The State Of Natural Language Processing Today

Readers of this blog may have realized that Natural Language Processing (NLP) was missing from our ‘5 Data Analytical Trends To Watch For in 2018’ post. Our in-house team of predictive data analysts say it lost out to the other trends by a narrow margin. But that in no way takes away from the importance of NLP and its growing influence in the world of big data analytics. The loser by a whisker surely deserves an honorable mention, hence this 2-part post.

How machines are learning from customers and predicting human behavior

Customers leave behind an incomprehensible amount of data while they go about shopping. Making sense of that data and reacting in real time are the two things that will keep companies one-step ahead of their customers (and competition) in the present-day customer-centric world.

Today, the average customer is spoilt for choice. Every time he goes shopping, he expects highly personalized, relevant offers. One poor interaction with a brand, and poof, the customer’s gone, almost-certain never to return. Customer retention’s turning into customer obsession, and only companies with the ability to paint a highly granular image of their customers will survive. Marketing teams cannot take 24 hours or more to react, their response to customer needs must be almost instantaneous.

From Data To Decision: Why companies fail to benefit from such a long journey

Can you name that one big challenge that businesses which have deployed data analytics face? It’s not failure to get qualified data scientists or IT professionals, nor is it finding the right analytical tools. The problem confounding companies is – how to implement actionable insights derived from analytics. For businesses that have started deploying analytics, the journey starts with data, moves on to its collection, analysis and visualization, finally ending in a decision that has to be then implemented. Yet, like a decathlon athlete who fails to clear that last hurdle, many companies falter at the last mile.

Why data governance is important and why organizations fail at it

So far as organizational strategy goes, the one that has been in focus of late is data governance. Ever since data analytics as a technology and technique was added as a major weapon to an Enterprise’s competitive armory, governance of the data, too, has gained importance. Yet, many Enterprises are found to be faltering on this front. There are surveys in the past that have shown that about 80 per cent

How menu engineering can push up a restaurant’s profits

In the first part of this blog post, we had read about how data analytics is being used by restaurants and fast food chains in customer segmentation. Today, we will look at how analysis helps to build, or rather, re-build a restaurant’s menu. That’s right. Analytics can play a big role in not only determining a menu but also working “behind the scenes” and improving kitchen efficiency. One of the oft-quoted examples

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