Given the enthusiasm that companies in different segments arouse about using BigData to expand business and profitability, it is natural for many executives to ask themselves: Wouldn't it be a case of internalizing this effort with the formation of a data analysis department?
While promising, large database analysis requires a specific effort and can be costly. It is true that data storage and processing software is multiplying and cheapening, with the aim of being increasingly operated by the final audience, say, a layperson in Data Science. Still, the cost of licensing a platform capable of processing large amounts of raw information, which cannot be easily organized and interpreted manually and is true for most even midsize businesses - can be quite high.
The high cost of adequate software, however, is not the only obstacle for companies looking to internalize the DataMining process. The main issue is the operation of the software and the subsequent analysis of the data. Now, more than owning a software, it is necessary to know how to operate it and this can not always be solved with a training given by the manufacturer. Applying the most appropriate algorithms, clearing the extracted data, reading statistics and, above all, interpreting the processed information requires a specific knowledge aggregated by data analysts or scientists, beyond systems analysis.
Data Analyst Profile
Professionals in this area are able to identify consistent patterns across large amounts of data, as well as association rules or temporary sequences, in search of clusters composed of systematic relationships between variables that are mainly related to the challenge / problem that motivated the analysis. . The data scientist, in fact, is a great solver of real problems by combining knowledge of statistics, engineering and economics, as well as being able to program computer codes.
“So why not hire such a professional for my company?” You might ask yourself. Depending on the size of the company, the amount of data to be interpreted routinely, or the complexity of the problems to be solved, only one data scientist may be insufficient for the job. It turns out that when creating a team, the cost not only increases, but it is also necessary to justify the constant performance of such qualified professionals. Meanwhile, configuring DataMining software and even analyzing data is often a one-off thing that companies often need once or twice a month. Thus, your team of data scientists will probably not have work associated with BigData every day, making it impossible to hire them.
Staff Composition and Retention
More than specialized, the data analysis team needs high retention. Obviously, when having access to confidential company data, it is not positive for these professionals to change jobs every six months or a year. Therefore, if the output is to outsource the data analysis service, the service provider's analyst retention rate should preferably be high. The high turnover of younger professionals and the hot market for the service sector make retention of data analysts increasingly rare and therefore worthwhile.
Outsourcing BigData analysis, however, may sound like process standardization. After all, who would know how to analyze data from a particular industry without knowing the segment deeply? Data scientists are trained and selected by the market according to their rapid learning ability. Understanding the problem that a CEO has at hand is a key factor in directing work, which will also require some familiarity with the nature of the business associated with healthy distance, the scientist's non-addicted gaze to identify solutions.
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