Time to Awareness: Responding to Changes in the Market

Markets change. That’s what they do. And change is becoming even faster, broader, more complex, and more drastic than ever.

In such troubled times, organizations have to be smart about customer behaviors, segment adjustments, and budget allocations. Collecting the right data, internally and externally, is a crucial first step in tracking the viability of any business and making business decisions. This data needs to get into the right hands quickly—the goal is to decrease the time to awareness, or how long it takes to make sure that problems come to the attention of people who can act on them.

When you put appropriate data governance and a complementary data-driven culture in place, you ensure that relevant and accurate information flows to decision makers consistently, continuously, and promptly. This helps them to make better decisions faster, even when change is swirling all around them.

Time to Market: Smoothing Your Processes

Reducing time to market is a key business driver: when you have an innovative product, you need to get it into the market before your competitors do. But before deploying a new product or marketing campaign, you need to ensure that the underlying data is reliably reported and accessible.

Did you know that data scientists, on average, spend 80% of their time identifying, cleaning, validating, and organizing data and just 20% on analyzing it?² Making these processes more efficient can help get products to market much faster.

One aspect of achieving that efficiency is putting the right policies in place to ensure well-documented metadata about ingested data and generated intermediate results. This makes it easier to share results and methodologies. Here, again, we see the distinction between governance and management: the data governance body creates those policies and oversees their use, while the data managers (in this case, metadata managers) implement them.

Time to Value: Efficient Mergers and Acquisitions

During mergers and acquisitions (M&A), the spotlight will shine directly on your data. Are you ready for that? This happens at the highest levels during the due diligence process, but it continues at a smaller scale well into the integration period. Projects migrate, teams and units learn to work together, and everyone looks to the data to find their footing.

In addition to integrating two companies and their products, management, processes, and systems at every level, the M&A process means integrating those companies’ data. It all amounts to a daunting job under the best of circumstances; even two fully data-driven businesses might well have different principles, policies, and standards that must be compared, rationalized, and brought into harmony. Good data governance can, however, significantly simplify many aspects of this undertaking.

At the highest level, the challenge of M&A is about realizing value. We do M&A to increase value, but we only see that increase once the integration is complete and updated processes and policies are in place across the newly larger enterprise. A data governance program can make business processes more transparent and ensure that clear, well-organized metadata eases the process of combining data. The more efficiently that happens, the faster you can get to that increase in value—that’s time to value.

Growing a Culture: Social Attitudes Toward Data

Your organizational culture isn’t just about ping-pong tables in the break room or the tone of chatter in the office—it’s about how every person in the enterprise approaches their work.

Organizational culture starts with the CEO, who plays a fundamental role in creating, shaping, and fostering it. But it extends to every level of the enterprise. No one is untouched by it. Yet a culture can’t be taught; it must be set up and nurtured. It lives in the interactions of people: the interdependent day-to-day behavior and group dynamics of every single person working in or with the organization. In this way, it’s just like any other kind of culture in the world, from nations to religions to youth subcultures.

Culture determines how employees see themselves and their place within the company. It shapes what they do and how they do it. An unhealthy culture can damage a business, but a healthy, positive, data-driven culture can do wonders—improving not only the quality of the work or product, but also the company’s brand image, the happiness of its customers and employees, and its resilience in the face of change. It shapes what the company builds (or rebuilds). Just as a national culture, for example, might include a food culture, a musical culture, and an artistic culture, data culture is an important component of a company’s culture.

The principles of data governance (Chapter 1) are the soil in which your data culture will grow. The company’s mission and values plant the seeds, determining what kind of culture you’ll cultivate. And what waters and weeds and shines sunlight to make it grow? Social behavior.

Human beings demonstrate what they value by how they behave. Their principles show up in what they do and how they do it, how they treat one another, and—yes—how they treat their data. When people care about the quality of the data and respect it as a fundamental part of the business (following the asset principle), they’re willing to adopt habits that support this value. They do the right thing because they understand that the data is an important business asset; they would no more harm the data than a farmer would trample their field. (This brings us back to the due diligence principle.)

You’ve seen that policies and standards are tangible, operational, measurable artifacts that embody principles. They are introduced and adjusted over time. Data culture is less tangible, but very much present. It becomes an intrinsic characteristic of the group, so people come to respect the principles in a natural way, without cumbersome controls and tedious processes.

When values become habits, there is social pressure to demonstrate the values by performing the habits. When someone breaks a principle—for example, by treating data carelessly and violating the audit principle—it makes everyone noticeably uncomfortable: that’s just not how we do things around here. The offender is quickly set straight; their colleagues are eager to help them understand why the habit in question is supported by a fundamental principle. Such interactions allow knowledge about principles, policies, and standards to spread through the organization organically. That kind of environment makes good data habits sustainable.

Growing Accountability: Data-Driven Decisions

More than ever, as you’ve seen throughout this report, businesses and consumers expect decisions to be based on data. Indeed, it sounds almost silly to talk of basing decisions on anything else, yet the history of business is full of decisions based on hype, fear, gut feelings, personal relationships, inflated optimism, and many other powerful forces. Furthermore, even when you think you’re working from evidence, it can be easy to see what you want to see or to present facts selectively. With data, anyone can dig in and examine the facts.

Data-driven decision making offers two considerable advantages. First, it’s easier to explain decisions. Rather than simply issuing an edict, leaders can provide the data behind their actions, showing clearly why the action is important. A company’s decision to pivot toward a new product, for example, will get a great deal more buy-in if everyone can see that consumer interest in that type of product has risen strongly and consistently for the last few years.

Sometimes turning to the data helps to hone the approach. In a case study, Stanford professors David Larcker and Brian Tayan cite the example of a fast-food chain that began an effort to reduce employee turnover as a way to improve customer satisfaction. But when they turned to the data, they found a different problem. Journalist Michael Mauboussin summarizes:

As the turnover data rolled in, the executives were surprised to discover that they were wrong: Some stores with high turnover were extremely profitable, while others with low turnover struggled. Only through proper statistical analysis of a host of factors that could drive customer satisfaction did the company discover that turnover among store managers, not in the overall employee population, made the difference. As a result, the firm shifted its focus to retaining managers, a tactic that ultimately boosted satisfaction and profits.³

The second advantage is that data-driven decision making provides accountability. From decision to execution to results, having good data helps us follow the trail and assess what we’ve done well and how we can improve. Let’s say that sales of a new product are much softer than anticipated. What happened? Why is there such a gap between the company’s initial expectations and the actual results?

If the company based its decision on data, a postmortem review of the product launch can easily show why those expectations were set. If it was based on other factors, examining the data might help explain why the expectations were unrealistic. Data about how, where, and when the product was launched and who did (or didn’t) buy it can inform decisions about where the missteps happened and how to do better next time. Data about who did what, and clear ownership of data, are also important drivers of accountability.

Here’s my take on accountability: it is largely composed of being able to explain what is happening, why it’s happening, and how things got there, with the goal of determining the next best actions to take. Accountability is not the same thing as blame. It’s about facts. When this is clear to everyone, the accountability principle becomes an attitude, a duty to self—not a sword of Damocles hanging over everyone’s heads.

Can you name three companies that have recently suffered major data breaches? I bet you can. Companies that fail to maintain ownership and accountability for their data risk serious damage to their brand image. They face more than hefty fines and legal issues—they also face public anger and become the butt of jokes and memes on social media and television. That’s embarrassing, but far worse is the loss of trust in the eyes of customers, partners, and shareholders, not to mention the advantage their competitors gain.

Growing Respect: Protecting PPI

One of the first signs that an organization’s data governance is maturing (a process we’ll discuss in the next chapter) is a culture of strong respect for personal private information (PPI) and personally identifiable information (PII).

Corporations generate and collect a monumental amount of personal data about individuals, much of it quite sensitive, including home addresses, government ID numbers, credit card information, GPS data, medical records, marital status, criminal record, and internet history, just to name a few. The consequences of a failure to steward such data are very serious, sometimes life and death. In the wrong hands, PPI can be used to find someone’s location, blackmail them, gain a competitive advantage, conduct credit card fraud, or discriminate against members of protected groups.

The data governance body monitors and enforces compliance with data protection laws. As you’ve learned, it creates the policies and standards that govern how aspects of each person’s job can be done in ways that maximize respect for and protection of data. That means helping everyone understand how even their small actions can affect the data, the data subjects, and the company’s overall compliance. That understanding allows for a healthy data culture to grow.

Holding PPI data is a responsibility that carries significant risk. Companies that hold PPI are obligated, legally and ethically, to protect it. Sometimes that obligation can get in the way of an innovative or potentially valuable idea—but the need for protection against widespread misuse of data has imposed this constraint. That sense of obligation and responsibility needs to suffuse the company’s data culture. When everyone, from the CEO on down, understands that protecting PPI means protecting the people behind the data and acts accordingly, the ensuing sense of trust becomes attached to the brand’s image.

Growing Profits: Realizing Value from Data Assets

The rights associated with PPI and PII are not the only constraints to innovating with data. One of the biggest challenges in data innovation is the legacy of silos. Older methods of data storage were localized and hard to share, and over the decades, most organizations dealt with this by not sharing it. Instead, each department or division found its own way to handle data. The eventual result was a set of silos, or separate, walled-off parts of the organization, each with different storage, organizational, and processing methods, database software, and quality levels. As cloud architect Mike Kavis notes, “This model served its purpose well when software was built as large, monolithic applications that were deployed on physical infrastructure and planned in quarterly or biannual release cycles.”⁴

The advent of cloud computing has removed those technological limitations, but in most enterprises, the silos remain in place. Today, when software might deploy multiple times a day, silos slow things down—and, as you learned in “Time to Value: Efficient Mergers and Acquisitions”, no company can afford to lose any time in leveraging the value of its data assets.

Data silos don’t shut innovation down entirely—certainly the last few decades saw enormous innovation despite them. However, even aside from their impact on speed, they’re harmful to company culture. The existence of silos fosters a culture of separation, where people focus on how to optimize their use of data within their own small domains in competition with others, instead of working systematically to improve the value the company overall can generate from its data. It’s hard to monetize your data quickly when everyone handles the data differently and no one talks to one another about it.

It might seem strange to talk about the need for openness and data sharing right after discussing the dire risks of losing control of PPI. But the openness I’m talking about happens within an enterprise, and open doesn’t mean irresponsible, or uncontrolled, or delaying the risk-management process to a later stage. What it means is lowering the cost of introducing new data into the business, finding cost-effective and efficient ways to store and work with data, and improving knowledge sharing and collaboration across the organization. All of those things have a direct effect on the bottom line.

In Chapter 1, you learned that data’s ability to be sold more than once makes it unique as a type of asset. Data monetization—selling lists of data to third parties or sharing them with partners—is a major source of value. A data governance program can ensure that this is done safely, legally, and ethically. In addition, as we’ve seen, proper data governance increases the quality of the data, which in turn makes it more valuable. In the data market, a reputation for well-managed, high-quality data is a competitive advantage.