Businesses Cope with Non-tech Challenges of Big Data Analytics

Businesses Cope with Non-tech Challenges of Big Data Analytics

by December 30, 2020

Big Data

Which Non-tech challenges of Big Data Analytics do Businesses face?

On a massive scale, big data analytics technologies and techniques allow to analyze data sets and take away new information, which helps organisations make informed business decisions. Business Intelligence (BI) queries answer basic questions about business operations and performance.

Big data analytics is a form of advanced analytics that involve complex applications with elements like predictive models, statistical algorithms and what-if analysis powered by analytics systems.

Although big data analytics helps businesses sharpen their market reach and fuel growth, big data initiatives come with their own set of challenges. Interestingly, the challenges are not associated with technology. Improvements in storage technologies and scalability of infrastructure have made it easier to pave the way for the deployment of big data analytics, but it does not automatically ensure the security of data whether at rest or in transit.



Insufficient Skills

One of the significant challenges that businesses face in their big data initiatives is that most data belongs to silos. Apart from being difficult to combine, this siloed data poses quality and consistency issues. Businesses don not have adequate tools that can help them harness disparate data to create actionable insights. It is because of technology, and hence solutions are still evolving. Not only do businesses face inadequate solution, but they are also finding it tough to find the right talent with appropriate analytical skills. The problem further gets compounded due to the high costs of hiring experienced data engineers and data scientists.


Trust Issues

Many C-level executives do not entirely trust the use of analytics in their organisations. KPMG’s Guardians of Trust survey in Australia revealed interesting facts about the level of trust such executives had in the several analytics tools used in their respective organisations. While CEOs were more reliable of these tools at 77%, Directors reposed 68% trust, and at the Department Head level, it fell to 52%. However, this phenomenon is not limited to one country and is visible in organisations globally.


Expert Mindset

A surprising challenge that many businesses are facing for their big data initiatives is creating a data-driven culture in their organisations. Big data initiatives require organizations to operate in a data-driven ecosystem. This is where organizations face a challenge, as they believe in and operate with an expert mindset. Adapting to a learning-driven mindset poses a massive challenge for most businesses because of the resistance to change.



Challenges notwithstanding businesses stand to gain from the benefits that big data analytics brings. Businesses know it well that big data analytics will be a key component in the increasingly digital economy. Henceforth, to gain a competitive edge, businesses will need to manage and harness the data they possess. To end this, it will be essential that businesses create a business-first strategy for any big data initiatives they are considering for their organisations. Its objective is to reduce operational costs, increase revenue, and mitigate risks.

Often businesses know the potential gain areas and probable use cases, but do not have data to back their claims. Thereby they must recognize such use cases- and begin with smaller projects-that can enable them to create value, powered by data. Then they replicate the learning for other use cases. Success is dependent on the right tools and, therefore, businesses must judiciously choose the tools that help them achieve their business goals.