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The Real Reason Analysts Get Sidelined

2 minutes

A curious phenomenon on LinkedIn is the constant influx of unsolicited offers for analytics services. Most feel copy-paste with little differentiation or real experience. How did the analyst role reach this point, and can it be saved?

IT Pecking Order

It seems the de facto IT hierarchy puts analysts at the bottom. With outputs limited to reports and metrics, they are seen as optional and easily cut compared to other roles. This should not be the case. In fact, analytics should not sit within IT at all, and it only has because the deliverables amount to little more than processed data with no meaningful impact.

IT hierarchy places programmers at the top with their ability to write functional code. After them are administrators who maintain operational status. At the bottom are analysts who generate random reports. The lack of concrete deliverables makes the role of analysis easy and doable by anyone.

Make Analytics Great Again

Customers do not need more data that requires further analysis and may never be useful. They need clear, tangible, immediate answers to their problems. If analysts cannot deliver that, they will remain expendable within the IT hierarchy.

Generating haphazard analytics consists of dashboards, metrics, insights...etc. which is not what customers need. Transforming this into proper analytics ensures precise, clear, and optimal outputs which is exactly what customers need.

Making analytics an essential business function is challenging. It is only one part of the solution and requires support from a broader team. The overall approach must be effective while remaining accessible for non-technical stakeholders.

Performing proper analytics requires overcoming expertise scarcity, since it is very rare to find a single person with all the required expertise. Solution framing to allow for optimization. Finally, problem structuring in order to allow for the quantification of problem and subject expertise and incorporation within the solution.

Transforming Analytics

Addressing expertise issues requires a structure that aligns team members so they can excel in their strengths while covering gaps in their skills.

Overcoming expertise scarcity requires an approach that coordinates the knowledge of different experts. The inductive approach coordinates problem and subject expertise from the customer and consultant along with data expertise from the analyst and optimization expertise offered by the machine. Analysts can't solve problems they don't understand and must be supported by other experts to arrive at a proper solution to the problem.

The data effectiveness methodology enables machine optimization and frames problems for analysts that are solvable and aligned with their core skills.

Data effectiveness provides solution framing that supports analytics and allows for optimization. Analytics results are framed then fed into the machine that performs optimization. Both the analyst and machine require properly structured problems to output quantitative results.

Another key challenge is adapting qualitative knowledge into the problem structure. This is what makes human intelligence essential for obtaining real analytics results rather than random data summaries.

Problem structuring requires a straightforward method for quantifying qualitative knowledge. This is where the business problem framework is essential in that it offers an easy to use and clear method for inputting knowledge from problem and subject experts. It is essential that the customer understand their business in order to lead the solution process.

The Future of Analytics

Reducing analytics to data summaries will be the profession’s downfall, as even current generative AI can easily replicate those outputs. Analysts, as part of the solution team, need to focus on what they are uniquely able to do, which is modeling the underlying behavior of mechanisms. This work is difficult but essential for solving real-world problems and delivering complete solutions to customers.

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