Posted on Oct. 9, 2015
Nature is full of symbiotic relationships between different species: the three-toed sloth and its moth , the leaf-cutter ant and its fungus, the agouti rodent and its Brazil nut tree, the hermit crab and its algae, are just a few examples.
Similar to the way these pairs of species complement each other, data scientists and business experts complement each other. One, alone, can not identify and realize the extraordinary benefits that can be harvested from the vast quantities of data generated, but not typically analyzed, by complex organizations.
While the data scientist needs data — lots of it — for his or her survival, the business expert, along with client teams, makes sure the meaning extracted from the data is real and relevant from a business perspective. When a firm has tried and failed to implement a “data analytics” initiative the root cause is nearly always linked to a failure to recognize the importance of combining these complementary skillsets. In such cases either a bunch of analytics was produced that gained little interest or traction from the business, or the business struggled to handle the volume and complexity of information encountered when they tried to move beyond spreadsheets and pre-cooked reporting systems.
Working together, the data scientist and the business expert “clean” the data, combine it, and evaluate it at a high level. The data scientist then analyzes the data by applying sophisticated tools and algorithms to identify performance improvement and risk mitigation opportunities. Enter again the business expert to help interpret the data as it is being analyzed, sifted and manipulated and to communicate the interpretation and implications to clients.
Without the business expert, the data scientist risks analyzing data for the sake of analyzing, without focus on real business issues that have relevancy to actual problems. For example, in the context of real estate management, a data scientist might naturally want to link swipe-card identification data with sensor location data. The resulting information, the location of an employee at some point in time, could produce nice charts but would have no relevance if the client’s problem is to establish the cost impact of real estate space optimization. By understanding the client’s issues, the business expert reigns in and focuses the data scientist to identify relevant links, to add relevant data feeds such as financial information and lease contract information in the example above, and to conduct problem-centered analyses.
Similarly, without the data scientist, the business expert is often ineffective, limited to conducting analyses with convoluted spreadsheets or pre-cooked tools that limit required manipulations and analyses of the data. Without the data scientist, the business expert is typically overwhelmed when asked to explore large raw datasets—often complaining that the data is “too messy” (not understanding that nearly all data, apart from the pre-cleaned reports they are used to analyzing, looks this way). The real estate management data of a large organization includes portfolios of hundreds or thousands of buildings, with tens of thousands of floors and offices, and hundreds of thousands of desks. Multiply these figures by days of the week or hours of the day, and analytical tasks become daunting, if not impossible, for a business expert. But with the help of the data scientist, who can harness the power of tools like Python, R, Hadoop or SQL, the volumes of data can be appropriately exploited for meaningful benefit. Equally important, the data scientist can craft these analytics to ingest live or near real-time data to drive daily decision-making and help the business expert move beyond summarized historical metrics.
Together, the data scientist and the business expert can win the data battle for their clients. These two — dare we say, species — need each other. The combination of skills of data scientist and business expert is essential to the quality of the solution to their client’s problems.
While it is sometimes hotly debated in the offices of CKM Advisors who is the sloth and who is the moth, what is not debated is the recognition that the data scientist and the business expert need each other in order to provide extraordinary results for their clients.
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