Top 4 Reasons Why Big Data Projects Fail (Plus Solutions)

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According to Gartner, 85% of big data projects end in failure. These are staggering numbers and the majority of the time, companies are failing in more than one place.

Here are the top 4 reasons why big data projects fail...

1. Poor Communication.

C-suite fails to effectively communicate crucial strategies leading to project failures and money wastage.

Poor communication is the primary contributor to big data project failure one-third of the time and can have a negative impact on project success more than half the time.


Once the C-suite has identified crucial strategic initiatives, they must communicate these initiatives down to the line managers. Now, it is extremely important NOT to pass it down without equipping these managers with the relevant technology and talent.

Being in this vulnerable state can lead to ambiguity, noise and complexity, especially if teams aren’t ready to discover, interpret and use the data in decision-making.

Line managers from various department need to come together and exchange notes on what data is required to help achieve the problem/strategic statement defined by the C-suite. Having a clear mission will help to recognise data that is going to support business objectives and the sources that might distract from achieving goals.

Everybody says critical thinking is a must-have skill but never has this been more true than when it comes to investigating insights. Don’t purely and blindly take data as fact without ensuring its accuracy or assessing the potential impact this data-driven decision could have on your company.

2. Too Ambitious.

Companies often counter-productively overreach to become data-driven.

Nearly all companies that embark on becoming data-driven organisations or digital transformation initiatives are too ambitious. They either spend millions on infrastructure or claim a framework for analytic or digital transformation that might not be wholly sustainable. 


Data is based on reality by examining what is actually happening. Therefore, decisions should be grounded in facts as often as possible.

Emerging victories in this landscape of digital transformation will not be possible by making huge bets. Winners of the digital age must be agile, pragmatic and disciplined. They follow a carefully devised transformation roadmap to optimise performance in the functions and operations that create the most value, while building the technical proficiency and resources to sustain the transformation.

3. Lack of Leadership.

C-Suite underestimates their involvement to carve out problem statement.

Leadership and strategy work hand-in-hand. In the past, Big Data Analytics would be a strategy in itself but with the maturity of the technology and abundance of data, the strategy is to sit and look at the crucial problem statement.

It is true but very upsetting to know that the biggest failure in Big Data implementation lies in the C-suite underestimating their involvement in carving out the problem statement.


The key decision-makers in C-suites should sit and discuss the primary pain points the company is facing. They will need to work on a few that would make the biggest impact.

These issues are usually left to the Chief Data Scientist (who is usually a very intelligent geek) or the Chief Data Officer (again – a very smart person who knows how to manage the company’s overall data governance and sees the big picture for data priorities and strategy).

When this is left to the data science group, line managers or individual departments, you are then left with 200-300 Big Data projects from which, I predict, more than half will fail. After all, failing to establish order and governance over Big Data projects, leads to chaos and poor business decisions and places businesses at a severe disadvantage in today’s data-driven world.

Harvard Business Review indicates that a data strategy helps organisations “clarify the primary purpose of their data and guides them in strategic data management.” Astoundingly, according to management consultants McKinsey, 30% of banks have no data strategy.

Deciding to become data-driven can be a long, difficult process that once decided upon, can spur a rush to try to attract data specialists and make scientific inferences before knowing the real problem.

This may not seem like a problem because after all, we need data and these specialists know how to handle it. However, do we stop to think what data we need? This is where a data strategy can be overlooked and is therefore crucially missing from a business’ overall strategy.

4. Skills Shortage.

30% of big data project failures stem from an inability to embed big data analytics into the workplace culture.

The lack of skills in organisations contributes 30% of the failure. This affects or takes effect on several levels:

  • C-suite not having the digital leadership mindset to drive strategy
  • Line managers not understanding the data they have within them
  • The rest of the company, not understanding the lingo of analytics

Most people, let alone companies are not prepared to adopt radical changes and become data-driven.


The challenge lies in ensuring Big Data projects perform reliably and efficiently enough so that organisations can flip their mindsets from considering Big Data only as a defensive tool for current activities to using it as a catalyst for business growth.

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