What is a data scientist? And do you really need one?

Regardless of the down economy, the amount of data being captured and stored didn’t slow down in the last few years. If anything, it has increased since 2008 when the recession hit. Even as business growth slowed and budgets were cut, most organizations continued to capture data.

The question is, what were you doing with your data? You might have used it to cut costs or be more efficient, but what else can you do with it now that we’re coming out of the recession and we have “big data” solutions available that offer faster processing speeds, more iterations of models and easy access to analyze your full arsenal of data?

Today, that data represents a significant asset that can be used for more opportunistic ventures. You can use it to prevent fraud, to get to know your customers better, or as an asset to develop new lines of business.

There are a couple of obvious prerequisites:

  1. Do you have the data? Yes, in most cases, more so now than ever.
  2. Do you understand the difference between summary statistics and advanced analytics that allow you to move your businesses forward?
  3. Do you have the talent or the expertise within your organization to help translate between the analyst and the business?

Number one is a no-brainer and I’ve covered number two before (see previous post: Is big data overhyped?). I’d like to discuss number three here by talking about this new career title: the data scientist.

When I first heard the term data scientist 18 months ago, Frankenstein’s laboratory came to mind. As we continue to research the job descriptions of data scientists, I now realize it’s a legitimate role that is useful in a lot of organizations to help you get the most out of your data and to help bridge the gap between IT and business needs.

So, what does a data scientist look like? Not like Frankenstein. It is somebody who has a background in mathematics, statistics and computer science. Data scientists are not necessarily experts in any one of those fields but they can understand all three. They have to be very good at translating the business value of data to the business and helping analysts understand what they possess.

The communication piece is a missing link in a lot of organizations, and data scientists can really help take full advantage of data to help overcome that challenge.

One obvious question that a lot of people ask is: Where do these data scientists live in the organization? They’re not IT. They’re not analysts. They’re not programmers. They’re the thing that brings it together and helps organizations communicate about the stories and the answers available in the data.

We’ve seen a lot of businesses find success with data scientists situated inside a Center of Excellence (CoE). That is one viable possibility, and it offers other benefits to really streamline and unify your efforts around analytics.

After all, you go to the trouble of creating a help desk to provide your employees with support for questions about email, hardware and other technical problems, right? If data is really important to you and you want to increase the use of data to drive decisions in your organization, why don’t you have a help desk for what could be one of your most valuable assets going forward?

Of course, a true CoE is more than a help desk, and a data scientist is more than a call center trouble shooter – but you get my point. You can’t just bring in the tools to solve your business problems and expect them to do all of the work. You need to have the right people in the right positions asking the right questions and teaching others how to use analytics to solve your biggest problems.

Ask yourself: How are you staffing your business analytics projects? Do you see a future for a data scientist role in your organization? What benefits could a CoE provide for your analytics projects?

3 Comments

  1. Bhaumik
    Posted June 1, 2012 at 8:14 am | Permalink

    sir it is really useful for those who never heard about data science.really it is very nice and helpful.

  2. Posted June 18, 2012 at 4:09 pm | Permalink

    Jim: as you indicated, data scientists are not programmers or analysts. But they understand
    Computer Science and Mathematics.
    In reality though, it is very hard for corporations to find "ideal" data scientists with knowledge in
    all the important disciplines.

    Point #6 in the McKinsey's report (link given below) explains this very well.

    http://www.mckinsey.com/Insights/MGI/Research/Technology_and_Innovation/Big_data_The_next_frontier_for_innovation

  3. Tom Kari
    Posted June 23, 2012 at 12:57 pm | Permalink

    Excellent article; I hope that organizations will seriously consider your recommendations.

    A couple of comments:

    Many companies will find it very difficult to staff their CoEs with individuals that possess all of the qualifications you mention. They can still succeed if they build teams whose members possess the skills in aggregate, and more importantly accept that they must work together to succeed. As you pointed out, this "team-oriented" mentality with strong communication skills is important for success anyway, as the CoE members can only succeed by building bridges to both the business people and IT. If they act like an island, they will almost certainly fail.

    I've talked to a lot of people in this type of position, and a problem that is frequently mentioned is that IT has little interest in BI and analytics; rather, they see themselves as responsible for the line-of-business systems and infrastructure. Sometimes this goes as far as IT impeding the BI / analytics processes by insisting that more traditional IT tools be used for these projects.

    It is incumbent on senior management to impress on the CIO and the IT management team that BI and analytics projects are critical for company success, and require full support and cooperation even if the toolset is foreign to their experience.

    My two cents worth!

    Tom

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