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Data Sherlock: What Does It Take?

By now you have seen a few cases in our Data Sherlock series. These cases are pulled from real life events and only tweaked slightly to both hide the identity of the team or prospect involved and to make sure the message resonates. 

One question you might be asking is, what does it take to be a Data Sherlock? Fair question, and one that is pretty easy to answer when you appreciate the layered response.

First, know that I believe every organization already has one, if not many, Data Sherlocks roaming the halls, as the skills below are not unique nor hard to learn. This is important, as I believe some Data Sherlocks are just waiting for that spark of inspiration that will lead them to tackle their destiny and move to new heights in their individual careers.

The basic traits of a Data Sherlock are broken down into three key attributes. After reviewing these, I challenge you to ask yourself, “could this be me?” and “what could I do for my career or my company if I became a committed Data Sherlock?”

  1. The first trait of a Data Sherlock is the ability to stop and take yourself out of the daily grind of work. We are all extremely busy, the days don’t get longer but the task list always does. Thus, it is the committed Data Sherlock that can, at least for brief moments, look up, get out of the daily grind and try and see the bigger picture. It is in the bigger picture where the Data Sherlock finds inspiration as they look to address big questions with high potential to add value to their career and their company. It should be noted that it often doesn’t take a lot of elapsed time to find the bigger questions, but it does take committed time with no distractions of daily work. Hence, it can be an early Saturday morning before the kids get up, a plane ride to a company meeting or a late evening where you lock yourself in a conference room. In the end, Data Sherlocks find the time to come up with high-impact questions.
  2. The second trait is the ability to flip the script on what it takes to get their normal jobs done. During the day, a Data Sherlock may work on a set of tasks or action items to keep the lights on, but a Data Sherlock knows that the high-value questions start higher and often from an entirely different perspective. This alternative perspective is key as it could be in the form of a customer perspective, a revenue perspective or some transformational angle that is not part of their current daily tasks.
  3. The last trait of a Data Sherlock is probably the most important and one that requires an appreciation for what Splunk has to offer in its unique platform. The Data Sherlock has to make sure all data, including machine data, structured data and context data, is part of the final answer. Machine data is everywhere and it is under-appreciated by most organizations because it is treated as digital exhaust, only to be forgotten or ignored. This is not okay for our Data Sherlocks because answers to game-changing questions are often found in the marriage between machine data and structured data. Data Sherlocks create the question and then make sure the answer is complete by incorporating all the data including machine data.

In the end, if you want to be a Data Sherlock for your company and career, I suggest you build on your understanding of what Splunk can offer, find a way to create a few small windows of time to think big and finally review what is important to your CXOs. If you can do all three of these, I promise you will find multiple areas where you can make a game-changing difference and rise to the next level. Most importantly, once you do this once you will have unlocked a key framework that will take you far in your career.

Good luck, and let us know about the cases you solve as you become a Data Sherlock.

Z

Michael Zuber
Posted by Michael Zuber

Michael is a well-rounded resource with experience in Accounting, Professional Service, Pre Sales, Sales and Go to Market Strategy.  This unique background provides him the opportunity to identify opportunities or topics for discussion for multiple angles.

 

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