Almost every company worth its salt now talks about Big Data in every breath they take, the early adopters have been able to ride the wave and work their way around the challenges to identify new opportunities for growth.

Recently, McKinsey had estimated that retailers exploiting data analytics at scale across their organizations could increase their operating margins by more than 60 percent and that the US healthcare sector could reduce costs by 8 percent through data-analytics efficiency and quality improvements. Optimistic estimates!

Whilst successful companies like Amazon, Facebook, and Google have been built with Data Analytics in their DNA, disruptors like Uber, food delivery companies like JustEat, Deliveroo, travel, and e-commerce startups- like AirBnB etc.. actively employ Big Data Analytics to stay ahead and stay relevant. For most legacy companies, including traditional retailers and High street banks, the adoption and therefore the success of Big Data Analytics has been severely restricted. With three-fourths of them achieving even less than 1 % of revenue increment or cost improvement, having implemented Analytics in some shape and form, within the business.

This article is an attempt to highlight some of the major obstruction in the adoption of Big Data Analytics:

• To have the business appreciate the potential of big data analytics. Considering there aren’t enough immediate financial returns to justify additional investments.
• To bring together the often scattered, structured and unstructured data coming from multiple channels from within and outside the organization.
• To find skilled resources to bind and analyse the data.
• The lack of understanding and confidence in the analytics and therefore the hesitation to employ it by the business users.
• The rigidity of the organizational structure and processes impeding advancements in analytics and automation.

While in long-established businesses, several of these key challenges can be overcome by having an organization that can overcome fear, adapt to new technologies, and catalyse change. Having a management with the strength and the vision to shift focus from a patchwork of data analytics scattered across different areas of business, into a coherent and comprehensive data analytics strategy spread across the entire organization. Altering the organization structure and processes to rapidly adapt to new tools and technologies to achieve large-scale benefits from its data-analytics efforts should also reap some benefits.

Along with the steps that organizations can take in-house, a reliable external partner/vendor can help:

• Help design a medium-term strategy around the fast-evolving landscape of data analytics.
• Assess, pick up and run POCs on business cases that could bring relatively quicker returns on investment.
• Provide the flexibility of having experienced teams and latest tools on demand with minimum to no upfront investment.

For startups, the big question remains on how much time, effort, and money to invest in data analytics. Whether to do it in-house whilst deploying the tools and building a team from scratch or to work within an experienced partner on an outcome based model!!

Having said all the above, Big Data Analytics, as we all know is an area of rapid advancements and to have a successful strategy around data gathering, structuring, filtering, reporting and analysing will continue to be an evolutionary process for the foreseeable future and will indeed be the cornerstone for major decisions, not just for businesses but even how people vote to decide their leaders!