Resilience in transport and logistics

The transportation-and-logistics sector is especially susceptible to economic shocks. Here’s how to prepare your operations for a smoother ride.
Resilience in transport and logistics

Five Fifty: Soft skills for a hard world

As workers interact with ever-smarter machines, the demand for soft skills is beginning to surge.
Five Fifty: Soft skills for a hard world

Join Our Twitter Chat: Goal Setting

At the start of a new year, setting goals — personal and professional — may very well be top of mind. In business, there are acronyms aimed at helping us set the kinds of goals that can be achieved with reasonable effort. (You’ve probably heard of SMART goals, but if you haven’t considered FAST ones, check out the suggested article below.) Typically, teams gather for offsite meetings, leadership teams set short- and long-term strategic priorities, and companies award annual bonuses when targets are met.

We’d like to hear from you about what works — and what doesn’t — about the way you set goals at work. Given that there’s no single best practice for identifying key targets, operationalizing advancement toward desired outcomes, or measuring the impact of those efforts, we’d like to talk about the approaches you find work best.

We hope you’ll join us Tuesday, Feb. 11, for this conversation.

To participate, head over to MIT Sloan Management Review’s Twitter feed (@mitsmr) at the chat start time, or search Twitter for the hashtag #MITSMRChat to follow along.

Add this event to your Outlook or iCal calendar.

Questions we’ll discuss include the following:

1. How do you approach goal setting at work?
2. What’s good (and bad) about the approach?
3. How far into the future do you, your teammates, and your organization’s leadership plan goals?
4. Do you follow any particular methodology or rubric when it comes to goal setting?
5. When and how do you measure your performance against your goals?

In advance of this chat, consider reviewing the following content from MIT SMR:

With Goals, FAST Beats SMART

MIT Sloan’s Donald Sull and Charles Sull argue that goals should be frequently discussed, ambitious, specific, and transparent.

John Doerr on OKRs and Measuring What Matters

Author and Kleiner Perkins chairman John Doerr discusses key benefits of objectives and key results (OKRs) for metrics-driven organizations.

Why Hypotheses Beat Goals

Research scientist Jeanne Ross from MIT’s Center for Information Systems Research observes that failing to meet goals is normal, but a more constructive process might be to generate and test hypotheses.

Join Our Twitter Chat: Goal Setting

The Innovator’s Legacy

Last spring, the editorial team at MIT SMR began kicking around ideas for a special issue with longtime Clayton Christensen collaborator Karen Dillon. The topic? The future of disruptive innovation. Christensen introduced his landmark theory in 1995 and further popularized it in his 1997 book The Innovator’s Dilemma and subsequent works. Our hope with the special issue was to investigate how the nature of disruption has changed over the past 25 years as technology itself has evolved so dramatically.

Over the course of development of the spring issue, which will be released in print and online next month, Dillon spent time with Christensen, discussing his influential body of work and his thoughts on the future of disruptive innovation. Both reflective and forward looking, the conversation is an important addition to the canon of disruption. Christensen died Jan. 23 of complications from cancer, and we at MIT SMR are in the company of many across the world who feel profoundly sorry for the loss of a great thinker and warmhearted person.

A driving characteristic of Christensen’s research and groundbreaking work was how meaningful and impactful they were for real people working in companies every day. With this interview, we are honored to publish a final contribution from an influential leader and longtime friend, and we hope it will have meaning for you too.

Read the interview: “Disruption 2020: An Interview With Clayton M. Christensen.”

The Innovator’s Legacy

Disruption 2020: An Interview With Clayton M. Christensen

Clayton M. Christensen

Image courtesy of Jason Grow

In the decades since Clayton M. Christensen first shared his Theory of Disruptive Innovation with the world, his thinking has led to the creation of billions of dollars of revenue, hundreds of companies, and an entirely new paradigm for how industry entrants upend established giants. Karen Dillon — Christensen’s longtime collaborator and guest editor of this special issue of MIT Sloan Management Review — had a chance to sit down with him before his death in January to learn how he had refined his thinking, what the future of innovation looked like through that lens, and what questions he was still wrestling with. This is an edited version of their conversation.

MIT Sloan Management Review: Over the years, the phrase disruptive innovation has come to mean all manner of things to people. But the broad, sweeping implication that “disruptive” is synonymous with “ambitious upstart” is not correct, is it? How would you like to define disruptive innovation for the record?

Clayton M. Christensen: Disruptive innovation describes a process by which a product or service powered by a technology enabler initially takes root in simple applications at the low end of a market — typically by being less expensive and more accessible — and then relentlessly moves upmarket, eventually displacing established competitors. Disruptive innovations are not breakthrough innovations or “ambitious upstarts” that dramatically alter how business is done but, rather, consist of products and services that are simple, accessible, and affordable. These products and services often appear modest at their outset but over time have the potential to transform an industry. Robert Merton talked about the idea of “obliteration by incorporation,” where a concept becomes so popularized that its origins are forgotten. I fear that has happened to the core idea of the theory of disruption, which is important to understand because it is a tool that people can use to predict behavior. That’s its value — not just to predict what your competitor will do but also to predict what your own company might do. It can help you avoid choosing the wrong strategy. 

You have been a big proponent of the benefits of causal theory. What do you think of the argument that big data obviates the need to seek causality?

Christensen: Well, it’s important to first recognize that the data are not the phenomena. They are a representation of the phenomena. Also, we must recognize that God did not create data; any piece of data you or I have ever encountered was created by a human being. Unable to fully capture this wonderfully complex world, we human beings use our bounded rationality to make “decisions” about what aspects of the phenomena to include, and which to exclude, in our data.

These decisions become embedded in the tools we use to create and process data. By definition, these decisions reflect our preexisting ways of thinking about the world. These ways of thinking are sometimes good and reliable — guided by known causal relationships. But oftentimes they are not. No quantity, velocity, or granularity of data can solve this fundamental problem.

I believe that in order for our scientific understanding of the world to progress, we must continually crawl inside companies, communities, and the lives of individuals to create new data in new categories that reveal new insights.

As an example, in my early research on the disk drive industry, I catalogued by hand every disk drive that had been bought or sold over the years after scouring hundreds of “Disk/Trend” reports. And while I was starting to see a pattern of the low-end companies quickly rising to prominence and challenging established leaders, it wasn’t until I went out to Silicon Valley and spoke with executives in the space that I fully grasped how incapable incumbent leaders are of responding to disruptive entrants. The data alone would have never generated those insights.

Big data also tends to gloss over or ignore anomalies unless it’s crafted carefully to surface these to humans. That is, big data tends to be far more focused on correlation rather than causation and as such ignores examples where something doesn’t follow what tends to happen on average. It’s only by exploring anomalies that we can develop a deeper understanding of causation. If you think about it, following a big data approach is what powered our understanding of the sun, moon, stars, and Earth for years, but it was only when Galileo peered through a telescope that we could start to understand more deeply how these celestial bodies moved in relation to one another.

You have commented that the inability to create disruptive growth helps explain Japan’s economic malaise. Do you worry that the series of mergers resulting in bigger and bigger companies that seem to primarily focus on stock buybacks is creating the same conditions for the U.S.?

Christensen: I absolutely worry about this. In the latest book that you and I wrote together, The Prosperity Paradox, we describe three types of innovation, all of which have a different impact on the growth of a firm and — by extension — a nation. Sustaining innovation, which most understand, is the process of making good products better. This is important for any economy, but once a market is mature, it generates little net growth in terms of new factories, new jobs, new technology investments, and so forth. There is also efficiency innovation, which is when a company tries to do more with less. By their very nature, efficiency innovations don’t create new growth, because their purpose is to squeeze more out of what you’re putting in. They generate free cash flow for companies, which is important, but if not reinvested properly, that cash doesn’t necessarily lead to new growth. A third type of innovation consists of developing simple products for unserved populations who historically couldn’t afford or didn’t have access to something. These are what we call market-creating innovations, meaning they build a new market for new customers. These innovations are the source of growth in any economy, as they pull in resources, investment, operations, employees, and infrastructure in order to serve this larger population of customers.

My sense is that we in the United States, like many other developed countries, are investing far too much energy in efficiency and sustaining innovations, and not enough in market-creating innovations. Buybacks are not inherently wrong, but at an extreme they indicate an inability of a firm (and perhaps an entire economic system!) to identify market-creating opportunities. There are many reasons why this is occurring, but despite some recent incremental improvements to GDP and unemployment, the long-term economic picture doesn’t seem too rosy to me as long as this more fundamental problem goes unaddressed.

In 2013, you made an off-the-cuff prediction that 50% of the 4,000 colleges and universities in the U.S. would go bankrupt in 10 to 15 years. I know that at the time, you were saying that in a spontaneous conversation, but this observation has been cited many times since as the “doomsday knell” of higher education. Now that you’ve had more time to think through this prediction, do you want to revise it?

Christensen: I’ll clarify a few things about the prediction. Rather than focus on bankruptcy, which is hard for colleges to declare (for regulatory reasons), what we’ll ultimately see is a lot of college closures and mergers. Since 2015, 14 schools have closed and nine have merged in New England alone. A new consulting firm was recently developed to help colleges merge. So this problem is not going away. I think 50% is on the high end of the scale, but not out of the realm of possibility, and 25% to 30% of colleges failing over the next couple of decades is very realistic.

My colleagues have been extremely insightful and have added enormous precision and insight to what I predicted many years ago. Michael Horn, one of my coauthors on Disrupting Class and a cofounder of the Clayton Christensen Institute, has recently written a very detailed summary of what in reality was a prediction of 25% that we made together in The New York Times in 2013. Although disruption — in the form of faster, more affordable, and more convenient college alternatives powered by online learning — is accelerating and a huge threat to established institutions, ultimately I’ve always felt that the bigger imminent danger is that their business models simply aren’t sustainable.

We’d love to hear your thoughts on the nature of disruption today versus two decades ago. How has the threat to incumbents evolved? How has the opportunity to disrupt established markets transformed? We assume that everything has sped up and that the threats of displacement are greater today — but is that really so?

Christensen: The mechanics of disruption are the same as ever, but recent technological and business model innovations present unique opportunities and challenges for both incumbents and entrants. For example, the hotel industry hadn’t been disrupted for decades, only to be completely caught off guard by the likes of Airbnb. The internet, combined with near-ubiquitous mobile access, is continually creating very creative entry points for companies to target nonconsumers with more affordable offerings. So I don’t believe that the threat of displacement is necessarily greater, but certainly the fact that digital platforms can emerge and expand is something that I just hadn’t conceived of early in our research and deserves further study.

Does the rise of “digital transformation” present any anomalies to your theories?

Christensen: Certainly there are anomalies waiting to be discovered, and further research into digital-focused firms will yield profound insight into the boundaries of disruptive innovation theory. But I believe that the fundamental questions we’ve been asking for decades now apply just as much in a digital context as they do in an analog one. Who are your best customers? What is your organization capable or incapable of doing? What “jobs” are you trying to help customers get done in their lives? In what circumstances should you integrate, and in what circumstances should you modularize your firm’s and product’s architecture? Who are the nonconsumers, and what is limiting their access? These strategic questions are universal.

The theory of disruptive innovation predicts what an incumbent will do in the face of a disruptive new entrant. That means incumbents should be well versed in what not to do. So why haven’t more companies solved the innovator’s dilemma?

Christensen: Companies certainly know more about disruption than they did in 1995, but I still speak and write to executives who haven’t firmly grasped the implications of the theory. The forces that combine to cause disruption are like gravity — they are constant and are always at work within and around the firm. It takes very skilled and very astute leaders to be navigating disruption on a constant basis, and many managers are increasingly aware of how to do that.

And in my experience, it seems that it’s often easier for executives to spot disruptions occurring in someone else’s industry rather than their own, where their deep and nuanced knowledge can sometimes distract them from seeing the writing on the wall. That’s why theory is so important. The theory predicts what will happen without being clouded by personal opinion. I don’t have an opinion on whether a particular company is vulnerable to disruption or not — but the theory does. That’s why it’s such a powerful tool.

Many of your other theories are vital to understand for companies that not only wish to avoid disruption but also for companies that aspire to be the disrupter. Your Theory of Jobs to Be Done explains how a would-be disrupter nails the right product or offering when an incumbent often can’t get it right. Can you explain what this is and why it’s so powerful?

Christensen: My colleagues and I have spent years trying to understand customer behavior — why someone would choose to buy one product or service over other options. What we know is that most companies tend to focus on data to help guide their decisions: They know market share to the nth degree, how products are selling in different markets, profit margin across hundreds of different items, and so on. But all this data is focused on customers and the product itself — not what the customer is trying to accomplish in making the purchase. We believe that there’s a better way to understand that choice. We call it the Theory of Jobs to Be Done.

There is a simple, but powerful, insight at the core of this theory: Customers don’t buy products or services; they pull them into their lives to make progress. We call this progress the “job” they are trying to get done, and in our metaphor we say that customers “hire” products or services to do these jobs. When you understand that concept, the idea of uncovering consumer jobs makes intuitive sense.

Each “job” has not only functional dimensions but emotional and social ones, too. Unless you understand the full context in which your customers are making a choice to “hire” your product or service, you will be unlikely to create the right offering for them. You’ll just be treading water with them until they “fire” your product and hire one that understands them better. Successful disrupters often nail the Job to Be Done with their offering right out of the gate. Incumbents try to layer more bells and whistles on their product to make it appealing, but in reality, they are missing the fundamental insight of what customers are trying to accomplish. That’s why Netflix was so successful in disrupting Blockbuster. Reed Hastings intuitively understood that his customers hired Netflix to relax in their own homes, whenever they wanted. Blockbuster focused on increasing its profitability (for instance, through the horrendous late fees we all sheepishly paid) rather than understanding why we chose to hire a video in the first place. Understanding the Job to Be Done provides a road map for successful innovation.

I know that you relish the opportunity to challenge and strengthen your own theories. There is a sign at your office at Harvard Business School that reads “Anomalies wanted.” Are you ever “done” refining your theories?

Christensen: I have always welcomed challenges to my thinking. I think understanding anomalies — what a theory doesn’t explain — helps make the theory better and stronger. We refine the theory with those insights. My own thinking about the theory of disruption has evolved tremendously since I first published its findings in 1995. My goal has never been to be right but to find the right answer. They’re very different things. I’ve long believed that asking the right questions is the only way to get to the right answer. And understanding what questions to ask takes real work.

What do you think people misunderstand about the theory of disruption?

Christensen: Apart from what you’ve already mentioned, which is that disruption does not mean “breakthrough” or “new and shiny,” far too many people assume that disruption is an event. Rather, disruption is a process. It’s intertwined with the resource allocation process in the firm, in the changing needs of customers and potential customers, and in the constant evolution of technology.

There is a growing set of companies that seem to be more fluid in how they approach strategy — like Amazon, Alibaba, and Tencent. Are these companies inoculated against the innovator’s dilemma?

Christensen: This is a very interesting question. I am always wary when we hear that whatever is the high-flying company of the day has solved such a deep systemic problem. Remember, Sears, Digital Equipment Corp., and Eastman Kodak were all once hailed as paragons of good management, until circumstances changed.

That said, there do seem to be some interesting connection points between the companies you mentioned. They have all built organizations that have put the customers, and their Job to Be Done, at the center. They also have demonstrated the ability to manage emergent strategy well. However, they also have been in the fortunate circumstance where their core businesses have been growing at phenomenal rates, and they have had the presence of the founder to help, to personally get involved in key strategic decisions.

One of my former doctoral students, Howard Yu (who now teaches at IMD), noted how important what he called “CEO deep dives” are to wrestling with common innovation challenges, and all of these companies have had the good fortune to have leaders that are ready, willing, and able to do such deep dives. The question for each is, when growth inevitably slows, and when those founders inevitably move on, have they developed the systems, processes, and culture to keep that fluidity? Or, when circumstances change, will the story end the same way it did for other paragons of good management? We will learn something interesting either way.

Anything you got wrong, in hindsight?

Christensen: I’ve gotten my share of things wrong. One of the joys of being a professor is that I am challenged on a daily basis by my students, and I know I’ve learned as much from them as they have from me over the years.

Perhaps most notably, I initially misread the Apple iPhone. When the iPhone first launched, I suggested that Apple had entered late into an established category with a sustaining strategy, and my research showed the odds of success of that strategy was low. I did not see it as disruptive. But then one of my former students, Horace Dediu, taught me that I had framed the problem incorrectly. I viewed Apple as a late entrant into the mobile phone business, where in Horace’s view it was an early mover in the “computer in your pocket” business. Horace was right. And, to its credit, Apple then developed a business model that allowed it be a portable PC better than anyone else. People forget this now, but when the iPhone launched, the only applications you could run on it were those that were created by Apple. Indeed, the company was famously protective of its interdependent, proprietary architecture. To Steve Jobs’s credit, he and the team created the App Store and opened the architecture up enough to allow an explosion of useful add-ons.

This example reinforced to me the importance of getting the categories right. When someone tells me they are disruptive, the first question I always ask is, “To what?” This is an important question, because disruption is a relative concept.

What questions are you still eager to answer?

Christensen: Last year I had a conversation with Marc Andreessen about The Prosperity Paradox, and we were discussing the role firms play in economic growth. Having just come back from an Airbnb board meeting, Marc described how Airbnb gives ordinary people a platform to offer their services, whether they are cooking a meal for their guests, hosting a class, or giving a tour of their hometown. These citizens would otherwise be unable to participate in the tourism industry, but because of the digital platform of Airbnb, they now can.

It occurred to me that in nearly every case, the firms we profiled to demonstrate how economies are built were those that built physical products. This meant they manufactured, distributed, sold, serviced, and designed goods for a non-consuming population, resulting in tremendous growth for their firm and their nation. But Airbnb and others like it don’t have to do any of those things, and yet they are creating opportunities all over the world. I am eager to explore further the growth potential of digital-first firms and understand what growth looks like in the years ahead.

One of the topics I’ve loved exploring with you over the years has nothing to do with technology but something far more important, in my mind. I know you’ve thought a lot about educating children — both in your personal life and in your research. What advice would you give parents of young children about how best to educate their children in today’s tumultuous world?

Christensen: One of my favorite quotes says to let people “be anxiously engaged in a good cause.” Far too often, parents smother their children with lists, extracurriculars, and other “good” things so that children don’t learn how to self-manage and regulate their own lives. In our world, that’s a vital skill kids need to have because of how distracted we are becoming.

Your theories have provided guidance not only for the senior statesmen of Silicon Valley but for a new generation of entrepreneurs all around the world. And you may have reached a pop culture pinnacle when you were the answer to a Jeopardy! question a few years ago. But what is it that you would most like to be remembered for?

Christensen: I want to be remembered for my faith in God and my belief that he wants all of mankind to be successful. The only way to make this happen is to help individual people become better people, and innovation is the key to unlocking evermore opportunities to do that.

Disruption 2020: An Interview With Clayton M. Christensen

Reimagining What It Takes to Lead

Related Reading

Douglas A. Ready, Carol Cohen, David Kiron, and Benjamin Pring, “The New Leadership Playbook for the Digital Age,” MIT Sloan Management Review.

The leadership brand is under siege. In MIT SMR’s recent study, “The New Leadership Playbook for the Digital Age,” only 12% of respondents strongly agreed that their own business leaders have the right mindsets to lead them forward, and only 9% agreed that their organization has the skills at the top to thrive in the digital economy. How should leaders answer these findings?

In this webinar, our study coauthors, Douglas A. Ready, Carol Cohen, and Benjamin Pring, explore the future of leadership in a changing world. The speakers review the study findings and discuss the leadership values and behaviors the research indicates are most valuable to organizations now and in the future.

In this webinar, you’ll learn:

• The three categories of leadership skills — eroding, enduring, and emerging — that organizations should focus on for leadership development.
• What the results from the 2020 study tell us about the state of leadership.
• How to avoid dangerous “blind spots” that cause organizations to get stuck in a state of cultural inertia.
• Four steps to advance leadership development in your organization.
• The four mindsets that are the hallmarks of leadership in the digital economy.

Reimagining What It Takes to Lead

What Separates Analytical Leaders From Laggards?

Information technology changes at a rapid pace, but organizational adoption of it often doesn’t. Fourteen years ago, one of us (Davenport) wrote an article about how companies were beginning to compete on analytics. In the years that followed, data and analytics seemed to become embedded in business culture. Whether these tools were called analytics, big data, or artificial intelligence, organizations of all sizes and types supposedly embraced these resources as a way to improve decision-making and enhance offerings.

How to explain, then, a recent Deloitte survey of U.S. executives that found that only 10% of companies are competing on their analytical insights, and that the most popular tool for analyzing data — used by 62% of companies responding to the survey — is the spreadsheet?

Our survey results clearly show that analytical competitors represent a minority of businesses today, despite the number of years technologies like big data and analytics have been readily available. Becoming an organization that’s driven by data and analytics is not the result of any single factor; it is multidimensional. For organizations to fully leverage the insights they derive and embed them into decisions and actions, a combination of three drivers is required: data and tools, talent, and culture.

Forward Progress

There has been progress, of course. In April 2019, Deloitte posed questions about the use of analytics to 1,048 executives working at large U.S. companies (those with 500-plus employees) who interact with, create, or use analytics as part of their job. The survey found that many companies have invested in creating the requisite data initiatives, analytics, or data science groups. Many have created chief data officer or chief analytics officer roles, and the vast majority have invested in tactical solutions. Many legacy issues that traditionally posed barriers have been eliminated or reduced, including the high cost of data storage, expensive proprietary software, and the need to devote capital to expensive data centers.

Three-quarters of the survey respondents reported that their organization’s analytical maturity increased over the past year, and nearly as many — 70% — expect business analytics to be more important in the next three years than it is now.

Accompanying these indicators of increased organizational awareness is the finding that over the next few years, business analytics as an organizational priority is expected to be on par with such critical drivers of business value as risk management, reputation management, product and service innovation, and managing growth expectations. In other words, analytics is becoming an established fact of business life.

AI — the more technology-intensive relative of business analytics — is not yet used as commonly as some other business and management tools. However, other Deloitte surveys, such as the one conducted for 2018’s “State of AI in the Enterprise” report, suggest that its use is growing even more rapidly as we move into what we call the Age of With — a world where humans work side by side with machines.

Stubborn Challenges

However, few companies have truly evolved into organizations driven by analytics and data. A data-driven culture is one in which important decisions are made based on data and analytics (assuming that data is available) and executives have a willingness to act on analytically derived insights rather than intuition. Among our April 2019 survey findings:

Most executives do not believe their companies are analytics driven. Only 37% of those surveyed said they would describe their organizations as either “analytical companies” or “analytical competitors.” Just 10% placed themselves into the highest category. The remaining 63% said that they are aware of analytics but that their companies lack infrastructure, are still working in silos, or are expanding analytics capabilities in an ad hoc manner.

Most executives are not comfortable accessing or using data. Sixty-seven percent of those surveyed, who were all senior managers or higher, said they are not comfortable accessing or using data from their tools and resources. Even at companies with strong data-driven cultures, 37% of respondents still expressed discomfort.

Spreadsheets are the most commonly used analytical tools. Spreadsheets have shortcomings as an analytics technology: Many have errors, and it is easy to create “multiple versions of the truth” with them. Fortunately, 67% of the companies surveyed also use at least one advanced tool such as SAS, an open-source tool like R, a programming language such as Python, or an AI tool.

Most organizations use limited data types. About two-thirds (64%) reported relying on structured data from internal systems or resources. Far fewer (18%) have taken advantage of unstructured data, such as product images or customer audio files, or comments from social media. This is a missed opportunity: Executives who said unstructured data is one of the most valuable sources of insights were 24% more likely to have exceeded their business goals.

The majority of companies today adopt a fragmented, siloed approach to analytics tools and data. This approach correlates with diminished business success.

A Culture That Acts On Insights

Organizations with the strongest cultural orientation to data-driven insights and decision-making were twice as likely to have significantly exceeded business goals: Among the 37% of companies in the survey with the strongest analytical cultures, 48% significantly exceeded their business goals in the past 12 months. In the 63% of companies that do not have as strong an analytics culture, only 22% significantly exceeded their business goals.

Among the key drivers that help companies scale their analytical insights, a data-driven culture is the most difficult to establish. Culture appears to be the one factor holding back many organizations.

In our experience — reinforced by our survey — the vast majority of companies do not have initiatives in place to address the data-driven culture issue. Here are some recommended steps to bring about the changes needed:

Aim high for analytics champions. Executive sponsorship is vital to this level of organizational change, and the best champion sits in the corner office. According to the survey, the CEO is the lead champion of analytics in 29% of companies surveyed, and these companies are 77% more likely to have significantly exceeded their business goals. They are also 59% more likely to derive actionable insights from the analytics they are tracking. Companies should hire or promote leaders with a strong orientation toward analytics-based strategy and competition.

Encourage leaders to model examples. In meetings, for example, leaders should demonstrate the importance of analytics by asking for data points to back up business decisions. There is a major opportunity for companies to provide more education and improve the user experience if they want every employee to use insights as part of their work.

Spread analytical talent broadly across an organization. The survey data shows that two-thirds of organizations rely on a select group of employees who have been trained in analytics or data science. However, how companies assign responsibility for analytics is a crucial factor in exceeding business goals: Survey data indicates that spreading responsibility across organizational lines is more effective than localized responsibility. (See “The Great Payoff of Shared Analytics Responsibility.”) In fact, the study found that 57% of companies where only key members of each team are responsible for analytics exceeded business goals, whereas 82% of companies in which all employees are responsible exceeded business goals. Companies would do well to cultivate a wide variety of people throughout the organization who are curious, numerate, and capable of translating between analytics/data science methods and business requirements. This might be called the democratization of data science.

The Great Payoff of Shared Analytics Responsibility

How companies assign responsibility for analytics insights is a crucial factor in whether they exceed business goals. As organizations spread responsibility more broadly, more report surpassing their business goals.

Source: Deloitte’s 2019 survey “Becoming an Insight-Driven Organization”

Implement individual performance assessments tying the use of analytics to incentives. Make it easy for employees to act on data and analytics through behavioral economic “nudges” — an effective way to motivate desired actions. Reward data-oriented ingenuity and risk-taking, even if efforts fail. Create a culture that respects the notion of honorable failure.

Know the limits of analytics. If you can’t get the data, you can’t gain the insights.

Buying and using analytics tools is not hard, but changing behaviors is. By enlisting executive sponsors, emphasizing education, and modeling and rewarding the right behaviors, businesses can eliminate traditional analytics silos and adopt a truly integrated approach to analytics and AI.

What Separates Analytical Leaders From Laggards?