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Nov 29 2023
Finding The Right ETL/ELT Solution – What Is Estuary And Should You Use It?


Data warehousing would be easy if all data were structured and formatted in the data source. Maybe we wouldn’t even need to build a data warehouse. But as anyone who has worked with data from more than one source knows, that’s rarely the case. Businesses today need to pull data from a plethora of sources,…
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Author: research@theseattledataguy.com

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Nov 28 2023
Using Data to Nurture Long-Term Customer Relationships

By now, all marketers know that they need data to successfully engage customers over the course of their entire customer 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 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, 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 customer, 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 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 platform can help you transform your customer relationships and accelerate your marketing goals. Take a free trial and experience the platform for yourself.

Related resources you may find useful:

The post Using Data to Nurture Long-Term Customer Relationships appeared first on Actian.


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Author: Becky Staker

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Nov 28 2023
The Power of Data in Overcoming Customer Churn

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 Actian 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 platform for free for 30 days to see how it can drive outcomes for your business. 

Related resources you may find useful:

6 Predictive Analytics Steps to Reduce Customer Churn

Prioritizing a Customer Experience (CX) Strategy to Drive Business Growth

Actian Makes It Easy for Insurance Providers to Know Their Customers Better

The post The Power of Data in Overcoming Customer Churn appeared first on Actian.


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Author: Becky Staker

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Nov 27 2023
Common Pitfalls in Deploying Airflow for Data Teams


If you’re a data engineer, then you’ve likely at least heard of Airflow. Apache Airflow is one of the most popular open-source workflow orchestration solutions that gets used for data pipelines. This is what spurred me to write the article “Should You Use Airflow” because there are plenty of people who don’t enjoy Airflow or…
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Author: research@theseattledataguy.com

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Nov 27 2023
Winning in the 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 Platform & Portfolio

Actian provides a hybrid cloud data platform and data 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 hybrid cloud 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. Take the next and start a free trial today!

The post Winning in the Automotive Industry with CX appeared first on Actian.


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Author: Derek Comingore

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Nov 17 2023
Apache Druid: Who’s Using It and Why?


Image Source: Druid The past few decades have increased the need for faster data. Some of the catalysts were the push for better data and decisions to be made around advertising. In fact, Adtech has driven much of the real-time data technologies that we have today. For example, Reddit uses a real-time database to provide…
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Author: research@theseattledataguy.com

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Nov 17 2023
Machine Learning: Challenges and Opportunities for Modern Data Executives


The transformational promise of artificial intelligence (AI) and machine learning (ML) for enterprises has fueled enormous excitement and massive investment by data executives. One estimate predicts that AI’s contribution to the global economy could reach an extraordinary $15.7 trillion by 2030. That’s more than the current combined economic output of China and India. Yet, there seems […]

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Author: Nina Zumel

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Nov 17 2023
Unlocking the Full Potential of Data Collaboration Through PETs


What’s the future of data collaboration? It’s a question that should be on the lips of every C-suite executive in global organizations right now. The unrealized potential of consumer data is immense for businesses wanting to forge deeper connections with their customers and unlock new opportunities, but many are unsure how to proceed when faced […]

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Author: Alistair Bastian

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Nov 16 2023
Gen AI Best Practices for Data Scientists, Engineers, and IT Leaders

As organizations seek to capitalize on Generative AI (Gen AI) 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 Gen AI is in its infancy.

Granted, with Gen AI, 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

Gen AI 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 Gen AI.

The potential impact of Gen AI 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, Gen AI 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 Gen AI.

Data Accuracy is a Must

Data preparation and data quality are essential for Gen AI, 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 Gen AI. 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 Gen AI 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. Gen AI 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 Gen AI 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 Gen AI projects. The unified data can be used to train models to query your data store for Gen AI 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 Gen AI

What I’m hearing from our Actian partners is that organizations interested in implementing Gen AI 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. Gen AI 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 Gen AI.

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 Gen AI. 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 Gen AI

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

Customers are telling us that they want solid use cases that utilize the strength of Gen AI 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 Gen AI 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 Gen AI. We are also exploring how our data storage capabilities can be utilized alongside TensorFlow to ensure storage is optimized for Gen AI use cases.

Go From Trusted Data to Gen AI 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 Gen AI and understand its implications and applications. It’s now broadly accepted that AI and Gen AI are going to be critical for businesses. Even if the picture of exactly how Gen AI will be beneficial is still a bit hazy, the awareness and enthusiasm are real.

We’re excited to see the types of Gen AI 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 Gen AI projects. Try the platform for free for 30 days to see how easy data can be.

Related resources you may find useful:

The post Gen AI Best Practices for Data Scientists, Engineers, and IT Leaders appeared first on Actian.


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Author: Vamshi Ramarapu

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Nov 16 2023
No Database Is Perfect: Applying CAP Theorem to Database Choice


Since its introduction to the marketplace in 2000, the consistency, availability, and partition theorem, or CAP theorem, has been a guiding principle in database management. Computer scientist Eric Brewer presented the CAP theorem in a talk about distributed systems that provide web services. Two MIT professors later proved the theorem. It states that a database can be […]

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Author: Shrivathsan Kumar

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Nov 15 2023
Choosing Tools for Data Pipeline Test Automation (Part 1)


Those who want to design universal data pipelines and ETL testing tools face a tough challenge because of the vastness and variety of technologies: Each data pipeline platform embodies a unique philosophy, architectural design, and set of operations. Some platforms are centered around batch processing, while others are centered around real-time streaming.  While the nuances […]

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Author: Wayne Yaddow

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Nov 15 2023
Data Observability vs. Data Quality
Data empowers businesses to gain valuable insights into industry trends and fosters profitable decision-making for long-term growth. It enables firms to reduce expenses and acquire and retain customers, thereby gaining a competitive edge in the digital ecosystem. No wonder businesses of all sizes are switching to data-driven culture from conventional practices. According to reports, worldwide […]


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Author: Hazel Raoult

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Nov 15 2023
Metadata Governance: An Outline for Success (Part One)
Organizations are driven by their mission and the underlying strategies to accomplish that mission. Organizations that fail to understand their mission and strategies will at best flounder and at worst fail. The outline in the following article will help an organization manage its metadata about itself (mission, strategies, etc.) as well as help an organization identify and […]


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Author: Scott Benson

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Nov 15 2023
Game-Changing Data Definitions You Cannot Afford to Ignore
In today’s data-driven world, the terms “data governance” and “data stewardship” have become buzzwords, often thrown around without a clear understanding of their significance. However, defining these terms is not about adding complexity, it’s about establishing clarity and accountability and providing a sense of practicality and pragmatism. As I define it, data governance is “the […]


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Author: Robert S. Seiner

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Nov 15 2023
Data Speaks for Itself: Data Love and Data Limerence
Now that “data” is finally having its day, data topics are blooming like jonquils in March. Data management, data governance, data literacy, data strategy, data analytics, data engineering, data mesh, data fabric, data literacy, and don’t forget data littering. In keeping with this theme, I’d like to propose a couple of new data topics not […]


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Author: Dr. John Talburt

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Nov 15 2023
Eyes on Data: The Right Foundation for Trusted Data and Analytics
Trust. Trust is defined as the assured reliance or belief on the character, ability, strength, or truth of someone or something (Webster’s Dictionary). It’s a term we use often to describe how we feel about the people, the institutions, and the things around us. But I would argue that the term “trust” was used differently […]


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Author: EDM Council

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Nov 15 2023
Synchronized Video Data Collaboration for Multi-Layered Insights
In this digital age, large amounts of data are produced and consumed every second. And particularly for experienced bloggers in the data management or IT industry, synchronized video data collaboration represents an untapped wellspring of potential. By harnessing this data revolution, businesses can gain multi-layered insights into consumer behavior and enhance organizational performance. The Undeniable […]


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Author: Cris Mark Baroro

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Nov 14 2023
Maximizing IT Investments and Enhancing End-User Experience with Data


In an age defined by data-driven decision-making, where 91.9% of organizations have already leveraged analytics to enhance their operations, a question remains: What if there were even more sources of untapped data, capable of helping businesses increase the quality of the end-user experience and elevating the functionality of systems, applications, and cloud investments within their business? This is […]

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Author: Amitabh Sinha

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Nov 13 2023
Introducing The Actian Data Platform: Redefining Speed and Price Performance

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 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 

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. Try it free for 30 days. You can be up and running in just a few minutes. I think you’ll be impressed.   

Related resources you may find useful: 

The post Introducing The Actian Data Platform: Redefining Speed and Price Performance appeared first on Actian.


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Author: Vamshi Ramarapu

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Nov 13 2023
Building a Strong Community for Women in Data Management and Governance


In September, I had the privilege of co-hosting a new special interest group (SIG), Women in Data Management and Governance, alongside DATAVERSITY’s Shannon Kempe, at a pre-conference Enterprise Data World (EDW) event. I’m so honored to be part of building this community and to better serve a critical and growing constituency in our industry.  Supporting the growth of […]

The post Building a Strong Community for Women in Data Management and Governance appeared first on DATAVERSITY.


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Author: Kelle O’Neal

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Nov 13 2023
Managing Missing Data in Analytics


Today, corporate boards and executives understand the importance of data and analytics for improved business performance. However, most of the data in enterprises is of poor quality, hence the majority of the data and analytics fail. To improve the quality of data, more than 80% of the work in data analytics projects is on data […]

The post Managing Missing Data in Analytics appeared first on DATAVERSITY.


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Author: Prashanth Southekal

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Nov 10 2023
Data Governors, First Govern Yourselves


Data Governance, as currently practiced, is failing. There have been some successes, but by and large, even these efforts have fallen short. Worse, many of those tasked with contributing to Data Governance find the effort painful.  We have enormous sympathy for data governors. (We use the term “data governors” – DGs – as the most […]

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Author: John Ladley and Thomas Redman

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Nov 10 2023
Why Are Companies Demanding DLP Functionality?


In an age where data breaches, cyber threats, and privacy violations are commonplace, companies are placing greater emphasis on safeguarding their digital assets. Data Loss Prevention (DLP) functionality has emerged as a critical tool in this endeavor. Although we all understand the consequences and the benefits of protecting data, it is interesting to delve into what’s […]

The post Why Are Companies Demanding DLP Functionality? appeared first on DATAVERSITY.


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Author: Anastasios Arampatzis

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Nov 9 2023
Data Management for a Hybrid World: Platform Components and Scalability

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 

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 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. 

The post Data Management for a Hybrid World: Platform Components and Scalability appeared first on Actian.


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Author: Derek Comingore

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Nov 9 2023
Transforming Data Management with AI-Driven Data Catalogs


In today’s data-driven world, where every byte of information holds untapped potential, effective Data Management has become a central component of successful businesses. The ability to collect and analyze data to gain valuable insights is the basis of informed decision-making, innovation, and competitive advantage. According to recent research by Accenture, only 25% of organizations are […]

The post Transforming Data Management with AI-Driven Data Catalogs appeared first on DATAVERSITY.


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Author: Gaurav Sharma

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