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

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

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

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

Data Management

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.

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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