Data is being generated and collected at an astonishing rate. Companies hoping to duplicate the success of data-first organizations like Amazon are jumping on the bandwagon, collecting every scrap of information they can. But without a plan to analyze and act upon this data, it is easy to get bogged down by minutiae with no real impact.
The first (and often skipped) step in creating an analytics strategy is mapping it to business goals. Dazzled by the wealth of available data, companies don’t know what is important and what is a distraction. They treat data analysis like panning for gold — sift through everything, and sooner or later you’re bound to find a nugget of value.
Smart use of data analytics can create efficiencies and build synergies across the organization. Marketing can find more qualified leads. Sales can negotiate personalized pricing based on risk profiles. Supply chains can move inventory more efficiently. Customer service can build deeper relationships or repair damaged ones. Accounts receivable can predict and reduce late payments. But you cannot reach a goal that has never been defined. When you know the desired outcome, data analytics can map a path to achieve it.
Identifying the issue
Once you have named the problems, it is critical not to try fixing everything at once. Implementing analytics programs at scale is notoriously difficult. Successful companies prioritize, focusing first on the areas with the greatest potential impact.
If information overload is one side of the coin, focusing too narrowly is the other. Looking at finite metrics like revenue alone, companies can miss out on opportunities to improve in areas like logistics or human resources. Keeping an open mind to new data can increase return on investment by offering insights into inefficiencies no one has considered.
Many legacy companies still think artificial intelligence is just the latest tool to plug into their existing infrastructure. But to gain a real competitive edge, leaders must stop seeing data analytics as an IT project. In a 2019 survey, management consulting firm McKinsey & Co. found companies with the greatest overall growth in revenue and earnings grew by transforming the corporate culture into one driven by data. Twenty-one percent of respondents who had met their corporate goals ranked a data and analytics strategy as their No. 1 key to success – while a growing share of those falling short of their goals acknowledged being hindered by a lack of such strategy.
Patience is a virtue
There’s a belief, often perpetuated by vendors of analytics software, that data analytics is a plug-and-play solution that can turn things around fast. Executives who start looking for significant return too soon after implementing analytics are bound to be disappointed. Like anything, data science requires trained personnel, good data, and patience. In an on-demand world, patience is hard to come by. It’s disheartening when dreams of rapid evolution crash into the reality of day-to-day business.
Companies surveyed by Boston Consulting Group (BCG) in 2016 hoped to raise their data maturity by 53% over the next three years. The actual number was closer to 19%.
“It is deceptively easy to launch AI pilots and achieve powerful results. but it is fiendishly hard to move toward AI at scale,” BCG commented. “Isolated use cases … can sputter and grind to a halt when interacting at scale unless companies transform their operating models.”
The art of what is possible
Of the many lessons COVID-19 has to teach, data analysis is one of the least appreciated. A lack of quality data has led to unanswerable questions about the availability of ventilators, hospital beds, and personal protective equipment. Poor data collection has hindered contact tracing efforts. In a pandemic, collecting the right data and applying it in the right way can save lives. A hospital in Boston was lauded for using a forecasting model to anticipate how many bags of blood it would need. Singapore, one of the countries with the slowest spread of COVID-19, uses blockchain and analytics to reduce exposures through contact tracing.
Many of the economy’s heavy hitters, like Amazon and Facebook, were designed from the outset to apply data. If a shopper looks repeatedly at an item on Amazon, the site will show similar items, adjust the price, or offer promotions to prod a purchase. Facebook’s Cambridge Analytica scandal demonstrates what can happen when data is applied indiscriminately. People felt violated by the depth of information the company was able to glean from their internet use. Under different circumstances, having such focused personal data could allow a company to serve its customers in a way that makes them feel special and understood.
Data analysis is about the art of what is possible. Take Neuralink, Elon Musk’s effort to connect human brains with machines. Data science and machine learning are working hard behind the scenes of the interface, predicting and adjusting human reactions.
The human connection
Interestingly, people are the key to competing in a data-driven world. Machines can automate repetitive and analytical tasks, but they can never replace the creativity and innovation that are vital to success. People and machines triumph when they work together. Analytics can provide insights that help humans determine the best course of action. This only works when people listen to the data — especially if it seems counterintuitive.
Keep your eyes open for opportunities. Often, they are missed when employees mistrust data that doesn’t match their expectations. The book and movie Moneyball illustrate the concept: To construct the perfect baseball team, the Oakland A’s threw conventional wisdom out the window and turned to statistics.
When intuition and data work hand in hand, there is no limit to what can be accomplished.
Divya Prakash Srivastava is a senior-level program development executive whose 19 years of experience include leading analytics-focused digital transformations in the US and Europe. His primary areas of interest are defining corporate and project vision and initiating business solutions across the life science, retail, energy and consumer/industrial product sectors. He worked for Deloitte for 12 years but is currently an independent consultant.
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