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Data Disappointment | @CloudExpo @SFlakeSoftware #AI #BigData #Analytics
Insights from data professionals on challenges and opportunities for data analytics
By: Jon Bock
Feb. 5, 2017 10:15 AM
It's hard to find anyone, from cutting-edge technology services to venerable enterprises that have been in the same industry for generations, who doesn't agree that data is critical to their success. But less discussed is the surprising, and frankly embarrassing, reality that far too often data analytics projects are failing to meet expectations.
What is behind that reality? How often do projects fail, what causes them to fail, and what opportunities hold promise to change that? What barriers prevent better outcomes to data analytics initiatives? A recent survey provides some insights.
First, it's important to note that claims about the importance of data analytics are not simply hyperbole. According to a recent Dimensional Research survey of key data decision makers, when asked about the criticality of data analytics, all technology and analytics stakeholders say that it is important to their organization, with almost half of executives characterizing data analytics as critically important.
Why are data analytics important? According to the survey, business motivations for investing in data analytics vary tremendously. They range from tactical improvements such as finding operational efficiencies and managing costs to the strategic, including decision making, enabling growth and staying ahead of competition.
However, the survey exposes challenges that may leave executives wondering if their data analytics initiatives are a good investment, or if a new approach is needed. Eighty-eight percent of those surveyed confessed they have faced failures with data initiatives. Many had dissatisfied users (thirty-six percent) or poor user adoption (forty percent). Also common were issues related to costs including initiatives that went over budget (thirty-nine percent) or costs that far exceeded the value received (twenty-six percent). Thirty-one percent also noted frequent reports of "zombie projects," that were almost done but were never quite ready for users.
What's behind these outcomes? Survey respondents most commonly cited the inflexibility of existing infrastructure (fifty-one percent) as a cause of data analytics failures. Also frequently reported were troubles finding expertise (forty-seven percent), taking too long to access additional data sources (forty-two percent), and inadequate budgets (forty-two percent).
If infrastructure is the leading obstacle, why isn't it a bigger topic of discussion? One reason might be the difference in perspective between senior executives and the frontline staff who work hands-on with that infrastructure. Only a little more than a third (thirty-six percent) of frontline people reported that inflexible infrastructure was a barrier. At the same time, a clear majority (fifty-nine percent) of executives saw infrastructure complexity as a challenge. This would appear to indicate that the frontline people, who typically have a lot of personal investment in putting in place the current data infrastructure, are more willing to accept a complex architecture and continue to hope that they can make it work. The executives, on the other hand, are better able to recognize the challenges presented by complex and inflexible technology.
These challenges aren't just an inconvenience, they're having a real impact. Seventy percent of those surveyed say that their business stakeholders wanted to do things that existing infrastructure doesn't allow.
Not only do these challenges have business impact, they also create headaches for the teams supporting the data analytics needs of the business. For example, most participants (seventy-one percent) said that troubleshooting their data or analytics technologies is difficult. Most participants (eighty-five percent) reported that they had difficulties finding technical expertise, particularly in areas like database tuning, data science and data engineering. The area where the fewest number of participants reported issues with hiring was expertise with SQL (twenty-seven percent). This resource shortage becomes more pronounced at large companies, particularly in the skills needed for projects requiring Hadoop, data engineering and data science expertise.
What will it take to change this bleak picture? The ability to implement and deploy faster (fifty-six percent), reduced time to make data available (fifty percent), simplifying tool sets (forty-four percent) or reducing the overhead of managing infrastructure (forty-four percent).
Looking at that wish list, it's clear that cloud solutions can go a long way to changing data analytics projects from disappointments to successes.
Organizations appear to agree. Already more than half have adopted cloud analytics (fifty-three percent), and among those the majority (seventy-four percent) plan to increase their use in the coming year.
The benefits of cloud solutions for analytics also create new opportunities. The cost model for cloud solutions allows you to just pay for what you need and grow painlessly as you ramp up rather than force you to start with a big up-front investment. That isn't just about saving money, it's also about removing the hurdles that limit the number of projects that can be done. In fact, the vast majority of those surveyed (ninety-two percent) said pay-as-you-go would enable them to try more things with their analytics.
While there has been a lot of hype around data analytics, there is also a fair amount of concern. These concerns aren't entirely unfounded, given that almost nine out of ten data decision makers have experienced failure with their analytics projects. However, inflexible architecture and budgetary problems can be prevented with the right strategy. Cloud computing addresses these issues and provides the flexibility data professionals and executives require in order to deliver insights that improve the way their organizations work.
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