According to the very serious Harvard Business Review, Data Scientist is the sexiest job of the 21st century. And indeed, for the past few years, as the buzz around big data was reaching its paroxysm, data scientists have been in high demand. Many organizations, drowning in incredible amounts of data, have been unable to reap the benefits that were expected by executive management, and the data scientist was the person who was supposed to make it happen.
However, as big data is now past this peak of buzz, companies are learning and morphing their organizations to the new needs around data. And here are a few jobs that are hot today -- they might not land you a date, they are sure to land you a cushy paycheck, assuming you live up to the expectations.
The data scientist remains on the list. Combining the rare ability to understand the business model and processes of the enterprise with the technical expertise to navigate in the data lake and explore uncharted territories, the data scientist is both a master of the business and a master of the technology.
Today, your typical data scientist curriculum focuses on instilling technology expertise: understand the Hadoop architecture, learn data mining languages like Python and R, master relational and NoSQL databases.
Somehow, the data scientist role embodies the failure of big data software to deliver answers easily. Hence the need for this combination of business and technology expertise. I would almost want to say that this role will fade away when technology matures, but our industry is still employing Cobol developers and Oracle DBAs -- so investing into data scientist training does not sounds a risky endeavor.
A couple years ago, a new generation of software solutions have started to emerge: data discovery combined with ETL in an Excel-like interface. After struggling with terminology, the industry settled on the term Data Wrangling for this technology class.
Therefore, the data wrangler became the person who is using this kind of non-technical interface to navigate across data sources and discover the hidden gems in the data lake. Typically a business user with a technical acumen, the data wrangler works more in prototyping mode, letting big data developers industrialize what they sketched.
In a big data world where metadata and documentation are not even an afterthought but more a delusion, the data shaman will bring his profound knowledge of data nature, his understanding of the business, and more importantly his instinct, to help business users make sense of the data they are drowning in.
Clearly, the data shaman is a short term remedy to a usually bad situation. But some shamans have been known to deliver extraordinary and hard-to-understand results....
Also known as the data story-teller, or the data journalist, the data interpreter helps the C-level exec understand the data that is presented to them. In theory, dashboards and reports should be speaking for themselves, but they often lack context. And some CxOs are great leaders and possess excellent business acumen, but lack the ability and analytical skills to "get" it when they look at a pie chart or other business intelligence contraption.
In the same way that diplomacy would be simpler if all world leaders spoke the same languages, CxOs and dashboards should speak the same language. But they don't, and because of this the CxO has to rely on someone they absolutely trust to tell them what it all means -- but only what it means, the decision remains the CxO's job. Gain that trust, and you'll keep flying on the corporate jet with your boss.
To borrow a line from Robert Redford in The Horse Whisperer, truth is, the data whisperer helps data with people problems -- not the opposite. While all the other hot jobs in data are about making sense of data, the data whisperer job gets the data in shape so that is can be used.
Sounds familiar? It should. In a pre-big-data world, it might have been called data governance, or data stewardship.
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