In an uncertain global market like that which we are experiencing as a result of fallout from the coronavirus and recent OPEC fubar, inexperienced or unnerved business leaders tend to hunker down, tighten the belt, jettison the ballast, or worse, just stay the course. More experienced and steely leaders on the other hand take to heart what M.F. Weiner first wrote in 1976: “Don’t Waste a Crisis.”
But how does one understand a crisis and anticipate and take advantage of its ramifications? One word: data.
True, most crises are unique and remain unpredictable in many ways. They are times of great uncertainty. Certainty, or at least predictable activity, can be gleaned from the deterministic chain of responses, and taken advantage of. In fact, the definition of information dating back to the days of the father of information theory, Claude Shannon, specifically cites it as being that which reduces uncertainty and therefore chaos (i.e., the effects of entropy).
So while it may make sense to push the pause button on particular capital expenditures, investing in data and analytics should be accelerated not abated.
Remember, good information in its many forms, including analytics, insights, predictions, diagnoses, prescriptions, and so forth, often is a lower-cost substitute for inventory, property and even money. Uber and Lyft for example have substituted information about who needs a ride and who has a car for fleets of taxis. Airbnb and HomeAway have done the same for bedrooms.
Even most traditional retailers and manufacturers have been able to reduce their inventory levels, some to just-in-time inventory, based on detailed, near real-time supply and demand information. Moreover, more than 30% of companies today exchange information they collect or generate in return for goods and services from others. And this merely represents one of several ways to monetize your data.
Investors themselves even seem to favor organisations that make significant investments in data and analytics. Public companies with chief data officers, data governance programs, and data science organizations command a nearly 2x market-to-book valuation over the rest of the market. Companies like MSCI, Accretive Health, Betfair PLC, SPS Commerce, Amex, and Apple, have demonstrated a clear commitment to substituting information for traditional high-cost production factors and waste.
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Data to the rescue
Value chains are deterministic for the most part. Indeed, the vagaries of human nature inject a level of insuperable uncertainty. But this is generally white noise for most businesses. Supply and demand, pricing and elasticity, productivity frontiers — all models from Econ101 can be used to describe your value chain, with data plugged in to identify and understand the drivers and levers of business. At no time is this degree of understanding more important than during an economic crisis. So don’t turn your back on it.
Moreover, how well do you really understand your supply-chain? (“Of course we know who our suppliers are!”) But do you know their production capacities or costs at different levels of capacity? Do you know who their suppliers are or who their suppliers’ suppliers are and how resilient their businesses are to changing economic conditions? Most automakers and airline manufacturers have up to six-levels of supply chain visibility. If you don’t have it, collect it, buy it, or barter for it, and plug it into your value chain model.
Furthermore, how well have you used available information to identify and line-up alternate suppliers in the event that one falters or can’t deliver you widgets because its borders are closed, workers have been quarantined, or transportation methods are halted?
Same with the demand side of your business. How well are you tracking customer sentiment, purchasing power, competitor pricing or service changes, or any of hundreds or thousands of global economic factors affecting demand for your goods and services in real-time or over an extended horizon?
A burgeoning selection of data product companies, data marketplaces, and specialized analytics solution providers has emerged offering an array of alternative data sources that can provide unique insights–if integrated well.
How business professionals can improve their data management
Data is also necessary to give visibility to executives so they can act with more precision, especially in a volatile market. A few years ago, Gartner analysts Dale Kutnick and Saul Brand developed the “economic architecture” concept which is a brilliant method of defining an organization’s prospective aspirational balance sheet and income statement, then architecting the business to achieve it over some time horizon. As economic conditions change, so must various financial goals and ratios that in-turn dictate how the business must operate to achieve them.
Similarly, at the onset of the Great Recession, BMC Software was one company whose leaders went a step further–developing a series of financial risk scenarios (red, green, yellow, blue, etc.) based on economic triggers that they were ready to execute at a moment’s notice. As a result, the company was able to weather the storm better than others, not just by being “proactively reactive” but by instilling confidence in its employees, customers and investors. Yet the ability to define, communicate, and execute efficiently to economically architect the business or employ economic scenarios requires deep and broad data.
Even at an information technology level, organisations can free up needed cash in a crisis without the need to halt IT projects. Among the most expeditious ways to do this is moving data and processing to the cloud, salvaging existing hardware, stemming hardware-related capital expenses, and likely reducing labor costs.
Speaking of labour costs, have you automated all the processes and decisions that can be automated reasonably and swiftly? Now isn’t the time to be afraid of black-box algorithms, it’s the time to embrace them. But these forms of advanced analytics and process control need to be fueled. Data is the lifeblood of artificial intelligence (AI) and machine learning (ML) algorithms.
Finally, with recent trepidation of gatherings and spreading disease through the handling of objects, consider which of your physical products and mano-a-mano services can be digitalised. Digitalisation of course requires data too.
So, CIOs inevitably being asked over the next few weeks to reconsider IT budgets in the midst of this current financial crisis should learn from those companies that weathered and thrived in the last one. Rather than slashing budgets wholesale, consider shifting them into improved ways to manage and leverage data as an actual corporate asset not an expense. Or bolder yet, ask for increased budget to bring data to the rescue.
