As per a McKinsey report, most businesses simply talk about data analysis and do little else for its actual implementation. McKinsey conducted an analysis on five major business sectors to find out what’s lacking. In most cases, companies have not been able to scale up the experiments they carried out at smaller scale to reap the full benefits of data analytics. According to Gartner, companies do not understand big data fully and don’t ask the correct questions to reap the potential benefits.

The only sectors that seem to be utilising big data productively are retail and location-based services. Most of the other sectors such as manufacturing, public sector companies, and healthcare haven’t yet understood the potential of data analytics.  Moreover, as new opportunities come up, the gap between those who are leading and those who are lagging behind is becoming bigger.

 

Barriers to Big Data adoption

Culture, mindset patterns and organizational structure create the biggest hurdles in the adoption of big data and analytics. Businesses are still not able to adapt the key processes and create scalable solutions. This requires not just the adoption of tactic but a change in mind-set to make necessary changes in the company’s business model.

The next big challenge is to find and retain experts and data scientists. As such, there is a lack of such professionals in the market. Most data scientists, with the capability of modelling data, work with organizations such as CERN where there are huge amounts of data. Hence, there is a gap in the market for professionals trained in big data analytics. Institutions and IT giants must look at this as an opportunity to create a pool of data scientists by providing advanced data science and analytics courses.

Another problem is people who call themselves data scientists may not actually be so. Hence, when hiring, the HR managers must make sure that those who promote themselves as data scientists are actually fit for the tag. As such, there is a need for data scientists with business acumen so that they can relate and solve business problems with data solutions. The specific term for such talent is “business translators.” As per the McKinsey study, the demand for such talent will be between 2 to 4 million in the United States alone over the period of the next ten years.

As of now machine learning and AI are still in initial stages and not part of mainstream business. As their use proliferates the market, data analytics would rapidly bring changes to business models. Organizations must, therefore, invest in learning how to harness the power of data analytics and big data to their full potential lest they risk losing out to competition in future.

 

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