As we step into 2018, a new world order is afoot where bots have become the preferred assistants, machine learning algorithms are becoming better than most humans in creating predictive models. More and more companies are deploying ML models across all aspects of their business from split second decision making (streaming analytics) or even multi-year strategic decision making (simulation). Here's an attempt to comprehend what this year holds, with respect to data, analytics, all things in between, and beyond.
Full stack Data Scientists: The Full Stack Developers of the Analytics world
As the full stack developer craze is growing in the IT community so is the need for Full Stack Data Scientists. Gone are the days when Data Scientists used to develop statistical models used to be developed in silos and engineers used to get the KT and deploy it in production. Companies now want data scientists who are also data engineers.
One person who can take care of the end to end lifecyle of data science projects from formulating the business problem, translating it into data problem, developing the appropriate models, creating the data pipeline which will deploy the models into production. This change in expectations of a data scientists is also driven by the kind of companies which are carrying the machine learning mantle.
While in early 2000’s companies like GE which had a management focus was carrying the Data Science / Machine learning mantle, today it is technology & engineering focussed the companies like Google, Microsoft & AWS who are carrying the Machine Learning / AI mantle.
Deep Learning: The Machine Learning technique that is changing everyones life
As the availability of labelled data is increasing algorithms Deep Learning seems to be winning over the battle of machine learning algorithms from simple classification problems to complex problems involving cognitive capabilities. While Deep Learning is taking over the jobs of drivers by powering autonomous vehicles, we expect that it would increase the number of data scientists who can work on Deep learning, but Auto Machine Learning models seems to be doing a better job in fine tuning deep learning models than data scientists.
Does this mean everyone including data scientists will lose their job? Not really it does mean that data science toolbox would be accessible to a more wider number of people who doesn’t need to know complex algorithms. That also means there would be more citizen data scientists and the application of machine learning will widen from the current narrow corporate walls that it is confined to.
Augmented Analytics: The next disruption in BI
While one part of the world is comfortable moving towards a deep learning centered world, where it is difficult to decode what’s happening and why its happening. There is another part of the world which wants human intuition to be combined with artificial intelligence to create the best of both worlds. Gone are the days when analysts have to sift through multiple drill downs and drill throughs to get to the specific insights that they want.
Business users wants the insights to be automatically generated and waiting for them when they would want to start analysing the same. The concept of data democratization is marries well with Augmented analytics as well. It is not only important to provide access to all parts of the organization but the tollset which will make it easier for them to analyse the same. While Augmented Analytics is in a nacent stage this is a field likely to see multifold interest in the years to come.
Gartner predicted in 2017 that "Data and Analytics will Go Mainstream". And now, the technological sands are shifting further. No matter the size of the business, the demand for learning customers' wants and requirements by studying data, recognizing patterns, to make smarter predictions, formulate strategies and assessments is being embraced by all.
If you're already using these technologies to your advantage, get ready to up the ante in 2018, but if you're not, don't fret. Harvard Business Review claims that a range of businesses aren't even close to identifying what analytics can do for them. So, go ahead, rope in data analysis methodologies, cutting edge AI technologies into your business intelligence plans, and actualize your enterprise aims.
The author is Director and Head of Data Sciences, Happiest Minds.
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