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Navigating the Next-Generation Analytics Roadmap

Q: How are companies being impacted by the new pace of retail?

Phani Nagarjuna: The pace of retail is accelerating on a number of fronts. First, retailers must deliver personalized offerings that feature the right product to the right customer when and where s/he wants to shop. That requires an accelerated pace of innovation. Added to this, the shopping preferences of consumers are continually changing owing to the plethora of touch points – mobile, web, multiple devices, apps, in-store, etc. These trends force retailers to get smart at expertly servicing shoppers. Retailers also must deliver innovative merchandise and services to ensure they are maintaining and growing customer loyalty in an ever-competitive world.

Q: How can big data satisfy these customer demands?

Nagarjuna: First, big data has the power to create a more knowledgeable organization. Companies can learn to be more cognizant of what’s happening throughout the industry, as well as internally, and then have the insight needed to adapt workforces and processes to these trends to stay ahead of the competitive curve. Next, big data can truly build customer intimacy. Understanding consumers’ shopping intent and behaviors, and aligning processes that enable personalized engagement can create lasting customer loyalty. Lastly, infusing focused analytics into business processes leads to operational effectiveness and efficiency. By leveraging big data in these areas, retailers should see top line growth, stronger customer intimacy, and increases in market share.

Q: What does a big data analytics roadmap look like?

Nagarjuna: There is a clear trajectory retailers should follow to maximize value from analytics. Organizations are moving from “descriptive analytics” to “predictive analytics,” which provides a level of certainty into whether someone will buy from me or buy something specific. The next stage is to enable “prescriptive analytics,” which uses a portfolio of analytical capabilities that drive a particular decision and influence the desired action from the consumer. The third stage in this journey is to enable “closedloop analytics,” an end-to-end analytics process that sets business goals, monitors progress, assesses impact and then realigns objectives based on continual feeds of data that reflect the real-world changes in a business environment. The last stage involves powering “real-time analytics,” which accelerates the previous analytics processes via real-time updates/insights needed by the organizational leaders at the point of decision making.

We have relied on descriptive analytics for the last two decades. Now we are expanding the journey through predictive, toward closed loop, and finally into real-time analytics. The key to this analytics journey is establishing a roadmap that defines how a company expects to compete in the marketplace and deliver differentiated value in a sustainable manner.

Q: Tell me why the analytics roadmap is so important for retailers?

Nagarjuna: The roadmap provides retailers with practical growth trajectories driven by purpose-built analytics based on real consumer behaviors, real data and real phenomena. It delivers enhanced capabilities for smarter execution, monitors progress, and predicts/recalibrates outcomes, rather than move forward blindly. The roadmap should enable retailers to tackle specific business problems along a business process, while ensuring the entire process is eventually optimized. Retailers, however, should establish an analytics practice or leadership team to own their analytics strategy and execution.

However, the key to tapping into the full value of big data without burning through huge budgets and long time frames and deep disappointments is to recognize that the “right data” always trumps “big data.” So, retailers always should look for solutions that enable them to adopt analytics that are tied to achieving critical business outcomes and that do not cause much disruption to their technology infrastructure. More specifically, our experience shows that about 2% of all big data streams, not 100%, are actually relevant to solving a given business problem. The question is how to enable organizations to quickly identify this 2% of data, apply that to solving a given problem, and move on to the next 2% for the next problem. Do not try to boil the big data ocean.


First Published in RIS News, March'2014

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