Getting Up to Speed with Big Data

Foto: Shutterstock
Photo: Shutterstock

For Gartner analyst Kimberly Collins, turning Big Data into actionable customer insights and business opportunities is pretty much “the next big thing on the horizon.” But most companies aren’t yet up to speed where that’s concerned, she explained in a recent webinar. Oftentimes, said Collins, a company will have loads of data but its “chief customer officer or head of e-commerce can’t access it.”

In fact, there is quite a number of obstacles standing in the way of leveraging Big Data effectively. The biggest hurdles identified by Collins and her colleague Bill O’Kane include the following:

  1. The IT department doesn’t understand what the business needs
  2. Companies need to figure out how to deal with hybrid data
  3. Companies are making do with basic data analysis solutions
  4. Silo structures are hindering consistent handling of data
  5. People are concerned about data security

This story is part of our special focus on retail. All the articles related to this topic can be found here.

1. The IT department doesn’t understand what the business needs: A fundamental problem that is so commonplace it almost sounds like a cliché. As Gartner sees it, IT departments are technically capable of turning Big Data requirements into feasible solutions, but unfortunately, they don’t know what specific insights the businesses need. O’Kane stressed that the customer-facing executive needs to collaborate with the head of IT and explain the processes used to derive the customer data.

Data from multiple CRM and ERP systems

2. Companies need to figure out for themselves how to deal with hybrid data: According to O’Kane, aligning structured data from multiple CRM and ERP systems with unstructured recordings from the call center is still something of a “no man’s land.” There still aren’t any tried and true standard processes for dealing with this kind of data material, he said, which means that companies are forced to come up with an appropriate method on their own by trial and error.

3. Companies make do with basic data analysis solutions: Companies looking for quick results when analyzing customer data often rely on a “mash-up,” a platform that, quite simply, combines data from a variety of sources. While this method may be fast and relatively cheap, O’Kane warns that it does not lead to high-quality analyses. “A mash-up,” he points out, “can’t help you resolve problems with data quality.” Companies would be better off using CRM, ERP, or special industry software instead, he says. O’Kane’s preferred approach, however, his “holy grail” of data analysis options, is the master data management hub – a platform that recognizes semantic differences and improves the data quality. The downside? Using it costs a lot of time and money.

Next page: Too many separate strategies for managing data

4. Silo structures hinder consistent handling of data: In many companies, the sales, marketing, and e-commerce departments all use their own strategies when it comes to analyzing data. “This may bring about certain benefits, but only within the individual silos,” Collins explains, recalling a company in the automotive industry whose customers were sent varying quotes for products as a result of this very behavior. Collins believes situations like this could be avoided with a customer engagement hub, in other words, a central point of contact between customer and company that “facilitates a meaningful and consistent dialog” between the parties.

Data privacy is a real issue

5. People are concerned about data security: The two Gartner experts cited the situation in Europe in particular here, but stressed that critical attitudes towards corporate use of private data were not that uncommon in the United States and have even led to stricter privacy laws in California. “People are sharing more and more information with their friends in social networks, but that doesn’t necessarily mean they want to share that information with companies too,” said O’Kane. It’s a fact that companies ought to accept.

In addition to naming the main challenges in their webinar, Collins and O’Kane provided a framework for developing in-house Big Data strategies.

Next page: Big Data strategy uncovered

The Big Data strategy road map

Begin immediately: Clarify how Big Data analyses might be performed in your CRM system. Come up with 10 examples of how you might leverage customer data to identify consumer patterns and insights. Determine who needs to be involved in the process.

Steps for the next 90 days: Pick the best three examples from your list of 10 for realization. Draw up your business case. Put together a joint team of IT and business colleagues.

Steps for the next 12 months: Recruit experts, if necessary. Determine what additional software is needed. Launch and conduct a pilot project. Examine its added value for the company – in the best case scenario, continue with the project and evaluate the outcome.