Interview: Phani Nagarjuna, Nuevora on Right Data and Business Analytics Roadmap
We discuss the journey of Business Analytics, definition of Right Data, competitive differentiation of Nuevora, challenges in the large-scale consumerization of analytics, and more.
Phani Nagarjuna is founder and CEO of Nuevora, a Big Data analytics and apps firm. His global leadership experience of more than 16 years includes C-level positions across product management, sales and marketing, and corporate strategy and turnaround. Nuevora was recently ranked by CIO.com as one of the top 10 Big Data firms to watch out for.
First part of interview
Here is second and last part of my interview with him:
Anmol Rajpurohit: Q5. What do you mean by the journey of Business Analytics from predictive to prescriptive, to closed-loop, to real-time? As of today, what percentage of companies would you expect to be on each of those phases?
Phani Nagarjuna: There is a clear roadmap organizations should follow to maximize value from analytics. Organizations are moving from “descriptive analytics” to “predictive analytics,” which provides a level of certainty into knowing whether someone will buy a product from a company in the future and if so, by when. Having this predictive insight is great; however, not being able to take appropriate action based on this great insight would be tantamount to a wasted opportunity.
The next stage is to enable prescriptive analytics, which uses a portfolio of analytical capabilities to recommend a particular decision path, one that has the power to influence the desired action from a consumer at a given point in time. However, due to the dynamic nature of every business and the ecosystems we live in, any recommended prediction and/or prescription has a limited shelf life.
Thus, the third stage in this roadmap is to enable “closed loop analytics,” which is an end-to-end analytics process that sets business goals, predicts outcomes, prescribes actions, monitors progress, and assesses impacts. It then realigns objectives, and optimizes predictions and prescriptions based on continual feeds of data that reflect the real-world changes in a business environment.
The challenge lies, however, in enabling this closed-loop process to operate in as real-time mode as possible. That is what brings us to the last stage in this roadmap, which involves powering real-time analytics that accelerate the previous analytics processes – with real-time updates and insights needed by the organizational leaders at the point of decision making.
AR: Q6. How do you define "Right Data"? How does one identify "Right Data" for one's Analytics pursuits?
PN: Identifying the right data begins with the end objective in sight, and then going backwards in terms of identifying the data which will influence the end goal. These are the controllable variables, which can be acted upon within all the meaningful variables for a business.
Using unique hierarchical algorithmic modeling techniques and data discovery approaches, Nuevora identifies the right data from the ocean of big data based on the predicted influence they have on the end outcome. These preliminary analytics and data analyses, run on all the variables in the big data stream in relation to the dependent variable, identify the most influential and meaningful variables.
AR: Q7. How do you differentiate Nuevora from its increasing competition?
PN: Nuevora plays at the intersection of analytics, big data and cloud. Leveraging a powerful platform, Nuevora delivers scalable and repeatable analytics to organizations at rapid speeds. The platform, with its pre-built set of applications brings in 60 to 70 percent automation for analytics delivery, with the last mile configuration and contextualization delivered by data scientists and business consultants.
This approach for delivering analytics provides significant speed, scalability, and reliability, while enabling closed-loop recalibration capabilities as required by the changing business circumstances and data dynamics of a given business. Further, Nuevora’s unified algorithmic architecture enables marketing organizations to gain an integrated and predictive view into all stages of a customer lifecycle (retention, up-sell, cross-sell, profitability, LTV) in one glance. This is a big differentiator compared to a number of isolated solution providers.
AR: Q8. What are the biggest challenges in the large-scale consumerization of analytics, particularly to marketing managers?
- Not being aligned with the marketing business process from a customer perspective
- Not embedded/integrated with operational execution
- Not having proper organizational structures and processes to ensure results are delivered consistently and regularly
- Not connecting insights with one another to provide an integrated view into the key customer outcomes measured by a CMO (customer retention, up-sell, cross-sell, profitability and LTV)
AR: Q9. What is the best advice you have got in your career?
PN: Dream big! Stay glued to the vision; be practical; build and depend on a great team; be a great mentor yourself.
AR: Q10. What soft skills do you think are the most important for practitioners in the field of Data Science?
- An ability to leverage intuition and facts to form logical conclusions, rather than just numbers.
- An ability to apply the data and analysis data scientists have to the context of the business and market, such that the insights from analytics become meaningful and actionable for the business situation.
- Capability to tell a coherent story about the insights derived from models, in the form that business leaders can act upon
AR: Q11. What was the last book that you read and liked?
PN: “Rafa” an autobiography/biography of Rafael Nadal by Rafael Nadal with John Carlin.
This is a great story of how a combination of intense focus on one’s goals, consistent hard work, human determination, intestinal fortitude and mental toughness can deliver wonders and world-class results.