The single most important element for succeeding with digital analytics is the analytics team. People, one of the P’s of analytics, are the primary drivers of the perception of success (or not) of the analytics team. In fact, beyond technology, beyond process, beyond data science, beyond big data, beyond optimization and prediction, it is the analytics team that does the hard analytical work.
The people on the analytics team offer the essential human interface to stakeholders for analytics projects. The team provides the manual processing through which requests for analytics are assessed for feasibility, prioritized, tracked, delivered, followed-up, and closed. More important, the people on the team provide the front-office support and act forward-facing as the representation of the team.
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Thus, you must employ people who have the right combination of technical, business, and social skills to deliver effective and insightful analysis to business people. What is the right combination of technical, business, and social skills? The answer is certainly subjective based on the unique needs of each business.
Due to the mainstreaming and growth of the importance in analytics in today’s business world, I often hear the words “data scientist” and “growth hacker,” and long lists of advanced statistical skills, expertise with newer technologies (such as Hadoop or Cassandra), or fluency with Business Intelligence (BI) software packages. Although it is undoubtedly important for the team to have the necessary level of skills and capability to collect, process, and manage the technicality of analytics, these skills are not always necessary; however, relevant technical skills are useful to hire when building an analytics team.
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After all, even the most esoteric technical and mathematical skills can be taught to intelligent (and willing) people who want to learn. That said, it is necessary, of course, to have the appropriate level of technical skills and/or support from the right technology teams, but remember, analytics is not database operations. Take for example, the dearth of data scientists who understand digital data. It is not unheard of to hire data scientists who have statistical skills developed in other fields, and then teach them the digital concepts to execute data science on digital data.
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