Search for:
Apr 29 2024
The Rise of Augmented Analytics: Combining AI with BI for Enhanced Data Insights


Businesses today are drowning in data. The sheer volume and complexity of information available have made it increasingly difficult for organizations to extract meaningful insights using traditional business intelligence (BI) tools and the expertise of specialized data scientists. This is where augmented analytics comes in. This game-changing technology combines the power of artificial intelligence (AI) […]

The post The Rise of Augmented Analytics: Combining AI with BI for Enhanced Data Insights appeared first on DATAVERSITY.


Read More
Author: Nahla Davies

Red Button 0 Comments
Apr 26 2024
Understanding Linear Regression Intercepts in Plain Language


I am often asked about the role of intercepts in linear regression models – especially the negative intercepts. Here is my blog post on that topic in simple words with minimal statistical terms.   Regression models are used to make predictions. The coefficients in the equation define the relationship between each independent variable and the dependent variable. […]

The post Understanding Linear Regression Intercepts in Plain Language appeared first on DATAVERSITY.


Read More
Author: Prashanth Southekal

Red Button 0 Comments
Apr 4 2024
Demystifying Data Analytics Models


In today’s global landscape, organizations worldwide are increasingly turning to data analytics to enhance their business performance. Research conducted by McKinsey Consulting revealed that data-driven companies not only experience above-market growth but also witness EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) increases of up to 25% [1]. Additionally, Forrester’s findings indicate that organizations utilizing […]

The post Demystifying Data Analytics Models appeared first on DATAVERSITY.


Read More
Author: Prashanth Southekal

Red Button 0 Comments
Feb 23 2024
5 Key Strategies for Making Data Visualization Accessible


“If I can’t picture it, I can’t understand it.” —Albert Einstein Research has found that 65% of the general population are visual learners, meaning they need to see information as images to understand it. The business world confirms this: Visualization is essential in driving success. Take, for instance, data visualization, or, the art of translating data into […]

The post 5 Key Strategies for Making Data Visualization Accessible appeared first on DATAVERSITY.


Read More
Author: Daniel Jebaraj

Red Button 0 Comments
Jan 12 2024
Is Your Data Management Strategy Ready for the Future of Manufacturing?

In the rapidly evolving landscape of the manufacturing industry, data has become a cornerstone of innovation. From robotics and connected factories to operational efficiency, the potential for leveraging data is boundless. However, to harness the full power of data, manufacturers must ensure that their data management strategies are not only current but also future-ready. For this reason, organizations must consider critical needs when creating a robust data management strategy. They must ensure that this strategy aligns with manufacturing priorities and carefully consider the value of digital transformation.

Defining the Manufacturing Data Management Strategy

A data management strategy is the backbone of successful data utilization in manufacturing. It encompasses the integration, standardization, and secure data storage, ensuring it is governed and trusted. In the context of the future of manufacturing, this strategy must align seamlessly with industry priorities, such as enhancing efficiency, maintaining quality control, predicting delays, and fostering innovation while simultaneously reducing costs.

The Role of Data Strategy in Digital Transformation for Manufacturing

A forward-thinking data management strategy is indispensable for any manufacturer looking to embark on a digital transformation journey. As the manufacturing landscape becomes increasingly digital and automated, selecting the right platform is crucial. A well-crafted data strategy, as often stated, is at the center of every successful digital transformation. This ensures not just immediate gains but also future-proofs the business against evolving technological landscapes.

Technology is a catalyst for digital transformation in manufacturing, enhancing efficiency, agility, and innovation. Integrating advanced technologies empowers manufacturers to optimize processes, improve product quality, and respond more effectively to market demands. By leveraging technology, manufacturers can not only optimize operations but also get ahead of any disruptions to suppliers or supply chains.

Key Metrics for Measuring Manufacturing Digital Transformation

Measuring the success of digital transformation in manufacturing requires defined metrics that should be an integral part of any data management strategy. These metrics serve as benchmarks, allowing manufacturers to gauge the impact of their digital initiatives. According to Gartner, “36% of manufacturing enterprises realize above-average business value from IT spending in digitalization at a reasonable cost compared with peers.”

Other metrics to consider include:

Customer Engagement

Track metrics such as website traffic, social media interactions, and customer feedback to assess the level of engagement with digital platforms.

Customer Satisfaction (CSAT) Scores:

Use surveys and feedback mechanisms to measure customer satisfaction with digital services, products, and overall experiences.

Operational Efficiency

Assess improvements in operational efficiency through metrics like reduced process cycle times, decreased manual intervention, and streamlined workflows.

Employee Productivity:

Monitor changes in employee productivity resulting from digital tools and automation. This can include metrics like tasks completed per hour or efficiency gains in specific processes.

Cost Reduction:

Measure the cost savings achieved through digital optimization, such as reduced manual processes, lower maintenance costs, and improved resource utilization.

Data Quality and Accuracy:

Evaluate the quality and accuracy of data, ensuring that digital transformation initiatives contribute to improved data integrity.

Customer Lifetime Value (CLV):

Evaluate the long-term value generated from each customer, factoring in repeat business, upsells, and customer loyalty influenced by digital initiatives.

Net Promoter Score (NPS):

Measure the likelihood of customers recommending your products or services as an indicator of overall satisfaction and loyalty.

Contextualized Data in the Fourth Industrial Revolution

Industry 4.0 represents a paradigm shift in manufacturing, characterized by integrating advanced technologies, digitalization, and data-driven decision-making. Entering the era of Industry 4.0 necessitates manufacturers to have clear, concise, and contextualized data.

Real-time decision-making is a cornerstone of Industry 4.0, and clear data ensures that manufacturers can swiftly respond to dynamic conditions, optimize processes, and troubleshoot issues in real-time. Predictive maintenance, a key aspect of this industrial revolution, relies on contextualized data to anticipate equipment needs and minimize downtime. By harnessing clear and contextualized data, manufacturers can optimize production processes, implement robust quality control measures, and achieve end-to-end visibility in the supply chain. This level of data clarity facilitates customization and personalization in production, enhances energy efficiency, and supports the integration of connected ecosystems within the manufacturing environment.

Additionally, manufacturers can identify potential risks through clear data insights and implement strategies to mitigate uncertainties. Clear data is crucial for ensuring compliance with regulatory standards, a necessity in Industry 4.0, given the increasing focus on stringent regulations.

Actian’s Role in Manufacturing Data Management

Actian has decades of experience helping manufacturers create and implement robust data management strategies. Actian’s solutions enable data-driven decision-making processes, ensuring manufacturers not only stay competitive in the present but also remain agile and prepared for the future.

In the dynamic landscape of manufacturing, a well-crafted data management strategy is not just a necessity, it’s a roadmap to success. As the industry hurdles towards an era of unprecedented technological advancement, manufacturers must ensure their strategies are not only current but also forward-looking. It’s time to embrace the future of manufacturing by putting data at the forefront of operations, and Actian is here to guide that transformative journey. Start a free trial now.

The post Is Your Data Management Strategy Ready for the Future of Manufacturing? appeared first on Actian.


Read More
Author: Traci Curran

Red Button 0 Comments
Jan 12 2024
Four Perspectives on the Art of Data Analytics


As data science professionals, we are often viewed as people who draw conclusions based only on data and minimize other factors. This perception usually becomes contentious when the insights and evidence from the data are inconsistent with somebody else’s “hypothesis.” Or we are confused and maybe frustrated when “qualitative” analysis trumps quantitative analysis. The next time […]

The post Four Perspectives on the Art of Data Analytics appeared first on DATAVERSITY.


Read More
Author: Jason Jue

Red Button 0 Comments
Jan 3 2024
Elevating Data and Analytics for 2024: A GenAI Imperative
As GenAI ascends in priority for CIOs, CDOs, and business leaders, 2023 has placed data and analytics in the spotlight. The hidden challenge is that entities lagging in data industrialization find themselves trailing in business transformation. GenAI and machine learning are touted to address myriad problems. Morgan Vawter, global vice president of data and analytics […]


Read More
Author: Myles Suer

Red Button 0 Comments
Dec 18 2023
Data Catalog, Semantic Layer, and Data Warehouse: The Three Key Pillars of Enterprise Analytics


Analytics at the core is using data to derive insights for measuring and improving business performance [1]. To enable effective management, governance, and utilization of data and analytics, an increasing number of enterprises today are looking at deploying the data catalog, semantic layer, and data warehouse. But what exactly are these data and analytics tools […]

The post Data Catalog, Semantic Layer, and Data Warehouse: The Three Key Pillars of Enterprise Analytics appeared first on DATAVERSITY.


Read More
Author: Prashanth Southekal and Inna Tokarev Sela

Red Button 0 Comments
Dec 8 2023
Data Activation: The Key to Taking Data Reports to the Next Level


Let’s talk about an inconvenient truth: For the typical business, data reporting has a tendency to fall short of producing the desired outcomes. Despite the significant resources that organizations often invest in producing data reports – and in the data collection, governance, and analytics processes that happen prior to reporting – the people who actually […]

The post Data Activation: The Key to Taking Data Reports to the Next Level appeared first on DATAVERSITY.


Read More
Author: Daniel Zagales

Red Button 0 Comments
Dec 5 2023
Gen AI for ESG Reporting and Compliance

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

Companies using manual processes for data collection will find it difficult to keep up with the breadth and depth of these mandates. This is why generative AI will begin to play a significant role to streamline data collection, automate reporting, improve accuracy and transparency, identify risks, and resolve compliance gaps.

How Generative AI Can Help with ESG Reporting and Compliance

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

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

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

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

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

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

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

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

How the Actian Data Platform Can Help

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

The post Gen AI for ESG Reporting and Compliance appeared first on Actian.


Read More
Author: Teresa Wingfield

Red Button 0 Comments
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.


Read More
Author: Becky Staker

Red Button 0 Comments
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.


Read More
Author: Derek Comingore

Red Button 0 Comments
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 […]


Read More
Author: EDM Council

Red Button 0 Comments
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.


Read More
Author: Vamshi Ramarapu

Red Button 0 Comments
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.


Read More
Author: Prashanth Southekal

Red Button 0 Comments
Nov 7 2023
Data Monetization: How You Can Deliver More Value

Because data is one of an organization’s most critical assets, monetizing it should be a top priority for business leaders. Data monetization is the act of generating economic benefits from data, either internally or externally through selling data to third parties.  Benefits typically fall within three broad categories, increase revenue, lower costs, and manage risks. A previous blog post Real-Time Data Analytics During Uncertain Times lists some examples of each of these categories and delves into the business value of data.

Although many organizations use data analytics to achieve these types of benefits, many leaders are constantly seeking even greater opportunities to monetize their data. So, how can you do more?  Here are a few tips to realize additional monetization advantages:

#1. Assess the Data You Have

Your first step should be to identify what data you have that can generate value by looking at its relevance to use cases and its potential impact on a business.  You should also assess data quality and fix known issues as this is key to successful data monetization.

#2. Define Your Business Objectives

Once you understand your data assets, you can decide on your monetization goals, defining new ways you or your potential customers can use it to create more revenue, lower costs, and manage risks. This is often a balancing act, weighing quick wins versus long-term strategy, costs versus benefits, and risks versus potential impact. Ultimately, the prioritization process should be based on expected business value.

#3. Develop a Data Monetization Strategy

Internal data monetization involves understanding users and how they view or measure success. Your strategy will need to include data that will help users meet their goals. Selecting the right data often depends on which key performance indicators (KPIs) they use to measure performance.

External data monetization strategies involve many factors.  You will need to determine:

  • The best market – Research buyers, their requirements, and their willingness to pay for data.
  • The most appropriate monetization model – Choose among options such as data licensing, data subscriptions, one-time data sales, or others.
  • The optimal packaging and pricing – Understand market dynamics such as supply and demand and consumption preferences.
  • The right channel(s) – Determine third-party platforms and data marketplaces that can broaden customer reach.

#4. Ensure Privacy, Compliance, and Ethical Use

Whether you are pursuing internal or external data monetization, you will need to ensure that data sharing and usage: a) comply with data privacy regulations, b) protect sensitive information, and c) does not harm individuals or society. If you are selling data, you will need to be transparent about where and how you collect data.

#5. Invest in Data Infrastructure

Data monetization is enabled through ingesting, transforming, analyzing, and storing data using a trusted, flexible, and easy-to-use data platform. Having the ability to run the platform in the cloud will:

  • Make it easier to facilitate connectivity among locations spread across the world. ​
  • Provide the ability to grow or shrink CPU, memory, and storage resources to adapt to the changing demands of data monetization workloads​.
  • Help you stay current with the latest technologies to support innovation.

Additionally, data monetization is most effective when a data platform provides real-time data analytics so that users are empowered to make important decisions in the moment.

#6. Promote Data Monetization Efforts

Internally, gaining leadership support, training, and education, communicating benefits and successes, and creating data champions, can all help to encourage data monetization adoption.  Externally, data is a product that will require appropriate sales and marketing strategies. And, just like most new initiatives, you will need to continuously evaluate and optimize the performance and impact of your data monetization efforts.

Get Started with Actian

For your data monetization initiative to succeed, you will need data that everyone has confidence in and can easily use. Learn how the Actian Data Platform delivers easy-to-use data you can trust.

The post Data Monetization: How You Can Deliver More Value appeared first on Actian.


Read More
Author: Teresa Wingfield

Red Button 0 Comments
Nov 2 2023
Analytics Program and Project Justification During Cautious Spending

The economy is currently in a state of flux, and there are both positive and negative signals regarding its future. As a result of factors, such as the low unemployment rate, growing wages, and rising prices, businesses find themselves in a spectrum of states. 

Recent pullbacks appear to be driven primarily by macro factors. I have a positive outlook on IT budgets in 2024 because I anticipate a loosening of IT expenditures, which have been limited by fears of a recession, since 2022. This will allow pent-up demand, which was cultivated in 2023, to be released. Because data is the key to success for these new endeavors, the demand for data cleansing and governance technologies has increased to address broad data quality issues in preparation for AI-based endeavors. 

Taking a broader perspective, despite the instability of the macro environment, the data and analytics sector is experiencing growth that is both consistent and steady. However, there is a greater likelihood of acceptance for business programs that concentrate more on optimization than on change. As a means of cutting costs, restructuring and modernizing applications as well as practicing sound foundational engineering are garnering an increasing amount of interest. For instance, businesses are looking at the possibility of containerizing their applications because the operation costs of containerized applications are lower. 

At this point in time, in this environment, project approval is taking place; nonetheless, the conditions for approval are rather stringent. Businesses are becoming increasingly aware of the importance of maximizing the return on their investments. There has been a resurgence of interest in return on investment (ROI), and those who want their projects to advance to the next stage would do well to bring their A-game by integrating ROI into the structure of their projects. 

Program and Project Justification

First, it is important to comprehend the position that you are attempting to justify: 

  • A program for analytics that will supply analytics for a number of different projects 
  • A project that will make use of analytics 
  • Analytics pertaining to a project 
  • The integration of newly completed projects into an already established analytics program 

Find your way out of the muddle by figuring out what exactly needs to be justified and then getting to work on that justification. When justifying a business initiative with ROI, it is possible to limit the project to its projected bottom-line cash flows to the corporation in order to generate the data layer ROI (which is perhaps more accurately referred to as a misnomer in this context). In order for the project to be a catalyst for an effective data program, it is necessary for the initiative to deliver returns. 

The question that needs to be answered to justify the starting of an existing data program or the extension of an existing data program is as follows: Why architect the new business project(s) into the data program/architecture rather than employing an independent data solution?  These projects require data and perhaps a data store, if the application doesn’t already come with one, then synergy should be established with what has previously been constructed.   

In this context, there is optimization, a reduction back to the bare essentials, and everything in between. The bare essentials approach can happen in an organization in a variety of different ways. All of these are indications of an excessive reach and expanded data debt: 

  1. Deciding against utilizing leverageable platforms like data warehouses, data lakes, and master data management in favor of “one-off”, and apparently (deceptively) less expensive, unshared databases tight fit to a project. 
  2. Putting a halt to the recruiting of data scientists. Enterprises that take themselves seriously need to take themselves seriously when it comes to employing the elusive genuine data scientist. If you fall behind in this race, it will be quite difficult for you to catch up to the other competitors. Even if they have to wrangle the data first before using data science, data scientists are able to work in almost any environment. 
  3. Ignoring the fact that the data platforms and architecture are significantly more important to the success of a data program than the data access layer, and as a result, concentrating all of one’s efforts on the business intelligence layer. You should be able to drop numerous BI solutions on top of a robust data architecture and still reach where you need to go. 
  4. Not approaching data architecture from the perspective of data domains. This leads to duplicate and inconsistent data, which leads to data debt through additional work that needs to be done during the data construction process, as well as a post-access reconciliation process (with other similar-looking data). Helping to prevent this is master data management and a data mesh approach that builds domains and assigns ownership of data.   

Cutting Costs

If your enterprise climate is cautious spending, target the business deliverable of your data project and use a repeatable, consistent process using governance for project justification. Use the lowering of expenses for justifying data programs. Also, avoid slashing costs to the extreme by going overboard with your data cuts, since this can cause you to lose the future.  

Although it should be at all times, it’s times like these when efficiencies develop in organizations and they become hyper-attracted to value. You may have to search beyond the headlines to bring this value to your organization. People in data circles know about Actian. I know firsthand how it outperforms and is less costly than the data warehouses getting most of the press, yet is also fully functional. 

All organizations need to do R&D to cut through the clutter and have a read on the technologies that will empower them through the next decade. I compel you to try the Actian Data Platform. They have a no-cost 30-day trial where you can setup quickly and experience its unified platform for ingesting, transforming, analyzing and storing data. 

The post Analytics Program and Project Justification During Cautious Spending appeared first on Actian.


Read More
Author: William McKnight

Red Button 0 Comments
Nov 2 2023
How to Eliminate Barriers to Adopting Advanced Financial Analytics

Financial analytics is the process of collecting, analyzing, and interpreting financial data to gain insights and make informed decisions regarding an organization’s financial performance and strategy. Advanced financial analytics uses more sophisticated techniques, algorithms, and tools to extract insights, recognize patterns and make predictions from large data sets. Using advanced financial analytics, organizations can gain deeper and more actionable insights that help them uncover potential risks and predict and improve performance.

Barriers to Adopting Advanced Financial Analytics

Unfortunately, there are many barriers to advanced financial analytics that a business may encounter. Here are some of the common ones alongside a brief recommendation for how to overcome them.

Over-Reliance on Spreadsheets

Practically all businesses use spreadsheets to handle some aspects of their data analytics. However, spreadsheets don’t offer the integration, scale, real-time data, and advanced analytics required to realize the full potential of your financial data. For these capabilities, companies will need to supplement spreadsheets with a data platform and the right financial tools and techniques to meet specific financial analysis objectives.

Data Silos

Data availability is key to advanced financial analytics. It requires access to comprehensive data, both current and historical. This may include financial records in financial management software, sales data in CRM systems, external market data and economic indicators, news feeds, social media data, and more. To be effective, organizations will need to break down these silos, bringing together data to develop mission-critical insights.

Data Quality Issues

Business users who rely on advanced analytics to make important decisions need to know that they can trust the integrity of its results. While data quality challenges are prevalent across all types of business data, financial data is particularly prone to issues. This is due to manual data entry, complexities when dealing with multiple currencies, customers with multiple accounts, intricate financial calculations, and lack of standard data formats, measurements, and naming conventions. These are reasons why data quality tools to ensure that data is accurate, complete, consistent, reliable, and up to date are so important.

The Complexity of Advanced Analytics

Advanced financial analytics uses techniques such as machine learning and statistical modeling to make accurate forecasts and uncover patterns buried in large volumes of data. Deploying these techniques correctly requires expertise in data modeling and proficiency in programming languages such as Python and R. Even business users will need to shore up their skills in math and how to accurately interpret results.

Strict Regulatory and Compliance Requirements

Financial data is heavily regulated, and violations are expensive. Understanding and enforcing regulations can be challenging, compounded by the need to understand unique requirements across geographies and industries. Businesses will need to establish and enforce policies and processes for collecting, storing, using, and sharing information for advanced analytics.

Communication

Communication can be challenging since various stakeholders throughout a company don’t typically have any background in advanced analytics. Conveying insights and implications of any analysis in a clear and understandable manner is a crucial skill for financial and business analysts to hone.

Sourcing From Multiple Vendors

Data integration, data quality, and other management workloads add more costs and complexity when sourced from multiple vendors. This can limit further investment in financial analytics if its costs exceed the business value it delivers. When possible, look for one data platform that offers many capabilities.

Overcoming Challenges

Overcoming these challenges isn’t always easy, but it’s well worth the effort since advanced analytics can help your business derive valuable insights and make more informed decisions.

Financial analytics success starts with the right platform. Actian has 50 years of experience helping customers manage some of their most critical data. The Actian Data Platform simplifies how you connect, manage, and analyze your financial data. Its unified data management gives you the ability to integrate, transform, orchestrate, and store your data in a single, easy-to-use platform.

Related resources you may find useful:

Data Silos Suck. Here’s How to Break Them Down

5 Common Factors that Reduce Data Quality—and How to Fix Them

How to Maximize Business Value with Real-Time Analytics

Real-Time Data Analytics During Uncertain Times

The post How to Eliminate Barriers to Adopting Advanced Financial Analytics appeared first on Actian.


Read More
Author: Teresa Wingfield

Red Button 0 Comments
Oct 19 2023
Understanding ESG: For a Better Tomorrow

There’s a growing movement emerging within socially responsible companies at a time when the world is faced with environmental crises, inequality, and economic disparities. Enter ESG: Environmental, Social, and Governance.

ESG criteria play a pivotal role in shaping how companies are evaluated. ”Five ways that ESG creates value” by McKinsey & Company highlights how ESG factors extend beyond financial performance by influencing decisions made by investors, stakeholders, and society at large. Decision-making is now greatly influenced by ESG factors, which compel firms to demonstrate their commitment toward a sustainable environment, social responsibility, and fairness in business practices. 

Let’s take a deeper dive into each aspect of ESG:

1.    Environmental: This is the environmental impact of a company. It includes things like CO2 emissions, consumption of resources, waste handling, and a shift toward renewable energy. Often, organizations follow eco-friendly practices, aim at reducing their carbon footprint, and take active measures for the mitigation of climate change.

2.    Social: ESG’s societal dimension refers to how well an enterprise treats its society, including stakeholders. These include variables like culture and inclusion, health, social welfare, and human rights. To achieve such a culture, companies embrace social responsibility by fostering inclusion and promoting the local community while treating employees and suppliers with respect.

3.    Governance: Governance refers to the system of rules, practices, and processes that control a company. This includes the makeup of the board, compensation of executives, openness of operations, and accountability. Good governance encourages ethical decisions, shields shareholders’ rights, and promotes long-term wealth creation. 

Why is ESG important?

There are several reasons ESG has been growing in importance. Initially, there were a growing number of individuals who focused on ESG in making investment decisions. These investors believed that environmental, social, and corporate governance (ESG) issues influence the potential for high returns in the future.

According to the World Economic Forum, investments in sustainability are expected to exceed $53 Trillion globally by 2025. This demonstrates that ESG is no longer just a niche concept but has evolved into a mainstream investment approach. Consumers are also more aware of how their choices affect the environment and society. They tend to make purchases from companies that align with their value system and exert an influence in positive ways. Additionally, regulators are increasing strictures in ESG, and companies must meet those standards.

ESG within Actian

ESG is a critical component of our operations at Actian. Our focus on environmental sustainability has led us to explore how to continuously reduce our carbon footprint, optimize energy consumption, and promote eco-friendly initiatives. This is coupled with nurturing a workplace that accommodates diversity and upholds fairness in employment of every worker. Actian participates in several outreach programs that provide assistance to its surrounding community through volunteering initiatives.

ESG signifies an underlying change in how corporations are assessed and viewed. Stakeholders demand this understanding as well as implementation of ESG principles; it is our duty for a better tomorrow. At Actian, our focus is to make a tangible difference through our software solutions and responsible corporate behavior.

The post Understanding ESG: For a Better Tomorrow appeared first on Actian.


Read More
Author: Jennifer Jackson

Red Button 0 Comments
Oct 17 2023
10 Ways Data Observability Gives Organizations a Competitive Advantage

Data observability is a specific aspect of data management that gives organizations a comprehensive understanding of the health and state of the data within their systems. This helps to understand the relationships and interdependencies between data elements and components within an organization’s data ecosystem, including how data flows from one source to another and how it is used and transformed.

Why is Data Observability Important?

According to a recent Gartner Report Innovation Insight: Data Observability Enables Proactive Data Quality, data observability is a critical requirement to both support and enhance existing and modern data management architectures. Organizations that prioritize data observability are better positioned to harness the full potential of their data assets and gain a competitive advantage in the digital age.

If you haven’t done so already, here are a few of the reasons why you may want to prioritize data observability as a strategic investment:

  1. Improved Decision Making: Data quality is an essential underpinning of a data-driven organization. Data observability helps organizations identify and rectify data quality issues early in the data pipeline, leading to more accurate and reliable insights for decision-making.
  2. Less Downtime: Continuously tracking the flow of data from source to destination and having a clear view of data dependencies enables quicker issue resolution and minimizes downtime in data operations.
  3. Lower Costs: Enterprise Strategy Group estimates that advanced observability deployments can cut downtime costs by 90%, keeping costs down to $2.5M annually versus $23.8 million for observability beginners. Real-time monitoring, early issue detection, and automated responses help organizations more proactively identify and address data issues, which reduces the cost of fixing downstream issues.
  4. Greater Productivity and Collaboration: Data observability fosters IT collaboration and productivity by providing a collective understanding of data and its lineage, promoting transparency, and providing real-time feedback on the impact of changes.
  5. Stronger Data Security: Data observability can improve security by enhancing an organization’s ability to detect, investigate, and respond to security threats and incidents. Real-time insights, comprehensive visibility, and automated responses enhance an organization’s overall security posture.
  6. Regulatory Compliance: Monitoring and controlling data access helps organizations comply with data privacy and security regulations.
  7. Change Control: Data observability helps manage changes in data schema, data sources, and data transformation logic by ensuring that changes are well understood, and their impacts are thoroughly assessed.
  8. Accelerated Digital Innovation: Data observability supports digital innovation by providing organizations with the data-driven insights and change control needed to continuously experiment, adapt, and create new solutions. It can also optimize digital experiences by ensuring the reliability, performance, security, and personalization of digital services.
  9. Operational Efficiency: By observing data flows, organizations can detect and resolve bottlenecks, errors, and inefficiencies in their data pipelines and processes.
  10. Optimized Resource Allocation: By identifying which data components are most critical and where issues occur most frequently, organizations can allocate, manage, and adjust their resources more efficiently.

Summary

Data observability strengthens an organization’s competitive edge in today’s data-driven business landscape. It ensures that organizations can maintain data quality, which is crucial for informed decision-making. It allows businesses to proactively detect and rectify issues in their data pipelines, reduce downtime, and lower costs. By enhancing visibility into data workflows, organizations can foster greater collaboration and improve security and compliance. Data observability provides change control that makes digital innovation less risky and provides operational and resource allocation efficiency.

Getting Started with Actian

Incorporating data analytics into data observability practices can significantly enhance an organization’s ability to identify and address issues promptly, leading to more reliable data, improved decision-making, and a stronger overall data management strategy. The Actian Data Platform includes many capabilities that assist organizations in implementing data observability, including built-in data integration with data quality as well as real-time analytics. Try the Actian Data Platform for 30 days with a free trial.

The post 10 Ways Data Observability Gives Organizations a Competitive Advantage appeared first on Actian.


Read More
Author: Teresa Wingfield

Red Button 0 Comments
Oct 10 2023
The Benefits of Generative AI for Banking & Financial Leaders

Generative AI is a subset of Artificial Intelligence (AI) that focuses on creating artificial data or content. It uses deep learning algorithms to generate images, videos, or audio based on the data given to it. Instead of learning from data, generative AI creates brand-new data.

Generative AI is transforming data analytics in the financial services industry, presenting new opportunities to enhance customer service, increase revenue, improve security, reduce risks, optimize investments and strategic planning, and more. Here are some common uses and benefits of generative AI in financial services:

Chatbots: Banks can use generative AI to create chatbots that mimic human conversation through text or voice interactions. Using chatbots can improve customer service, cut costs, and boost revenue.  For example, chatbots can save banks money by automating routine customer service functions such as answering questions about account balances and performing routine tasks such as making transfers and sending messages. More advanced uses include providing personalized recommendations and sales based on a customer’s history and activity.

Fraud Detection and Prevention: Generative AI is supplementing traditional fraud analytics with models that can identify abnormal patterns in large volumes of financial transactions so that financial institutions can halt suspicious transactions faster. Financial companies are also using generative AI to create synthetic data that simulates fraud so they can develop more robust fraud detection algorithms.

Anti-Money Laundering: Using generative AI to analyze large volumes of financial data such as transactions, accounts, customer profiles, and company information. Know Your Customer (KYC) data can identify patterns and anomalies that may indicate money laundering activities.

Credit Risk Assessment: Generative AI models can determine credit risk more accurately and much faster by analyzing vast amounts of data, including financial statements, credit scores, transaction histories, and other relevant data. This can lead to better lending decisions that reduce credit risk.

Credit Reporting: Companies in the financial services industry can use generative AI to automatically create credit reports and other financial documents. This can streamline loan application and approval processes, reducing paperwork and improving efficiency.

Algorithmic Trading: Traders can use generative AI to potentially achieve higher returns. Generative AI helps develop trading algorithms that produce trading signals for when to buy or sell a security and that predict market movements.

Portfolio Management: Generative AI can help optimize portfolio allocations by generating asset combinations and simulating their performance. Portfolio managers can use this information to build efficient portfolios based on criteria such as risk tolerance and return objectives.

Asset Management: Businesses can use generative AI to analyze market data and forecast asset prices, interest rates, and other economic trends. This information is valuable for making investment decisions and managing financial assets. Generative AI excels in analyzing unstructured data, such as social media sentiments and news articles to help investment managers gain insights into investor perceptions and market shifts.

Strategic Planning: A company in financial services can leverage generative AI to develop predictive models for financial metrics such as customer churn, account balances, and revenue. Better forecasts of these metrics can improve strategic planning and resource allocation.

Generative AI and the Actian Data Platform

Generative AI is a versatile tool that presents many opportunities for data analytics within the financial service industry. However, generative AI requires the right data platform to be successful. The Actian Data Platform is the first as-a-service solution to unify analytics, transactions, and integration. Its flexible cloud, on-premises, and hybrid cloud architecture brings you trusted, real-time insights, making it easier to get from data source to decision with confidence. The Actian platform’s low, no-code integration with data quality and transformation options make it easier and more flexible to address more generative AI needs/use cases.

The post The Benefits of Generative AI for Banking & Financial Leaders appeared first on Actian.


Read More
Author: Teresa Wingfield

Red Button 0 Comments
Sep 26 2023
How to Optimize Customer Analytics to Improve the Post-Purchase Customer Experience

In a recent Martechcube survey, only 18% of retail leaders believe that they could significantly improve the post-purchase customer experience. In contrast, a whopping 80% of consumers feel otherwise.  Providing a poor post-purchase customer experience can prevent you from building customer loyalty. Customer analytics can provide valuable insights and data-driven strategies to help you get to know your customers, personalize customer experiences, and improve customer satisfaction.

Over-Reliance on Customer Segmentation

One of the biggest culprits underlying a poor post-purchase customer experience is segmentation. Analytics allows you to segment your customers into similar groups with similar characteristics such as income, gender, age, etc., or behaviors such as purchases, path-to-purchase, and promotional responses.

Marketers use segmentation to help them tailor their campaigns, promotions, and communication to each segment, hoping that these will resonate with customers in the same segment.  But do they? Not always. People falling within a segment often have different needs, values, and motivations, and, even if they have the same behaviors, their reasons or motivations for that behavior can be very different.

Insufficient Personalization

By analyzing a customer’s purchase history, browsing behavior, demographics, and other customer activities, you can deliver targeted content, product recommendations, and offers that are more likely to resonate with the customers. More savvy retailers are bringing zero-party data into the personalization mix. Zero-party data is information from customers that they voluntarily and deliberately share with you. The use of zero-party data has risen in popularity after Google announced its intended phase-out of support for third-party tracking cookies in Chrome back in early 2020. Since this time, marketers have realized that zero-party data is more than a replacement strategy for cookie data and now understand that one of the best ways to know what a customer really wants is to simply ask the customer. 

Predictive Analytics Can’t Always Forecast Churn

There’s no doubt that predictive analytics is a valuable tool that can help you predict customer behavior, such as their likelihood of churning or making a repeat purchase. Insights can assist you in proactively addressing issues and engaging at-risk customers.

On the downside, there are tons of factors that cause predictive analytics to fail to predict customer churn. Insufficient or poor-quality data will impact the accuracy of results for any type of modeling.  Predictive models base their predictions on trends in historical data.  As such, they might fail to predict that a customer has decided to churn abruptly due to a recent negative experience. This is a big shortfall for predictive accuracy because 76% of shoppers will stop doing business with a company after just one negative experience.  In addition, the competitive landscape is constantly evolving, and historical data may not reveal this.

These shortcomings have several implications for users of predictive analytics. It’s important to regularly update predictive analytics models, validate results, and incorporate a variety of data sources, both internal and external.  Also, predictive analytics needs to be part of a comprehensive data analytics approach that includes adaptive analytics strategies. For example, analyzing current data from customer support interactions, including call logs, chat transcripts, and email can quickly identify if a customer is experiencing an issue. And keeping track of new social media mentions and conversations can help you spot unhappy customers faster.

Let’s Make CX Easy Together

Customer analytics provide valuable insights to help you know your customers better to help you deliver a more engaging customer experience.  But more is needed than traditional segmentation. You’re going to need to focus more on individual customers and engage with them directly to understand their needs. Advanced analytics such as predictive modeling are useful for understanding future customer behavior, but you’ll still need adaptive analytics to identify sudden changes in the customer experience or market dynamics.

According to a recent GigaOm TPC-H Benchmark Test, the Actian platform’s operational data warehouse is 9x faster and 16x cheaper than alternatives. The Actian Data Platform makes it easy to track, manage, and analyze customer analytics to better identify areas that need improvement and help improve business outcomes. Contact us to start your journey to improving CX.

The post How to Optimize Customer Analytics to Improve the Post-Purchase Customer Experience appeared first on Actian.


Read More
Author: Teresa Wingfield

Red Button 0 Comments
Sep 25 2023
Empowering Power BI with the Semantic Layer


Today’s organizations widely acknowledge the significance of leveraging data and analytics. Virtually every executive envisions establishing a data-driven organization. However, a survey conducted by New Vantage Partners reveals that only a mere 26.5% of companies have effectively achieved this transformative goal. Part of the problem lies in the ineffective collaboration between business and technology teams.  In the […]

The post Empowering Power BI with the Semantic Layer appeared first on DATAVERSITY.


Read More
Author: David Mariani

Red Button 0 Comments
Sep 20 2023
How to Use Analytics in Web Development
It may sound a little dramatic, but in today’s ever-evolving world, data is vital. Data can be huge in boosting your business revenue and exposure. Knowing your data means being able to offer continuous improvement and optimization, which in turn will allow you to deliver even better user experiences. You can track who’s looking at your […]


Read More
Author: Priyanka Damwani

Red Button 0 Comments
Sep 15 2023
Introducing the Data Analytics Fabric Concept


Organizations all over the world – both profit and nonprofit – are looking at leveraging data analytics for improved business performance. Findings from a McKinsey survey indicate that data-driven organizations are 23 times more likely to acquire customers, six times as likely to retain customers, and 19 times more profitable [1]. Research by MIT found that digitally mature firms are 26% […]

The post Introducing the Data Analytics Fabric Concept appeared first on DATAVERSITY.


Read More
Author: Arun Marar and Prashanth Southekal

Red Button 0 Comments