Research Hub > Artificial Intelligence Transforms Big Data into Smart Data

February 09, 2018

3 min

Artificial Intelligence Transforms Big Data into Smart Data

Taking a strategic approach to data analytics helps organizations minimize pain points and maximize benefits.


Work smarter, not harder. When it comes to managing and extracting insights from Big Data, that’s easier said than done.

Volume is a major part of the challenge: Businesses have to mine terabytes to find the nuggets of actionable insights. New sources of data, especially the Internet of Things (IoT), add to that challenge considerably.

Silos are another major hurdle. Regardless of whether data comes from traditional or new sources, it typically winds up being used by only the department that’s responsible for it. Siloing not only prevents organizations from wringing maximum business value from their data, but also undermines the return on investment for Big Data analytic tools because organizations don’t use them to their full potential.

From AI to KPI

As a business development manager, I’ve helped countless businesses overcome such challenges to get a holistic view of their Big Data so they can work smarter and more efficiently. One example is a major apparel brand, whose sales vary significantly by weather. By analyzing disparate types of data — from regional sales to weather trends — this company knows exactly how many degrees the temperature must drop before it’s time to start ramping up production of thermal underwear, and exactly which stores in which states will need more.

What’s the secret to taking advantage of Big Data? Artificial intelligence, which makes it easier and faster to analyze all that information. AI also facilitates different types of analysis, such as predictive analytics and linear regressions. But, like any other analytics tool, AI is only as effective as its implementation.

With most customers, I start by identifying how AI-powered Big Data can address each their unique goals and key performance indicators. In the case of financial services firms, for example, these often include fraud detection, market analysis, customer analysis and compliance. I then look at the types of data the customer has and whether any of it must be converted into a format that AI can handle. That might include, for example, unstructured data such as social media posts and surveillance video from the sales floor.

Tap Experts to Find the Right Solution

Myself and my colleagues at CDW work with partners such as Dell, HPE, IBM, Intel and Nvidia whose solutions transform Big Data into smart data. Our partner portfolio gives us the insights and flexibility necessary to recommend the ideal combination of computing, networking, storage and analysis software solutions to match each customer’s unique goals and data environments. For instance, the apparel brand needed a solution capable of ingesting data from disparate internal and external sources, overlaying those data sets on one another and then ferreting out correlations. Other customers increasingly need the ability to process data from myriad IoT sources, from wearables to connected cars.

The relationships I have with vendors also give me insights into their product roadmaps, enabling me to advise customers about what AI solutions will be available in a year or two. That marketplace guidance is critical because AI is a quickly growing industry right now.

Finally, a successful, AI-powered Big Data implementation needs to be accessible to more than just data scientists and other specialists. After all, siloing is the enemy of business intelligence, so the analytic tools shouldn’t create new barriers to insights.

Learn more about how financial services firms can successfully develop and execute AI-powered Big Data strategies.

This blog post brought to you by: