Why Everyone Will Become a Part-Time Data Scientist

Why Everyone Will Become a Part-Time Data Scientist


Your job description just changed.

Take a look around you – Big Data is no longer a buzzword. Data volumes are exploding and so are the opportunities to understand your customers, create new business, and optimize your existing operations.

No matter what your current core competencies, if you’re not a part-time data scientist now, you will be.

The ability to do light data science (you don’t have to become a full bore PhD data maven in this new environment) will be as powerful a career tool as an MBA. Whether you’re in finance, marketing, manufacturing or supply chain management, unless you take on the mantle of part-time data scientist in addition to your other duties, your career growth might be stymied.

Successful companies today are data-driven.  Your role is to be one of those drivers. As a “data literate” employee comfortable slicing and dicing data in order to understand your business and make timely, innovative decisions, you have can have a positive impact on your company’s operations and its bottom line.

For example, I’ve personally found that being able to drill into Talend’s marketing data has yielded critical insights. Analysis indicates that the adoption of Big Data – a key driver for part of our business – is much further along in some countries compared to others. As a result, different marketing messages resonate better in certain locales than others. Combine that data with web traffic activity, the impact of holiday schedules (France, for examples, has a rash of holidays in May), weather patterns and other factors, and we come up with much clearer picture of how these various elements impact our marketing efforts.  I can’t just look at global trends or make educated guesses – I need to drill into campaign data on a country by country basis.

So my recommendation to you is to dive in and get dirty with data. The good news is that you can become data literate now without spending years in graduate school.

Start by becoming comfortable with Excel and pivot tables – a data summarization tool that lets you quickly summarize and analyze large amounts of data in lists and tables. (Microsoft has put quite a lot of work into its pivot tables to make them easier to use.)

Learn how to group, filter, and chart data in various ways to unearth and understand different patterns.

Now, once you’ve mastered these basics, you’ll feel comfortable bringing new data sources into the mix – like web traffic data or social media sentiment.  You will realize that you can aggregate this data in much the same way as you are able to analyze basic inventory levels or discounting trends.

In the case of Talend’s marketing operation, we are using the Talend Integration Cloud to bring together data from our financial, sales and marketing systems. This allows us to better understand and serve our customers and determine who should be targeted for new products and services. By taking this approach, you don’t have to wait for weeks or months for IT to conduct the analysis – these new tools provide results in hours or even minutes.

In the future, with the introduction of new data visualization tools, working with big data will become far easier for the growing ranks of your part-time data scientists.  If you’re already comfortable with spreadsheets and statistics and have the core competence to spot different patterns in your data as you roll it up by week or by month, spotting trends using data visualization will be 10 times easier as you make the transition from a spreadsheet.

And, be sure to update your job description.  You’ve just joined the growing ranks of smart business users who have earned their part-time data scientist chops. Today this is a highly desirable option; tomorrow it will be mandatory.

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