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Why do data-driven projects fail?

Aktualisiert: 19. Okt. 2021

Over the last couple of years, companies around the globe have amassed a lot of data and tools to analyze it. According to IDC, the global big data and business analytics market was valued at 168.8 billion U.S. dollars in 2018 and is forecast to grow to 274.3 billion U.S. dollars by 2022, a staggering 21% growth from 2021 market value projections (221.3 billion U.S. dollars) [1] . While numbers are affirmative of ongoing, tremendous investments in data and analytics solutions, the question of why a large share of data-driven projects fail has never been more relevant to address.

Through 2022, Gartner predicts that only 20% of analytic insights will deliver business value [2]. A groundbreaking discovery? Unfortunately not. In their 2015 study, IT consulting provider Capgemini already found that only 27% of respondents described their Big Data initiatives as “successful” and only 8% described them as “very successful” [3]. Market studies confirm each other to the extent that large-scale technology investments have barely moved the needle over the years when it comes to overwhelmingly high failure rates. Much less clarity and consensus exists on the root causes underlying this stagnating dynamic.

Interviewing 112 business practitioners nowadays engaging in data-driven projects, a recent in-depth study conducted by Ermakova et al. (2021) eventually puts this question through its paces. 41% of respondents claim that less than 50% of their data-driven projects created real business value in the past [4]. The reasons why? We have extracted our three key take-aways for you below:

  1. Business understanding followed by data understanding and user need understanding are qualified as the top 3 critical project phases to non-success

  2. 54% of respondents agree with the conceptual distance between business strategies and the implementation of analytics solutions being influential when it comes to non-success in data-driven projects

  3. Shortly after, lack of organizational alignment (49%), lack of subject expertise (42%), lack of analytics understanding (38%), non-involvement of data scientists into problem definition (34%) are qualified as notable hurdles to successful project completion

Lessons to be learned

With business, data and user understanding at the heart of today’s data-driven project failures, the identification of remedies catering to these collaboration and communication intensive phases will be key to pivot to project success tomorrow. Yet, possible cures need to find application right at the start. Especially early into data-driven projects, when the project team must align on different objectives as well as reconcile business ambitions with technical requirements and possibilities, promoting understanding between all stakeholders involved is instrumental. We can’t agree more that the status quo has to change to turn things around, but what can be done, effective immediately?

The detective take on things

Setting yourself up for success, it always comes down to three ingredients: People, Process and Technology. Great people can be hired externally or trained organically. Great technology can be bought. Great processes can be defined in theory but making them great in practice is hugely dependent on enabling great people with great technology the right way. The best technology is useless if users cannot work it. Conversely, great talent can’t be brought to bearing if the tools in use do not gear towards collaboration. In fact, creating business and data understanding early on during project execution grounds on the process of having team members of technical and non-technical backgrounds as well as varied expertise and relationships with data, share and communicate knowledge efficiently and effectively. If we can agree that data-driven projects only succeed when treated as team sport, we must acknowledge that it is extremely hard for business and IT personnel to play together today when the aim is to score business value through data. According to a recent study by Accenture & Qlik, 74% of employees feel overwhelmed or unhappy when working with data while 59% of users feel unproductive or frustrated when dealing with data analytics or BI tools [5] (Learn more about why today’s data collaboration rituals suck here).

Bridging the conceptual gap between business strategies and the implementation of analytics solutions as teased out by Ermakova et al.'s (2021) study requires not “yet another analytics tool” but a collaboration platform where the entire project team can jointly figure out the messy bits of data-driven projects. This is to weed out the kinds of costly misunderstandings, misalignments and the I-wish-we-would-have-talked-about-this-earlier revelations that have killed far too many project in the past. Such a platform does not substitute the analytical arsenal companies entertain nowadays but establishes the necessary understanding between people from the get-go so that great technology finally realises the business outcomes we aspire.

At, we aim to revolutionise the way business and IT personnel collaborates on enterprise data so that data-driven projects can finally be successful.

You can find out more about our whiteboard-based platform approach here. We are thrilled to show you more and keep in touch with you. Sign up to our newsletter to stay up-to-date on data collaboration best practices and to learn more the detective platform. Want to see it live? Simply schedule a demo with us.


[1] Mlitz, K. (2021) - Revenue from big data and business analytics worldwide from 2015 to 2022 -

[2] White, A. (2019) - Our Top Data and Analytics Predicts for 2019 -

[3] Colas, M., Finck, I., Buvat, J., Nambiar, R., & Singh, R. R. (2014). Cracking the data conundrum: How successful companies make big data operational. Capgemini Consulting -

[4] Ermakova, T., Blume, J., Fabian, B., Fomenko, E., Berlin, M., & Hauswirth, M. (2021, January). Beyond the Hype: Why Do Data-Driven Projects Fail?. In Proceedings of the 54th Hawaii International Conference on System Sciences (p. 5081) -

[5] Qlik (Firm) Accenture (Firm). (2020). The human impact of data literacy. -

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