Interview with Ravi Rao on Analytics and overcoming challenges

Interview with Ravi Rao on Analytics and overcoming challenges

In today’s technology world, optimal use of analytics is of utmost importance because it will not only bring benefits such as cost reduction but also enable you to make better decisions. However, there are various hurdles which prevent companies from leveraging the rich benefits of analytics.

The biggest technical challenge is to rightly handle data veracity and silos. Moreover, inefficient use of analytics for decision making and lack of relevant analytical talent are the two main issues, which affect companies.

To successfully adopt analytics, companies should be willing to change the culture, currently adopted by many companies. There should be a change in the mindset of top organizations.

In an exclusive interview, Ravi Rao, Senior VP of pre-sales, Infogix revealed the need to adopt advanced analytics solutions and also the steps required to overcome the common challenges.

How can we overcome the challenges posed by Analytics?

In order to successfully leverage analytics, there are a few aspects that first need to be clarified, that are not directly related to analytics. Organizations must first clearly define the business problem(s) to be addressed along with the specific metrics or KPIs that can be used to monitor the state of the problem(s) at hand.

Then, an assessment should be made on the following three components of putting together a successful analytics solution.

  • Technology: The technology and tools that will be used to build the solution to address the identified problem(s)
  • Resources: The team and skill sets required to drive the project to completion
  • Culture: The approach to get leadership teams to encourage their organization to make analytics-driven decisions.
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This helps develop a culture that leverages analytics for better decision making.

Finally, a cost-benefit analysis should be done to ensure that the projected impact on ROI is acceptable. If the right team and technology is not in place, the cost may seem lower and ROI higher, but then the right solution may not be created and, therefore, the ROI may not be realized. On the other hand, if the cost of the technology and team is over-estimated, then the right ROI may not be realized.

How do Analytics offer cost reduction?

Analytics can usually be used to reduce cost of operations. For example, in any business, it costs more to acquire a new customer than to retain existing customers. So, deploying more accurate analytics to retain customers or, more precisely identifying which of the new marketing leads are likely to become customers, can help reduce the marketing costs in an organization. Another example is using analytics to identify risks that new customers may pose. Being able to identify that risk early on in the process, even at the point-of-sale, can help manage and reduce the costs of provisioning the new customer. Yet another example is using analytics to optimize the collections process; this can help reduce the cost of collections.

Is it necessary for a company to implement Analytics for better decision making?

Assuming the right management team in place, the successful implementation of analytics inevitably facilitates better and timely decision making. This is especially true in the scenarios where vast amounts and variety of data is available. The analytics and data driven decision making approach has repeatedly proven to produce better results.

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Do you think data collected from Analytics are always accurate?

Data is collected from various sources as input into the analytics process. Most of these data sources are from core systems that were put in place to fulfill specific functions which usually means that the data produced by those systems may not contain the consistency and accuracy required for analytics. This means that the results from the analytics may not be as accurate as desired since the source data is erroneous. For this reason, it is almost as important to put in place mechanisms to monitor data integrity and quality, as it is to deploy advanced analytics on that data.

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