The big obstacles to big data adoption are simple to overcome

BIG DATA analytics holds tremendous potential for businesses. It is capable of helping enterprises across multiple industries make better business decisions, both operational and strategic.

The technology allows companies to analyze a variety of diverse datasets from different sources in real time, providing businesses with valuable insights, be it regarding employees, customers, or markets.

Further, the insights could also be deployed to forecast future outcomes reliably, minimize uncertainties, and mitigate risks, among other things.

However, despite the obvious benefits, companies still struggle to integrate big data into their operations.

According to a recent study, up to 53 percent of respondents admitted to lack of adequate expertise within their companies while 56 percent of them said that they are yet to find valid business cases for investment in big data.

Major impediments

As big data aggregates data from various sources, it is incredibly complex to sort, scrub, and structure it. The integration of data into the business requires taming data, developing algorithms, and rigorous testing.

And for a management team that is used to managing the business based on static transactional data, switching the mindset and approach could be a tall order.

In addition to that, big data reporting is often carried out by data scientist that may or may not keen on the business outcome it produces, while business leaders will demand actionable business insights.

These disconnects, among other factors, further deter big data adoption.

A case for an empirical approach

Organizations can’t do without big data. It’s up to the CIO and IT leaders to remedy the situation.

To that end, they first need to find data integration tools that automate data ingestion and integration.

By doing so, the IT departments will save valuable time and resources developing custom APIs and user interfaces for data integration.

Next, big data analytics need to address business pain points directly, to build a precise business case. Examples of these paint points could be dwindling sales numbers, uncertainty in the market, or delayed services response.

Figuring out the business value from the get-go allows for more streamlined development of algorithms, data collection, and analytics.

Finally, the insights garnered from the analysis have to be presented in a compelling manner.

Visualizing data using charts, maps, and other aids allow business leaders to digest the report quickly, fostering confidence among users to deploy big data in other aspects of operations.

In conclusion, big data adoption could be complicated and challenging to implement, but the cost of not utilizing big data is too big to ignore.

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