In the ever-evolving business world in which technology enables rapid changes and evolution providing more robust tools, paving the way for better and more effective business management, it is hard to argue with the impact of prospective. Be it the burning the desire to be more data driven or the ability to have near complete operational awareness; the access and subsequent analysis of such data is likely to result in even more questions and potentially more doubt about the validity of such analysis. Without going into intrigues of data analysis and bias i.e. information bias, selection bias, and confounding, let’s have a look how it can be reasonably mitigated in a startup setting.
Let’s start with a realistic assumption: startups have some inherit limitations, mainly around resources i.e. funding and attraction of high quality talent. Both of those are equally relevant to decision making at macro and micro level, hence it stands to reason that decision making at both levels are limited. This particular conclusion, though semi subjective has a real impact on the explicit and/or implicit expectations of virtually all stakeholders.
Once the realization of those limitations are taken into account, the next step would be seeking a method to mitigate those perceived shortcomings. The usual reaction tends to be an increase in resource allocation to either tools or human capital; and frankly there is nothing fundamentally wrong with that approach. However, it raises the questions about feasibility, effectiveness and efficiency.
Human capital vs tools
It is no secret that previously mentioned advancements in business related technology has resulted in an amazing array of advanced and sophisticated tools that allow even non experts to compile, visualize and interpret a wide range of data points. The real question is however the utility and impact: can such tools replace expertise? The answer is not straight forward: generally speaking, even amazing tools that democratize availability of complex data cannot be expected to provide appropriate strategy within the context of individual organizations. Sure, the data sets and factual conclusions that are not a matter of “opinion” are a great start; but how the decision makers can use that business intelligent in the context of specific setting are a point of contention.
Tools, no matter how sophisticated are truly at the mercy of the user. We all have heard the expression – tools are only as good as the user; and there is a lot of truth to that. Simple factor such as the breadth and depth of the tool its elves can have an immense impact on the output; which brings us back to the human capital i.e. expertise. But does that imply that human capital re: expertise trumps tools? Again the answer is not that “black and white”: essentially those two function symbiotically. In an optimal setting the end user has an expert level knowledge of the tool combined with matching expertise to use the conclusions both tactically and strategically in context of the field and said organization.
External vs. Internal
Now that we logically concluded that the said expertise has to be in context in order to maximize the output of the tool, it is important to explore the human capital strategic impact.