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

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.

Databases

Smart Stores, Savvy Shoppers: Data’s Role in Reinventing Retail

Kasey Nolan

August 12, 2024

businessperson looking at data in retail

Navigating the Complexity of Modern Retail With Data

In today’s digital age, retail is evolving at a breakneck pace. Gone are the days when a great product and a welcoming smile are enough to secure customer loyalty. Modern shoppers demand seamless, personalized experiences, whether they’re browsing online from their couch or strolling through a brick-and-mortar store.

Customer loyalty has also evolved. In the past, shoppers would often stick with a single brand or store out of habit or familiarity. However, today’s consumers are more informed and have more choices at their fingertips. Loyalty is no longer guaranteed by proximity or tradition; it must be earned through consistent, high-quality, and personalized experiences.

To stay competitive, retailers need to harness the power of data to anticipate needs, optimize operations, and create memorable shopping experiences that keep customers coming back—across every channel and each interaction.

Leveraging Data to Improve Customer Acquisition and Loyalty

To improve customer acquisition and loyalty, retailers must leverage a variety of data types that often exist in different silos within the retail environment.

1. Behavioral Data

Behavioral data is all about tracking customers’ online browsing history, click patterns, and purchase history on websites and mobile apps. For example, understanding which products a customer frequently views but does not purchase can help to craft targeted promotions.

In stores, IoT devices and sensors can track how customers move through physical aisles, identifying popular paths and frequently visited sections. This information allows retailers to optimize store layouts and product placements to enhance the shopping experience and increase sales.

2. Transactional Data

Analyzing purchase history provides insights into customer preferences and buying habits. Retailers can identify trends, like which products are frequently bought together, or which times of year certain items are in high demand. This data aids in inventory management, ensuring that popular products are always available to meet customer demand.

3. Demographic Data

Collecting demographic information such as age, gender, location, and income levels helps retailers segment their customer base and create targeted marketing campaigns. Understanding the geographic distribution of customers can inform decisions about where to open new stores or focus advertising, while data on age group preference can allow retailers to tailor their marketing messages to the right audience.

4. Psychographic Data

Psychographic data is all about customer interests, values, and lifestyle choices. Retailers can gather this information through online / browsing behavior, social media interactions, and other engagement tools. By aligning marketing messages with customers’ values and interests, retailers can build stronger emotional connections and brand loyalty.

5. Feedback Data

Finally, customer feedback collected through reviews, surveys, and direct interactions offers invaluable insights into customer satisfaction and areas for improvement. Positive reviews can be leveraged in marketing campaigns to build trust and attract new customers. Negative feedback can highlight pain points and opportunities for improvement. By addressing customer concerns promptly, retailers can improve their products and services and boost customer loyalty and retention.

Connect, Manage, and Analyze With Confidence Using the Actian Data Platform

Knowing what data to look for is only part of the solution. Integrating it to get a full view of your business is another issue entirely. Retailers often struggle with data scattered across various systems like POS, CRM, and e-commerce platforms, and need help connecting, managing, and analyzing the data points to make fast, accurate, data-driven decisions. This entails capturing data in both on-premises systems and in the cloud. That’s why retailers need a hybrid platform that enables:

Connecting Data

Imagine a customer browsing your online store, adding items to their cart, and later deciding to complete the purchase in a physical store. With connected data, you can track their journey seamlessly, offering personalized recommendations and ensuring inventory is synchronized across channels. This level of integration creates a cohesive shopping experience that delights customers and drives loyalty.

The Actian Data Platform provides this solution by seamlessly connecting these data sources, providing a unified view of operations. This integration not only streamlines workflows but ensures that all departments have access to accurate and up-to-date information.

Managing Data

Managing vast amounts of data can be daunting, but the Actian Data Platform makes it easy. The platform’s ability to handle data from multiple sources means you can manage everything from sales transactions and customer profiles, to inventory levels and supply chain logistics. Secure data management also protects sensitive customer information—like customer names—while still allowing you to target customers for marketing activities, building trust and confidence in your brand.

Analyzing Data

The true power of data lies in its analysis. The Actian Data Platform supports analytics capabilities that transform raw data into meaningful insights. Retailers can identify trends, forecast demand, and make data-driven decisions that improve their bottom line. Whether it’s optimizing inventory or personalizing marketing campaigns, the possibilities are endless.

Drive the Future of Retail With Confidence

The Actian Data Platform is a game-changer for the retail industry, offering unparalleled capabilities in connecting, managing, and analyzing data. By leveraging this powerful tool, retailers can achieve greater efficiency, enhance customer experiences, and accelerate strategic growth. Actian’s commitment to innovation and excellence ensures that businesses like yours are equipped to meet the challenges of today’s data-driven world. Discover the future of retail with Actian with a custom demo.

About Kasey Nolan

Data Integration

Connect Disparate Data Sources With Confidence

Derek Comingore

August 8, 2024

abstract concept of connecting data sources with confidence

Now more than ever, businesses in every vertical are inundated with vast amounts of data coming at them from various sources. And those sources keep growing as data is created by an ever-expanding number of apps, systems, and devices. Whether it’s customer interactions, supply chain operations, or financial transactions, data is the lifeblood of the modern enterprise.

However, the sheer volume and variety of data that’s available creates a significant challenge. You must ensure information is accessible, accurate, trusted, and actionable. This is where data integration has become crucial.

As I shared during a recent TDWI conference in San Diego, unifying data from multiple sources enables you to utilize the full potential of all your data to drive informed decision-making and strategic growth of the business. This includes hybrid data integration, which connects data from across cloud and on-premises environments.

Four Business Reasons to Integrate Your Hybrid Data

Unifying disparate data sources while ensuring quality and compliance are essential for success. Following proven approaches for integration, implementing a robust data integration strategy that supports your growth objectives, and using a modern data platform are all required to connect your data.

Reasons to bring your data together in a single platform include:

1. Overcoming Data Silos

Silos isolate data sets, making them inaccessible to other parts of your organization. These silos can arise from different software systems, geographic locations, or employees using their own data because they don’t trust or can’t easily access enterprise data. Data silos hinder collaboration and lead to incomplete insights. Data integration breaks down these silos, providing a unified view that enhances collaboration, decision-making, and comprehensive analysis.

2. Ensuring Data Consistency & Quality

With data flowing from so many sources, maintaining consistency and quality becomes a daunting task. Inconsistencies and inaccuracies in data can lead to flawed analysis and poor decision-making. By contrast, comprehensive data integration ensures that data is standardized, trusted, and accurate, providing a single source of truth that gives you confidence in your outcomes. Consistent, high-quality data is critical for accurate reporting and reliable business intelligence.

3. Enhancing Operational Efficiency

A unified view of critical information allows analysts and decision-makers to identify trends, optimize processes, and allocate resources more effectively. Integrated data also streamlines workflows, reduces redundancies, and minimizes errors, leading to improved operational efficiency. That’s because data integration helps you operate more smoothly, have agility to respond to market changes, and maintain a competitive edge.

4. Supporting Compliance & Security

In our era of stringent regulatory requirements, ensuring compliance is essential. Modern data integration platforms offer robust security features and compliance controls, helping you manage sensitive data across various environments. This includes implementing data quality rules, orchestration workflows, and secure data transfers, which are essential for maintaining regulatory compliance and protecting data integrity.

Four Benefits of Hybrid Data Integration

The ability to master data integration and achieve seamless operations across cloud, on-premises, or hybrid environments can unlock significant value across the business. With hybrid data integration, you realize these benefits:

1. Improved Organizational Decision-Making

Connected hybrid data provides a comprehensive view of business operations, enabling data-driven decision-making. By having access to accurate, up-to-date data, business leaders can make more informed choices that drive strategic growth and competitive advantage. When hybrid data is fully integrated, decision-making increases across all aspects of the business.

2. Increased Efficiency & Cost Savings

Bringing together data pipelines reduces the time and resources required for ongoing data management. This efficiency, coupled with automated data processes, translates into cost savings, reduced manual intervention, and optimized resource utilization. Plus, integrated data reduces the need for multiple data management tools, especially when using the right platform, which further lowers costs.

3. Enhanced Collaboration & Coordination

Data integration encourages data sharing across various departments and systems. When you have a data platform that offers easy data integration and accessibility, analysts and organizational teams can seamlessly share data and work together using the same information. Enhanced coordination leads to better alignment of efforts, more cohesive strategies, and improved overall performance, which also improves trust in your data.

4. Barrier-Free Access to Valuable Insights

Integrated data offers richer, more contextual insights than single data sets. This lets you uncover details that may have previously been hidden. These details give you a better understanding of customers, markets, and internal operations. As a result, you can make informed decisions, develop highly targeted strategies, and respond more effectively to changing market conditions.

Four Best Practices to Integrate Hybrid Data

One of the main questions I get asked during presentations is how to get started with data integration—especially with data spanning the cloud and on-premises systems. Many analysts and other data users are accustomed to complex processes that require IT help or advanced skill sets.

That is no longer the case! With the right strategy and data platform, hybrid data integration is easier than you may think. Here are four steps to ensure success:

1. Assess Your Data Integration Needs

Determining your organization’s specific needs is the essential first step. You’ll want to identify the data sources that need to be integrated, the types of data being handled, and the business processes that will benefit from integration. This assessment helps you choose the right data integration tools and strategy.

2. Pick the Right Data Platform

Select a robust data platform that simplifies data integration processes and makes it easy to build data pipelines to new sources. Also look for a platform that offers flexibility, scalability, and ease of use. Features such as codeless API integration, pre-built connectors, and data profiling capabilities significantly streamline the integration process and reduce the time to value.

3. Ensure Data Quality & Governance

Comprehensive integration should not come at the expense of data quality. Maintaining quality is a continuous process that entails enacting data quality rules, performing regular data profiling, and establishing governance policies to ensure integrated data remains accurate and reliable. This approach helps mitigate data inconsistencies and ensures compliance with internal and regulatory standards.

4. Benefit From Automated Processes

Automating data integration processes greatly reduces manual efforts and minimizes errors. Integration tools and data pipeline orchestration can automate data workflows. Automation enhances efficiency while also enabling real-time data integration to deliver timely insights.

Consider a Complete Data Platform That Simplifies Integration

Data integration is a necessity for businesses that want to thrive in our data-driven world. It requires a modern platform that allows you to connect data in hybrid environments without using a variety of tools. For example, the Actian Data Platform offers end-to-end integration, data warehousing capabilities, and analytics across your entire hybrid environment.

This single, unified data platform offers real-time insights along with superior price performance. Users across all skill levels can connect, manage, and analyze data using a fully integrated suite of data solutions, eliminating the need for multiple tools or manual code.

We can meet you wherever you are on your data journey while making data easy to access and use. Our platform can help you go from data to decisions with confidence, enabling you to:

Increase revenue
Reduce costs
Mitigate risk
Win market share
Support a data-driven culture

With the Actian Data Platform, you also benefit from native integration and data quality, flexible deployment, and ease of use. In addition, our dashboards give you visibility into activities so you can see information in an easy-to-understand format. Curious how the platform can transform data integration and management at your organization? Get a custom demo. I think you’ll be impressed.

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 Analytics

A Day in the Life of a Marketing Operations Specialist

Savannah Bruggeman

August 2, 2024

marketing operations specialist showing day to day

My day begins early, fueled by a strong cup of coffee, a protein smoothie, and a quick glance at the day’s agenda. As a marketing operations specialist, my role revolves around leveraging data to drive strategic decisions to improve our marketing efforts. I need a holistic, cross-channel view across the entire global marketing organization. I also need to be able to trust my data, having the confidence to know that it’s giving me the most accurate and up-to-date information.

The first task is usually a review of content performance metrics. This morning, I’m diving into the performance of content we created to support the new Actian Zen 16.0 launch. I not only need to be able to slice and dice content metrics such as views, clicks, and scroll depth, but I also have to be able to layer in lead acquisition information to see if I can attribute any new leads to the launch content. To do this effectively, I need de-siloed, integrated data that I can trust, so having a platform that allows me to connect a multitude of sources together is imperative.

Using Real-Time Dashboard to Spot Trends

Mid-morning is typically spent in a strategy meeting with the marketing team. For example, today I pulled up and shared real-time dashboards to present the latest performance trends and customer behaviors. We discussed optimizing our current launch efforts and brainstormed new strategies based on the data-driven insights I presented to make informed decisions that optimize our marketing efforts and resources.

By lunchtime, it’s time to step away from my computer, grab another cup of coffee, and have lunch. Knowing that my data is being integrated, stored, and managed, and dashboards are up to date allows me to feel good about taking 15 minutes to myself, sitting outside, and playing with my cat.

The afternoon is dedicated to taking a deeper dive into campaign content performance. I look through a number of sources to understand how content performs in various markets and channels. This segmentation helps tailor our messaging for upcoming campaigns, ensuring that we target the right audience.

The Need for Trusted, Easy-to-Use Data

Actian products play a pivotal role in my daily routine. The Actian Data Platform allows me to unify all my marketing data into a single dataset in a warehouse that is built for easy, no-code reporting and analytics. Plus, the pre-built marketing connectors and APIs to marketing data sources allow self-serve, so I don’t have to wait or rely on IT to get the insights I need. Most importantly, there is no fear of stale or duplicative data with native data quality rules. My critical dashboards are reliable and function as expected.

Reliable Tools are a Marketer’s Best Friend

Wrapping up my day, I feel confident that the Actian Data Platform has empowered me and others across our global marketing team to make informed decisions and optimize our marketing strategies effectively. With its efficiency and reliability, the Actian Data Platform is an indispensable tool in my daily workflow, driving better outcomes for our marketing initiatives.

Customers are using our products for similar use cases. Learn about how the AA uses the Actian Data Platform to make split-second decisions to deliver faster results to their customers.

About Savannah Bruggeman

Savannah Bruggeman is a Marketing Operations Specialist at Actian, bringing a data-driven mindset to campaign optimization. A recent Loyola University Chicago graduate, Savannah has quickly integrated fresh ideas into Actian's marketing processes. She specializes in marketing tech, analytics, and streamlining lead generation workflows. Her blog contributions cover marketing automation, lead management, and performance tracking. Explore her articles for actionable insights on driving marketing ROI.

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