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

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.

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.

Data Management

The Power of Data in Overcoming Customer Churn

Becky Staker

November 28, 2023

Retaining customers is a high priority and a constant challenge for businesses. Today’s customers expect frictionless and consistent experiences across all channels. They also expect you to anticipate and meet their needs. If you don’t, they are likely to switch to a competitor. Likewise, if you miss market trends, which can change incredibly fast, or don’t provide the benefits, features, and services customers want, you run the risk of losing them. Just a single bad experience may be all it takes for a customer to leave. If poor experiences pile up, your risk of customer churn increases. Delivering the right experiences to build customer loyalty requires you to truly know your customers. How do you do that? By analyzing data and building a real-time view of customers.

Take a Data-Driven Approach to Customer Retention

The first step in overcoming customer churn is to integrate customer data and perform analytics. You need to bring together all relevant data, including social media data, on a single platform to gain a complete customer view. Building 360-degree profiles can reveal the insights needed to understand customer behaviors, preferences, buying habits, and other critical information. Analytics can then identify customers at risk of churn based on customer journeys and other information.

Getting accurate, granular information allows you to determine if there are issues with customer service, customer experiences, product design, or another area that is negatively impacting customers. This critical step alerts you to any major issue that’s turning away customers, so you can address it and mitigate churn. Customer churn analysis lets you predict which customers are at risk. Data analytics looks for factors that can signal customers may be likely to leave, such as:

Long periods of inactivity, including no longer using a service, not opening emails from the organization, and not visiting the company’s website. A sudden and prolonged drop in interaction is a red flag.
Negative feedback and complaints from customers. This can include direct feedback to call centers or from surveys, or indirect feedback in social media posts. Unhappy customers are likely to leave.
Subscription services are reaching their expiration date. This is the critical time when customers decide if they want to recommit to your service. It’s an opportune time to engage them and nurture the next phase of their customer journey.
Cancellations or non-renewals of subscriptions, memberships, or contracts. You can reach out to these customers, and maybe offer a discount or other exclusive incentive, to entice them back as a customer.

Creating a retention strategy allows your organization to have an established process for identifying customers likely to churn, and offering a course of action.

Engage At-Risk Customers Early

Once you’ve identified customers who are likely to churn, the next step is to quickly engage them with timely, personalized, and relevant offers. In some cases, the right offer at the right time delivers the experience needed to rebuild customer loyalty. Data can reveal what motivates customers to take action, such as making a purchasing decision or visiting a particular page on a website. These insights can help you craft the right message to connect with at-risk customers. Going forward, you will need to establish the right cadence of engagement for each customer. This can be a delicate balance—too much and you could turn away the customer, while too little can result in missed opportunities. Using data to understand behavior patterns, such as the frequency at which a customer visits your site or opens your emails, can help inform how often you communicate with each individual.

Make Data Easy-to-Use to Inform Customer Retention Strategies

Some churn is to be expected, especially if you have an extremely large customer base with varying needs. At the same time, minimizing churn is less expensive than acquiring and onboarding new ones, and can also boost revenue. Bringing all data together on a single platform helps you better understand customers and what can lead to churn. You can learn from historic customer behaviors and patterns, customer surveys and feedback, and other data points that tell a story about what motivates customers to churn. Building customer profiles and analyzing large volumes of customer data requires a scalable platform, like the Actian Data Platform. It can help reduce customer churn by offering a complete and accurate picture of each customer to understand their wants, needs, and preferences—and predict what they’ll want next.

These views can identify high-risk customers and your highest-value customers, as well as all customers in between, so you can deliver the best offer to guide the next phase of their journey. This allows you, for example, to connect with those most likely to leave in order to retain them as customers, and also deliver tailored offers to the customers most likely to increase sales and grow revenue. The platform makes it easy for everyone across the business, including marketing, sales, and other departments to access, share, and analyze data to mitigate customer churn and improve experiences. Try the Actian Data Platform to see how it can drive outcomes for your business.

Additional Resources:

- 6 Predictive Analytics Steps to Reduce Customer Churn
- Prioritizing a Customer Experience (CX) Strategy to Drive Business Growth

About Becky Staker

Data Management

Winning in the Automotive Industry With CX

Derek Comingore

November 27, 2023

customer experience automotive industry with cx

Modern automotive customers expect engaged and superb user experiences. Automotive companies can collect, store, and analyze data across a spectrum of assets. By architecting better customer experiences (CX), automotive companies will reduce customer churn and increase new vehicle sales.

Intelligent Vehicles

Connected cars, beginning with the GM OnStar service, provided the world an early glimpse into the future of automotive innovation. The GM OnStar service relied primarily on CDMA phone technology. Cellular providers and technology added support to transmit data, and this ushered in the era of GPS vehicle connectivity.

Fast forward twenty years and the connected car is no longer sufficient. Modern automotive consumers require not only connected but also intelligent vehicles that provide a host of Customer Experience services. Modern-day intelligent vehicle services can include hands-free driving, navigating traffic, fastest route navigation, weather and road condition navigation, and accident prevention. Additional complimentary services can include vehicle health reports, preventative maintenance, automatic parking, automatic vehicle system updates, remote vehicle start and stop, in-car hotspots, in-vehicle entertainment systems, stolen vehicle tracking, and mobile application support. And with the replacement of mechanical parts and combustion engines with electronic ones, intelligent vehicle capabilities further increase.

The CX services and features mentioned above have the inherited requirement to collect and analyze data both in real-time and in historical batches. The modern intelligent vehicle must be able to access, query, analyze, and predict data and model scores in real time. Modern intelligent vehicles will need to easily transmit and receive ever-increasing volumes of data to provide this portfolio of customer experiences. This combination of macro events (i.e. weather, quickest route) coupled with micro-events (i.e. tire pressure, road conditions, driverless) lays the foundation for quickly moving and processing data across a variety of cloud and in-vehicle environments. In effect, the modern intelligent vehicle is becoming a mobile data generator and processing unit.

The Future of Intelligent Vehicles

Data processing and model scoring tasks will need to be done in vehicle progressively more into the future as vehicles continue to get smarter with regard to their immediate surroundings. Customers will expect all the above-mentioned experiences and services with a new vehicle purchase. Automotive manufacturers will continue to invest in edge and hybrid cloud data processing architectures for product development.

The Actian Data Platform & Portfolio

Actian provides a data platform and portfolio that includes edge data processing technologies. Customers can easily process and store data on the edge while easily moving data up and across a variety of cloud data processing environments. Our data platform includes built-in features to reduce the total cost of ownership (TCO). This makes common tasks such as data integration, management, and analytics easy with compelling price performance. The demands of modern intelligent vehicles have arrived and Actian is here to help.

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 Management

GenAI Best Practices for Data Scientists, Engineers, and IT Leaders

Vamshi Ramarapu

November 16, 2023

generative AI for data scientists, engineers, and IT leaders

As organizations seek to capitalize on Generative AI (GenAI) capabilities, data scientists, engineers, and IT leaders need to follow best practices and use the right data platform to deliver the most value and achieve desired outcomes. While many best practices are still evolving GenAI is in its infancy.

Granted, with GenAI, the amount of data you need to prepare may be incredibly large, but the same approach you’re now using to prep and integrate data for other use cases, such as advanced analytics or business applications, applies to GenAI. You want to ensure the data you gathered will meet your use case needs for quality, formatting, and completeness.

As TechTarget has correctly noted, “To effectively use Generative AI, businesses must have a good understanding of data management best practices related to data collection, cleansing, labeling, security, and governance.”

Building a Data Foundation for GenAI

GenAI is a type of artificial intelligence that uses neural networks to uncover patterns and structures in data and then produces content such as text, images, audio, and code. If you’ve interacted with a chatbot online that gives human-like responses to questions or used a program such as ChatGPT, then you’ve experienced GenAI.

The potential impact of GenAI is huge. Gartner sees it becoming a general-purpose technology with an impact similar to that of the steam engine, electricity, and the internet.

Like other use cases, GenAI requires data—potentially lots and lots of data—and more. That “more” includes the ability to support different data formats in addition to managing and storing data in a way that makes it easily searchable. You’ll need a scalable platform capable of handling the massive data volumes typically associated with GenAI.

Data Accuracy is a Must

Data preparation and data quality are essential for GenAI, just like they are for data-driven business processes and analytics. As noted in eWeek, “The quality of your data outcomes with Generative AI technology is dependent on the quality of the data you use.

Managing data is already emerging as a challenge for GenAI. According to McKinsey, 72% of organizations say managing data is a top challenge preventing them from scaling AI use cases. As McKinsey also notes, “If your data isn’t ready for Generative AI, your business isn’t ready for Generative AI.”

While GenAI use cases differ from traditional analytics use cases in terms of desired outcomes and applications, they all share something in common—the need for data quality and modern integration capabilities. GenAI requires accurate, trustworthy data to deliver results, which is no different from business intelligence (BI) or advanced analytics.

That means you need to ensure your data does not have missing elements, is properly structured, and has been cleansed. The prepped data can then be utilized for training and testing GenAI models and gives you a good understanding of the relationships between all your data sets.

You may want to integrate external data with your in-house data for GenAI projects. The unified data can be used to train models to query your data store for GenAI applications. That’s why it’s important to use a modern data platform that offers scalability, can easily build pipelines to data sources, and offers integration and data quality capabilities.

Removing Barriers to GenAI

What I’m hearing from our Actian partners is that organizations interested in implementing GenAI use cases are leaning toward using natural language processing for queries. Instead of having to write in SQL to query their databases, organizations often prefer to use natural language. One benefit is that you can also use natural language for visualizing data. Likewise, you can utilize natural language for log monitoring and to perform other activities that previously required advanced skills or SQL programming capabilities.

Until recently, and even today in some cases, data scientists would create a lot of data pipelines to ingest data from current, new, and emerging sources. They would prep the data, create different views of their data, and analyze it for insights. GenAI is different. It’s primarily about using natural language processing to train large language models in conjunction with your data.

Organizations still want to build pipelines, but with a platform like the Actian Data Platform, it doesn’t require a data scientist or advanced IT skills. Business analysts can create pipelines with little to no reliance on IT, making it easier than ever to pull together all the data needed for GenAI.

With recent capability enhancements to our Actian Data Platform, we’ve enabled low code, no code, and pro code integration options. This makes the platform more applicable to engage more business users and perform more use cases, including those involving GenAI. These integration options reduce the time spent on data prep, allowing data analysts and others to integrate and orchestrate data movement and pipelines to get the data they need quickly.

A best practice for any use case is to be able to access the required data, no matter where it’s located. For modern businesses, this means you need the ability to explore data across the cloud and on-premises, which requires a hybrid platform that connects and manages data from any environment, for any use case.

Expanding Our Product Roadmap for GenAI

Our conversations with customers have revealed that they are excited about GenAI and its potential solutions and capabilities, yet they’re not quite ready to implement GenAI technologies. They’re focused on getting their data properly organized so it’ll be ready once they decide which use cases and GenAI technologies are best suited for their business needs.

Customers are telling us that they want solid use cases that utilize the strength of GenAI before moving forward with it. At Actian, we’re helping by collaborating with customers and partners to identify the right use cases and the most optimal solutions to enable companies to be successful. We’re also helping customers ensure they’re following best practices for data management so they will have the groundwork in place once they are ready to move forward.

In the meantime, we are encouraging customers to take advantage of the strengths of the Actian Data Platform, such as our enhanced capabilities for integration as a service, data quality, and support for database as a service. This gives customers the benefit of getting their data in good shape for AI uses and applications.

In addition, as we look at our product roadmap, we are adding GenAI capabilities to our product portfolio. For example, we’re currently working to integrate our platform with TensorFlow, which is an open-source machine learning software platform that can complement GenAI. We are also exploring how our data storage capabilities can be utilized alongside TensorFlow to ensure storage is optimized for GenAI use cases.

Go From Trusted Data to GenAI Use Cases

As we talk with customers, partners, and analysts, and participate in industry events, we’ve observed that organizations certainly want to learn more about GenAI and understand its implications and applications. It’s now broadly accepted that AI and GenAI are going to be critical for businesses. Even if the picture of exactly how GenAI will be beneficial is still a bit hazy, the awareness and enthusiasm are real.

We’re excited to see the types of GenAI applications that will emerge and the many use cases our customers will want to accomplish. Right now, organizations need to ensure they have a scalable data platform that can handle the required data volumes and have data management practices in place to ensure quality, trustworthy data to deliver desired outcomes.

The Actian Data Platform supports the rise of advanced use cases such as Generative AI by automating time-consuming data preparation tasks. You can dramatically cut time aggregating data, handling missing values, and standardizing data from various sources. The platform’s ability to enable AI-ready data gives you the confidence to train AI models effectively and explore new opportunities to meet your current and future needs. The Actian Data Platform can give you complete confidence in your data for GenAI projects.

Additional Resources:

About Vamshi Ramarapu

Vamshi Ramarapu is VP of Actian Data Platform Engineering, leading cloud data management development. He has 20+ years of experience, previously at Mastercard and Visa, focusing on scalability, user experience, and cloud-native development. Vamshi is passionate about FinTech and data engineering, often contributing to research on secure, scalable platforms. His Actian blog contributions explore next-gen cloud data solutions, security, and innovation. Read his articles or insights on building resilient data infrastructures.

Data Management

Introducing the Actian Data Platform: Redefining Speed and Performance

Vamshi Ramarapu

November 13, 2023

As the Vice President of Engineering at Actian, I have been very involved in the recent launch of our Actian Data Platform. My role in this major upgrade has been twofold—to ensure our easy-to-use platform offers rewarding user experiences, and to deliver the technology updates needed to meet our customers’ diverse data management platform needs.

On a personal level, I’m most excited about the fact that we put in place the building blocks to bring additional products onto this robust data platform. That means, over time, you can continue to seamlessly add new capabilities to meet your business and IT needs.

This goes beyond traditional future-proofing. We have provided an ecosystem foundation for the entire Actian product suite, including products that are available now and those that will be available in the coming years. This allows you to bring the innovative Actian products you need onto our hybrid platform, giving you powerful data and analytics capabilities in the environment of your choice—in the cloud, on-premises, or both.

Blazing-Fast Performance at a Low Price Point With the Actian Data Platform

One of the Actian Data Platform’s greatest strengths is its extreme performance. It performs query optimization and provides analytics at the best price-performance when compared to other solutions. In fact, it offers a nine times faster speed advantage and 16 times cost savings over alternative platforms.

This exceptional price performance, coupled with the platform’s ability to optimize resource usage, means you don’t have to choose between speed and cost savings. And regardless of which of our pricing plans you choose—a base option or enterprise-ready custom offering—you only pay for what you use.

Our platform also offers other modern capabilities your business needs. For example, as a fully managed cloud data platform, it provides data monitoring, security, backups, management, authentication, patching, usage tracking, alerts, and maintenance, freeing you to focus on your business rather than spending time handling data processes.

Plus, the platform’s flexible and scalable architecture lets you integrate data from new and existing sources, then make the data available wherever you need it. By unifying data integration, data management, and analytics, the Actian Data Platform reduces complexity and costs while giving you fast, reliable insights.

Easy-to-Use Offering for High-Quality Data and Integration

Another goal we achieved with our platform is making it even simpler to use. The user experience is intuitive and friendly, making it easy to benefit from data access, data management, data analytics, and integrations.

We also rolled out several important updates with our launch. One focuses on integration. For example, we are providing stronger integration for DataConnect and link customers to make it easier than ever to optimize these platforms’ capabilities.

We have also strengthened the integration and data capabilities that are available directly within the Actian Data Platform. In addition to using our pre-built connectors, you can now easily connect data and applications using REST- and SOAP-based APIs that can be configured with just a few clicks. To address data quality issues, the Actian Data Platform now provides the ability to create codeless transformations using a simple drag-and-drop canvas.

The platform offers the best mix of integration, quality, and transformation tools. It’s one of the reasons why our integration as a service and data quality as a service are significant differentiators for our platform.

With our data integration and data quality upgrades, along with other updates, we’ve made it easy for you to configure and manage integrations in a single, unified platform. Plus, with our native integration capabilities, you can connect to various data sources and bring that data into the data warehouse, which in turn feeds analytics. Actian makes it easy to build pipelines to new and emerging data sources so you can access all the data you need.

Providing the Data Foundation for Generative AI

We paid close attention to the feedback we received from customers, companies that experienced our free trial offer, and our partners about our platform. The feedback helped drive many of our updates, such as an improved user experience and making it easy to onboard onto the platform.

I am a big proponent of quality being perceptive and tangible. With our updates, users will immediately realize that this is a high-quality, modern platform that can handle all of their data and data management needs.

Many organizations are interested in optimizing AI and machine learning (ML) use cases, such as bringing generative AI into business processes. The Actian Data Platform lends itself well to these projects. The foundation for any AI and ML project, including generative AI, is to have confidence in your data. We meet that need by making data quality tooling natively available on our platform.

We also have an early access program for databases as a service that’s been kickstarted with this platform. In addition, we’ve added scalability features such as auto-scaling. This enables your data warehouse to scale automatically to meet your needs, whether it’s for generative AI or any other project.

Breaking New Ground in Data Platforms

The Actian Data Platform monitors and drives the entire data journey, from integrations to data warehousing to real-time analytics. Our platform has several differentiators that can directly benefit your business:

A unified data platform improves efficiency and productivity across the enterprise by streamlining workflows, automating tasks, and delivering insights at scale.
Proven price performance reduces the total cost of ownership by utilizing fewer resources for compute activities—providing a more affordable solution without sacrificing performance—and can process large volumes of transactional data much faster than alternative solutions.
Integration and data quality capabilities help mitigate data silos by making it easy to integrate data and share it with analysts and business users at all skill levels. You can cut data prep time to deliver business results quickly with secure integration of data from any source.
REAL real-time insights meet the demand of analytics when speed matters. The platform achieves this with a columnar database enabling fast data loading, vectorized processing, multi-core parallelism, query execution in CPU cores/cache, and other capabilities that enable the world’s fastest analytics platform. 
Database as a service removes the need for infrastructure procurement, setup, management, and maintenance, with minimal database administration and cloud development expertise required, making it easy for more people to get more value from your data.
Flexible deployment to optimize data using your choice of environment—public cloud, multi- or hybrid cloud, or on-premises—to eliminate vendor lock-in. You can choose the option that makes the most sense for your data and analytics needs.

These capabilities make our platform more than a tool. More than a cloud-only data warehouse or transactional database. More than an integration platform as a service (iPaas). Our platform is a trusted, flexible, easy-to-use offering that gives you unmatched performance at a fraction of the cost of other platforms.

How Can Easy-to-Use Data Benefit Your Business?

Can you imagine how your business would benefit if everyone who needed data could easily access and use it—without relying on IT help? What if you could leverage your integrated data for more use cases? And quickly build pipelines to new and emerging data sources for more contextual insights, again without asking IT? All of this is possible with the Actian platform.

Data scientists, analysts, and business users at any skill level can run BI queries, create reports, and perform advanced analytics with our platform with little or no IT intervention. We ensure quality, trusted data for any type of analytics use case. In addition, low-code and no-code integration and transformational capabilities make the Actian Data Platform user friendly and applicable to more analysts and more use cases, including those involving generative AI.

Our patented technology continuously keeps your datasets up to date without affecting downstream query performance. With its modern approach to connecting, managing, and analyzing data, the Actian platform can save you time and money. You can be confident that data meets your needs to gain deep and rich insights that truly drive business results at scale.

Experience Our Modern Data Platform for Yourself

Our Actian platform offers the advantages your business needs—ease of use, high performance, scalability, cost effectiveness, and integrated data. We’ve listened to feedback to deliver a more user-friendly experience with more capabilities, such as an easy-to-understand dashboard that shows you what’s happening with consumption, along with additional metering and monitoring capabilities.

It’s important to note that we’ve undertaken a major upgrade to our platform. This is not simply a rebranding—it’s adding new features and capabilities to give you confidence in your data to grow your business. We’ve been planning this strategic launch for a long time, and I am extremely proud of being able to offer a modern data platform that meets the needs of data-driven businesses and puts in place the framework to bring additional products onto the platform over time.

I’d like you to try the platform for yourself so you can experience its intuitive capabilities and ultra-fast performance. You can be up and running in just a few minutes. I think you’ll be impressed.

Additional Resources:

Actian Beats Snowflake and BigQuery in GigaOm TPC-H Benchmark Test
REAL Real-time Data Analytics: When Seconds Matter

About Vamshi Ramarapu

Data Intelligence

What is Sensitive Data Discovery?

Actian Corporation

November 12, 2023

Protecting sensitive data stands as a paramount concern for data-centric enterprises. To navigate this landscape effectively, one must first embark on the meticulous task of accurately cataloging sensitive data – this is the essence of sensitive data discovery.

Data confidentiality is a core tenet, yet not all data is created equal. It is imperative to differentiate between sensitive data and information requiring heightened security and care. Sensitive data encompasses a broad spectrum, including personal and confidential details whose exposure could lead to significant harm to individuals or organizations. This encompasses various forms of information, such as medical records, social security numbers, financial data, biometric data, and details about personal attributes like sexual orientation, religious beliefs, and political opinions, among others.

The handling of sensitive data necessitates relentless adherence to rigorous security and privacy standards. As part of your organizational responsibilities, you are required to implement robust security measures to thwart data leaks, prevent unauthorized access, and shield against data breaches. This entails employing techniques such as encryption, two-factor authentication, access management, and other advanced cybersecurity practices.

Once this foundational principle is acknowledged, a pivotal question remains: Does your business engage in the collection and management of sensitive data? To ascertain this, you must undertake the identification and protection of sensitive data within your organization.

How do you Define and Distinguish Between Data Discovery and Sensitive Data Discovery?

Data discovery is the overarching process of identifying, collecting, and analyzing data to extract valuable insights and information. It involves exploring and comprehending data in its entirety, recognizing patterns, generating reports, and making informed decisions based on the findings. Data discovery is fundamental for enhancing business operations, improving efficiency, and facilitating data-driven decision-making. Its primary objective is to maximize the utility of available data for various organizational purposes.

On the other hand, sensitive data discovery is a more specialized subset of data discovery. It specifically centers on the identification, protection, and management of highly confidential or sensitive data. Sensitive data discovery involves pinpointing this specific type of data within an organization, categorizing it, establishing appropriate security protocols and policies, and safeguarding it against potential threats, such as data breaches and unauthorized access.

What is Considered Sensitive Data?

Since the enforcement of the GDPR in 2018, even seemingly harmless data can be deemed sensitive. However, it’s important to understand that sensitive data has a specific definition. Here are some concrete examples.

Sensitive data, to begin with, includes Personally Identifiable Information, often referred to as PII. This category covers crucial data like names, social security numbers, addresses, and telephone numbers, which are essential for the identification of individuals, whether they are your customers or employees.

Banking data, such as credit card numbers and security codes, holds a high degree of sensitivity, given its attractiveness to cybercriminals. Customer data, encompassing purchase histories, preferences, and contact details, is invaluable to businesses but must be diligently safeguarded to protect the privacy of your customers.

Health data, consisting of medical records, diagnoses, and medical histories, stands as particularly sensitive due to its deeply personal nature and its vital role in the realm of healthcare.

However, the realm of sensitive data extends far beyond these examples. Legal documents, such as contracts, non-disclosure agreements, and legal correspondence, house critical legal information and thus must remain confidential to preserve the interests of the parties involved. Depending on the nature of your business, sensitive data can encompass a variety of critical information types, all necessitating robust security measures to ward off unauthorized access or potential breaches.

What are the Different Methodologies Associated With the Discovery of Sensitive Data?

The discovery of sensitive data entails several essential methodologies aimed at its accurate identification, protection, management, and adherence to regulatory requirements. These methodologies play a crucial role in securing sensitive information:

Identification and Classification

This methodology involves pinpointing sensitive data within the organization and categorizing it based on its level of confidentiality. It enables the organization to focus its efforts on data that requires heightened protection.

Data Profiling

Data profiling entails a detailed analysis of the characteristics and attributes of sensitive data. This process enhances understanding, helping to identify inconsistencies, potential errors, and risks associated with the data’s use.

Data Masking

Data masking, also known as data anonymization, is pivotal for safeguarding sensitive data. This technique involves substituting or masking data in a way that maintains its usability for legitimate purposes while preserving its confidentiality.

Regulatory Compliance

Complying with laws and regulations pertaining to the protection of sensitive data is a strategic imperative. Regulatory frameworks like the GDPR in Europe or HIPAA in the United States establish stringent standards that must be followed. Non-compliance can result in significant financial penalties and reputation damage.

Data Retention and Deletion

Effective management of data retention and deletion is essential to prevent excessive data storage. Obsolete information should be securely and legally disposed of in accordance with regulations to avoid data hoarding.

Specific Use Cases

Depending on the specific needs of particular activities or industries, additional approaches can be implemented. These may include data encryption, auditing of access and activities, security monitoring, and employee awareness programs focused on data protection.

Managing sensitive data is a substantial responsibility, demanding both rigor and an ongoing commitment to data governance. It necessitates a proactive approach to ensure data security and compliance with ever-evolving data protection standards and regulations.

About Actian Corporation

Data Management

Data Management for a Hybrid World

Derek Comingore

November 9, 2023

For most companies, a mixture of both on-premises and cloud environments called hybrid cloud is becoming the norm. This is the second blog in a two-part series describing data management strategies that businesses and IT need to be successful in their new hybrid cloud world. The previous post covered hybrid cloud data management, data residency, and compliance.

Platform Components for a New Hybrid World

There are essential components for enabling hybrid cloud data analytics. First, you need data integration that can access data from all data sources. Your data integration tool needs a high degree of data quality management and transformation to convert raw data into a validated and usable format. Second, you should have the ability to orchestrate pipelines to coordinate and manage integration processes in a systematic and automated way. Third, you need a consistent data fabric layer that can be deployed across all environments and clouds to guarantee interoperability, consistency, and performance. The data fabric layer must have the ability to ingest different types of data as well. Last, you’ll need to transform data into formats and orchestrate pipelines.

Scaling Hybrid Cloud Investments

There are several costs to consider for hybrid cloud such as licensing, hardware, administration, and staff skill sets. Software as a Service (SaaS) and public cloud services tend to be subscription-based consumption models that are an Operational Expense (Opex). While on-premises and private cloud deployments are generally software licensing agreements that are a Capital Expenditure (Capex), subscription software models are great for starting small, but the costs can increase quickly. Alternatively, the upfront cost for traditional software is larger but your costs are generally fixed, pending growth.

Beyond software and licensing costs, scalability is a factor. Cloud services and SaaS offerings provide on-demand scale. Whereas on-premises deployments and products can also scale to a certain point, but eventually may require additional hardware (scale-up) and additional nodes (scale-out). Additionally, these deployments often need costly over-provisioning to meet peak demand.

For proprietary and high-risk data assets, leveraging on-premises deployments tends to be a consistent choice for obvious reasons. You have full control of managing the environment. It is worth noting that your technical staff needs to have strong security skills to protect on-premises data assets. On-premises environments rarely need infinite scale and sensitive data assets have minimal year-over-year growth. For low and medium-risk data assets, leveraging public cloud environments is quite common including multi-cloud topologies. Typically, these data assets are more varied in nature and larger in volume which makes them ideal for the cloud. You can leverage public cloud services and SaaS offerings to process, store, and query these assets. Utilizing multi-cloud strategies can provide additional benefits for higher SLA environments and disaster recovery use cases.

Hybrid World Data Management Made Easy

The Actian Data Platform is a hybrid and multi-cloud data platform for today’s modern data management requirements. The Actian platform provides a universal data fabric for all modern computing environments. Data engineers leverage a low-code and no-code set of data integration tools to process and transform data across environments. The data platform provides a modern and highly efficient data warehouse service that scales on-demand or manually using a scheduler. Data engineers and administrators can configure idle sleep and shutdown procedures as well. This feature is critical as it greatly reduces cloud data management costs and resource consumption.

The Actian platform supports popular third-party data integration tools leveraging standard ODBC and JDBC connectivity. Data scientists and analysts are empowered to use popular third-party data science and business intelligence tool sets with standard connectivity options. It also contains best-in-class security features to support and assist with regulatory compliance. In addition to that, the data platform’s key security features include management and data plane network isolation, industry-grade encryption, including at-rest and in-flight, IP allow lists, and modern access controls. Customers can easily customize Actian Data Platform deployments based on their unique security requirements.

The Actian Data Platform components are fully managed services when run in public cloud environments and self-managed when deployed on-premises, giving you the best of both worlds. Additionally, we are bringing to market a transactional database as a service component to provide additional value across the data management spectrum for our valued customers. The result is a highly scalable and consumable, consistent data fabric for modern hybrid cloud analytics.

How Partitioning on Your Data Platform Improves Performance

Why Distributing and Partitioning Tables are Critical to Performance

Partitioning in the Actian Data Platform

When Should You Partition?

About Colm Ginty

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Common Healthcare Data Management Issues and Solutions

Data Silos are an Ongoing Problem

Barriers to Data Integration and Quality

Complying With Ever-Changing Regulations

Dealing With Duplicate Patient Records

Delivering Trust in Healthcare Data

About Scott Norris

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

Trend #1: Edge AI as a Promise of Responsiveness

Trend #2: Responsible AI as an Ethical Promise

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

About Actian Corporation

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The Future of Automation in Manufacturing

Automation in Modern Factories Comes in Many Varieties

Automating Both Data and Manufacturing Processes

Keeping Assembly Line Belts Moving Efficiently

Improving Product Lifecycle Management

The Future of Automation Will Rely on Data

About Robert Gorsuch

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Generative AI for ESG Reporting and Compliance

How Generative AI Can Help With ESG Reporting and Compliance

Data Integration

Natural Language Processing (NLP)

ESG Reporting Automation

Data Analysis

Regulatory Change Analysis

Compliance Chatbots

Risk Assessment

ESG Investment

How the Actian Data Platform Can Help With Generative AI

About Teresa Wingfield

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Using Data to Nurture Long-Term Customer Relationships

Integrate All Relevant Data to Build Customer Profiles

Using Predictive Analytics to Anticipate Changing Needs

Deliver Engaging and Hyper-Personalized Communications

Have Complete Confidence in Your Customer Data

About Becky Staker

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The Power of Data in Overcoming Customer Churn

Take a Data-Driven Approach to Customer Retention

Engage At-Risk Customers Early

Make Data Easy-to-Use to Inform Customer Retention Strategies

About Becky Staker

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