It’s hard to deny the buzz around Data Science. Harvard Business Review called Data Scientist the sexiest job of the 21st century. Nearly every major university now offers some type of Data Science program and even President Obama hired a Chief Data Scientist. Just look at the explosion of interest on Google.
Why all the hype? And what does a Data Scientist actually do?
The need for Data Science arose from the realization that better use of data leads to a huge competitive edge. While there are established roles for administering databases, performing market research and developing software, these areas of the business have been isolated from each other. As the size and complexity of the datasets has grown exponentially, analytical capabilities are necessary to extract information to gain useful insights. This void is being filled by Data Scientists.
The main role of a Data Scientist is to craft the data into a story that can be reproduced, packaged and “shipped” to a client or customer (even if the customer is your own boss or company). This typically comes in the form of analysis and algorithms. It may be building scalable personalized recommender systems (think Netflix or Spotify), fraud detection or automating an existing process by leveraging the right data. The data is usually unstructured, “dirty” and sourced from multiple locations around the organization. Therefore combining, cleaning and parsing the signal from the noise is critical to success. Simply put, the Data Scientist is responsible for making sense of sparling mess of information for a non-data oriented audience.
Data Science sits at the intersection of statistics, technology and business. Although creating analytical models and data products may be the key output, a firm understanding of technology and business acumen are necessary to get the job done right. This means breaking down the traditional silos that exist between departments and working cross-functionally.
From the business side, building an innovative algorithm is not enough. A good Data Scientist needs to be able to communicate the solution and tie it to a specific business objective for Business Development, Product Management and other customer facing teams. Here is the recipe for success:
- One part simplifying complex ideas
- One part anticipating future needs
- Two parts curiosity
- A spoonful of domain knowledge
- Add creativity, to taste
Access to the right data is paramount to getting meaningful results. On the technical side, fostering a partnership with the IT and data warehouse teams is essential. While the Data Scientist may not dictate the tech stack, storage solution or infrastructure, they are certainly a key influencer on the decision- making process. The Data Scientist is often a primary end user and a strong advocate of a solution that gives fast, easy access to all the data. As more and more organizations embrace a data driven culture, the Data Scientist has become a trusted advisor.
Does your company have a Data Scientist? Do you use your data wisely?