🛑The Data and Analytics leaders should STOP thinking about “data” and “analytics” altogether.
🚩The goal of data and analytics is to make data useful in organizations. This is done by trying to build the whole data management infrastructure as well as validation and cleaning processes at a large scale in organizations. It involves many leadership roles, such as Chief Data Officer (CDO), Business Leaders, Decision-makers to Data Governance and Compliance Officers, and individual contributors such as #DataAnalysts, #DataScientists, and #DataEngineers, as well as IT and Infrastructure teams.
Managing data and analytics in organizations requires a lot of effort, with the hope that making data useful will enable organizations to make better decisions.
💣Let me break it to you; “hope” is not a strategy. Managing data and analytics is VERY expensive and complex, and it does not help any organization to operate better and certainly will not change organizational behaviors. Additionally, we now know organizations do not use data to make decisions: “On average, between 60% and 73% of all data within an enterprise goes unused.” – said Forrester. There are many other reports and stats that show similar behavior. This result is the best a data-driven approach can offer.
✨What's the alternative? Rather than hope for the best, let's plan for the best using Decision Centric Approach®. Here is how it works:
1️⃣ Look at the decisions that are made and executed in organizations (decision logs, decision journals, operational decisions, etc.)
2️⃣ Create a holistic view of the business decisions and how they impact business objectives
3️⃣ Decompose the holistic model into smaller and more understandable decision units
4️⃣ Establish the relationships and dependencies between the units
With the roadmap and the blueprint in hand (the decision models), you now know for facts:
• What decision units can be improved in organizations
• What techniques are relevant to each unit based on the nature of the problem they deal with
• What data are needed to support those decisions (i.e., decision-driven data)
• what algorithms and techniques can help them for automation, e.g., #rules #machinelearning #optimization
🚀Model decisions explicitly and operationalize them proactively. This will influence the organization's behavior rather than just hoping for the best by managing data and analytics.
Learn more at https://lnkd.in/gBzdb4e9
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Published May 15th, 2026 at 07:30 am
