Companies are racing to apply machine learning to important business decisions, only to realize that the data they need doesn’t even exist yet. In essence, the fancy new AI systems are being asked to apply new techniques to the same old material. The result is a visible arms race as companies bring on machine learning coders and kick off AI initiatives alongside a behind-the-scenes, panicked race for new and different data.

Digital customer service agents (known as virtual assistants, chatbots, or softbots) are typically used to sift through and process only the most straightforward customer inquiries, such as requests for basic information. At most companies, complex issues get passed along to human agents. In that regard, public sector agencies in Australia are ahead of the curve: They are using digital agents to handle complex inquiries from citizens, and businesses stand to learn much from these applications.

Companies can monetize data internally to optimize operations and client services. They can also leverage that data across various client use cases.

Oftentimes contending with overwhelming quantities of data, the health care industry could lessen its reliance on intuitive decision-making by making better use of machine learning.

Our 2018 Strategic Measurement research shows that companies using machine learning to optimize business processes and decision-making have distinct advantages over those that aren’t investing in ML. By using ML technology to make KPIs more predictive and prescriptive, these data-driven companies are redefining how to create and measure value.

Each month, the MIT SMR Strategy Forum poses a single question to our panel of experts in the fields of business, economics, and management. This month’s question asks our panel whether privacy concerns will limit businesses’ use of consumer data.

Data reveals the C-suite recognizes that technical debt — the “price” companies pay for short-term technological fixes — hinders their ability to innovate and adapt in the digital age. One strategy to combat technical debt? Digital decoupling.

In Part 2 of our eight-part video series, we explore how technology affects product and component design. The digital thread not only streamlines product design via the ability to digitally scan an existing part or design a new one using computer-aided design (CAD) software, it can also accelerate the development process by affording previously unattainable levels of transparency and input.

According to a 2018 NewVantage Partners survey, executives now see a direct correlation between big data capabilities and AI initiatives. For the first time, large corporations report having direct access to meaningful volumes and sources of data that can feed AI algorithms to produce a range of business benefits from real-time consumer credit approval to new product offers.

An infographic based on the 2018 Data & Analytics Report by MIT Sloan Management Review illustrates how companies can better engage with customers using analytics.

Data has become a key input for driving growth, enabling businesses to maintain a competitive edge. Given the growing importance of data to companies, how should managers measure its value? An increasing number of institutions, academics, and business leaders have begun tackling the valuation problem to help organizations realize more value from their data.

Poor communication between managers and technical experts is an obstacle to technology innovation that literally has been present for centuries. To overcome these issues, leaders need to absorb three key lessons about how to manage the inherent tensions between defining technical requirements and achieving valuable business outcomes.

Until companies start to invest seriously in security, internet-enabled devices will offer far better ROI for thieves than for the companies seeking to build IoT capacity. But the benefits ultimately will make the effort worthwhile.

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.

Plummeting data acquisition costs have been a big part of the surge in business analytics. We have much richer samples of data to use for insight. But more data doesn’t inherently remove sampling bias; in fact, it may make it worse.

What is the value of data? Many businesses don’t yet know the answer to that question. But going forward, companies will need to develop greater expertise at valuing their data assets.

Coauthors Thomas H. Davenport and Stephan Kudyba discuss the many ways for organizations to monetize data, including selling “data products” directly to consumers. A seven-step model shows the way real-life companies are developing those products and services.

Analytics requires structured data, but we may be introducing errors in the processing we use to produce it. An alternative: Do more up-front structuring before we even collect the data so that less processing is needed.

On Dec. 1 at 11 a.m. EST, join MIT SMR coauthors Thomas H. Davenport and Stephan Kudyba in a free, live webinar, where they will discuss their recent article, “Designing and Developing Analytics-Based Data Products.” The authors will look at the ways in which the internet of things, market forces, and evolving technology are changing how companies plan the development of data products. This new product category requires a reworking of the traditional phases of product development.