One of the newest and most important players in the sports world isn’t a soccer phenom or an NBA all-star — it’s a multiplayer video gaming platform known as eSports. The universe of eSports relies heavily on data analytics, but the surprising twist is the role of social science analytics in this world of virtual contests between far-flung players. Counterpoints talks to eSports analytics guru Tim Sevenhuysen.

While AI is brilliantly placed to solve decisions that are concrete and well-defined, in other contexts it can fail spectacularly, showing connections between facts or events but stumbling when the need to disentangle cause from correlation arises. Human input in the form of subject matter knowledge and common sense are often needed to complement AI. And executives must understand which challenges are right for these new technologies to address.

Findings don’t have to be earth-shattering to be useful. In fact, obvious insights can help you overcome three barriers to change in your organization: resistance to new data (“But that’s not what my experience has shown”), resistance to change itself (“But that’s the way we’ve always done it”), and organizational uniqueness bias (“That will never work here”). You can also gain trust by confirming what people already believe.

E-commerce and omnichannel retail strategies have revolutionized the way consumers buy products, but maintaining consistent pricing across global markets can be an obstacle for organizations. By harnessing a focused data science strategy, organizations can avoid pricing pitfalls.

Companies are looking to artificial intelligence to create business value, and as MIT Sloan Management Review’s 2018 Global Executive Study and Research Report on AI shows, Pioneer organizations are pulling ahead of their counterparts. By deepening their commitment to AI and focusing on revenue-generating applications over cost savings, these early implementers are positioning themselves to reap the benefits of AI at scale.

Companies handling large volumes of data face a greater chance of erroneous linkages creeping into their analytics. Three key habits of data managers can reduce the risk of missed data connections.

Even when faced with evidence that an algorithm will deliver better results than human judgment, we consistently choose to follow our own minds. Why? MIT Sloan Management Review editor in chief Paul Michelman sat down with the University of Chicago’s Berkeley Dietvorst to find out.

The 2017 Data & Analytics Report by MIT Sloan Management Review finds that the percentage of companies deriving competitive advantage from analytics increased for the first time in four years. Incorporating survey results and interviews with practitioners and scholars, the report finds that companies’ increasing ability to innovate with analytics is driving a resurgence of strategic benefits from analytics across industries. The report is based, in part, on MIT SMR’s seventh annual data and analytics global survey, which includes responses from 2,602 business executives, managers, and analytics professionals from organizations located around the world.

When it comes to putting data to use, communication — or rather, lack of it — between the data scientists and the executive decision makers can cause problems. The two sides often don’t speak the same language and may differ in their approach to and respect for data-based decisions. Given these challenges, organizations may need to call upon a “data translator” to improve how data is incorporated into decision making processes.

Managers can guard against data deceit affecting their decision making by asking questions about the quality of their data, the strength of their models, and the assumptions behind their analysis.

There is a growing belief that sophisticated algorithms paired with big data will find relationships independent of any preconceived hypotheses. But in businesses that involve scientific research and technological innovation, this approach is misguided and potentially risky, as spurious correlations and “noise” may lead analysts astray.

Organizations across the business spectrum are awakening to the transformative power of data and analytics. They are also coming to grips with the daunting difficulty of the task that lies before them. It’s tough enough for many organizations to catalog and categorize the data at their disposal and devise the rules and processes for using it. It’s even tougher to translate that data into tangible value. But it’s not impossible, and many organizations, in both the private and public sectors, are learning how.

What’s happening this week at the intersection of management and technology: IoT implementation lessons; IBM’s AI primer for the White House; forecasting requires augmented intelligence.

How to build a data science team; on-boarding in the cloud; using big data to improve executive communication skills.

To manage a first-rate data science or quant group, leaders need to build an engaging environment, get the team the resources it needs and balance being involved while also staying out of the way. In banking, for example, division managers generally don’t review loan applications. But in analytics, the most successful leaders engage regularly in hands-on research and continue to publish regularly even as they move up the executive ladder. By staying active in line research, analytics managers are able to hone their abilities to judge how difficult projects are and how long they will take.

General Mills brought a data scientist into its Consumer Insights group because it wanted to use its existing data more effectively. The company thought it was making decisions based too much on outside data at the expense of what it knew. But figuring out what the company actually knew about its consumers was the challenge facing Wayde Fleener as he came on board. In an interview with MIT SMR’s Michael Fitzgerald, Fleener talks about how he got started in building a Big Data practice within his division.

Data analysts may have external agendas that shape how they address a data set — but Boston College’s Sam Ransbotham argues that a savvy manager can identify biases by learning to question the underlying assumptions that go into dataset cleanup and presentation.

When you’re dealing with data on the massive scale that a company like GE uses, a data warehouse just isn’t big enough to house it all. And organizing it ahead of analysis is more of a burden than a help. GE’s CIO Vince Campisi explains to MIT Sloan Management Review why his company is now storing data in a data lake — and how that approach changes the kind of human resources his company is looking for.

Companies will want hundreds of thousands more data scientists than exist, creating a much discussed skills gap. In the past, businesses have figured out how to automate in-demand skills, and some companies say they can automate what data scientists do. What does it mean for companies when they do the equivalent of putting their data scientists into a can?

Businesses are running into the issue of having analytics professionals who can’t communicate what they mean. Companies need to train their data scientists to explain how their work helps the business. A little communications 101 is in order, says Meta Brown, whose business has shifted from helping companies analyze data to helping them understand what their analysts are doing.