If information is the driving force behind businesses, why do so many Big Data projects fail? What is holding organizations back from fully utilizing their data?

To answer this we need to look at a report by Pricewaterhouse Coopers (PwC) and Iron Mountain, “How organizations can unlock value and insight from the information they hold,” which surveyed 1,800 senior business leaders in North America and Europe in SMBs with 250 employees and big enterprises with 2,500 employees. The results were astonishing; most organizations – the right talent, technologies, strategies – and are unable to fully utilize their data.

While data risk management and compliance are still important markers to evaluate a business’s success or failure, to gain an edge in a competitive market, it is necessary to put in place a powerful data management strategy.



Why Big Data projects fail

The most common reason for Big Data project failure is the mismatch of expectation and capability. It is not enough to simply capture big data, companies need to use the right talent, tools, solutions and strategies to tap into that data and derive maximum benefit. One sure sign of failure is when an enterprise gets hold of humongous data sets but has neither the skill nor strategy in place to analyze and extract tangible insights from it. According to one Gartner study, here are the top 5 reasons why Big Data projects fail in most companies:

  • Lack of Data Strategy – Any Big Data project that is not based on a carefully outlined plan, execution and deployment strategy is bound to fail. All involved parties must align their priorities and clearly map out the business objectives. It is necessary to first understand the problem, and then work towards a solution.
  • Business Case Evaluation – Most enterprises have joined the Big Data bandwagon by investing in or purchasing Big Data systems, but the percentage of companies that have actually deployed Big Data technology is abysmally low. The fear of ‘being left behind’ has prompted a number of businesses to get into Big Data without fully evaluating the business requirement. It is necessary to understand if at all Big Data is required before investing in it.
  • Leadership Resistance – In a survey of 750 business leaders titled “Only Human: The Emotional Logic of Business Decisions,” Fortune Knowledge Group in collaboration with Gyro, found that 62% decision-makers trust their gut instinct while 61% rely on life experiences; there were very few who factored hard analytics into their decisions. When the top management is set in its methods and reluctant to trust analytics and algorithms, it is difficult to fully utilize the available information.
  • Lack of Skill and Tools – While most companies flounder when it comes to employing the right skills and technology to implement their Big Data projects, some actually go a step further and employ traditional and legacy tools resulting in obvious failure. Since every business is different, it is important to tailor the data strategy as per the company goals and use the right tools and technologies to affect a successful project. Moreover, companies which underestimate the complexity of Data science – a blend of domain, mathematical and statistical knowledge and expertise – either end up hiring people with insufficient skills or do not realize the importance of hiring a data scientist.
  • Poor Communication – Big Data can be intimidating and when the information is shuttling to-and-fro between the data science lab, the management and sales teams, important data can get lost in translation and lost data equals to lost opportunities. It is important to ensure the clarity of the data and any related communication; using visual analytics helps in better presenting relevant insights.

We will talk about some of these failures in our article “Bad Analytics: 5 Places Where Big Data Projects Failed” next week, till then, do check out some of the other articles on our Blog page.

Reach out to us for more information on how you can make your Big Data project succeed.