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10 Reasons Your Data Vision Will Fail- Valutrics

Even as they aspire to be data driven, organizations are failing to align their vision with execution. Pitfalls lurk everywhere. We’ve uncovered 10 of the most common culprits. PreviousNext

(Image: geralt via Pixabay)

(Image: geralt via Pixabay)

Data-first companies represent the biggest competitive threat to more traditional players. Unlike their older counterparts, data is integrated into their DNA, so they think and operate differently. The disruptive effects of such companies are causing more mature businesses to redefine “business as usual.” However, transformation is often more difficult and more complex than business leaders may anticipate.

For example PricewaterhouseCoopers (PwC) recently surveyed 2,100 executives about their next big decisions and how their decision-making needs to improve by 2020. More than half, 53%, of survey respondents said consider their organizations “somewhat data-driven,” 8% said they are “rarely data-driven,” and 39% said they are “highly data-driven.”

“Even if these numbers are true by half, if you’re not in [the highly data-driven] group, you’re swimming with the sharks,” said Dan DiFilippo, US and global lead for Data and Analytics at PwC, in an interview with InformationWeek. “You can see competitive advantage and disadvantage for those who have the capability and those who don’t.”

Interestingly, the least sophisticated group in the PwC survey is the most bullish about its ability to compete with data by 2020. What these organizations may not realize yet is it’s one thing to define a vision and quite another to execute it. Execution takes considerable discipline and organizational fortitude to ensure that objectives are actually met. It also takes a bit of patience.

“In every industry, from e-commerce, to banking, to retail, the companies that are moving the fastest are those that made the investment in data science two or more years ago. Now in 2016, they are finally reaping the rewards,” said Ian Swanson, cofounder and CEO of data science consulting firm and vendor DataScience, in an interview. “Moving forward, companies have two options: Catch up and make that investment in your data or get left behind in three to four years.”

[See 12 Types of Data IT Can’t Afford to Overlook.]

The C-suite, business unit leaders, and IT need to work together to ensure that whatever vision that’s articulated meets not only the general goals of the enterprise, but also the specific needs of the operating units within it.

“It all comes down to aligning data initiatives with what you want to accomplish. If businesses don’t align their analytics decisions to their desired business outcomes, across the front office, middle, and back, they’ll continue to waste millions on initiatives that don’t deliver significant ROI,” said NV “Tiger” Tyagarajan, CEO of business process transformation firm Genpact, in an interview.

While cross-functional collaboration is necessary to make organizations more competitive, the process can get complicated. An obvious solution, assuming it’s practical, is one in which organizations hire a chief data officer (CDO) who can make sure data is available and of acceptable quality, there’s governance in place, and that business goals are being met.

Someone explicitly has to own defining the vision, and the process must include socializing the vision and incorporating feedback. It’s just too big and too important a job to add it to someone’s list of things to do who already has a day job,” said Gene Leganza, VP and research director at Forrester Research.

Meanwhile, companies are scrambling to implement the latest technology without a solid implementation roadmap. According to a study of nearly 450 executives by The Economist Intelligence Unit sponsored by marketing firm ZS, nearly all respondent companies claim to be investing in big data infrastructure and analytics capability improvements, but very few have achieved broad impact so far.

“Ninety-four percent report that they are putting in place a big data cloud-based infrastructure, but only 8% have fully integrated the infrastructure with their analytics capabilities,” said Dan Wetherill, associate principal on ZS’s Analytics Process Optimization team, in an interview. “[O]nly 20% reported transformation value from such investments to date.”

Companies are struggling at many levels, in other words. Following are some of the obstacles organizations have to overcome.

(Cover imgage: Sergey Nivens/iStockphoto)

Lisa Morgan is a freelance writer who covers big data and BI for InformationWeek. She has contributed articles, reports, and other types of content to various publications and sites ranging from SD Times to the Economist Intelligent Unit. Frequent areas of coverage include … View Full BioPreviousNext