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Top Five Big Data Challenges for CIOs

Big data is more than just a buzzword these days—but it can be both a massive opportunity and a huge problem for companies. Digital data collection has been a practice since the dawn of computing, but the exponential increase of information, in terms of smartphones, social, search and the IoT, have created a snowball effect when it comes to the data that’s generated, collected and stored. What’s that mean? It means lots of possibilities for smarter products and services, smarter marketing and smarter business practices. It means 88 percent of executives say big data is a top priority for their company. But it also means lots of challenges are coming their way.

The amount of data we create each year creates a staggering set of problems for those who are in charge of managing it. Thankfully, we’re no longer in an era where one person has all the collective responsibility for an organization’s data. That said, the CIO is most often tasked with developing systems of record that can leverage tech to drive better business processes.

We know CIOs face innumerable challenges when it comes to big data, but what are the biggest big data challenges for CIOs in 2016? Let’s break down.

  1. Collection. The challenge with collection doesn’t have to be the obvious one (how do we collect it)? What if—as Clorox’s CIO Manjit Singh discussed in an interview on the very subject—it was something more high level, like “how to get insight out of the data — what questions to ask and how to use the data to predict results in the business?” Singh is right. CIOs should ask themselves to determine what data is important to collect from a business case standpoint? What data isn’t? How is it decided? CIOs can start by looking at big data collection from a big picture perspective.
  1. Storage. Logic states that big data takes some big attention to storage, and it’s the truth. Besides the sheer volume requirements of all those byes, certain data also needs to be available on demand at any time. This can be for operations purposes or compliance. Even though storage is more available than ever, it isn’t all created equal. CIOs should examine infrastructure and cloud options thoroughly before any checks are signed. Purchasing too much is wasting money, but too little could mean crashes and costly downtime.
  1. Organization and Management. To make data useful, it needs to not just be stored and accessible but organized in some way that makes it easy to find and easy to pull. That way, data scientists and other users can locate, analyze, and apply the information in a way that is both efficient and measurable.
  1. Conversion. With companies turning to more and more Chief Data Officers, Architects, and Analysts, it is critical that business intelligence tools are readily available these individuals. While they may be involved in selecting the tools for creating business intelligence, the CIO needs to have systems in place—systems for collection, storage and organization, and management—so they can actually use them.

I’m sure I don’t have to tell you that internal infrastructure costs money, and many organizations want results from big data initiatives sooner rather than later. In these instances where financial and analytical expectations don’t jive—and in a ton of other big data situations, too—CIOs who have not yet done so should consider taking a look at cloud-based options, especially in the realm of increased operability provided by SaaS.

  1. Unstructured Data Growth. Big data is more than just data produced by and for a company. Even when consumers aren’t interacting specifically with a brand—when they’re making posts on social media, uploading videos, or generating any other type of personalized content—they’re giving businesses insight into their habits, preferences and consumer behaviors. Even when it’s not explicitly tied to their business pages or endeavors—perhaps, actually, especially when it’s not—CIOs need to develop systems to collect, store, organize, and make this valuable customer data usable for operations, sales, and marketing.

Big data has already had a substantial impact on the way businesses operate internally, interact with consumers, and navigate their respective markets. That big data snowball keeps rolling, though, and even more changes are forthcoming. If you’re a CIO who can conquer the challenges above without losing sight of the opportunities success will bring, 2016 will be a great year.

Additional Resources on this Topic:

Mobile Devices, Big Data and Big Displays: A World in Sync
CIO Matters: Leveraging Big Data for Big Business Value
The CIO’s Big Data Challenge: Asking the Right Questions, Connecting the Dots

This post was brought to you by IBM Global Technology Services. For more content like this, visit Point B and Beyond 

Photo Credit: shukladeepika via Compfight cc

Daniel Newman

After 12 years of running technology companies including a CEO appointment at the age of 28, I traded the corner office for a chance to drive the discussion on how the digital economy is going to forever change how business is done. I'm an MBA, adjunct business professor and 5x author of best-selling business books including "The Millennial CEO" and "The New Rules of Customer Engagement." Pianist, soccer fan, husband and father, not in that order. Oh and for work...I'm the CEO of Broadsuite Media Group and President of V3 Broadsuite, a family of marketing and media agencies that help companies be found, seen and heard in a cluttered digital world. I also give keynote speeches around the world on the topics such as digital transformation, technology and marketing.
  • DataH

    Daniel, with the explosion of big data, companies are faced with data challenges in three different areas. First, you know the type of results you want from your data but it’s computationally difficult to obtain. Second, you know the questions to ask but struggle with the answers and need to do data mining to help find those answers. And third is in the area of data exploration where you need to reveal the unknowns and look through the data for patterns and hidden relationships. The open source HPCC Systems big data processing platform can help companies with these challenges by deriving insights from massive data sets quickly and simply. Designed by data scientists, it is a complete integrated solution from data ingestion and data processing to data delivery. Their built-in Machine Learning Library and Matrix processing algorithms can assist with business intelligence and predictive analytics. More at http://hpccsystems.com/