1. Analytics has Moved from Manual to Machine-Driven – Don’t Become a Robot!
Most people in the field of analytics can remember writing their own analytical code. Today, our Data Scientists in the MSiA program at Northwestern, can produce analytical models from regression, decision trees, support vector machines (and more) – all with more or less one simple execution. The manual step is minor. In fact, the manual step is being removed as analytics moves into automation and artificial intelligence. Don’t be a robot! Help create the robot!
Career Take Away: Develop skills in many model types. Recognize that the role of the Analytics Professional (See more on Analytics Professional) is not now in hand-fitting a regression, but rather in examining many model types and in exploring the data. Move to business problems that involve more embedded analytics. The Analytics Professional’s input will be more valuable in those areas.
2. Competitive Advantage is Not in doing Analytics but the Speed and Scale of Analytics and Big Data Management
When I first starting working at Capital One, it would be safe to say that the business was still largely analog and manual. Let me explain. We took in hand-written credit card applications, developed risk models using logistic regression (manually programmed in SAS) and send letters to customers. There was no Internet or digital communication with the customer. The checks sent by customers filled a warehouse, every day! The data used in the regressions was almost exclusively credit bureau data. Capital One was the first major lender to use analytics to differentiate risk. Now, everyone in lending does it. The competitive advantage is no longer in being able to develop a logistic regression on credit bureau data, but rather in bringing together transactional data, mobile data, social media data (like LinkedIn: see blog on LinkedIn Data Valuation), and doing it at a scale and speed and with big data sets that creates first mover advantages. This is exactly what is fueling growth opportunities for Analytics Professionals in FinTech and Social Media.
Career Take Away: Examine your firm. Are you solving problems that are run of the mill, that only meets analytical requirements? If so, move in another direction. Look to career opportunities that support enterprise innovation through analytics. Focus on opportunities that bring together data in a novel way and disrupt markets (like Airbnb and Uber: See blogs on Data Disruption at Airbnb and the Economics of Uber-fication) with data and analytics. Look for firms with strong Analytics Professional leaders and a commitment to innovating with analytics.
3. Big Value is in Creating Data Products
There is always a big step from an academic presentation of analytics to a business deployment of analytics. In the academic world, we consume ourselves with root mean squared error and various goodness of fit tests, for instance. In short, we are interested in the statistical performance of the algorithm. Indeed, that is important. But it is not everything and often not the most important thing.
In the business deployment of analytics, there is a question to answer or an investment opportunity to evaluate. Moving from an algorithm to an answer is a big leap. A Data Product (See blog on Economics of Creating Data Products) does that! A data product is a transformation of data to serve a business goal. It involves algorithms. In the case of a model being evaluated, a data product might show the output of many algorithms and why one is considered better or show how customer behavior has changed over time.
Career Take Away: Be that Analytical Professional that take the algorithm to a business answer. Examine your statistical output – does it really answer the business question? Consider scorecards, model comparisons, the consideration of decision-making sans data, and the trends in the market as ways to take your algorithm to an answer.
4. Analytics is Now Outward Looking – So, Look Outward!
For many Analytics Professionals, work is inward-looking. It examines customer or operational data for the use by other colleagues, only. While at Capital One, we largely took credit bureau data, analyzed it, and then used it to make internal decisions. The data nor the analysis was never used to advance relationships with customers. That is changing, with the reality that data comes to the firm and is put back to customers in various ways. Consider the rise of healthcare monitors like FitBit (See blog on data opportunity for health monitors and startups). Their success is highly tied to being able to offer data to consumers – that is to offer data products outside of the firm. Even in financial services, we see firms offering advice on improving credit scores, and financial management tools, like Mint. Amazon’s famous decision-engine is not about selecting new items for Amazon’s warehouse, but rather about pushing items to the customer. The consumption of analytics is not just inward, but now outward.
Career Take Away: Move to business problems that are closer to the customer, especially if you are focused on marketing. These problems will, generally, have greater value and some of that value will be retained in your personal earnings.
5. Mastering Big Data and Analytics is a prerequisite to Automation, Artificial Intelligence, IoT, Machine Learning, and Self-Learning
As we continue to deploy more automation, AI, IoT, and Self-Learning capabilities into our lives, the technology we once celebrated will be just expected. Consider a simple Google Nest thermostat (my blog on Google Nest and its creation of data). It nicely uses data capture and feedback loops to optimize heating and cooling expenses. The algorithms behind this are deployed across millions of units. Behind the scenes is an analytical model that interfaces with customer apps, HVAC sensors and actuators, and even power utility pricing models. The opportunity is made possible because the analytics and success with big data were figured out. Who would have guessed thermostats would need so much analytical firepower?
Career Take Away: Next generation analytics will involve driving analytics into businesses that could never economically afford manual models (as we could in banking). We see analytics solving problems in speech recognition, senor controls, automation, and, as I will call it, “life management” (See blog on Revolv and impact of digital systems on our lives). The bots doing this, will have rule-engines and analytics and will need Analytical Professionals developing them. Look for seemingly nontraditional and unexpected openings and analytical careers. Automobile manufactures, appliance manufacturers, and pharmaceutical firms are all moving to business models that involve customer measurement and deployment of analytics. With the benefit of robust skills in analytics and big data management, a variety of new industries will offer great careers for Analytics Professionals.
We explore analytics, digital strategies and big data in our upcoming Northwestern University executive education program in Evanston, IL, June 15-16, 2017: Big Data to Big Profits: Strategies for Monetizing Social, Mobile, and Digital Data with Data Science, led by professors Russell Walker and Edward Malthouse, will examine how firms can take big data to big profits through data monetization strategies and the best use of data science for growth and innovation across your organization. Join us!
In this program, we will examine:
About Russell Walker, Ph.D.
Professor Russell Walker helps companies develop strategies to manage risk and harness value through analytics and Big Data. He is Clinical Professor of Managerial Economics and Decision Sciences at the Kellogg School of Management of Northwestern University. He has worked with many enterprises and leading marketing organizations through the Analytical Consulting Lab, an experiential class that he founded and leads at Kellogg.
His most recent book, From Big Data to Big Profits: Success with Data and Analytics is published by Oxford University Press (2015), which explores how firms can best monetize Big Data through digital strategies. He is the author of the text Winning with Risk Management (World Scientific Publishing, 2013), which examines the principles and practice of risk management through business case studies.
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