Your Future as a CIO Depends on Big Data: How Are You Going to Overcome Current Obstacles?

Posted by Mayank Pant on Aug 19, 2015 8:00:00 AM

We all read the tech news and have noticed an emerging trend. You can’t open your browser, check out your Pulse channel or go through your LinkedIn stream without seeing a headline that has something to do with Big Data and the promises of the future. We all agree that the potential is enormous. Yet now what we are seeing is that to realize this enormous potential, a CIO needs to deal with many obstacles: internal politics, data sourcing, organizational inertia, skill set shortage and the list goes on…

Yet even with these obstacles, we have found that by articulating a clear business problem statement, partnering with the right people, and making a series of “little bets,” that you can realize benefits such as:

• Identifying the root cause of your company’s most expensive problems, and gaining the insight needed to know which levers to pull that will change the outcomes

• Building prediction engines that uses customer shopping data and contextual information to come up with new customer engagement methods that drive revenue.

• More rapidly evolving product decision-making and more quickly bring new products to market by harnessing third party data combined with company information such as customer feedback and reviews, sales information and competitive data.

• Realize the promise of omni-channel and improve customer interactions and thus enhance the overall customer experience to build brand loyalty.

Still, once big companies start down the road to big data projects, many find themselves stalled out. As we discussed in our previous blog, organizations have a hard time moving beyond the basics and have not been able to realize the promise of big data and advanced analytics. This prevents them from making the jump to real insights leading to high confidence decision-making, which in turn, brings significant differences in business processes and the bottom line.

It’s easy to see why this happens. The reality is that for enterprises to “compete on data” they cannot take a cookie cutter approach using pre-built products/solutions. Rather, they need to bring technology, insights and user experience together in a way that is relevant to their own organization and provides them the competitive advantage.

The “Too many Data Sources ” challenge

One big challenge in using big data analytics effectively is to identify which data is relevant (internal as well as external) and bring it all together for analysis. Most companies have various tools and systems in place that track different things, such as CRM, supply chain management, social media, etc. The number and type vary, but can often reach 25 disparate systems. Imagine trying to determine what is important and relevant in each system, and then trying to bring it all together. To further complicate things, some of these systems could be legacy, and don’t lend themselves to easy data analysis.

The Data Overload challenge

In addition, enterprises need to deal with fast moving and rapidly accumulating data from the different systems and sources. 90% of the data available today has been created in the past two years. The sheer volume and variety of data makes acquiring it much more complex. Given these complexities, it is absolutely necessary to create data about the data you acquire, and track its lineage. This gives the rest of the business confidence in the data, the findings and subsequent decisions.

The People challenge

And often they have to deal with a culture that is used to making decisions based on “gut feel” or have problems finding people with the right skillset to tackle big data projects. Add to these challenges the fact that people often tasked with delivering insight from data have another “day job” and cannot focus entirely on analytics.

How do those of us involved in big data solve these challenges and get to a point where we can realize the potential and future of big data analytics?

At Brillio, we believe technology is important but mindset is critical, as is having the right team with a mix of technology and industry expertise, and a diverse partner ecosystem. It’s only when you are able to bring all of the key elements together that you can capitalize on the promise of big data.

We have seen that companies that have the most success with big data, and those that will be able to embrace the future of big data, develop a system and environment where people can experiment — i.e. making a series of “little bets” — essentially taking an innovation lab approach, where rapid prototyping, evaluating impacts, adjusting, optimizing and continuously learning can create the breakthroughs that catapult Big Data projects to success.

To learn more about how the Brillio approach can help companies overcome common challenges to decision making like black box output and process automation, download a copy of our white paper.