Keeping Up With Data #118

5 minutes for 5 hours’ worth of reading

Adam Votava
Data Diligence



In today’s data-driven business environment, companies must convert data outputs into tangible business outcomes to stay competitive.

While generating vast amounts of data is relatively easy, transforming it into actionable insights requires a deliberate strategy. To be successful, companies must identify key stakeholders, create compelling data outputs, collaborate effectively, and continuously refine the process to build a lasting habit.

The trick lies in a realisation that converting data outputs (your dashboards, reports, or machine learning models) into business outcomes (impacting the bottom line) requires someone to make a decision or take an action.

Logically, the first step in the process is to identify the people who will be making decisions or taking actions based on the data. This requires a deep understanding of the organisation’s structure and processes, as well as the specific needs of each stakeholder.

The next step is to compel the decision makers. This is best done by actively listening to their needs and understanding their ways of working to design a data solution with the decision maker in mind. All while continually validating the design, building trust in data, and explaining the analytics.

The best success is achieved when professionals across functions collaborate effectively. One should bring together different departments such as data, IT, marketing, sales, and finance and ensure that everyone is working towards the same goals.

Finally, companies must continuously refine this process to achieve success. This involves analysing the data, identifying areas for improvement, making adjustments to the data sources and data outputs, and evolving business processes to ensure that they are jointly delivering value.

Always remember that data-driven decision making is not a destination, it’s a means to an end. Business outcomes is what we are after.

Today’s reading list looks at analytics-adverse cultures, strategies for scaling AI capabilities, key question to ask when evaluating AI tools, and PitchBook’s new AI exit predictor.

  • Everyone Wants to be Data-Driven, but Few Want to do the Driving: Organisations are increasing their investment into data and analytics. Yet the senior data leaders are still citing analytics-adverse cultures as their main challenges. This article holds a mirror to the leaders stating that part of the reason is a “lack of dedicated leadership to evangelise and guide the transformation.” It is important to note that the survey is taken among executives from very large organisations. Those that are the most conservative and slow moving, yet constantly jumping on the latest trends. (RTInsights)
  • Scale your enterprise AI capabilities: “While the hype around AI continues to grow and dominate the news and conversations, organizations still struggle to successfully deploy responsible AI algorithms and models across real-world environments. In fact, only about half of AI projects make it from pilot to production.” Having worked on many data due diligences involving AI (but also way simple rule-based models) I’ve seen this first hand. IBM offers four strategies to scale AI. Guess what is the first point — identify how AI platforms and machine learning align with key goals. “If you’re a data leader, think about the things your teams are being asked for the most, and how AI could make life easier for those lines of business.” (IBM)
  • The №1 Question to Ask When Evaluating AI Tools: “The quality of an AI tool — and the value it can bring your organization — is enabled by the quality of the ground truth used to train and validate it. […] In AI, ground truth refers to the data in training data sets that teaches an algorithm how to arrive at a predicted output; ground truth is considered to be the ‘correct’ answer to the prediction problem that the tool is learning to solve.” The issue for the companies evaluating AI tools, as well as for us data due diligence professionals, lies in the fact, that “many AI solutions on the market focus on more subjective decision contexts, where experts often disagree about whether a decision was ‘true,’” making the search for ground truth very difficult. (MIT Sloan)
  • PitchBook’s new tool uses AI to predict which startups will successfully exit: We have seen many investors using similar tools to scale and improve their deal origination processes. EQT and their Motherbrain comes to mind. “VC Exit Predictor’s ability to process large amounts of data and identify patterns can give investors an edge in making informed investment decisions.” But as ever, “no predictive tool is perfect.” The investors need to understand that the models are as good as the data they are trained on — not seeing new trends and reinforcing biases in the training data. (TechCrunch)

Enjoy the weekend and remember that keeping up with data is easier than catching up.

In case you missed the last week’s issue of Keeping up with data

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Adam Votava
Data Diligence

Data scientist | avid cyclist | amateur pianist (I'm sharing my personal opinion and experience, which should not to be considered professional advice)