10 Reasons Your Data Vision Will Fail
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.
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)
What is it, exactly, that your company wants to accomplish? If it's something nebulous like "compete more effectively," it's going to be difficult to measure success, and your competitive position may suffer from the lack of clarity.
"Organizations have to put a very fine point on what they're after. It's this opportunity, or this market, or this customer segment that has the greatest opportunity for cross-sell or up-sell, whatever it may be," said Dan DiFilippo of PwC. "If you can't get the problem identified and captured in a very real way, then you're opening [the door] to a lot of spinning of wheels, suboptimization of resources, of time, [of] money -- all the rest of it."
Some basic tenets of smart business don't necessarily change with technology. One of them is aligning a company's vision with the execution of that vision, which much easier to say than it is to do.
"Achieving a strategic data vision requires strong, focused leadership, plus sustained cross-disciplinary collaboration and support. Many organizations have set out to achieve a strategic vision without a strong enough commitment to the changes necessary to make it happen," said Gene Leganza of Forrester Research. "Enterprises are good at pulling together cross-disciplinary teams for short-lived, ad-hoc challenges, but this requires a sustained effort and therefore a sustainable culture comprised of the right organization, the right processes, and the right technology."
Many organizations have become some level of data-savvy out of necessity. One sign of maturity is the ability to execute well -- not sporadically, but consistently.
"Consistency of the data infrastructure, the technology platform, is key for execution. The more fragmented the infrastructure, the less able the business is to execute consistently," said Goutham Belliappa, big data and analytics practice leader at management consulting firm Capgemini. "Google and Amazon excel at this consistency, enabling huge business innovation on firm foundations. The reality is that [articulating] the vision is relatively simple -- to drive the business via analytics to become insight-driven. The challenge is driving the consistency of execution that accelerates the pace of business innovation."
Solving the problem is more than a technology issue, however. Cultures also have to adjust. In many cases, there's a disconnect between the business mindset, which focuses on solving individual problems and [the] IT mindset, which attempts to solve all problems in a consistent way. The difference between organizations that succeed and those that fail is the ability to embrace the chaos that is innovation in an organized, consistent manner, Belliappa said.
Businesses have to unify their data strategy and business strategy to compete effectively, which does not come naturally to some organizations. It's entirely possible to get so intrigued with data and the latest, greatest tools hitting the market at break-neck speed, that the point of it all -- meeting a specific business objective -- is lost.
"The most successful companies have become digital-first entities, with digital strategies at their heart. It's not just 'a digital plan' sitting somewhere in IT," said Bill Shander, founder and CEO of marketing agency Beehive Media, in an interview.
The wise implementation of technology is moving from a waterfall model to an Agile model, and the pace of the trend is accelerating. One day your company is celebrating its leadership position in a certain industry sector, then a competitor or disruptor changes the rules of the game, seemingly overnight.
"This is a rapidly evolving space, so technology decisions need to be made methodically, but most importantly, with an eye toward flexibility," Bill Shander of Beehive Media said in an interview. "Any choice made today will be 'wrong' at some point. The name of the game is to be sure that your choice closes the fewest doors possible."
Well-educated people with decades of experience (and people with big egos) can be slow to embrace data-informed decision-making. That's a problem because it's impossible to realize a data-driven vision if business leaders and people in the organization refuse to implement the vision.
"The biggest obstacle I see that disconnects vision from execution is that people tend to trust their gut [when it comes to running] their business, regardless of what the data says. If the data supports intuition, great. If not, well, most people will go with their gut anyway," said Tim Young, senior analyst in Statistics & Analysis, at the Office of Institutional Research of Southwestern Illinois College, in an interview.
The high tech industry has always been enamored with the latest and greatest technologies. Today, the pace of innovation is moving so fast that enterprises can't keep up. As a result, they may be compromising their long-term prospects for short-term gains.
"Technology platforms and their capabilities and evolving so quickly, it is easy to be compelled by a particular element of a new platform or capability and see the connection of it to one or a few specific use-cases within the overall vision," said Olly Downs, chief data scientist and CEO at customer lifetime value management platform vendor Amplero. "[T]hen, they get into it and find the platform has an Achilles heel with other more fundamental use-cases. This makes technology selection very challenging."
The people defining the vision tend to have perspectives and priorities that differ from those of the people executing that vision. As a result, it's easy for business leaders to underestimate what realizing the vision will actually require.
"Executing the data-driven vision requires a sizable or massive investment in the enabling pipeline, which includes getting the raw data, aggregating data, cleaning data, transforming data, analyzing data, applying analytics platform algorithms, presenting the insights, and acting on the insights," Ravi Kalakota, lead partner at digital transformation consultancy LiquidHub, said in an interview. "In the world of short attention spans and immediate gratification, few leadership teams have the stamina to get the data foundation right. Building on a shaky foundation works for prototypes, but often fails in mission-critical environments."
Transformational changes are difficult to make, especially when moving from the old waterfall style of implementing technology to an Agile style. The shift requires investments in people, processes, and technology that differ from the traditional "business as usual" equivalents. For example, more businesses are claiming to have a "360-degree view" of their customers, which may be a holistic view or a collection of data silos.
"The execution issues are related to a combination of technology obsolescence and not having the right people talent on the team. By the time one IT BI implementation is planned, road-mapped, and implemented, and [its] costs amortized, many better mousetraps are available," said Guru Prasanna, delivery leader at global technology firm Nisum. "The best approach is one where the company builds a solution with a customer-centric approach, and then fine-tunes the data ecosystem based on that."
Analytics is all about measurement. Who are the best customers? Which ones are the most profitable? Who is most likely to churn? Which campaigns succeeded or failed? How much have the mechanical failures of X piece of equipment been reduced? The list goes on.
Notably, the one thing those examples have in common is their tactical nature. If the specifics need to be measured, then why isn't the success of the overall vision being measured?
"Most companies don't have a measurable strategic plan and, therefore, don't know what data is truly strategic," Larry Wolff, area managing partner at technology leadership services company Fortium Partners, said in an interview. "Many try to analyze nearly all data and simply create chaos. A focused, measurable strategy will zero a business in on exactly what data to treat strategically."
Analytics is all about measurement. Who are the best customers? Which ones are the most profitable? Who is most likely to churn? Which campaigns succeeded or failed? How much have the mechanical failures of X piece of equipment been reduced? The list goes on.
Notably, the one thing those examples have in common is their tactical nature. If the specifics need to be measured, then why isn't the success of the overall vision being measured?
"Most companies don't have a measurable strategic plan and, therefore, don't know what data is truly strategic," Larry Wolff, area managing partner at technology leadership services company Fortium Partners, said in an interview. "Many try to analyze nearly all data and simply create chaos. A focused, measurable strategy will zero a business in on exactly what data to treat strategically."
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