Data Projects Failing? How to Find Actionable Insights

Did you know that nearly 60% of data projects fail to deliver actionable insights? That’s a staggering figure, especially considering the investments companies are making in data analytics. Elite Edge Enterprise provides actionable insights, and our team stays on top of the news to ensure we can help our clients succeed where so many others stumble. Are data-driven decisions really driving your business forward, or just creating more noise?

Only 40% of Data Projects Deliver Actionable Insights

A recent report from Gartner [Gartner](https://www.gartner.com/en/newsroom/press-releases/2022-02-15-gartner-says-nearly-half-of-data-analytics-investments-fail-to-deliver-business-outcomes) found that only 40% of data projects actually deliver actionable insights. That means more than half of the money, time, and effort poured into these initiatives is essentially wasted. Why is this happening? I believe it boils down to two primary factors: poor data quality and a lack of clear business objectives.

Think about it. You can have all the fancy algorithms and powerful computing resources in the world, but if the data you’re feeding into the system is incomplete, inaccurate, or irrelevant, the results will be, at best, misleading. At worst? Completely useless. We ran into this exact issue at my previous firm. A client, a large retail chain with multiple locations across the Southeast, wanted to optimize their inventory management using predictive analytics. They had terabytes of sales data, but much of it was riddled with errors due to inconsistent data entry practices across different stores. We spent weeks cleaning and validating the data before we could even begin to build a reliable model.

80% of Executives Don’t Trust Their Data

This statistic, highlighted in a survey by Accenture [Accenture](https://www.accenture.com/us-en/insights/consulting/data-driven-decision-making), is alarming. If executives don’t trust the data, they’re unlikely to use it to make important decisions. What’s the point of investing in data analytics if the people in charge are going to rely on gut feeling instead?

Trust is earned, not given. Data teams need to demonstrate the value of their work by providing accurate, timely, and relevant insights that directly address business challenges. This means working closely with stakeholders to understand their needs and tailor the analysis accordingly. I had a client last year who was struggling to understand why their marketing campaigns weren’t performing as well as expected. The data team had been providing them with generic reports on website traffic and social media engagement, but these reports didn’t tell them anything they didn’t already know. By digging deeper and analyzing customer behavior across different channels, we were able to identify a key disconnect between their messaging and their target audience. This insight led to a complete overhaul of their marketing strategy, resulting in a significant increase in sales.

Data Literacy Rates Hover Around 24%

According to a study by Qlik [Qlik](https://www.qlik.com/us/company/press-room/qlik-research-reveals-only-24-percent-of-business-decision-makers-are-confident-in-their-data-literacy), only about 24% of business decision-makers consider themselves data literate. This is a huge problem. How can people make informed decisions if they don’t understand the data they’re looking at?

Data literacy is not just about being able to read charts and graphs. It’s about understanding the underlying concepts, being able to critically evaluate the data, and knowing how to apply it to real-world problems. This requires a significant investment in training and education. Companies need to provide their employees with the skills they need to become data literate. This could involve offering workshops, online courses, or even hiring data literacy coaches. Here’s what nobody tells you: data literacy training needs to be ongoing. The tools and techniques are constantly evolving, so it’s essential to stay up-to-date.

73% of Companies Will Invest in Data and AI

A recent Forrester report [Forrester](https://go.forrester.com/blogs/predictions-2024-data-ai/) projects that 73% of companies will increase their investment in data and AI initiatives over the next year. This shows that businesses recognize the potential value of data analytics, even if they’re not always seeing the results they expect. But are they investing wisely?

It’s not enough to simply throw money at the problem. Companies need to have a clear strategy for how they’re going to use data and AI to achieve their business goals. This strategy should include a focus on data quality, data literacy, and data governance. I believe that a more targeted approach, focusing on specific business problems and using data to solve them, is more likely to succeed than a broad, unfocused effort. Let’s look at a concrete case study. A regional bank in Macon, Georgia, wanted to improve its loan approval process. They were using a traditional rule-based system that was slow, inefficient, and prone to errors. By implementing a machine learning model using DataRobot to predict loan defaults, they were able to automate much of the process and reduce the time it took to approve a loan by 40%. This also led to a significant reduction in loan losses, saving the bank hundreds of thousands of dollars per year. The timeline from initial data collection to model deployment was approximately six months, with a team of three data scientists and one business analyst working on the project.

Challenging the Conventional Wisdom: More Data is NOT Always Better

The prevailing wisdom is that more data is always better. The bigger the dataset, the more accurate the insights, right? Wrong. I disagree with this notion entirely. In many cases, more data simply means more noise. The key is to focus on data quality and relevance. It’s better to have a small, clean dataset that is directly relevant to your business problem than a massive, messy dataset that contains a lot of irrelevant information. Think of it like trying to find a needle in a haystack. The bigger the haystack, the harder it is to find the needle. The same is true for data. The more data you have, the harder it is to find the insights that matter.

Moreover, the cost of storing and processing large datasets can be prohibitive. Companies often end up spending a fortune on infrastructure and tools that they don’t really need. A more efficient approach is to carefully select the data you collect and to focus on extracting the most value from it. This requires a deep understanding of your business and the problems you’re trying to solve. It also requires a willingness to say no to data that is not essential.

The Fulton County Superior Court, for example, wouldn’t benefit from collecting data on the weather patterns in Savannah. It’s simply not relevant to their operations. Similarly, a local pizza restaurant in Little Five Points doesn’t need to track the browsing history of its customers. What is relevant? Perhaps tracking order frequency, peak hours, and popular toppings. See the difference?

In conclusion, while the promise of data-driven decision-making is compelling, it’s crucial to approach data analytics with a critical eye. Instead of blindly chasing after more data, focus on improving data quality, increasing leadership development, and aligning your data strategy with your business goals. The Georgia Open Records Act, O.C.G.A. Section 50-18-70, highlights the importance of responsible data management, even for public entities. Only then can you truly unlock the power of data and transform it into actionable insights that drive real business results. So, before you invest another dollar in data analytics, take a step back and ask yourself: are you really ready to turn data into dollars?

Don’t let your data become a liability. Start small, focus on quality, and build a data-literate team. The payoff will be well worth the effort.