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.

What’s happening this week at the intersection of management and technology: Data analytics for a better work-life balance; cybercrime as a managerial mindset; how to put IoT data to work.

An infographic provides highlights of MIT SMR‘s 2016 Internet of Things report and illustrates the three key areas business leaders need to address in order to realize the IoT value proposition.

The city of Amsterdam is becoming a model for “smart cities” through its innovation efforts to improve the lives of its employees and inhabitants. This case offers insights into what it takes to achieve these goals, including: taking the crucial step of doing an initial inventory of data available; using and integrating data from the private sector; and experimenting and learning from pilot projects.

Blockchain is a data storage technology with implications for business that extend well beyond its most popular application to date — the virtual currency, Bitcoin. Managers need to build their organization’s absorptive capacity around this topic for at least three reasons: (1) the potential effects on organizational value chains, (2) communication within and between organizations, and (3) benefits from cooperation.

The past several years have been period of exploration, experimentation, and trial and error in Big Data among Fortune 1,000 companies, and the result has been a different story. For these firms, it is not the ability to process and manage large data volumes that is driving successful Big Data outcomes. Rather, it is the ability to integrate more sources of data than ever before — new data, old data, big data, small data, structured data, unstructured data, social media data, behavioral data, and legacy data. Guest blogger Randy Bean, CEO of NewVantage Partners, explains why the “variety challenge” has emerged as the top data priority.

Thanks to social media and an increasing flood of data, the capacity to generate causes and controversies almost instantly has become the new norm in today’s “super-transparent society.” Individuals and organizations produce a voluminous, mostly involuntary, “digital exhaust,” which reveals much more about them than they think it does. Most business leaders have not yet come to grips with the new reality — and what it means for their organizations.

With analytics as a hammer, so many questions can start to look like nails. It is difficult for organizations to know what to do. But the “should” in “What should we do?” goes beyond just selecting what to hammer on for maximum insight. Companies need to pay attention to the ways in which the possibilities that analytical abilities create involve responsibilities as well.

Hugh Scandrett, VP of engineering for EnerNOC, is bringing transparency to an energy system that works against clarity. The goal: help companies realize more cost savings and cut back on energy usage. In a Q&A, Scandrett says that one big issue for companies is predicting future demand. “We predict a company’s usage based on analytics that look at weather, degree of sun azimuth, and a whole set of other parameters,” he says. “We then can provide techniques for minimizing peak usage, like pre-cooling a building.”