Why Data Projects Fail to Deliver Real-Life Impact: 5 Critical Elements to Watch Out for as an Analytics Manager | by Jordan Gomes

A Simple Guide to Understanding the Macro-Elements That Can Negatively Impact Your Work

Have you ever found yourself deeply invested in a data project, only to realize that it’s not going anywhere? This is a more common feeling than you might think. According to VentureBeat, 87% of data science projects fail to make it into production. In 2018, Gartner predicted that 85% of AI projects would deliver erroneous outcomes. And in 2016, they estimated that 60% of big data projects fail.

Last week, we discussed how to perform quality data analyses, but producing a high-quality analysis is only half the battle. Many impressive works never actually make it into real life and end up being nothing more than “displays of data acumen” at best. So, how do you bridge the gap between quality work and impactful work?

The first step is to understand the rules of the game and have a good grasp of the macro-elements that will determine whether your project will succeed or fail.

Macro-elements Impacting the Success of a Data Analysis

If you have experience in consulting or come from a consulting background, you may have heard of the term “PESTEL” – Political, Economic, Social, Technological, Environmental, Legal. This framework is used to understand the macro-environmental factors affecting an organization and gain a better perspective of its strengths, weaknesses, opportunities, and threats.

To some extent, the same principle can be applied to assess the potential success of your data projects, with a twist. For our variant, we have Data Availability, Skillset, Timeframe, Organizational Readiness, and Political Environment. Each of these factors is like a puzzle piece in the big picture of your data project’s success. Understanding and aligning these elements is like tuning an engine – get it right, and your project will run smoothly; get it wrong, and you’ll encounter bumps along the way.

Data Availability – It may seem obvious, but for any data project, you need data. The availability and accessibility of relevant data are crucial. If you find that the necessary data is unavailable or impossible to obtain, your project will face significant challenges. However, it’s important not to give up immediately. Explore alternative options to acquire the data or identify a suitable proxy. If all efforts fail, it may be justifiable to reconsider the feasibility of the project.

Skillset – Having the right skillsets to analyze the data is essential. It’s not just about technical skills like SQL or Python; it’s also about possessing the specific knowledge required for the type of analysis you’re undertaking. If the project’s requirements fall outside your team’s expertise, consider upskilling the team, provided it aligns with the project timeline.

Timeframe – Time plays a crucial role in a project’s success. If you don’t allow enough time for a project to be completed, the quality of the project can be compromised. However, there’s also a point of diminishing returns, where adding more time doesn’t necessarily lead to a significant improvement in quality. Finding the right balance is key.

Organizational Readiness – It’s not just about having the data and analysis; it’s about having the right structure and processes in place to act on the insights. If the organization is not prepared to make the most out of data insights, the project’s success will be limited.

Political Environment – Navigating the political landscape within an organization is crucial for the success of a data analysis project. You need alignment among stakeholders regarding the project’s goals, roles, and responsibilities. Competing interests and lack of consensus can pose high risks for your project, so it’s essential to address these issues before starting the project.

These key elements – Data, Skillset, Timeframe, Organizational Readiness, and Political Environment – are the gears that drive the success of any data project. Without the right data and skills, even the most skilled team can’t build meaningful insights. Time is a canvas that needs to be managed effectively. Organizational readiness ensures that insights can be acted upon, and navigating the political landscape ensures alignment and cooperation among teams.

By understanding and aligning these macro-elements, you can increase the chances of your data project soaring rather than sinking.

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