data quality work in tandem to ensure that the data feeding these advanced analytics solutions is accurate, complete, and fit-for-purpose, unlocking the full potential of these emerging technologies to drive strategic business outcomes.

In the age of data-driven transformation, the synergistic relationship between data quality and data governance is a crucial competitive advantage. By seamlessly integrating these two pillars of data management, organizations can unlock unprecedented insights, enhance operational efficiency, and position themselves for long-term success.

About Traci Curran

AI & ML

Using Data to Build Democratized AI Applications: The Actian Approach

Steven B. Becker

August 23, 2024

blue graphic showcasing using data to build democratized AI applications

Artificial intelligence (AI) has become a cornerstone of modern technology, powering innovations from personalized recommendations to self-driving cars. Traditionally, AI development was limited to tech giants and specialized experts.

However, the concept of democratized AI aims to broaden access, making it possible for a wider audience to develop and use AI applications. In this post, we’ll explore the pivotal role data plays in democratizing AI and how Actian’s cutting-edge solutions are enabling this shift.

What is Democratized AI?

Democratized AI is all about making AI tools and technologies accessible to a broad range of users—whether they’re analysts at small businesses, individual developers, or even those without technical backgrounds. It’s about breaking down the barriers to AI development and enabling more people to incorporate AI into their projects and business operations to transform ideas into actionable solutions, accelerate innovation, and deliver desired business outcomes faster. Actian is a key player in this movement, offering tools that simplify data management and integration for AI applications.

The Role of Data in AI Democratization

Data is essential to AI. It trains AI models and informs their predictions and decisions. When it comes to democratized AI, data serves several critical functions, including these four:

Training Resources: Open datasets and pre-trained models empower developers to create AI applications without needing extensive proprietary data.
Personalization: User-generated data allows even small applications to deliver personalized AI experiences.
Transparency: Open data practices enhance the transparency of AI systems, which is vital for building trust.
Continuous Improvement: User feedback data helps refine AI models over time, making them more accurate and relevant.

Actian’s DataConnect and Actian Data Platform are central to these processes, providing powerful, easy-to-use tools for data integration, management, and analysis.

5 Key Components of Data-Driven, Democratized AI Applications

User-Friendly AI Platforms: Tools like AutoML simplify the creation and deployment of AI models.
Data Integration and Management: Actian’s DataConnect excels here, offering robust extract, transform, and load (ETL) capabilities that make it easy to prepare data for AI.
Scalable Data Processing: The Actian Data Platform offers high-performance data processing, essential for handling the large datasets required in AI.
Cloud-Based AI Services: API-based services provide pre-trained models for common AI tasks like image recognition or natural language processing.
Collaborative Platforms: These spaces allow developers to share models, datasets, and knowledge, fostering community-driven AI development.

Actian’s Role in Democratizing AI

Actian’s products play a crucial role in democratizing AI by addressing some of the most challenging aspects of AI development, including these four:

Data Integration With Actian’s DataConnect: This tool simplifies the process of aggregating data from various sources, a critical step in preparing datasets for AI. Its intuitive interface and robust capabilities make it accessible to users with varying levels of technical expertise.
Scalable Data Processing With Actian Data Platform: This platform provides the necessary infrastructure to manage large-scale data processing tasks, enabling businesses of all sizes to extract insights from their data—a fundamental step in AI applications.
Real-time Data Analytics: Actian’s solutions support real-time data analytics, crucial for AI applications that require immediate decisions or predictions.
Hybrid and Multi-Cloud Support: Actian’s flexible deployment options span on-premises, cloud, and hybrid, allowing organizations to build AI applications that align with their infrastructure and data governance needs.

3 Examples of Democratized AI Applications Powered by Actian

Predictive Maintenance for Small Manufacturers: By using Actian’s DataConnect to integrate sensor data and the Actian Data Platform for analysis, small manufacturing businesses can implement AI-driven predictive maintenance systems.
Customer Behavior Analysis: Retailers can use Actian’s tools to integrate point-of-sale data with online customer interactions, feeding this data into AI models for highly personalized marketing strategies.
Supply Chain Optimization: Actian’s solutions allow businesses to integrate and analyze data from multiple supply chain points, facilitating AI-driven optimization strategies.

Understanding Challenges and Considerations

While democratized AI offers significant potential, it also presents four primary challenges:

Data Quality and Bias: Ensuring high-quality, representative data is crucial. Actian’s DataConnect’s data profiling and cleansing/data quality features help address this issue.
Privacy and Security: As AI becomes more accessible, safeguarding data privacy and security becomes increasingly important. Actian’s solutions include robust security features to protect sensitive information.
Ethical Use: The widespread adoption of AI requires education on its ethical implications and responsible usage.
Technical Limitations: While tools are becoming more user-friendly, there’s still a learning curve. Actian provides comprehensive support to help users overcome these challenges.

Future Outlook: 5 Emerging Trends

The future of democratized AI is bright, with several key trends on the horizon:

No-Code/Low-Code AI Platforms: Expect more intuitive platforms that make AI development accessible without coding expertise.
Edge AI: Bringing AI capabilities to resource-constrained devices will become more prevalent.
Explainable AI: Emphasizing transparency in AI decisions will help build trust.
Growth of AI Communities: Expanding communities and knowledge-sharing platforms will foster collaborative AI development.
AI Integration in Everyday Tools: AI will become increasingly embedded in common software and tools.

Actian is well-positioned to support these trends with ongoing advancements in its data management and analytics solutions to meet the evolving needs of AI applications.

Empowering Innovation With Accessible AI

Democratized AI, driven by accessible data and tools, has the potential to revolutionize our interaction with technology. By making AI accessible to a diverse group of creators, we unlock new possibilities for innovation.

Actian’s suite of products, including DataConnect and the Actian Data Platform, plays a crucial role in this democratization by simplifying the essential steps of data integration, management, and analysis in the AI development process. These products also ensure data is properly prepped for AI.

As we continue to democratize AI, it’s essential to prioritize responsible development practices, ensuring that AI systems are fair, transparent, and beneficial to society. With Actian’s powerful, secure, and user-friendly tools, businesses and developers are well-equipped to confidently explore the exciting possibilities of democratized AI, transforming data into actionable insights and innovative AI-driven solutions.

About Steven B. Becker

Steven B. Becker is Global Vice President of Solution Engineering at Actian, with over 20 years of technology experience. He has a history of helping Fortune 10 companies modernize apps, data, analytics, AI, and GenAI initiatives. Steven prioritizes bridging technology, people, and business. Steven has led successful transformations for both large enterprises and startups. His Actian blog posts explore modern app architectures, AI-driven insights, and enterprise data challenges. Dive into his articles for proven strategies on leveraging technology for growth.

Databases

A Day in the Life of an Application Owner

Nick Johnson

August 15, 2024

blue technology arrows and lines depicting life as an application owner

The role of an application owner is often misunderstood within businesses. This confusion arises because, depending on the company’s size, an application owner could be the CIO or CTO at a smaller startup, or a product management lead at a larger technology company. Despite the variation in titles, the core responsibilities remain the same: managing an entire application from top to bottom, ensuring it meets the business’s needs (whether it’s an internal or customer-facing application), and doing so cost-effectively.

Being an application owner is a dynamic and multifaceted role that requires a blend of technical expertise, strategic thinking, and excellent communication skills. Here’s a glimpse into a typical day in the life of an application owner.

Morning: Planning and Prioritizing

6:30 AM – 7:30 AM: Start the Day Right

The day begins early with a cup of coffee and a quick review of emails and messages. This is the time to catch up on any overnight developments, urgent issues, or updates from global teams.

7:30 AM – 8:30 AM: Daily Stand-Up Meeting

The first official task is the daily stand-up meeting with the development team. This meeting is crucial for understanding the current status of ongoing projects, identifying any roadblocks, and setting priorities for the day. It’s also an opportunity to align the team’s efforts with the overall business goals and discuss any new application needs.

Mid-Morning: Deep Dive into Projects

8:30 AM – 10:00 AM: Project Reviews and Code Reviews

After the stand-up, it’s time to dive into project reviews. This involves going through the latest code commits, reviewing progress on key features, and ensuring that everything is on track, and if it’s not, create a strategy to address the issues. Code reviews are essential to maintain the quality and integrity of the application.

10:00 AM – 11:00 AM: Stakeholder Meetings

Next up are meetings with stakeholders. These could be product managers, business analysts, or even end-users. The goal is to gather feedback, discuss new requirements, and ensure that the application is meeting the needs of the business.

Late Morning: Problem Solving and Innovation

11:00 AM – 12:00 PM: Troubleshooting and Bug Fixes

No day is complete without some troubleshooting. This hour is dedicated to addressing any critical issues or bugs that have been reported. It’s a time for quick thinking and problem-solving to ensure minimal disruption to users.

12:00 PM – 1:00 PM: Lunch Break and Networking

Lunch is not just a break but also an opportunity to network with colleagues, discuss ideas, and sometimes even brainstorm solutions to ongoing challenges.

Afternoon: Strategic Planning and Development

1:00 PM – 2:30 PM: Strategic Planning

The afternoon kicks off with strategic planning sessions. These involve working on the application’s roadmap, planning future releases, incorporating customer input, and aligning with the company’s long-term vision. It’s a time to think big and set the direction for the future.

2:30 PM – 4:00 PM: Development Time

This is the time to get hands-on with development. Whether it’s coding new features, optimizing existing ones, or experimenting with new technologies, this block is dedicated to building and improving the application.

Late Afternoon: Collaboration and Wrap-Up

4:00 PM – 5:00 PM: Cross-Functional Team Standup

Collaboration is key to the success of any application. This hour is spent working with cross-functional teams such as sales, UX/UI designers, and marketing to analyze and improve the product onboarding experience. The goal is to ensure that everyone is aligned and working toward the same objectives.

5:00 PM – 6:00 PM: End-of-Day Review and Planning for Tomorrow

The day wraps up with a review of what was accomplished and planning for the next day. This involves updating task boards, setting priorities, and making sure that everything is in place for a smooth start the next morning.

Evening: Continuous Learning and Relaxation

6:00 PM Onwards: Continuous Learning and Personal Time

After a productive day, it’s important to unwind and relax. However, the learning never stops. Many application owners spend their evenings reading up on the latest industry trends, taking online courses, or experimenting with new tools and technologies.

Being an application owner is a challenging yet rewarding role. It requires a balance of technical skills, strategic thinking, and effective communication. Every day brings new challenges, opportunities, and rewards, making it an exciting career for those who love to innovate and drive change.

If you need help managing your applications, Actian Application Services can help.

>> Learn More

About Nick Johnson

Nick Johnson is a Senior Product Marketing Manager at Actian, driving the go-to-market success for HCL Informix and Actian Zen. With a career dedicated to shaping compelling messages and strategies for databases, Nick brings a wealth of experience from his impactful work at leading technology companies, including Neo4j, Microsoft, and SAS.

Actian Life

Actian’s Interns Contribute Across all Areas of the Business

Katie Keith

August 13, 2024

As we wrap up our internship season, I want to reflect on the brilliance of this program. It’s been a great experience so far and like the other interns, I’m impressed with how much I’m learning and the opportunities to actively contribute to the business. From collaborating on real-world projects to brainstorming innovative solutions, our intern team is making tangible impacts that help drive the company forward.

Since I came on board in June, my first three impressions are what I refer to as “The Three Cs.” They consist of community, culture, and capstone projects. I am incredibly grateful that these foundational pillars are integral to the distinctive character of the program. Actian’s internship is truly structured to move its participants from interns to capable, confident employees who will be ready for the next stage of our careers.

Experiencing a Sense of Community

Given the remote nature of my internship—I’m based in Illinois—I was initially unsure how I would be able to connect with my fellow interns and Actian employees. To my relief, when we attended the in-person orientation at the Round Rock Center of Excellence in Texas, it became abundantly clear that despite the mostly remote work environment, Actian cultivates a supportive community of employees who not only care for the success of the company, but for one another, regardless of where we’re working.

It was extremely encouraging to have such incredible support from so many individuals within the company. Every employee with whom I’ve interacted has invited me to connect with them.

Without exception, they genuinely want to see us succeed and have provided us with the individual investment, tools, and resources to do so. This strong sense of community fosters collaboration and ensures that we all thrive together. As an intern, I feel like I’m part of a team that’s making a difference in the company.

Participating in a Culture Worth Celebrating

Every Actian employee I’ve spoken to has genuine praise for the company’s incredible people and culture. Given this fact, it is no surprise that this positive culture extends to interns as well. During our in-person orientation, interns were able to meet each other face-to-face and engage in activities that allowed us to connect with one another.

This allowed us to get to know each other on a personal and a professional level. Whether it was the group dinners or the cohort favorite “GameOn! ATX” competition—for which I would like to extend a humble apology and thanks to my team’s opponents for continuing to be gracious following their loss!—we were able to share some incredibly fun memories.

Although we have all returned to our various work environments, including remote locations, thanks to the brilliant design of Employee Experience leaders Rae Coffman and Sara Lou, we are fortunate to have a continuing calendar of upcoming fun events. This allows us to interact and share, regardless of where we’re located or what team we’re working with at Actian.

Personally, I’m looking forward to the mini campfire. For this annual Actian intern tradition, each of us is sent supplies to build a candle campfire in their home. The supplies are complete with ingredients to build s’mores, which we’ll eat while we share scary stories with each other. Eek!

This is one example of how the recognizable culture that Actian cultivates globally is scaled to the internship program. The culture ensures that each intern feels seen, supported, and connected throughout the entirety of our experience with Actian.

Delivering Powerful Results With Capstone Projects

There tends to be a cliché that an intern’s only tasks are those that are miniscule to the company. You know, making copies or running errands. That’s certainly not the case here. No Actian intern will ever find themselves simply fetching their manager a cup of coffee. Instead, we are all given a unique opportunity to learn and showcase our hard work.

Each intern is assigned a capstone project at the beginning of our 12 weeks. We work on it, collaborate with others in the company, and ultimately deliver a structured, substantive outcome at the completion of the internship.

We are each given a team consisting of our manager and a buddy who create a reliable balance of support and autonomy as we work toward our project—honing our skills while adding value to the organization. Although I do make a mean cup of coffee, I am more excited about the project management skills and transferable, real-world experiences these capstone projects afford each one of us.

Our Unique Internship Opportunities Extend Globally

The brilliance of our internship program is not limited to inside the U.S. borders. Actian has an incredible cohort of interns working in Germany as well—and they hail from various parts of the globe. One difference between the U.S. and the German program is that those interning in Germany have the ability to be hired at any time of the year. Actian provides these interns with incredible opportunities that include an internship, academic thesis supervision, or a part-time position.

In the last year alone, the Actian office in Germany has supervised 11 students. This includes three academic thesis students and one who will be joining Actian full time this fall. It’s exciting for everyone involved in the program!

Coming from all levels of education and diverse experiences, these interns work on the Actian Vector team under the leadership of Steffen Kläbe and Thomas Schweser to contribute to the success of one of the industry’s fastest analytical database management systems. These interns start their program by completing an extensive onboarding experience that introduces them to the codebase and explains how to successfully optimize it.

Following the completion of these first one to two weeks, interns are assigned a task designed to provide hands-on experience with the codebase system. This task usually entails solving a problem or something similar that delivers actual business value, such as fixing a bug in the code. The initial task allows interns to not only advance their skillset but also gain the confidence needed to move into their selected projects.

Following this task comes the fun part! Interns choose a project that aligns with their interests. So far this year, there have been 17 projects that directly influence the current usage and future innovation of our Vector database. These projects range from “Memory Loading” to “Cloud Usage” to “Data Compression.”

The impact that these interns and projects have on the company is not only recognizably impressive but also incredibly powerful. Their dedication and innovation that they bring to the company every day continues to demonstrate a significant impact that advances our products and our business.

Making a Lasting Impression

Overall, the brilliance of the Actian internship program continues to reveal itself the more I experience it. I am extremely grateful for the opportunity to be here. I am certain that this experience will be one I carry on far longer than my 12 weeks here. Thank you to everyone who makes it possible!

About Katie Keith

Katie Keith is pursuing a BBA in Finance at Loyola University in Chicago, contributing to Actian's Employee Experience team. She has collaborated on a Pilot Orientation Program for new go-to-market employees, leveraging her academic research and interpersonal skills. Katie has studied the intersection of psychology and business, providing unique perspectives on employee engagement. Her blog entries at Actian reflect her passion for organizational development and onboarding. Stay tuned for her insights on creating impactful employee experiences.

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