Data Integration

Top Capabilities to Look for in Database Management Tools

Derek Comingore

January 2, 2024

As businesses continue to tap into ever-expanding data sources and integrate growing volumes of data, they need a solid data management strategy that keeps pace with their needs. Similarly, they need database management tools that meet their current and emerging data requirements.

The various tools can serve different user groups, including database administrators (DBAs), business users, data analysts, and data scientists. They can serve a range of uses too, such as allowing organizations to integrate, store, and use their data while following governance policies and best practices. The tools can be grouped into categories based on their role, capabilities, or proprietary status.

For example, one category is open-source tools, such as PostgreSQL or pgAdmin. Another category is tools that manage an SQL infrastructure, such as Microsoft’s SQL Server Management Studio, while another is tools that manage extract, transform, and load (ETL) and extract, load, and transform (ELT) processes, such as those natively available from Actian.

Using a broad description, database management tools can ultimately include any tool that touches the data. This covers any tool that moves, ingests, or transforms data, or performs business intelligence or data analytics.

Data Management Tools for Modern Use Cases

Today’s data users require tools that meet a variety of needs. Some of the more common needs that are foundational to optimizing data and necessitate modern capabilities include:

Data Management: This administrative and governance process allows you to acquire, validate, store, protect, and process data.
Data Integration: Integration is the strategic practice of bringing together internal and external data from disparate sources into a unified platform.
Data Migration: This entails moving data from its current or storage location to a new location, such as moving data between apps or from on-premises to the cloud.
Data Transformation: Transformative processes change data from one format or structure into another for usage and ensure it’s cleansed, validated, and properly formatted.
Data Modeling: Modeling encompasses creating conceptual, logical, and physical representations of data to ensure coherence, integrity, and efficiency in data management and utilization.
Data Governance: Effective governance covers the policies, processes, and roles used to ensure data security, integrity, quality, and availability in a controlled, responsible way.
Data Replication: Replicating data is the process of creating and storing multiple copies of data to ensure availability and protect the database against failures.
Data Visualization: Visualizing data turns it into patterns and visual stories to show insights quickly and make them easily understandable.
Data Analytics and Business Intelligence: These are the comprehensive and sophisticated processes that turn data into actionable insights.

It’s important to realize that needs can change over time as business priorities, data usage, and technologies evolve. That means a cutting-edge tool from 2020, for example, that offered new capabilities and reduced time to value may already be outdated by 2024. When using an existing tool, it’s important to implement new versions and upgrades as they become available.

You also want to ensure you continue to see a strong return on investment in your tools. If you’re not, it may make more sense from a productivity and cost perspective to switch to a new tool that better meets your needs.

Ease-of-Use and Integration Are Key

The mark of a good database management tool—and a good data platform—is the ability to ensure data is easy-to-use and readily accessible to everyone in the organization who needs it. Tools that make data processes, including analytics and business intelligence, more ubiquitous offer a much-needed benefit to data-driven organizations that want to encourage data usage for everyone, regardless of their skill level.

All database management tools should enable a broad set of users—allowing them to utilize data without relying on IT help. Another consideration is how well a tool integrates with your existing database, data platform, or data analytics ecosystem.

Many database management tool vendors and independent software vendors (ISVs) may have 20 to 30 developers and engineers on staff. These companies may provide only a single tool. Granted, that tool is probably very good at what it does, with the vendor offering professional services and various features for it. The downside is that the tool is not natively part of a data platform or larger data ecosystem, so integration is a must.

By contrast, tools that are provided by the database or platform vendor ensure seamless integration and streamline the number of vendors that are being used. You also want to use tools from vendors that regularly offer updates and new releases to deliver new or enhanced capabilities.

If you have a single data platform that offers the tools and interfaces you need, you can mitigate the potential friction that oftentimes exists when several different vendor technologies are brought together, but don’t easily integrate or share data. There’s also the danger of a small company going out of business and being unable to provide ongoing support, which is why using tools from large, established vendors can be a plus.

Expanding Data Management Use Cases

The goal of database management tools is to solve data problems and simplify data management, ideally with high performance and at a favorable cost. Some database management tools can perform several tasks by offering multiple capabilities, such as enabling data integration and data quality. Other tools have a single function.

Tools that can serve multiple use cases have an advantage over those that don’t, but that’s not the entire story. A tool that can perform a job faster than others, automate processes, and eliminate steps in a job that previously required manual intervention or IT help offers a clear advantage, even if it only handles a single use case. Stakeholders have to decide if the cost, performance, and usability of a single-purpose tool delivers a value that makes it a better choice than a multi-purpose tool.

Business users and data analysts often prefer the tools they’re familiar with and are sometimes reluctant to change, especially if there’s a long learning curve. Switching tools is a big decision that involves both cost and learning how to optimize the tool.

If you put yourself in the shoes of a chief data officer, you want to make sure the tool delivers strong value, integrates into and expands the current environment, meets the needs of internal users, and offers a compelling reason to make a change. You also should put yourself in the shoes of DBAs—does the tool help them do their job better and faster?

Delivering Data and Analytics Capabilities for Today’s Users

Tool choices can be influenced by no-code, low-code, and pro-code environments. For example, some data leaders may choose no- or low-code tools because they have small teams that don’t have the time or skill set needed to work with pro-code tools. Others may prefer the customization and flexibility options offered by pro-code tools.

A benefit of using the Actian Data Platform is that we offer database management tools to meet the needs of all types of users at all skill levels. We make it easy to integrate tools and access data. The platform offers no-code, low-code, and pro-code integration and transformation options. Plus, the unified platform’s native integration capabilities and data quality services feature a robust set of tools essential for data management and data preparation.

Plus, Actian has a robust partner ecosystem to deliver extended value with additional products, tools, and technologies. This gives customers flexibility in choosing tools and capabilities because Actian is not a single product company. Instead, we offer products and services to meet a growing range of data and analytics use cases for modern organizations.

Additional Resources:

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

The Actian Data Platform’s Superior Price-Performance

Phil Ostroff

December 27, 2023

When it comes to choosing a technology partner, price and performance should be top of mind. “Price-performance” refers to the measure of how efficiently a database management system (DBMS) utilizes system resources, such as processing power, memory, and storage, in relation to its cost. It is a crucial factor for organizations to consider when selecting a DBMS, as it directly impacts the overall performance and cost-effectiveness of their data management operations. The superior Data Management Platform, the Actian Data Platform, can provide the price-performance you’re looking for and more.

Getting the most value out of any product or service has always been a key objective of any smart customer. This is especially true of those who lean on database management systems to help their businesses compete and grow in their respective markets, even more so when you consider the exponential growth in both data sources and use cases in any given industry or vertical. This might apply if you’re an insurance agency that needs real-time policy quote information, or if you’re in logistics and need the most accurate, up-to-date information about the location of certain shipments. Addressing use cases like these as cost-effectively as possible is key in today’s fast-moving world. Key benefits of the Actian Data Platform include:

The Importance of Prioritizing Optimal Price-Performance

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

Technical users of various 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 discussing how to effectively “game” their DBMS platforms to get the best price-performance possible, sometimes leading to the development of shadow database solutions just to try and save costs.

According to a December 2022 survey by Actian, 56% of businesses struggle to maintain costs as data volumes and workloads increase. These types of increases affect the total cost of ownership and related infrastructure maintenance, support, query complexity, the number of concurrent users, and management overhead, which have a significant impact on the costs involved in using a database management system.

Superior Price-Performance

Having been established over 50 years ago, Actian was in the delivery room when enterprise data management was born. Since then, we’ve had our fingers on the pulse of the market’s requirements, developing various products that meet various use cases from various industries worldwide.

The latest version of the data management platform, the Actian Data Platform, includes native data integrations with 300+ out-of-the-box connectors and scalable data warehousing and analytics that produce REAL real-time insights to more confident support decision-making. The Actian Data Platform can be used on-premises, in the cloud across multiple clouds, and in a hybrid model. The platform also provides no-code, low-code, and pro-code solutions to enable a multitude of users, both technical and non-technical.

The 2023 Gigaom TCP-H Benchmark Test

At Actian, we’re really curious about how our platform compared with other major players and whether or not it could help deliver the price-performance being sought after in the market. In June of 2023, we commissioned a TCP-H Benchmark test with GigaOm, pitting the Actian Data Platform against both Google Big Query and Snowflake. This test involved running 22 queries against a 30TB TCP-H data set. Actian’s response times were better than the competition in 20 of those 22 requests. Furthermore, the benchmark report revealed that:

In a test of five concurrent users, Actian was overall 3x faster than Snowflake and 9x faster than Big Query.

In terms of price-performance, the Actian Data Platform produced even greater advantages when running the five concurrent user TPC-H queries. Actian proved roughly 4x less expensive to operate than Snowflake, based on cost per query per hour, and 16x less costly than BigQuery.

These were compelling results. Overall, the GigaOm TCP-H benchmark shows that the data management platform, the Actian Data Platform, is a high-performance cloud data warehouse that is well-suited for organizations that need to analyze large datasets quickly and cost-effectively.

Actian customer, the Automobile Association (AA), located in the United Kingdom, was able to reduce their quote response time to 400 milliseconds. Without the speed provided by the Actian Platform, they wouldn’t have been able to provide prospective customers the convenience of viewing insurance quotes on their various comparison pages, which allows them to gain and maintain a clear advantage over their competitors.

Let Actian Help

If price-performance is a key factor for you, and you’re looking for a complete data platform that will provide superior capabilities and ultimately lower your TCO, do these three things:

Contact us! One of our friendly, knowledgeable representatives will be in touch with you to discuss the benefits of the Actian Data Platform and how we can help you have more confidence in your data-driven decisions that keep your business growing.
Check out our technology solutions.

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 Management

Do You Have a Data Quality Framework?

Emma McGrattan

December 21, 2023

We’ve shared several blogs about the need for data quality and how to stop data quality issues in their tracks. In this post, we’ll focus on another way to help ensure your data meets your quality standards on an ongoing basis by implementing and utilizing a data quality management framework. Do you have this type of framework in place at your organization? If not, you need to launch one. And if you do have one, there may be opportunities to improve it.

A data quality framework supports the protocols, best practices, and quality measures that monitor the state of your data. This helps ensure your data meets your quality threshold for usage and allows more trust in your data. A data quality framework continuously profiles data using systematic processes to identify and mitigate issues before the data is sent to its destination location.

Now that you know a data quality framework is needed for more confident, data-driven decision-making and data processes, you need to know how to build one.

Establish Quality Standards for Your Use Cases

Not every organization experiences the same data quality problems, but most companies do struggle with some type of data quality issue. Gartner estimated that every year, poor data quality costs organizations an average of $12.9 million.

As data volumes and the number of data sources increase, and data ecosystems become increasingly complex, it’s safe to assume the cost and business impact of poor data quality have only increased. This proves there is a growing need for a robust data quality framework.

The framework allows you to:

Assess data quality against established metrics for accuracy, completeness, and other criteria.
Build a data pipeline that follows established data quality processes.
Pass data through the pipeline to ensure it meets your quality standard.
Monitor data on an ongoing basis to check for quality issues.

The framework should make sure your data is fit for purpose, meaning it meets the standard for the intended use case. Various use cases can have different quality standards (e.g. a customer’s bank account number must be 100% accurate, whereas a customer’s age or salary information might be provided within a range, so it won’t be 100% accurate). However, it’s common best practice to have an established data quality standard for the business as a whole. This ensures your data meets the minimum standard.

Key Components of a Data Quality Framework

While each organization will face its own unique set of data quality challenges, essential components needed for a data quality framework will be the same. They include:

Data Governance: Data governance makes sure that the processes, policies and roles used for data security, integrity, and quality are performed in a controlled and responsible way. This includes governing how data is integrated, handled, used, shared, and stored, making it a vital component of your framework.
Data Profiling: Actian defines data profiling as the process of analyzing data, looking at its context, structure and content, to better understand how it’s relevant and useful, what it’s missing, and how it can be augmented or improved. Profiling helps you identify any problems with the data, such as any inconsistencies or inaccuracies.
Data Quality Rules: These rules determine if the data meets your quality standard, or if it needs to be improved or transformed before being integrated or used. Predefining your rules will assist in verifying that your data is accurate, valid, complete, and meets your threshold for usage.
Data Cleansing: Filling in missing information, filtering out unneeded or bad data, formatting data to meet your standard, and ensuring data integrity is essential to achieving and maintaining data quality. Data cleansing helps with these processes.
Data Reporting. This reporting gives you information about the quality of your data. Reports can be documents or dashboards that show data quality metrics, issues, trends, recommendations, or other information.

These components work together to create the framework needed to maintain data quality.

Establish Responsibilities and Metrics

As you move forward with your framework, you’ll need to assign specific roles and responsibilities to employees. These people will manage the data quality framework and make sure the data meets your defined standards and business goals. In addition, they will implement the framework policies and processes, and determine what technologies and tools are needed for success.

Those responsible for the framework will also need to determine which metrics should be used to measure data quality. Using metrics allows you to quantify data quality across attributes such as completeness, timeliness, and accuracy. Likewise, these employees will need to define what good data looks like for your use cases.

Many processes can be automated, making the data quality framework scalable. As your data and business needs change and new data becomes available, you will need to evolve your framework to meet new requirements.

Expert Help to Ensure Quality Data

Your framework can monitor and resolve issues over the lifecycle of your data. The framework can be used for data in data warehouses, data lakes, or other repositories to deliver repeatable strategies, processes, and procedures for data quality.

An effective framework reduces the risk of poor-quality data—and the problems poor quality presents to your entire organization. The framework ensures trusted data is available for operations, decision-making, and other critical business needs. If you need help improving your data quality or building a framework, we’re here to help.

About Emma McGrattan

Emma McGrattan is CTO at Actian, leading global R&D in high-performance analytics, data management, and integration. With over two decades at Actian, Emma holds multiple patents in data technologies and has been instrumental in driving innovation for mission-critical applications. She is a recognized authority, frequently speaking at industry conferences like Strata Data, and she's published technical papers on modern analytics. In her Actian blog posts, Emma tackles performance optimization, hybrid cloud architectures, and advanced analytics strategies. Explore her top articles to unlock data-driven success.

Data Management

Is Your Data Quality Framework Up to Date?

Emma McGrattan

December 19, 2023

A data quality framework is the systematic processes and protocols that continually monitor and profile data to determine its quality. The framework is used over the lifecycle of data to ensure the quality meets the standard necessary for your organization’s use cases.

Leveraging a data quality framework is essential to maintain the accuracy, timeliness, and usefulness of your data. Yet with more data coming into your organization from a growing number of sources, and more use cases requiring trustworthy data, you need to make sure your data quality framework stays up to date to meet your business needs.

If you’re noticing data quality issues, such as duplicated data sets, inaccurate data, or data sets that are missing information, then it’s time to revisit your data quality framework and make updates.

Establish the Data Quality Standard You Need

The purpose of the framework is to ensure your data meets a minimum quality threshold. This threshold may have changed since you first launched your framework. If that’s the case, you will need to determine the standard you now need, then update the framework’s policies and procedures to ensure it provides the data quality required for your use cases. The update ensures your framework reflects your current data needs and data environment.

Evaluate Your Current Data Quality

You’ll want to understand the current state of your data. You can profile and assess your data to gauge its quality, and then identify any gaps between your current data quality and the quality needed for usage. If gaps exist, you will need to determine what needs to be improved, such as data accuracy, structure, or integrity.

Reevaluate Your Data Quality Strategy

Like your data quality framework, your data quality strategy needs to be reviewed from time to time to ensure it meets your current requirements. The strategy should align with business requirements for your data, and your framework should support the strategy. This is also an opportunity to assess your data quality tools and processes to make sure they still fit your strategy; and make updates as needed. Likewise, this is an ideal time to review your data sources and make sure you are bringing in data from all the sources you need—new sources are constantly emerging and may be beneficial to your business.

Bring Modern Processes into Your Framework

Data quality processes, such as data profiling and data governance, should support your strategy and be part of your framework. These processes, which continuously monitor data quality and identify issues, can be automated to make them faster and scalable. If your data processing tools are cumbersome and require manual intervention, consider modernizing them with easy-to-use tools.

Review the Framework on an Ongoing Basis

Regularly reviewing your data quality framework ensures it is maintaining data at the quality standard you need. As data quality needs or business needs change, you will want to make sure the framework meets your evolving requirements. This includes keeping your data quality metrics up to date, which could entail adding or changing your metrics for data quality.

Ensuring 7 Critical Data Quality Dimensions

Having an up-to-date framework helps maintain quality across these seven attributes:

Completeness

The data is not missing fields or other needed information and has all the details you need.

Validity

The data matches its intended need and usage.

Uniqueness

The data set is unique in the database and not duplicated.

Consistency

Data sets are consistent with other data in the database, rather than being outliers.

Timeliness

The data set offers the most accurate information that’s available at the time the data is used.

Accuracy

The data has values you expect and are correct.

Integrity

The data set meets your data quality and governance standards.

Your data quality framework should have the ability to cleanse, transform, and monitor data to meet these attributes. When it does, this gives you the confidence to make data-driven decisions.

What Problems Do Data Quality Frameworks Solve?

An effective framework can address a range of data quality issues. For example, the framework can identify inaccurate, incomplete, and inconsistent data to prevent poor-quality data from negatively impacting the business. A modern, up-to-date framework can improve decision-making, enable reliable insights, and potentially save money, by preventing incorrect conclusions or unintended outcomes caused by poor-quality data. A framework that ensures data meets a minimum quality standard also supports business initiatives and improves overall business operations. For instance, the data can be used for campaigns, such as improving customer experiences, or predicting supply chain delays.

Make Your Quality Data Easy to Use for Everyone

Maintaining data quality is a constant challenge. A current data quality framework mitigates the risk that poor quality data poses to your organization by keeping data accurate, complete, and timely for its intended use cases. When your framework is used in conjunction with the Actian Data Platform, you can have complete confidence in your data. The platform makes accurate data easy to access, share, and analyze to reach your business goals faster.

Additional Resources:

About Emma McGrattan

Data Intelligence

What is Cloud FinOps?

Actian Corporation

December 17, 2023

Cloud Financial Management Finops Conceptual Illustration

As organizations pursue their digital transformation journey, Cloud Computing has become an essential foundation for business performance. However, the unlimited flexibility of Cloud services is sometimes accompanied by rising costs, prompting companies to consider ways of controlling expenditure without degrading employee usage. To do so, they are implementing a Cloud financial management approach, also known as Cloud FinOps.

Does the term FinOps ring a bell? Derived from the contraction of Financial Operations, the term refers to a financial management methodology applied in Cloud Computing. The emergence of Cloud FinOps is linked to the need to control costs associated with the exponential growth in the use of Cloud services. This approach aims to reconcile the actions of financial, operational, and technical teams to optimize Cloud spending and guarantee optimal use of resources.

Cloud Finops focuses on cost transparency, identifying optimization opportunities, and empowering teams to take responsibility for their use of Cloud resources. By fostering collaboration between IT, finance, and business teams, Cloud Finops improves visibility, cost predictability, and operational efficiency, enabling companies to maximize the benefits of the Cloud while maintaining strict financial control.

How Does Cloud Finops Work?

Cloud Finops works through a combination of specific practices, processes, and architecture. In terms of architecture, cost monitoring tools, such as Cloud Financial Management platforms, are deployed to collect real-time data on resource usage. This information is then analyzed to identify opportunities for optimization.

In terms of processes, Cloud Finops encourages close collaboration between financial, operational, and technical teams, establishing regular review cycles to evaluate costs and adjust resource allocations. This iterative approach enables you to optimize spending on an ongoing basis, ensuring that your company makes efficient use of Cloud services while creating the conditions for total cost control.

What are Cloud FinOps Best Practices?

The practice of Cloud FinOps relies on a combination of methods, tools, processes, and vision. To take full advantage of your Cloud Finops approach, you’ll need to foster the emergence of a number of best practices.

Transparency & Synergy

The founding principles of Cloud FinOps are based on cross-functional collaboration. This involves the close involvement of financial, operational, and technical teams. This synergy enables a common understanding of business objectives and associated costs, promoting continuous optimization of Cloud resources.

Automation & Control

Automating processes is essential to ensure optimum cost management on a day-to-day basis. The use of automation solutions for automatic resource provisioning, instance scheduling, and all repetitive cloud management tasks, improves operational efficiency and avoids unnecessary waste.

Reporting & Analysis

To guarantee cost transparency, you need to be able to provide detailed, accessible reports on resource utilization. These reports enable teams to make informed decisions. This greater visibility encourages users to take responsibility and makes it easier to identify areas for improvement.

What are the Main Challenges for Cloud Finops?

To deliver its full potential, Cloud FinOps must overcome the complexity of Cloud pricing models. Indeed, the diversity of these models, which vary from one Cloud provider to another, makes it difficult to accurately forecast costs. As a result, expenditure can fluctuate according to demand, making budget planning more delicate.

Finally, compliance management, data security, and Cloud migration considerations are also complex aspects to integrate into an effective FinOps approach.

What Does the Future Hold for Cloud Finops?

As companies move further along the road to cloudification, the future of Cloud FinOps looks brighter month after month. Tools and platforms specializing in the financial management of Cloud resources, offering advanced cost analysis, automation, and forecasting capabilities, are likely to continue to grow in line with Cloud adoption.

Closer integration and collaboration between financial, operational, and technical teams will enable companies to place greater emphasis on financial governance in the Cloud, integrating FinOps principles right from the start of their Cloud projects.

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 Management

How IT Leaders Leverage Unstructured Data

Emma McGrattan

December 15, 2023

Data-driven organizations are accustomed to using structured data. This type of data is well-defined, organized, stored in a tabular form, and typically managed in a relational database management system (RDBMS). The data is predefined and formatted to fit a set structure. A vast range of tools have been developed to optimize this type of data, which includes customer names, sales data, and transaction dates. The data is easily searchable by programming languages and data analytics tools, unlike unstructured data.

Unstructured data is different. It does not have a predefined data model or structure, making it more challenging to organize, process, and analyze using traditional databases or structured data formats. Unstructured data lacks a specific schema or format, and it can take many forms, including text, images, videos, audio recordings, social media posts, and more.

Let’s explore how IT leaders can leverage unstructured data to gain a better business advantage.

Automate Workflows for Unstructured Data

The majority of data—between 80% and 90%, according to some estimates, is unstructured. This means the data represents a huge treasure trove of value to businesses that can leverage it and use it effectively.

Bringing automated processes to unstructured data can help ensure the data is properly ingested and stored in a way that makes it accessible and usable across the enterprise. Automating processes improves efficiency, yet automation is oftentimes complex due to the data’s variability, size, and lack of a standard format. At the same time, organizations that can successfully automate unstructured data can unlock insights faster to drive decision-making.

According to TDWI, “Automating workflows to curate and deliver data to cloud-native analytics tools will help IT organizations efficiently leverage massive stores of unstructured data while reducing the manual effort required for data curation by data analysts and researchers. Data workflow automation is becoming a new requirement of unstructured data management platforms.”

IT leaders who implement the tools and technologies to harness unstructured data and make it available to analysts and business users can realize a variety of benefits such as:

Extracting information from texts to better understand customer needs, customer sentiments, and market trends.
Reviewing social media and other unstructured data to understand customer sentiment, preferences and behaviors, then delivering personalized recommendations for products, services, or content.
Analyzing text in documents such as legal contracts to ensure compliance.
Performing analysis on images for use cases spanning medical imaging diagnosis to quality control.
Identifying positive and negative customer reviews to understand how customers view a brand and to inform marketing strategies.
Reviewing unstructured data sources, including emails, text data, and transaction records to help detect fraud.
Integrating unstructured data with structured customer data to provide a complete view of customers, which can be used to personalize campaigns, improve customer service, and enhance customer experiences.

Using Unstructured Data for AI

Organizations across all industries are looking to implement Artificial Intelligence (AI) or Generative AI use cases. These use cases require data—often large volumes of data—and that can include unstructured data.

Fast Company writes that “unstructured data is the fuel needed for AI, yet most organizations aren’t using it well. One reason for this is that unstructured data is difficult to find, search across, and move, due to its size and distribution across hybrid cloud environments.”

Making all data readily available can support a diverse range of use cases, including those involving AI. For example, chatbots can analyze unstructured data to route customer questions to the appropriate source for an answer.

In addition, unstructured data, including streaming data from social media posts, news articles, sensor data, and other sources, can enable new possibilities for AI and machine learning. These possibilities include enabling AI to understand context and quickly analyze large data sets or volumes of text to identify relationships or summarize the information.

Integrate Data on an Easy-to-Use Platform

Managing and leveraging unstructured data allows organizations to gain deeper, richer insights into all aspects of the business. Likewise, implementing a data management strategy that includes unstructured data gives IT visibility into where the data is stored, which team owns the data, the costs to store it, and other pertinent information.

The ability to leverage alternative data, such as unstructured data, helps businesses make more informed decisions, identify changing market conditions sooner, and reach business objectives faster. Accessing unstructured data can advance priorities that may not be readily apparent. For instance, it can help with environmental, social, and governance (ESG) initiatives by enhancing transparency, assisting with ESG reporting and disclosure, and benchmarking ESG performance against industry leaders.

The unified Actian platform makes data easy across cloud, on-premises, and hybrid environments to empower business users and drive data-intensive applications. It also supports businesses’ confidence in their data, improves data quality, assists in lowering costs, and enables better decision-making across the business.

The Actian Data Platform is unique in its ability to collect, manage, and analyze data in real-time with its transactional database, data integration, data quality, and data warehouse capabilities in one easy-to-use platform.

About Emma McGrattan

Data Management

How Partitioning on Your Data Platform Improves Performance

Colm Ginty

December 14, 2023

One of my goals as Customer Success Manager for Actian is to help organizations improve the efficiency and usability of our modern product suite. That’s why I recently wrote an extensive article on partitioning best practices for the Actian Data Platform in Actian communities resource.

In this blog, I’d like to share how partitioning can help improve the manageability and performance of the Actian platform. Partitioning is a useful and powerful function that divides tables and indexes into smaller pieces and can even subdivide them further into even smaller pieces. It’s like taking thousands of books and arranging them into categories—which is the difference between a massive pile of books in one big room and having the books strategically arranged into smaller topic areas; like you see in a modern library.

You can gain several business and IT benefits by using the partitioning function that’s available on our platform. For example, partitioning can lower costs by storing data most optimally and boost performance by executing queries in parallel across small, divided tables.

Why Distributing and Partitioning Tables are Critical to Performance

When we work in the cloud, we use distributed systems. So instead of using one large server, we use multiple regular-sized servers that are networked together and function like the nodes of a single enormous system. Traditionally, these nodes would both store and process data because storing data on the same node it is processed on enables fast performance.

Today, modern object storage in the cloud allows for highly efficient data retrieval by the processing node, regardless of where the data is stored. As a result, we no longer need to place data on the same node that will process it to gain a performance advantage.

Yet, even though we no longer need to worry about how to store data, we do need to pay attention to the most efficient way to process it. Oftentimes, the tables in our data warehouse contain too much data to be efficiently processed using only one node. Therefore, the tables are distributed among multiple nodes.

If a specific table has too much data to be processed by a single node, the table is split into partitions. These partitions are then distributed among the many nodes—this is the essence of a “distributed system,” and it lends itself to fast performance.

Partitioning in the Actian Data Platform

Having a partitioning strategy and a cloud data management strategy can help you get the most value from your data platform. You can partition data in many ways depending on, for example, an application’s needs and the data’s content. If performance is the primary goal, you can spread the load evenly to get the most throughput. Several partitioning methods are available on the Actian Data Platform.

Partitioning is important with our platform because it is architected for parallelism. Distributing rows of a large table to smaller sub-tables, or partitions, helps with fast query performance.

Users have a say in how the Actian platform handles partitions. If you choose to not manage the partition, the platform defaults to the automatic setting. In that case, the server makes its best effort to partition data in the most appropriate way. The downside is that with this approach, joining or grouping data that’s assigned to different nodes can require moving data across the network between nodes, which can increase costs.

Another option is to control the partitions yourself using a hash value to distribute rows evenly among partitions. This allows you to optimize partitioning for joins and aggregations. For example, if you’re querying data in the data warehouse and the query will involve many SQL joins or groupings, you can partition tables in a way that causes certain values in columns to be assigned to the same node, which makes joins more efficient.

When Should You Partition?

It’s a best practice to use the partitioning function in the Actian Data Platform when you create tables and load data. However, you probably have non-partitioned tables in your data warehouse, and redistributing this data can improve performance.

You can perform queries that will tell you how evenly distributed the data is in its current state in the data warehouse. You can then determine if partitioning is needed.

With Actian, you have the option to choose the best number of partitions for your needs. You can use the default option, which results in the platform automatically choosing the optimal number of partitions based on the size of your data warehouse.

I encourage customers to start with the default, then, if needed, further choose the number of partitions manually. Because the Actian Data Platform is architected for parallelism, running queries that give insights into how your data is distributed and then partitioning tables as needed allows you to operate efficiently with optimal performance.

For details on how to perform partitioning, including examples, graphics, and code, join the Actian community and view my article on partitioning best practices. You can learn everything you need to know about partitioning on the Actian Data Platform in just 15 minutes.

About Colm Ginty

Colm Ginty is a Customer Success Engineer at Actian, committed to helping businesses maximize value from the Actian Data Platform. With 8 years as a Data Engineer specializing in distributed systems like Spark and Kafka, Colm brings hands-on expertise in real-time data processing. He has presented case studies at data engineering meetups, focusing on system scalability and cost optimization. On the Actian blog, Colm writes about deployment best practices, performance tuning, and big data architectures. Check out his latest articles for practical guidance.

Data Management

Common Healthcare Data Management Issues and Solutions

Scott Norris

December 12, 2023

Summary

This blog addresses prevalent data management challenges in healthcare, emphasizing the need for modern solutions to ensure data quality, compliance, and integration across various systems.

Data Silos and Shadow IT: Departments often create isolated data repositories, bypassing IT protocols, leading to disconnected and outdated information. Implementing scalable data platforms with user-friendly integration capabilities can unify data and promote a data-driven culture.
Integration and Quality Barriers: Legacy systems may lack interoperability, hindering seamless data sharing. Adopting modern platforms that automate data profiling and ensure quality can provide comprehensive patient records and support data analytics.
Regulatory Compliance Challenges: Healthcare data is subject to strict regulations like HIPAA. Utilizing compliant data management technologies, role-based access controls, and encryption can protect patient data and maintain compliance.

A modern data management strategy treats data as a valuable business resource. That’s because data should be managed from creation to the point when it’s no longer needed in order to support and grow the business. Data management entails collecting, organizing, and securely storing data in a way that makes it easily accessible to everyone who needs it. As organizations create, ingest, and analyze more data than ever before, especially in the healthcare field, data management strategies are essential for getting the most value from data.

Making data management processes scalable is also critical, as data volumes and the number of data sources continue to rapidly increase. Unfortunately, many organizations struggle with data management problems, such as silos that result in outdated and untrustworthy data, legacy systems that can’t easily scale, and data integration and quality issues that create barriers to using data.

When these challenges enter the healthcare industry, the impact can be significant, immediate, and costly. That’s because data volumes in healthcare are enormous and growing at a fast rate. As a result, even minor issues with data management can become major problems as processes are scaled to handle massive data volumes.

Data management best practices are essential in healthcare to ensure compliance, enable data-driven outcomes, and handle data from a myriad of sources. The data can be connected, managed, and analyzed to improve patient outcomes and lower medical costs. Here are common data management issues in healthcare—and how to solve them:

Data Silos are an Ongoing Problem

Healthcare data comes from a variety of sources, including patient healthcare records, medical notes and images, insurance companies, financial departments, operations, and more. Without proper data management processes in place, harnessing this data can get very complex, very fast.

Complexity often leads to data silos and shadow IT approaches. This happens when departments or individuals want to quickly access data, but don’t want to follow established protocols that could require IT help, so they take shortcuts. This results in islands of data that are not connected and may be outdated, inaccurate, or have other quality issues.

Breaking down silos and connecting data requires the right data platform. The platform should be scalable, have easy-to-use integration capabilities to unify data, and make data easy-to-access, without IT assistance. Making data easy discourages silos, fosters a data-driven culture that supports data management best practices, and allows all users to tap into the data they need.

Barriers to Data Integration and Quality

Many legacy systems used by healthcare organizations are not integration-friendly. They may have been built as a single-purpose solution and interoperability was not a primary concern. In today’s healthcare environment, connectivity is important to enable data sharing, automation, and visibility into the organization.

“The flow of data is as important as the flow of people,” according to FQHC Associates, which specializes in Federally Qualified Health Center (FQHC) programs. “One common issue in connected care is a lack of data standardization, in which the different platforms used by different departments are not mutually readable or easily transferable. This results in data silos, blocks productivity, and even worse, leads to misunderstandings or errors.”

Data integration—bringing together all required data from all available sources—on a single platform helps inform decision-making, delivers complete patient records, and enables healthcare data analytics. The Centers for Medicare & Medicaid Services (CMS) has mandates to prioritize interoperability—the ability for systems to “speak” to each other.

A modern platform is needed that offers simple integration and ensures data quality to give stakeholders confidence in their data. The platform must be able to integrate all needed data from anywhere, automate data profiling, and drive data quality for trusted results. Ensuring the accuracy, completeness, and consistency of healthcare data helps prevent problems, such as misdiagnosis or billing errors.

Complying With Ever-Changing Regulations

The healthcare industry is highly regulated, which requires data to be secure and meet compliance mandates. For example, patient data is sensitive and must meet regulations, such as the Health Insurance Portability and Accountability Act (HIPAA).

Non-compliance can result in stiff legal and financial penalties and loss of patient trust. Protecting patient data from breaches and unauthorized access is a constant concern, yet making data readily available to physicians when treating a patient is a must.

Regulations can be complex, vary by state, and continually evolve. This challenges healthcare organizations to ensure their data management plan is regularly updated to meet changing requirements. Implementing role-based access controls to view data, using HIPAA-compliant data management technologies, and encrypting data help with patient privacy and protection.

Similarly, data governance best practices can be used to establish clear governance policies. Best practices help ensure data is accurate, protected, and compliant. Healthcare organizations need a modern data platform capable of offering transparency into data processes to ensure they are compliant. Automating data management tasks removes the risk of human errors, while also accelerating processes.

Dealing With Duplicate Patient Records

The healthcare industry’s shift from paper-based patient records to electronic health records enabled organizations to modernize and benefit from a digital transformation. But this advancement came with a challenge—how to link a person’s data together in the same record. Too often, healthcare facilities have multiple records for the same patients due to name or address changes, errors when entering data, system migrations, healthcare mergers, or other reasons.

“One of the main challenges of healthcare data management is the complexity of managing and maintaining patient, consumer, and provider identities across the enterprise and beyond, especially as your organization grows organically and through partnerships and acquisition,” according to an article by MedCity News.

This problem increases data management complexity by having duplicate records for the same patients. Performing data cleansing can detect duplicate records and reconcile issues. Likewise, having a robust data quality management framework helps prevent the problem from occurring by establishing data processes and identifying tools that support data quality.

Delivering Trust in Healthcare Data

Many healthcare organizations struggle to optimize the full value of their data, due to a lack of data standards, poor data quality, data security issues, and ongoing delays in data delivery. All of these challenges reduce trust in data and create barriers to being a truly data-driven healthcare company.

Solving these issues and addressing common data management problems in healthcare requires a combination of technology solutions, data governance policies, and staff training. An easy-to-use data platform that solves issues for data scientists, managers, IT leaders, and others in healthcare organizations can help with data management, data visualization, and data accessibility.

For example, the Actian Data Platform gives users complete confidence in their data, improves data quality, and offers enhanced decision-making capabilities. It enables healthcare providers to:

Connect data sources. Integrate and transform data by building or using existing APIs via easy-to-use, drag-and-drop blocks for self-service, removing the need to use intricate programming or coding languages.
Connect to multiple applications. Create connections to applications offering a REST or SOAP API.
Broaden access to data. Use no-code, low-code, and pro-code integration and transformation options to broaden usability across the business.
Simplify data profiling. Profile data to identify data characteristics and anomalies, assess data quality, and determine data preparation needs for standardization.
Improve data quality. Track data quality over time and apply rules to existing integrations to quickly identify and isolate data inconsistencies.‌

Actian offers a modern integration solution that handles multiple integration types, allowing organizations to benefit from the explosion of new and emerging data sources and have the scalability to handle growing data volumes. In addition, the Actian Data Platform is easy to use, allowing stakeholders across the organizations to truly understand their data, ensure HIPAA compliance, and drive desired outcomes faster.

Find out how the platform manages data seamlessly and supports advanced use cases such as generative AI by automating time-consuming data preparation tasks.

Additional Resources:

About Scott Norris

Scott Norris is a veteran IT professional with 30+ years as a Program Manager, Solutions Architect, and System Engineer. He has managed complex implementations involving data integration, pre-/post-sales consultations, and advanced system design. Scott has led workshops on program/project management, training, and application development. On the Actian blog, Scott shares tips on unified data strategies, client engagement, and modernization. Check out his posts for strategic guidance.

Data Intelligence

3 AI Trends Identified by Gartner to Look Out for in 2024

Actian Corporation

December 11, 2023

Analyzing Digital Data Copy Space Statistics, Financial Chart, Economy

Gartner is the world’s leading data research and advisory firm. At the Gartner Data & Analytics Summit 2023, the firm shared its vision of the key trends likely to impact and shape the future of Data Science and Machine Learning. Here’s a look back at the 3 AI trends to watch for your business in 2024.

At its Data & Analytics Summit in Sydney this past summer, Gartner outlined the key trends that will influence the future of data science and machine learning (DSML). At a time when many industries are being impacted by the explosion in the use of AI in business, the firm highlights the growing importance of data in artificial intelligence which is embarking on a path that is both more ethical and more responsible.

Trend #1: Edge AI as a Promise of Responsiveness

One of the Gartner 2024 trends is Edge AI. It enables calculations to be carried out close to where the data is collected, eliminating the need for a centralized Cloud Computing center or external data center. This promotes making intelligent decisions more quickly, without the need to connect to the Cloud or remote data centers. By enabling faster execution of AI algorithms, latency is reduced and systems are more responsive.

Edge AI applies to IoTs, taking advantage of available local computing power. This approach is crucial for applications requiring real-time decision-making, such as autonomous driving or smart medical devices. Edge AI also offers advantages in terms of data confidentiality and security. Indeed, because certain sensitive information can be processed locally without being transmitted to remote servers, this eliminates unnecessary data exposure to external threats.

This convergence of AI and edge computing paves the way for solutions that are not only more efficient but also more responsible, as they are potentially more energy-efficient. According to forecasts by the Gartner Institute, more than 55% of all data analysis performed by deep neural networks will take place at the point of capture in an Edge system by 2025, compared to less than 10% in 2021!

Trend #2: Responsible AI as an Ethical Promise

Gartner highlights the key role of Responsible AI in its AI trend forecast for 2024. This set of principles and practices aims to ensure that AI is used ethically and responsibly. It addresses the social, environmental, and economic impact of AI, and aims to minimize risks and maximize benefits.

In technological terms, Responsible AI translates into a series of measures aimed at improving the transparency, reliability, and safety of AI systems. The key focus is on data and algorithm transparency. This enables users to understand how AI systems work, and to detect any misappropriated biases so that data can be used in a virtuous and respectful way. The second major area is the reliability of AI systems, whose robustness must be guaranteed, even under complex conditions or in the event of computer attacks. Thus, AI systems must be secure to protect personal data and sensitive information.

According to the Gartner Institute, “Responsible AI makes AI a positive force rather than a threat to society and itself”. To achieve this, the advice is simple: adopt a risk-proportionate approach to bringing value to AI, while exercising extreme caution when applying solutions and models.

Trend #3: Data-Centric AI as a Promise of Relevance

Gartner’s third major AI trend for 2024 highlights the centrality of data in the mass adoption of AI. Artificial intelligence is based on algorithms, which determine its relevance and performance. But rather than focusing solely on algorithms, data-centric AI focuses more on the quality, diversity, and governance of data. The aim is to improve model accuracy by relying on rich, perfectly maintained data sets.

For companies, data-centric AI promises better customer understanding, more informed decision-making, and more robust innovations. By focusing on data quality, organizations can increase the effectiveness of their AI initiatives, reduce algorithmic biases, and boost user confidence. In doing so, data-centric AI offers a more reliable and sustainable way of harnessing the potential of artificial intelligence. According to Gartner forecasts, by 2024, 60% of AI data will be used to simulate reality, identify future scenarios, and reduce the risk of AI errors, compared with just 1% in 2021!

Between performance, ethics, compliance, safety, and responsibility, the AI 2024 roadmap is ambitious. Will you rise to the challenge?

About Actian Corporation

Manufacturing

The Future of Automation in Manufacturing

Robert Gorsuch

December 7, 2023

As manufacturers know, automation enables a range of high-value benefits, such as cost and time savings. The Outlook of Automation in 2023 from Thomas Insights captures these advantages succinctly by noting that “automation promises lower operating costs, improved worker safety, a higher return on investment (ROI), better product quality, operational efficiencies, and competitive advantage.”

While automation isn’t new, manufacturers have been automating processes for decades as opportunities to expand it into new areas of the factory floor continue to emerge. Meanwhile, customizing and modernizing automation to fit a manufacturer’s unique needs can bring additional benefits, such as filling the gap caused by a labor shortage, making manufacturing processes more efficient, and meeting the changing needs of contract and original equipment manufacturing.

As automation continues to shape the future of manufacturing, automating data-driven processes will likewise make growing volumes of data readily available to support manufacturing use cases. The data can also make existing manufacturing processes more efficient and potentially more sustainable.

Automation in Modern Factories Comes in Many Varieties

Manufacturers see automation as a priority area for investing. According to a Deloitte survey, 62% of large companies plan to invest in robotics and automation, making it the top focus. The next highest area of investment is data analytics at 60%.

Digital transformations, which have swept through almost every industry, have helped lay the groundwork for the future of automation. In fact, according to a survey by McKinsey, 94% of respondents said digital solutions will be important to their future automation efforts. Other key technologies that are enabling the future of automation, according to McKinsey, include soft programmable logic controllers, digital twins, and teach-less robotics.

Most people probably immediately think of robotics when they think of automation in manufacturing. While the use of robotics has certainly advanced the industry, automation also extends into areas that many people don’t see.

For example, I’ve worked on projects that were as straightforward as transitioning from paper-based processes and manual entries on a computer to automating digital workflows that didn’t require human intervention. This type of project delivers time and money savings, and transparency into processes, even though it’s not as visible as a robotic arm on a factory floor.

Automating Both Data and Manufacturing Processes

Traditionally, automation has played a key role in manufacturers’ process controls. This includes supporting quality assurance processes, identifying risks, and predicting outcomes. The driving force for all of this automation at an enterprise level, not surprisingly, is data. However, getting a consolidated and normalized view of data is challenging. It requires a modern data platform that offers data warehousing and integration capabilities that bring together data from all needed sources and automates data pipelines.

The more disparate that the application landscape, ecosystem, and infrastructure become for manufacturers, the more they are going to need efficient and scalable data preparation and management capabilities. Legacy technologies and outdated processes that still require a lot of manual intervention will delay insights and are not scalable.

One proven way to solve this challenge is to use a small footprint, low maintenance, high performance database management system like Actian Zen. It can be embedded as part of an Internet of Things (IoT) strategy to advance manufacturing operations, including automation. With Actian Zen, manufacturers can also reap the benefits of edge applications and devices, which enable data-driven improvements all the way down to the process controller level.

Performing analytics at the edge and transmitting the results, rather than moving the entire data set to a data warehouse or platform for analysis, avoids the task of transferring data. This is certainly a big advantage, especially when manufacturers are faced with large data volumes, limited bandwidth, and latency issues.

For example, Actian is currently setting up a proof of concept to intercept data streams from a satellite that was shot up by a space organization that tracks GPS data from endangered animals. There’s a big problem with poaching for these animals, but if we can monitor their GPS movements, we can detect and then alert authorities when there are anomalies. This type of capability can help manufacturers pinpoint potential problems in automation by recognizing patterns or behaviors that deviate from a baseline.

A lot of IT applications require 5G or Global System for Mobile Communications (GSM), but these options have limited bandwidth. That’s why smart driving vehicles have not taken off—the bandwidth doesn’t support the vehicles’ massive data needs. Once the bandwidth improves to move data at the speed required for data-intensive applications, companies across all industries can find new use cases for automation in everything from manufacturing to the automotive industry.

Keeping Assembly Line Belts Moving Efficiently

Automation and digital transformations often go hand in hand to drive process and operational improvements across manufacturing. “Organizations are now utilizing automation as their most up-to-date approach for innovating and operating,” according to Smartbridge. “Companies are putting automation at the forefront of their digital strategies, making it a core priority for the entire enterprise.”

Similarly, Boston Consulting Group calls digitization and automation core elements of the “factory of the future.” Part of the reason is because manual processes are not designed for automation. Digital processes are, so they lend themselves to automating key aspects of supply chains, manufacturing tasks, and other operations. For example, manufacturers need to ensure they have enough supplies on-premises to keep their assembly line belts moving efficiently, but without incurring bloated inventory that increases storage costs. This is all in the interest of keeping production moving while minimizing costs, and nowadays meeting sustainability goals.

Accurately predicting and meeting rolling forecasts is the holy grail in manufacturing. Rolling forecasts are continuously updated based on past performance, current trends and operations, and other factors. Automating data processes to feed these forecasts gives stakeholders the real-time insights needed to make informed decisions that can impact all aspects of manufacturing.

Our customer Aeriz is a good example. The company unifies and analyzes data to inform a wide range of decisions. Aeriz is a national aeroponic cannabis brand, but it runs manufacturing processes that are reminiscent of those used by pharmaceutical companies. The organization’s leaders put a lot of thought into processes and automation controls, such as controlling the humidity and temperature for growing cannabis as well as the speed of conveyor belts for manufacturing processes. Like other companies, Aeriz relies on data to tell a comprehensive story about the state of the business and what is expected to happen next.

What this demonstrates is that the more opportunities there are to automate, from data processing to assembly line interactions, the more companies benefit from accuracy and time savings, which can transform standard operating procedures. Every step that can be automated provides value.

Improving Product Lifecycle Management

Bringing automation into manufacturing can solve new and ongoing challenges. This includes expanding the use of automation to optimize efficiencies, encourage sustainable operations, and make processes less complex. When the International Society of Automation (ISA) published a blog on the four biggest manufacturing automation trends of 2023, it called out connecting automation to sustainability goals, using automation to address skills shortages, leveraging automation as a competitive differentiator, and implementing more accessible forms of automation such as turnkey robotics.

These trends can certainly bring welcome advantages to manufacturing. Yet, from a big-picture view, one key benefit of automation is how it advances overall operations. When we think of manufacturing, whether it’s a mid-sized custom manufacturer or a large global enterprise, we oftentimes think of automating repetitive tasks. Once tasks are automated, it doesn’t mean the job is done. There may be opportunities to make changes, even minor enhancements, to improve individual processes or large-scale operations.

For example, manufacturers may find that they can further optimize the movement of a robotic arm to be faster or more efficient. Plus, connecting data from automated robotics with other sources across a factory floor may uncover ways to minimize waste, identify any silos or duplicated processes, and inform planning strategies. All of this ultimately plays a role in improving product lifecycle management, which can include everything from product design to testing and development. Improvements made to product lifecycle management can trickle down to improvements made on the factory floor.

Optimizing automation to drive the future of manufacturing requires not only an accurate overview of everything going on inside the factory walls, but also insight into what’s going on outside. This includes understanding supply chain operations and tier one, tier two, and tier three vendors. This helps ensure the manufacturer doesn’t run out of an essential item that can shut down production and bring automated processes to a halt.

The Future of Automation Will Rely on Data

One aspect of modernization that’s been consistent over the decades—and is positioned to be the driving force into the future—is the use of data. As new use cases emerge, all available data will be needed to inform decisions and enable precision automation.

Manufacturers will need the ability to go from data source to decision with confidence. At Actian, we deliver by making data easy. We enable manufacturers and others to access unified, trusted data in real-time. The Actian Data Platform provides data integration, quality, and superior performance, along with native integration and codeless transformations that allow more users to access data to drive business goals.

With new capabilities such as integration as a service and database as a service, the Actian Data Platform meets the current and future needs of manufacturers.

Additional Resources:

About Robert Gorsuch

Robert Gorsuch is a Software Engineer at Actian, bringing 30 years of IT industry experience in architecture, design, and implementation. He specializes in enterprise-grade data integration, management, and analytics solutions, spanning multiple hardware and software ecosystems. Robert has led development teams across sectors, contributing to business process automation and optimization. On the Actian blog, Robert discusses advanced integration frameworks and best practices in data engineering. Read his recent posts to navigate complex data pipelines effectively.

ESG

Generative AI for ESG Reporting and Compliance

Teresa Wingfield

December 5, 2023

Environmental, social, and governance (ESG) initiatives assess and measure the sustainability and societal impact of a company or investment. The number of countries and even within the United States that are implementing mandatory ESG reporting is rapidly expanding. One of the most far-reaching laws is the European Union’s Corporate Sustainability Reporting Directive (CSRD), which requires companies to publish reports on the social and environmental risks they face, and on how their activities impact the rights of people and the environment. According to the Wall Street Journal, more than 50,000 EU-based companies and approximately 10,400 non-EU enterprises are subject to CSRD compliance and some of these companies will need to disclose as many as 1,000 discrete items.

Companies using manual processes for data collection will find it difficult to keep up with the breadth and depth of these mandates. This is why Generative AI will begin to play a significant role in streamlining data collection, automating reporting, improving accuracy and transparency, identifying risks, and resolving compliance gaps.

How Generative AI Can Help With ESG Reporting and Compliance

Data Integration

Generative AI can help address various integration challenges and streamline processes such as data mapping and transformation, data conversion, data cleansing, data standardization, data enrichment, data validation, and more. This assistance allows companies to consider a wider range of data and criteria, which can lead to more accurate assessments of a company’s ESG performance and compliance.

Natural Language Processing (NLP)

Generative AI models based on NLP can extract and analyze information from regulatory texts, legal documents, and compliance guidelines. This can be valuable for understanding and adhering to complex compliance requirements.

ESG Reporting Automation

Generative AI can automate compiling ESG compliance reports, reducing the time and resources required to gather, analyze, and present data.

Data Analysis

Generative AI can process and analyze vast amounts of data to provide insights related to ESG performance and compliance. It can identify trends, patterns, and areas to help a company improve its ESG practices.

Regulatory Change Analysis

Generative AI can monitor and analyze changes in regulatory requirements. By processing and generating summaries of new regulations and regulation updates, it helps organizations stay informed and adapt their compliance practices to changes.

Compliance Chatbots

Chatbots powered by generative AI can answer compliance-related questions, guide employees and customers through compliance processes, and provide real-time compliance information. Compliance chatbots can be particularly useful in industries with strict regulatory requirements, such as banking and healthcare.

Risk Assessment

Generative AI can analyze ESG data to identify potential risks that can lead to non-compliance, such as supply chain vulnerabilities, pollution, emissions, resource usage, and improper waste disposal, helping companies proactively address these issues.

ESG Investment

Generative AI can assist in creating investment strategies that help fill ESG compliance gaps by identifying companies or assets that meet ESG criteria.

How the Actian Data Platform Can Help With Generative AI

You may have clear and comprehensive ESG policies, but inadequate data collection, reporting, analytics, and risk assessment can lead to non-compliance and dramatically increase the time and resources needed for meeting extensive and demanding reporting mandates. The Actian Data Platform makes it simple to connect, manage, and analyze your compliance-related data. With the unified Actian platform, you can easily integrate, transform, orchestrate, store, and analyze your data. It delivers superior price performance as demonstrated by a recent GigaOm Benchmark, enabling REAL real-time analytics with split-second response times.

About Teresa Wingfield

Teresa Wingfield is Director of Product Marketing at Actian, driving awareness of the Actian Data Platform's integration, management, and analytics capabilities. She brings 20+ years in analytics, security, and cloud solutions marketing at industry leaders such as Cisco, McAfee, and VMware. Teresa focuses on helping customers achieve new levels of innovation and revenue with data. On the Actian blog, Teresa highlights the value of analytics-driven solutions in multiple verticals. Check her posts for real-world transformation stories.

Data Analytics

Using Data to Nurture Long-Term Customer Relationships

Becky Staker

November 28, 2023

By now, all marketers know that they need data to successfully engage customers throughout their entire customer experience journey. But, with customers sometimes having needs and expectations that are very different from others—and even very different from their own previous wants and needs—nurturing each long-term relationship can be difficult. Yet, with the right data and strategy, it can be done.

Building and sustaining relationships requires an in-depth understanding of each customer at an individual level. This includes knowing their past behaviors, what motivates them to take action, and also having the ability to predict what they will do next. Predicting and meeting changing needs and preferences are instrumental to creating customers for life.

Here are some key, data-driven approaches that can help you engage customers and sustain long-term relationships that improve sales and build loyalty.

Integrate All Relevant Data to Build Customer Profiles

Any customer experience initiative will entail using all relevant data to create comprehensive profiles, which is commonly known as building 360-degree customer views. This critical step involves integrating data on a single platform and then making it easily accessible to everyone who needs it. Profiles typically include transactional, demographic, web visits, social media, and behavioral data, as well as data from a myriad of other sources. Gathering this information may require you to build data pipelines to new sources.

Profiles allow you to truly know your customers, such as their buying habits, preferred shopping and delivery channels, and interests. The profiles ultimately give you the insights needed to engage each person with relevant, targeted offers, based on their behaviors and preferences to ensure effective campaigns and deepen customer relationships.

Keeping profiles current and accurate is essential to identify, predict, and meet customer expectations. Preferences and habits can change quickly and without warning, which is why continually integrating data is essential to understanding customers’ current and future needs, and ensuring their profiles are up-to-date. Having insights into what customers want next—and being able to deliver that product or service—is the key to successfully nurturing customers.

Using Predictive Analytics to Anticipate Changing Needs

Predictive analytics is one of your most important capabilities to gain an understanding of how customer needs are changing. This type of analytics can help you make informed decisions about delivering the next best offer to customers, enabling you to be proactive rather than reactive when meeting and exceeding customer expectations.

A proactive approach allows you to guide customers on their journeys and improve customer retention. It also helps you nudge, or motivate, customers who are not progressing on their journeys in order to reengage them and reduce the risk of churn.

The analysis looks at past behaviors to predict future actions. In addition to helping you identify shifting customer preferences, the analytics can help you uncover any emerging industry or consumer trends that could impact business or marketing decisions.

Another benefit of predicting actions is improving customer experience satisfaction by understanding their ongoing needs, which supports customer-for-life strategies. Likewise, performing predictive analytics on customer data can help you identify the most opportune moments to reach out to customers with a relevant offer—and determine what that offer should be.

Deliver Engaging and Hyper-Personalized Communications

Nurturing customers requires you to create a perfectly tailored experience for every single engagement. Today’s customers expect businesses to know and understand their individual needs, and then meet those needs with personalized offers. Customers are accustomed to companies providing targeted communications and recommendations based on their habits and preferences, which is why personalization is now tables stakes for interacting with customers.

Going beyond personalized offers to hyper-personalized or ultra-personalized experiences lets you separate yourself from competitors. Hyper-personalization involves more than using the customer’s first name in communications and lumping the person into a customer segment.

Hyper-personalization involves delivering highly customized offers, products, or services that are relevant and timely to the customer. With the right data platform, you can analyze large data volumes to truly know your customer and deliver the right offer at the right time. You can even personalize offers to small customer segments—even curating unique offers to a customer segment of just one person.

Have Complete Confidence in Your Customer Data

Turning leads into customers is a great success. The next goal is to continually stay ahead of customer needs to sustain long-term relationships. Some churn is inevitable, but using data can improve customer retention and drive higher sales.

To build trust with your customers and nurture relationships, you must be able to gather, analyze, and trust your data. The Actian Data Platform makes it easy for everyone across your organization to access, share, and trust data with complete confidence. This allows you to take a truly data-driven approach to customer engagement, to help you better understand each customer, and make predictions with a high degree of accuracy.

The Actian Data Platform can help you transform your customer relationships and accelerate your marketing goals.

Additional Resources:

About Becky Staker

Becky Staker is Actian's Vice President of Customer Experience, focused on elevating customer outcomes across the business. Her diverse background spans marketing, sales, and CX leadership roles, including at Deloitte and EY, where she honed a customer-centric approach. Becky has led global CX projects that improved retention and satisfaction scores. She frequently speaks at industry events on CX trends and innovations. Becky's Actian blog articles cover how data can transform customer engagement and experiences. Explore her recent writings for strategies to boost loyalty and ROI.

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Evaluate Your Current Data Quality

Reevaluate Your Data Quality Strategy

Bring Modern Processes into Your Framework

Review the Framework on an Ongoing Basis

Ensuring 7 Critical Data Quality Dimensions

Completeness

Validity

Uniqueness

Consistency

Timeliness

Accuracy

Integrity

What Problems Do Data Quality Frameworks Solve?

Make Your Quality Data Easy to Use for Everyone

About Emma McGrattan

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What is Cloud FinOps?

How Does Cloud Finops Work?

What are Cloud FinOps Best Practices?

Transparency & Synergy

Automation & Control

Reporting & Analysis

What are the Main Challenges for Cloud Finops?

What Does the Future Hold for Cloud Finops?

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How IT Leaders Leverage Unstructured Data

Automate Workflows for Unstructured Data

Using Unstructured Data for AI

Integrate Data on an Easy-to-Use Platform

About Emma McGrattan

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How Partitioning on Your Data Platform Improves Performance

Why Distributing and Partitioning Tables are Critical to Performance

Partitioning in the Actian Data Platform