Why Digital Transformation Does Not Ensure Success

Famous tech consultant and author Jaspreet Bindra was writing a book on the emerging technologies. He wanted to write a chapter on Artificial Intelligence. He thought why not make an AI write about itself. So, he got in touch with Massachusetts based AI-firm Findability Science. And a chapter came into being using unsupervised automatic sentence extraction and algorithms based on graph-based ranking. This same person who is often referred to as the Tech Whisperer is of the opinion that computers are not really very intelligent, they are machines with amazing data processing capacity.

 Superhuman intelligence

So, do we find a discrepancy in his statements? Is he being self contradictory here? Not really. If you pay attention, the keyword here is data. That is information. If human beings had the ability to skim through thousands of gigabytes of data and remember all of it at one given point of time, they would easily find patterns and start making logical predictions about the future. This does not require creativity, or superb intelligence. All it requires is a data processing.

What’s all the fuss about?

We have tried to establish that machines have not yet outsmarted human beings (or have they?), then why are people all over the world excited about emerging technologies like AI, cloud computing and block chain? Well, why wouldn’t they be excited, it is all happening so fast and with such intensity. Companies are spending billions on digital transformation and employing skilled data science professionals. All they want is to get a better grip on the market, judge its fluidity better and make more money. But oftentimes all they get is disappointment.

Where lies the problem?

Companies cannot really rely on machines to get them through the competitive rush. Most companies fail to reap the profits of digital transformation in spite of having spent millions just because of the lack of a progressive environment and a culture of innovation. Employing a data scientist and installing the tools is clearly not enough. There has to be an air of data centricity throughout the company. Will it not be great if all employees, the software developers, the testers, the marketing people, the sales people, all of them are aware of the importance and applications of data and contribute towards building a more data oriented environment?

Looking back at the basics

While it is great to become acquainted with emerging technologies it is also important that we do not forget the basics of big data analytics. It is not long ago when big data used to be a buzzword and people went berserks over it. Everyone who felt interested, read about big data and how it can transform industries. But just like all buzzwords it fell out of popularity and you hardly see it anymore. The problem is, we cannot really ignore big data if we are going to help ourselves succeed in the analytics industry.

As of 2018 India had 97,000 vacancies for data analysts. This could go up to 2 lakhs by the end of 2020. A lot of these roles require skills in basic big data tools like Excel, Hadoop, Spark, Mapreduce etc. There are tools like R, Python and SAS which can be used in various capacities – for basic as well as advanced analytics tasks. Then there is the need for business analytics experts who can guide businesses through the maze of data. The skill gap has to be bridged and aspirants need to be focused on what they want to do while also keeping the doors open for further general learning. Big data courses were and still are the principal source of data analytics talent to fill the skill gaps. Analytics institutes in Indian cities like Delhi, Bangalore, Hyderabad and Pune are responsible for supporting the booming analytics industry with sufficient supply of efficient employees. If you have skills you will have a break through and soon have multiple lucrative options to choose for.

To sum things up

Our primary contention, it seems, has two distinct facets. Firstly we have tried to make a point that businesses spending millions on digitization do not necessarily cut through the competitive market. What really is needed is building a work culture that incorporates the data centric approach naturally.

Our second point was that in order for the nation to profit from the data driven industries the talent pool has to be nurtured. Institutes must make sure that analytics education is dispersed at all levels while keeping the curriculum relevant to real time industries.