A 4-Step Approach to Building your Predictive Analytics Stack

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In this special guest feature, Slava Koltovich, CEO at EastBanc Technologies,  speaks to the infinite possibilities of cloud-based predictive analytics and provide key strategies companies should consider when building a predictive analytics stack. Slava grew up in Russia at a time when computers in the home were scarce. At the age of 10, however, he was introduced to his first computer in the classroom. From that point, there was no going back. Slava went on to study math and computer science at Siberian Federal University and soon after began a career as a software developer. Before joining EastBanc Technologies in 2010, Slava held positions with ITX Software, the United Nations Framework Convention on Climate Change (UNFCCC), and co-founded Astrosoft. During his time at the UNFCCC, Slava worked on the technology used to collect data from countries participating in the Kyoto Protocol for the study of greenhouse gas emissions. Slava assumed the role of CEO in 2014.

We live in a world filled with vast amounts of data that could open endless business opportunities. But with so much data scattered across disparate IT architectures, systems, and applications, these data resources often go untapped.

Traditional business intelligence (BI) tools go some of the way to help organizations harness the power of big data, but these are often enterprise-scale, complex, and rarely customized to the needs of each user.

Self-Service Business Transformation

The cloud has afforded a new approach. Gone are the days when you needed to engage IT to run a BI report. Today, the cloud has brought self-service to BI, making it easier than ever for non-technical users to own, manage, analyze, and visualize their data – wherever it resides.

Cloud-based predictive analytics (the practice of extracting information from data sets to predict outcomes and trends and inform decision making) also solves the problem of disparate data sets. By integrating systems and processing capabilities in the cloud, organizations benefit from a powerful and holistic view of the enterprise and its customers without the need to invest in new systems, software, or expertise. Instead of spending months integrating data siloes and systems into an inflexible traditional BI platform, the cloud makes it possible to connect and analyze legacy and real-time data without the headache or cost.

This makes it much easier for organizations, small or large, to improve services and achieve outcomes such as segmenting customers at the micro-level so they can track customer journeys, understand customer needs and behaviors, and develop more targeted marketing messages.

Thanks to the cloud, predictive analytics is fast becoming the norm and a core basis for business decision-making. But a pure-cloud-based BI approach has its shortcomings. For one thing, cloud BI is still evolving and tends to lack the functionality and range that the traditional (complex and costly) BI solutions provide. Today’s solutions, including cloud, also fall short in terms of future-readiness.

Future-Readiness Goes Beyond the Cloud

The BI tech industry is a fast moving one, today’s tools may not be the best for tomorrow’s business needs. Rather than “rip and replace”, forward-thinking organizations need to be able to adapt as technology changes. This means extending today’s investment in BI tools, something that’s easier said than done given the challenges of vendor lock-in and static technology that typify today’s enterprise-grade solutions. It’s a perplexing dichotomy.

A 4-Step Open Approach to Predictive Analytics

Organizations should start by utilizing open source software and an integrated infrastructure based on a flexible, open, and customizable architecture. Because the cloud and open source are intrinsically elastic, you can seamlessly boost your BI efforts with the addition of pre-packaged, production-ready, open source software components that meet your evolving needs at a low cost.

Change isn’t easy, but the insights and competitive advantage enabled by predictive analysis alone is a sufficient catalyst for change. To forge a path to cost-effective data-driven insights, organizations should consider the following four strategies as they build a predictive analytics stack:

  • Embrace the cloud–If you’re not using the cloud to collate your disparate data sources, now is the time to do so.
  • Build an open architecture – Avoid building monolith, instead build a solution based on component parts that work together through well-defined or standard protocols. This will ensure you can keep pace with innovations and re-build parts of your stack as new technologies become available.
  • Converge the priorities of the C-suite– Siloed decision-making is just as problematic as siloed data. The CTO/CIO and CMO must focus on the results they want to achieve from any investment in predictive analysis through agile joint planning sessions, rather than serialized planning, or no planning at all.
  • Embrace open source– Leverage open source technology and agile development processes and prioritize a DevOps culture and related technologies for faster turnaround and feature delivery.

A predictive analytics solution based on cloud and open source makes smart data more accessible while future-proofing your investment so you can harness a wealth of information to drive competitive advantage, today and into tomorrow.


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  1. A self-service approach allows business users to make data-driven decisions in real time without having technology (BI) staff. Article gives great details about predictive and self-service analytics.