The wealth and success and the sweet siren call of optimizing data has led us to overlook the collateral damage of forgetting, or neglecting to consider, the human side of the equation.
The Age of Data
Organizations are drawn to data for many reasons, not the least of which is the emergence of incredibly sophisticated technologies that allow us to process and parse information with speed and insight. But here are more reasons why the spreadsheet is dominating the story:
- Data is plentiful.
- Data is not messy or nuanced like feelings.
- Data feels certain, while hunches are uncertain.
- Data is a common language that everyone in a global world can read.
- Data drives wealth creation, as illustrated by the data-driven companies of Google and Facebook, whose algorithms are powerful prediction engines.
- Data can be displayed using stunning graphics.
But just because all things data are dominant doesn’t make data a bad thing. In fact, it’s a mistake to characterize the spreadsheet as bad and the story as good. Both are crucial for organizations. More to the point, achieving a balance between the two is the hallmark of great companies.
The problem is that data’s seductiveness throws the dynamic off balance. Data helps us justify our decisions. It seems to mitigate our risks. It provides insights into consumer behaviors, which can shape our product and service development. It can save us and make us a lot of money.
But it can also seduce us into believing that data is all we need. When that happens, we lose the agility, innovation, and inspiration upon which organizations thrive. Data can and should be meaningful in every sense of that word. We shouldn’t use it just to quantify stuff and increase efficiency and productivity, but to gain “softer” insights.
Different Types of Data: Math and Meaning
Simplistically, math is all the data flowing through organizations and meaning is all the intangible feelings and perceptions surrounding people, products, services, and the companies themselves. Less simplistically, math takes various forms—algorithms, AI, social media data, and so on. Meaning, too, can vary considerably, ranging from organizational purpose to employee beliefs about their companies to brand significance.
Organizations have always used data in various ways—surveys, budgets, focus groups—but because of technological advances, data has now become a ubiquitous presence in every nook and cranny. Data:
- Fosters customer insight. Data reveals opportunities like never before, from maximizing marketing to developing new product ideas.
- Spurs continuous improvement. By comparing key metrics against historical performance as well as that of competitors, companies can create benchmarks to improve performance and provide feedback to each individual in objective versus subjective terms.
- Provides competitive advantage. In a world where product differentiation is narrowing and price comparison places downward pressure on margins, data allows for new forms of competitive advantage and monetization. Data allows for low cost, speed, and high quality.
Now think about meaning. While it is less tangible than data, it is no less crucial to a company’s success. Consider the following forms of meaning and the questions that help elicit it:
- A brand’s reputation. Is it considered a quality product or a cheap one? Reliable or not? Does the brand connote trust and inspire loyalty or is it seen as utilitarian?
- Customer service beliefs. Do customers find servicepeople to be helpful and friendly or cold and bored? Do service representatives and salespeople forge relationships with their best customers or are relationships merely transactional?
- Company mission/values. Is the company known for consistent beliefs and principles or is it seen as amoral and fickle? Does the organization try to make its community, its industry, and the world a better place or is it motivated only by profit?
- Employee perceptions of the enterprise. Do they perceive the organization as a place where they can learn and grow or one that exploits their hard work and skills? Do they feel they are rewarded fairly or that the company is cheap? Do they feel included and affiliated with the organization or isolated and mercenary?
- The full significance of the data. What do all those facts and figures mean beyond the obvious? Yes, profits are up by 12 percent in June, but why? Is this an anomaly or is there an underlying trend to which we must pay attention? Did the new ad we ran have an effect or did our salespeople respond to a new incentive program?
Despite Its Limitations, Data Will Become More Pervasive
It is critical that we constantly remind ourselves about the challenges of relying on data, since we will likely encounter data more than ever because of a number of factors:
- Accessibility. Data is available on a much more real-time, granular, and unified basis than ever before. The easier it is to obtain information, the more likely organizations will capitalize on it.
- Storage and manipulability advances. It’s now possible to measure and store how a single individual interacts with every website component at every moment and link this information to other data about that individual. Lower storage costs combined with powerful computing capabilities make it possible to capitalize on this data and manipulate it in insightful ways.
- Leadership tool. Data is the spine that holds the organization together and affects every significant decision and communication. Leadership’s embrace of data has a trickle-down effect, causing all levels of the organization to buy in.
- The AI age. Increasingly powerful computers input huge amounts of data and “learn” as they process information, getting smarter just as humans get smarter from multiple experiences. But computers, unlike humans, can capitalize on data-driven algorithmic decision-making, and organizations are increasingly relying on algorithms rather than people to make decisions.
How to Extract Meaning from Data by Tapping into People
Over the years I have learned that the best way to gain insights and extract meaning from data is to follow what I call the six I approach: Interpret, Involve, Interconnect, Imagine, Iterate, and Investigate.
Interpret the data. Don’t just take all those facts and figures at face value. View ambiguous data from multiple perspectives. Develop hypotheses, search for patterns, look for outliers, create alternative scenarios to explain the information you’re receiving. Through interpretation you can enrich the data with meaning; you can identify the story it’s telling.
Involve diverse people. As important as your analytics people are, expand the group that examines the data. When you involve people with various skills and perspectives, you’re likely to receive a richer interpretation.
Interconnect to larger trends and events. Making these types of connections helps you take the data one step further, determining if it’s going to have a short-term or long- term impact, if it’s suggesting the end of a trend or the beginning of a new one.
Imagine and inspire solutions. Too often we look at the data and allow it to set boundaries: “We can’t go into Market Z as planned because the numbers indicate sales of our category is starting to fall off.” Rather than allowing the data to limit options and actions, explore the solutions it might inspire. If the numbers show that your product category isn’t doing as well as it once did in Market Z, is there an emerging opportunity because the market still has potential and competition will be reduced because of this data?
Iterate. Data can spawn new and better data. Is there a test you might run based on the information you’ve gathered that can produce more insightful facts and figures? Can you think of fresh ways to generate feedback that might provide multiple perspectives and explain surprising, disturbing, and promising data?
Investigate people’s experiences. In a given organization, you have hundreds or thousands of people with data-relevant insights because in the past—whether while part of your organization or with a previous employer—they experienced something applicable to the current information. Tapping into this by seeking out relevant employees and asking about the data may provide ideas that would not otherwise be articulated.
The Need for a Human-Centric Data Policy
Beyond questioning and exploring the data, organizations need to create policies and protocols for it. The tilt toward data wouldn’t be so harmful if companies enacted basic rules to mitigate the damage caused by overdependence. Meaning naturally flows back into an environment when companies filter all the facts, figures, and other information through a human lens.
To that end, here are some suggested filters:
- Determine what data is worth receiving and eliminate the rest.
- Flag bad data.
- Stop using data as a crutch.
- Ask questions data can answer, not data-driven questions.
- Measure judiciously.
Organizations can redress the balance between math and meaning by enacting data policies that allow people to use their creativity and ideas more effectively. Meaning can also be added in other ways to the numerical thinking that dominates companies.
Excerpted from Restoring the Soul of Business by Rishad Tobaccowala. Copyright © 2020 by Rishad Tobaccowala. Used by permission of HarperCollins Leadership.