Building an Insights Ecosystem: Part 4. AI and ML

  5 min read
panel discussion

An interview by Jason Rowe & Yustyna Velykholova with Iurii Milovanov, Director of AI and Data Science

As actionable insights remain top-of-mind for forward-thinking enterprises, so do artificial intelligence (AI) and machine learning (ML). These advanced technologies enable businesses to uncover value and insights hidden in their data. Whether it’s forecasting demand for products, improving the safety of operations, detecting fraud, predicting equipment failure, or exploring oil reserves — AI and ML are at the forefront of market leaders’ business intelligence.

However, to unleash the power of AI/ML and insights, enterprises need to build an Actionable Insights Ecosystem (AIE) with a centralized data platform, powerful data analysis and visualization tools, and highly scalable and flexible cloud infrastructure.

AIE provides the capabilities needed for operational intelligence and end-to-end visibility on current processes, data feeds, and insights — all within a single digital space.

“Organizations that offer stakeholders access to a curated catalog of internal and external data will realize twice the business value from analytics investments than those that don’t.”

In our previous installments of this digital panel discussion series, we’ve covered the technical (EDP), user-centric (XD), and strategic (BA) components of AIE.

Now let’s dive into the role of AI and ML for AIE.

Transforming data into intelligence that fuels day-to-day decisions is impossible without AI-empowered capabilities. From delivering more personalized experiences and offers, to revealing new revenue streams and hidden vulnerabilities — the possibilities and use cases are almost limitless.

In this installment of our virtual panel discussion, we're speaking with Iurii Milovanov—AI and Data Science expert for SoftServe’s insights-driven solutions.


JR: What is the role of AI/ML in the Actionable Insights Ecosystem and why is it important?

IM: AI and Machine Learning technologies play a crucial role in data-driven decision making by allowing companies to go far beyond traditional business intelligence and analytics. These technologies enable enterprises to tackle complex, multi-dimensional problems that are challenging for traditional rule-based software or require a significant amount of domain expertise.

pyramid


JR: AI/ML could be called the tip of the data wisdom pyramid—founded on raw data that is then perpetually optimized to data driven wisdom. What does insights wisdom look like for an enterprise?

IM: As part of an Actionable Insights Ecosystem, we use advanced analytics to help businesses predict unknown events, fill in missing information, identify risks, and detect anomalies.

Additionally, we:

  • Build forecasting solutions that rely on multiple factors of different natures to uncover forward-looking business insights and allow accurate long- and short-term planning
  • Use advanced what-if and causal analysis to inform critical business reasoning and decision-making
  • Use state-of-the-art Deep Learning algorithms to solve problems that involve human perception, cognition, and behavior, such as computer vision, natural language understanding, and personalization
  • Automate and streamline complex business processes via intelligent automation and optimization

All of which allows us to solve a wide variety of business problems across various industries and business domains.
 

JR: That’s quite a checklist. Your team’s role in AIE is clearly about delivering problem solving support to business stakeholders. Will you share examples of how AI is applied in the various industries you just mentioned?

IM: Sure! Are you ready for another list?

JR: Fire at will, sir.

IM: Ok, some very specific examples are:

  • In Retail, we are helping our clients derive deep customer understanding to optimize their supply chain and inventory management, and design new AI-driven customer experiences
  • In Supply and Transport, we build solutions to identify risks, inefficiencies, and bottlenecks across complex supply chains, optimize navigation and logistics, and uncover the cause of transport vehicle under-performance
  • In Manufacturing, we apply AI techniques to empower our customers with predictive maintenance, industrial automation, and visual inspection capabilities
  • In Energy, we accelerate oil and gas exploration by deriving deep subsurface insights from seismic and other sensing data and automate mission-critical offshore and onshore operations with AI
  • In Healthcare, we use AI to analyze clinical imaging, design personalized patient experiences and optimize complex billing and financing workflows
  • In the Public Sector, we are building smart environments, improving public safety and surveillance, and revolutionizing public education by building personalization systems that can uncover students' potential and boost their success
  • In the Legal and Financial industries, we are helping customers automate large-scale document processing, identify hidden risks, and detect fraudulent activities


JR: None of which is possible without first having your data “house” in order, which is the purpose of an Actionable Insights Ecosystem, right?

IM: Correct. Without properly managed and democratized data, optimal cognitive analytics and intelligent automations are not possible.
 

JR: Thanks, Iurii. This has been very informative.

IM: My pleasure.


And that wraps up our panel discussion series!
 

Every enterprise in today’s global marketplace is accumulating data at an unprecedented pace. We know that all business leaders recognize the need for, and value of actionable insights. We also know that most companies striving to become data-driven (and not just data aware) are failing at achieving the insights-driven imperative.

We hope, this panel discussion series has provided more perspective on why we say that there is no one size fits all insights solution. While we’re all in a hurry to “get there”, there is also no “out of the box” answer to achieving democratized data insights that connect business, tech, and users.

As we’ve just learned, artificial intelligence and machine learning deliver high impact benefits, but achieving these advanced technologies driven benefits requires quality data beforehand—which is the purpose of SoftServe’s Actionable Insights Ecosystem.

How do you define “Actionable insights”?

What stakeholders are currently working on achieving a data-driven reality, and what are they doing to accomplish this?

Let’s talk about your data and insights journey and how an Actionable Insights Ecosystem—featuring AI/ML—can help you achieve long-term operational excellence.