The Big Data Resolution for 2016!

The Big Data Resolution for 2016!

Big-Data

I love new years! After having taken a long delayed break towards the end of last year, finally spent some quality time with the family, finally had a time to just think about where I have reached and where I want to go, like most of you, I am all charged up to start another year.

With a fresh start feeling that 1st of January brings; a lot of us plan to figure out what we would do differently this year. Whether it’s a healthier lifestyle or taking the long planned entrepreneurial leap on the personal side… Or getting more done, finally getting to that long awaited innovation project on the professional side – we all want this new year to be better than the last. ‘This is going to be ‘the’ year’… we tell ourselves.

As I wrap up my first week of work this year, I notice another trend in most of my customer meetings this year. A bulk of New Year discussions have been around the theme of  ‘this is going to be the year when we finally do something about big data. We have a ton of it and everyone seems to be doing interesting stuff with it. I bet we can pull out something amazing out of it too. But where do we start?’

While I cannot help you get a start on a healthy lifestyle, I can offer my two cents on how to start with big data this year. Most impactful data science insights come out of large data sets of user interactions – purchases, views, swipes, clicks, emails, documents, images, receipts, etc. Here is a four-step outline to get started with big data this year:

  1. Figure out what is your largest and fastest growing data set around user interactions – emails, expense records, TV program views, purchases, profiles, etc.
  2. Extract 10% of this data and engage a data science company to go through it to extract meaningful patterns
  3. Look at other publicly available information online or from social media, etc. to find opportunities to enrich this data.
  4. Get the data scientists to look at this combined data to create 5 models:
    1. Marketing – What is in this data that can help me get more customers?
    2. Sales – How can this data help us sell more to our existing customers?
    3. Customer Service – What does this data have to serve our customers better or make their experience better?
    4. Finance – Are there any cost savings or fraud prevention opportunities available in this data?
    5. Risk – Can this data help us predict or mitigate any business risks?

With a good data science partner, you should be able to find at-least one model that can help your business do significantly better than it did in 2015. This should give you a direction to get started with your data science work.

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