September 24, 2021

3

min read

Why Data Analytics projects fail and how to fix them

Photo by Barn Images on Unsplash
Photo by Barn Images on Unsplash
Through 2022, only 20% of analytic insights will deliver business outcomes. - Gartner, 2019

Have you ever faced a failing data project? An article from Nick Hotz discussed a few common issues that have been around Data & Analytics and sabotaging project successes for over a decade. The 8 common reasons are:

  1. Not having the right data
  2. Not having the right talent
  3. Solving the wrong problem
  4. Not deploying value
  5. Thinking deployment is the last step
  6. Applying the wrong (or no) process
  7. Forgetting ethics
  8. Overlooking culture

Among all the reasons why big data and analytics projects fail, we can summarize them with 3 types of causes: unclear D&A strategy, loss control on portfolios, and lack of data culture.

Information grouped by author about why data analyaics projects fail
Information grouped by author

In fact, problems companies face nowadays can be less challenging once the entire organization is aligned on “WHY” the changes need to be there. It is too often that avoidance and miscommunication that block data & analytics projects from success and delivering impacts.


📌 Pinpoint the right problems to focus on. Organizations can avoid wasting time, effort, and investments in Data & Analytics. Then the value from data can be captured and well-used.

In the frame of that D&A strategy, organizations obviously need to invest in technology, strong processes and people’s skills and data literacy to deploy their data projects.

Within those dimensions, we believe, that two critical topics are often overlooked and can drive significant differences: end-to-end processes with portfolio management and developing data culture and people skills with data communities.

📌 Portfolio management tracks projects and assets throughout their complete lifecycle and gives a global view of the ongoing activities and priorities. It also ensures visibility and alignment along the way. With the supports of data, executives can know when to allocate what resources to which project.

📌 Data communities enable collaboration and sharing to support Data & Analytics projects. By engaging people around well-defined topics and goals from the data strategy, this is the best way to actually build a data culture, develop skills, and ensure proper adoption of data-driven approaches within operating models. According to NewVantage Partner’s survey in 2021, executives believe cultural challenges are the biggest impediment to successful digital transformation. People are the key to data-driven and insights-driven transformation.

Relationship between data strategy and its dimensions, illustrated by author
Relationship between data strategy and its dimensions, illustrated by author

Organizations and tech leaders need to map their data strategies accurately then hone their communities and resources together to reach the goals.

Projects don’t function 100% all the time, especially when facing rapid changes. When everyone in the industry has similar technologies implemented, what makes difference is the data culture and processes a company has in hand. As a recap, companies need a well-developed framework to define their data value management strategy. At the same time, they need to invest in people and support their growth in developing digital skills and data literacy.

🤝YOOI supports an organization’s journey in becoming data-driven by providing a cockpit to leaders to manage Data & Analytics portfolios flexibly. YOOI enables executives to implement data value management to support change management and fully leverage their Data & Analytics initiatives.

Want to know more about how YOOI can help you in your digital transformation ambitions, schedule a demo now!

Further reading: Why Big Data Science & Data Analytics Projects Fail (datascience-pm.com)

#CDO #DataStrategy #ValueManagement #PortfolioManagement #Datacommunities

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