Big Data

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Blockchain Data Storage May (Soon) Change Your Business Model

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.

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Complexity’s Competitive Edge

IHG is gaining a competitive advantage from applying advanced analytics to pricing and marketing. “Addressing complexity, if you can address complexity in modern marketing, gives companies a competitive advantage that can take time for competitors to replicate,” say IHG executives Larry Seligman, Jim Sprigg, Angela Galeziowski, and Dev Koushik, in a group interview.

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Variety, Not Volume, Is Driving Big Data Initiatives

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.

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Just How Smart Are Smart Machines?

Managers don’t expect to see machines displacing knowledge workers anytime soon. Instead, they expect computing technology to augment rather than replace the work of humans. But in the face of a sprawling and fast-evolving set of opportunities, what forms should that augmentation take? Davenport and Kirby, authors of “Only Humans Need Apply: Winners and Losers in the Age of Smart Machines,” examine what cognitive technologies managers should be monitoring closely and what they should be applying now.

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Cognitive Technologies: The Next Step Up for Data and Analytics

This free on-demand webinar offers context for understanding cognitive technology offerings. It focuses on what technology capabilities will be available — and what tasks will still require human input. Topics include artificial intelligence, automation, and business rules for making cognitive technology functional. Presenters Thomas H. Davenport and Julia Kirby are co-authors of the forthcoming book Only Humans Need Apply: Winners and Losers in the Age of Smart Machines.

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Tech Savvy: March 3, 2016

  • Blog
  • Read Time: 3 min 

This week’s Tech Savvy looks at what’s happening in wearables at work, virtual reality in hiring, enhancing big data ROI, and digital transformation preparation. For instance, in a Netherlands warehouse, employees wearing smart glasses to pick orders show a 25% improvement in efficiency. And in big data, Wharton’s Eric Clemons notes that “Where big data analytics may create local fiefdoms, online social networks create distributed pockets of autonomous connection, affiliation, and even affection.”

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Will the Internet of Trees Be the Next Game Changer?

  • Interview
  • Read Time: 9 min 

Unlike agriculture, where cutting-edge technologies are being aggressively adopted, forestry and its related industries are something of a technology laggard. But the prospect of the industry using sensors in the field, both in sawmills and even embedded within trees themselves, is emerging. Eric Hansen and Scott Leavengood, both professors at Oregon State University’s Wood Science and Engineering department, discuss how the Internet of Things could help drive efficiency and improve quality in the forestry sector.

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How Time-to-Insight Is Driving Big Data Business Investment

With the emergence of a digital economy over the course of the past two decades, leading companies have learned that they must act faster to respond to customer needs and competitive dynamics. The fourth annual Big Data Executive Survey confirms that Fortune 1000 firms recognize that faster time-to-insight correlates with success and will be the driving force behind Big Data investment for the years ahead.

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Avoiding Analytical Myopia

Analytics offers managers a great way to fine-tune processes, but too many executives focus on metrics at the expense of the bigger picture. The blinders and focus that work well to optimize the details of a problem may prevent managers from seeing other options, and intense focus on a narrow measure can address only the well-specified puzzle — resulting in a myopic view of the problem. Executives who desire bigger breakthroughs need to encourage exploration.

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Enough Health Care Data for an Army: The Million Veteran Program

The holy grail of medicine is therapy that is customized for the patient. But to get there, health care researchers need huge amounts of data to help identify which genes affect health. The Million Veteran Program has tapped one of the largest cohorts available — U.S. military personnel — to obtain the dataset, but managing the security of this sensitive data is a challenge. In a Q&A, two of the project’s lead scientists, J. Michael Gaziano and Saiju Pyarajan, explain the process.

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The Ethics of Wielding an Analytical Hammer

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.

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What Email Reveals About Your Organization

By studying data from email archives and other sources, managers can gain surprising insights about how groups should be organized and about which communications patterns are most successful. Anonymized analysis of internal information communication found that creative people, for instance, work more productively on projects with strong leaders than on collaborations without a clear leader. In addition, in many situations, groups of leaders taking turns worked better at sparking creativity.

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Secrets in the Age of Data

Secrets may be an unexpected casualty of increasing analytical prowess — just ask Volkswagen. Companies often have information they’d rather keep under wraps; sometimes it’s innocuous, like the timing of a new product launch, but other times it’s embarrassing details about unethical or even criminal behavior. But as data analytics becomes more broadly available, the chances of keeping secrets out of public view grow slimmer every day. Will this result in a change in how companies do business?

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Preparing for Disruptions Through Early Detection

In an adaption from his new book The Power of Resilience, MIT’s Yossi Sheffi explains how companies are learning to more quickly detect unanticipated problems that can interfere with their global operations. Sheffi looks at how leading companies are using an array of detection and response techniques, from sensors to supply chain control towers. These tools are helping companies become more resilient to disruptions such as hurricanes, the discovery of product contamination, and political events.

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General Mills Builds Up Big Data to Answer Big Questions

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.

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Analytics in E Major

The Echo Nest, a self-described “music intelligence” company recently acquired by Spotify, uses machine-learning technology to connect people with music. “At our core,” says CEO Jim Lucchese, “what we’re trying to do is what a great deejay does, or the friend that you rely on musically: to better understand who you are as a fan.” In a Q&A, Lucchese describes how the company merges machine learning and cultural analytics to describe music in an analytics-friendly way and help users find new music they’ll enjoy.

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Are Data Scientists Really a Breed Apart?

What differentiates data scientists from other quantitative analysts? It’s partly their skill set and partly their mind set. “The recent emergence of the digital enterprise has created a seemingly insatiable management appetite to amass and analyze data,” write Jeanne G. Harris and Vijay Mehrotra. Data scientists are particularly able to make sense of so much information. For instance, 85% said their projects often or always address new problems, compared to 58% of analysts who made that claim.

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Detecting Bias in Data Analysis

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.

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