In the age of information explosion companies face the challenge of collecting, integrating, managing and analysing data to arrive at actionable insights. It is interesting to note that all of the above needs to be done by an analyst. It’s her knowledge, experience and skill-set that matters the most when undertaking any of the above activities. In order for a firm to derive the maximum benefits from data and analytics it not only needs to invest in the data management and analytical tools but also in the right talent that manages them. As Avinash Kaushik once stated, “If you have a budget of $100 to make smart decisions about your websites… invest $10 in tools and vendor implementation and spend $90 on Analysts with big brains.”

I therefore concur that no tool is ever as valuable as the analyst.

First, we need to understand that a number presented differently can mean different things. For example, $1,500,000 can be mentioned as $1.5 million or $2 million if rounded to the nearest digit. Both the numbers are correct but the contexts in which the number might be mentioned can lead any one reading the number to draw different conclusion. Hence, it is the judgement of the analyst and how she chooses to represent the number for a particular exercise matters a lot.

Second, with the advent of big data and analytics most of the firms are looking to ride on this wave. They fail to give enough thought on whether they have the capability as well as the culture within their firm for data and analytics driven decision-making. Investing on data and analytics infrastructure is not a means to an end. It might turn out to be a situation wherein a novice surfer is provided with the best surfboard that money can buy in the market but all she needed was a great instructor and an average surfboard.

Third, while in computing there is no denying that quality of output is determined by the quality of the input, in analytics, many overlook that even the best quality data if fed in to a model that considers invalid assumptions will lead to inaccurate results and conclusions. Therefore the acumen and capability of the analyst once again comes in  to picture. The analyst not only needs to collect and structure data in the best possible format for processing and analysis but also ensure that the models used are statistically sound and viable for deriving relevant conclusions.

In the end, it is of utmost importance to weigh the options of investing in the right analytical talent and right analytical tools and infrastructure. It should not be a perfect balance but should lean more towards investing in the right analytical talent. As stated earlier analytics should not be a means to an end but an important enabler to effectively mine actionable insights from data. This can only happen when the analyst has the acumen to assess the shortcomings and strength of a model and suggests recommendation based on the outputs of the model.