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Q&A: Advanced Business Analytics Tools Promote Pervasive BI

As interest in business analytics continues to grow, companies are responding by making tools easier to use -- making analytics more pervasive. In this interview, the CEO of Infogix talks about the progress and future of business analytics.

• By Linda L. Briggs
• July 5, 2016

Current changes in advanced analytics to make tools easier for business users -- and thus more pervasive -- are similar to an earlier evolution in business intelligence, says Infogix CEO and president Sumit Nijhawan. "In five years, analytics will be as pervasive as simple rules-based technology is today," he says.

His company offers data integrity and advanced analytics solutions. In this interview, he discusses changes and challenges in business analytics and how companies can work through the data scientist shortage and position themselves to make the best use of advanced analytics.

Nijhawan holds a Ph.D. in engineering from Brown University, teaches graduate classes as an adjunct faculty member at DeVry Institute of Technology, and attended the Program for Leadership Development at Harvard Business School.

Upside: What is the biggest technical challenge that today's IT leaders face?

Sumit Nijhawan: Technology is not a new challenge, of course, but rather a constant hurdle faced by IT leaders. New technology constantly shapes the future, and IT leaders need to stay ahead of innovation to help drive change. However, identifying which technology to latch onto and which to avoid isn't an easy task.

Adding to the complexity is the amount of data generated by this constant change and innovation. IT leaders often find it daunting to successfully govern and use data in ways that drive business growth.

Data veracity and data silos are the biggest technical challenges. Diverse data sources and repositories make it difficult to keep data consistent and accurate all the time. To address the issue, we must identify data redundancies and inconsistencies with appropriate data management and governance strategies.

How will those challenges change in the next three to five years?

The amount of data generated will only increase as the world around us becomes increasingly digitized. Fortunately, expertise in this area will also increase in the next three to five years both internally and externally. Expertise in identifying the data needed to make good analytics decisions will be critical, as will be discarding noise in the data. We can expect more automation in this area so humans can focus their expertise on making good decisions rather than sifting through large amounts of data.

In a recent interview with Forbes magazine, you talked about the people resource problem many companies currently face with data scientists. What skills are often lacking in organizations?

Currently, the desired application of advanced analytics in multiple areas of business far outweighs the supply of qualified people to apply those analytics. In fact, Glassdoor.com recently identified the data scientist as the best job in America for 2016. If an organization lacks people with these skills, one option is to consider working with organizations that offer data-science-as-a-service so they aren't left behind.

How else can organizations best address the skill shortage -- especially when, as you say, some skills simply are in short supply right now?

In the short term, use of data-science-as-a-service offerings is a good way to address the skills shortage. Data-science-as-a-service is a convenient and cost-effective approach that leverages an experienced data science group to complement or extend in-house analytics teams, filling needs on a short-term or long-term basis. In parallel, data science must be operationalized in a way that minimizes the need for analytics silos or manual work.

What can companies do to position themselves better for challenges such as the mushrooming interest in analytics and the explosion in data size and velocity?

First, companies should not consider data and analytics as an afterthought. It needs to be thought of in conjunction with core operations and processing systems so that these systems and initiatives are designed to leverage data. Second, companies should not treat analytics as an activity that resides in a separate silo outside of day-to-day business users.

Finally, real-time monitoring and measurement of data to ensure ongoing governance must be treated as an imperative. With large enterprises moving to incorporate data lakes and other big data technologies, it's more critical than ever to ensure trustworthy data -- both for use in advanced analytics and increasingly digitized everyday operations.

How are businesses using advanced analytics now that they weren't five years ago?

Although we're in the early stages, most large organizations have embraced advanced analytics; smaller organizations are often struggling with it due to the high cost and scarcity of trained resources. Advanced analytics is being used in areas such as fraud management, customer life cycle management, and designing new products.

A typical use of advanced analytics is in the front office; in the near future, we can expect increased use in the back office as well. Advanced analytics can take many forms. It can be used for profiling entities, for detecting behavioral patterns, for fuzzy matching of identities, or for predictive models. Predictive modeling appears to be the most recent area of adoption, although lack of access to significant amounts of accurate and reliable historical data is often a limitation.

In what ways will businesses be using analytics in five years that they aren't today?

In five years, analytics will be as pervasive as simple rules-based technology is today. Analytics will be embedded in most if not all new software applications. Analytics will be used not just to provide insights but also to recommend the best course of action to both human and automated decision-making systems.

Finally, we should expect analytics to be a standardized offering for specific industries, using a software-as-a-service model rather than being built in-house.

How realistic is it to expect business users without technical training to understand and use analytics successfully? Is that a trend we'll see more of in the future?

It really depends on the level of analytics. With the increased need for analytics, I think you'll find more tools that will simplify the presentation of data and algorithms, enabling those who don't necessarily have much technical experience to become analytics-savvy. That said, enterprises will always need a deeper level of technical expertise for more complex problems.

A few years ago, similar questions were being asked of business intelligence. Self-service BI paradigms have now taken over, empowering business users to take on more data exploration on their own rather than waiting for specialized IT personnel to create reports.

Interestingly, many companies do not have the ability and resources to analyze all of their data; they are analyzing only small amounts of it. Forrester Research estimates most companies analyze a mere 12 percent of their data.

What does Infogix bring to this discussion?

Infogix provides an enterprise-class analytics platform that helps embed analytics into daily workflows without requiring significant in-house technical or data science expertise. Our built-for-purpose approach uses a proven, industry-agnostic software platform to package analytics solutions that address industry-specific issues in areas such as customer life cycle management and fraud management.

Furthermore, our analytics solutions ensure data integrity up front so clients don't build analytics using inaccurate data. To further support in-house analytics initiatives, Infogix also offers data-science-as-a-service to jumpstart efforts on internal initiatives that have stalled due to resource constraints.