With big data emerging as a necessity to survive in the current real estate marketplace, businesses are trying to understand the kind of skills associated with good big data professionals. However, only the bigger companies are spending on developing their own big data infrastructure. Other businesses, including refinancing and mortgage specialists, are evaluating quotes from big data service providers. These vendors have the expertise to create systematically indexed data warehouses. This makes it easier to collate, extract, and analyze data.
However, when choosing a big data provider, how can a business ensure that the analysis team can get the job done? Similarly, if you are a business planning to create your own data analysis team, how you can ensure that actionable conclusions are created and not just impressive recommendations?
This issue has been discussed at many recent global roundtable conferences. Big data industry specialists are now more forthcoming about the skill sets a good data scientist should have.
Who is a big data scientist?
A big data scientist is at the core of effective data analysis processes. Often, data scientists are people with an Applied Sciences background. They are experts at mathematical equations and using Applied Physics along with an understanding of comparative studies.
Are they same as data analysts? Actually, a data scientist is a step ahead of a typical data analyst. However, this difference might not be easily identifiable. A data scientist can be understood as a data analyst with a more creative application to traditional and emerging data challenges. In terms of analytical ability, a data scientist might be very similar to a data analyst. However, data scientists are expected to be more resourceful—ready to dive into silos of data that seem worthless.
What differentiates them? Along with logic, data scientists are known to function in tune with their intuition. They have a good business sense with a dedicated researcher-like working habit—small failures are not an issue as long as they are a stepping stone towards a bigger success. Good data scientists are known to function outside the realm of the usual. They might look for data in the least probable resources. Experimentation and creating test models are among their most sought after skills.
We believe that a good data scientist should be well versed with statistical studies. This is often needed for creating correlation and causality charts for real estate institutions. Along with exploration skills, data scientists are expected to be good at communication, i.e. ensuring that managers and employees understand the importance of big data.
How to ensure that your data scientists are performing effectively
For starters, don’t bind them with workplace policies and IT frameworks. Allow these data artists to create their own canvas and great results will come your way. Don’t put limitations on the kind of data channeled to them. The more data they play with, the better are the chances of getting actionable data from your analysis team. Don’t allow marketing managers to subdue the curiosity and natural knack for experimentation that data scientists possess. Good data scientists perform best when working in their private labs.
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