Stop and Smell the Data

How does a business stop and smell the roses? It steps back and inspects the data.

No one can deny the benefits of pausing, gazing into the meadow, or smelling the roses. Yet most of us are guilty of not heeding to this pastoral yearning of the mind. In this eternal cycle of work, earn, expense, repeat, the cost of ramping up for the fear of slowing down is dwarfed by the cost of not taking a minute to breathe. We too keenly take the Faustian choice of searching for short-term gains at the expense of long-term, strategic growth.

Every business stands to benefit from piecing together their data’s subtle messaging. Like the many details our mind obscures in the dash to our next task, many business intelligence teams don’t recognize the power of their data – the often overlooked yet omnipresent asset that has grown alongside their business.

Fibonacci in nature:
Floribunda blossoms in coworker’s garden

Smell is a unique sense in that it often invokes equal parts caution and curiosity upon encountering the unknown. To trace a smell takes attention. Your eyes squint. Your mind begins to tunnel. It takes a voluntary focus to get to the root of it. Businesses have been sowing seeds of data all along but never stopped to smell the blossom. Why? Are we more cautious than curious?

As is the nature of smell, your primary intake is a blended aroma from the whole garden. That can be very much likened to how data behaves, too. Every data point, like every flower adds its own unique fragrance to the melting pot that fills the data lake. It is the dependencies and correlations and causations of flower patches that help you better understand your garden. And it takes a mindset to absorb it.

The advent of easy data gathering has pushed analytics into more of an after-thought. To grasp the science behind your data takes pondering, and maybe even some wandering. The more complex your business is, the more intricate the relationships of your data.

Akin to smell, data science might seem to lack tangibility of results. This is because there is no fixed key or solution that can be broadly applied. Every business problem brings with it its own set of issues, conditions, needs, and goals. Data science is not any out-of-the-box solution that can be applied universally.

To tackle any business problem, one must begin to quantify the problem’s costs and business impacts. Not all quantification may be done in terms of dollar value. The business must identify both the acceptable impacts and those that must take priority for the data solution. These are not simple decisions that the business struggles with. To navigate this path takes a wide variety of inputs and many interdependent factors to attain an informed answer. In such cases, following your intuition can be too naïve.

Just as defining the problem is tricky, the ultimate impacts of these solutions may not always be objective or quantifiable, let alone readily apparent. Every solution must be guided according to business requirements and fundamentals. Even the results must be interpreted suitably. It is a rarity to immediately affirm the benefits gained from that fresh bouquet of flowers. But, once the pause becomes routine, your confidence in the interludes of quietude increases. You begin to feel healthy in ways you were not aware of. Your maturity towards the practice becomes more accepting. The more you dive into your data, the more trust you will lean towards data-driven decisions at your business.

Data storage as a vase

The sense of smell is more visceral than visual. The traditional data visualizations which have become the staple business strategy will be insufficient. The limitation with seeing or touching data is that it is sourced from a tangible past. Smell on the other hand is more aware – more neural. It helps transform the burgeoning interconnectedness of past data into plausible forward insight.

To be fair, not all data smells “flowery”. As a matter of fact, most data might even stink. If it does, you could begin to remedy data issues and data quality to reach a fresher fragrance. There are many cases where the data has no scent at all. This could mean no set learning pattern or insight is able to be extracted from the way your practice is structured. Deliberate data science might not benefit you any. But at least you learn that your data carries no scent – that itself is insightful. Until you allow that pause to assess what you have, the mysterious aroma remains unsolved.

So, the next time you rush towards that morning meeting, remember to take a pause at the entryway and allow the waft of that fragrant bouquet to wash over you. Trust me, apart from being happier at work, you will be way more effective in that morning meeting.

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