3 Theorems on the Economic Value of Data – InFocus Blog | Dell EMC Services. By Bill SchmarzoCTO, Dell EMC Services (aka “Dean of Big Data”) October 25, 2017 Since releasing the University of San Francisco research paper on “How to Determine the Economic Value of Your Data” (EvD), I have had numerous conversations with senior executives about the business and technology ramifications of EvD. Now with the release of Doug Laney’s “Infonomics” book that builds upon Doug’s EvD work at Gartner, I expect these conversations to intensify. In fact, I just traveled to Switzerland to discuss the potential business and technology ramifications of EvD with the management team of a leading European Telecommunications company. From these conversations, I am starting to form some “theorems” to guide organizations regarding how EvD could impact their business and technology investments.
A theorem is defined as “a general proposition not self-evident but proved by a chain of reasoning; a truth established by means of accepted truths.” Economic Value of Data Theorem #1: A Primer on Big Data for Business. Data Is Useless Without the Skills to Analyze It - Jeanne Harris. By Jeanne Harris | 9:00 AM September 13, 2012 Do your employees have the skills to benefit from big data? As Tom Davenport and DJ Patil note in their October Harvard Business Review article on the rise of the data scientist, the advent of the big data era means that analyzing large, messy, unstructured data is going to increasingly form part of everyone’s work. Managers and business analysts will often be called upon to conduct data-driven experiments, to interpret data, and to create innovative data-based products and services.
To thrive in this world, many will require additional skills. Companies grappling with big data recognize this need. In a new Avanade survey, more than 60 percent of respondents said their employees need to develop new skills to translate big data into insights and business value. Ready and willing to experiment: Managers and business analysts must be able to apply the principles of scientific experimentation to their business.
Education Technology. SEO. The Privacy Legal Implications of Big Data: A Primer. By now many lawyers and business managers have heard of the term “Big Data,” but many may not understand exactly what it refers to, and still more likely do not know how it will impact their clients and business (or perhaps it already is). Big Data is everywhere (quite literally). We see it drive the creative processes used by entertainment companies to construct the perfect television series based on their customer’s specific preferences. We see Big Data in action when data brokers collect detailed employment information concerning 190 million persons (including salary information) and sell it to debt collectors, financial institutions and other entities.
Big Data is in play when retailers can determine when its customers are pregnant without being told, and send them marketing materials early on in order to win business. Big Data may also eventually help find the cure to cancer and other diseases The potential uses and benefits of Big Data are endless. 1.0 What is “Big Data”? IBM’s CEO on data, the death of segmentation and the 18-month deadline.
Big data will spell the death of customer segmentation and force the marketer to understand each customer as an individual within 18 months or risk being left in the dust, according to IBM’s CEO Ginni Rometty. Speaking yesterday at the ‘CMO+CIO Leadership Symposium’ in Sydney, Rometty outlined three paradigm shifts marketers are poised to go through, giving the industry 18 months to sink or swim. The most telling for marketers was the shift from segmenting customers into groups to understanding and targeting each customer as an individual.
“Marketers will say my job has always been to understand customers segments,” Rometty explained. “The shift is to go from the segment to the individual. The second shift covered was the evolution from reaching out to customers to creating a “system of engagement” that keeps track of interactions between brand and customer, and uses insights to maximise value creation at every touch point. Transparency is important in achieving this, she says. Big Data is starting to bring big jobs to Greater Cleveland. Anil Jain, second from right, leads one of the stand-up meetings that occur frequently at Explorys, a Cleveland startup pioneering Big Data in the health care realm. Jain invented its early technology and helped assemble a staff that includes, from left, product manager Mark Modic, software engineer Scott Sosnowski, and director of infomatics Jason Gilder.
Lisa DeJong, The Plain Dealer CLEVELAND, Ohio -- The morning staff meetings at Explorys are called sprints. No one sits down. Software engineers and data scientists gather at a white board, busy with note cards, and scan the issues of the moment. Scribbles on each card denote a question to be answered, a problem to be solved. New knowledge pours endlessly into Explorys and a youthful, tech-savvy staff races to absorb the possibilities. Spun out of the Cleveland Clinic three years ago, Explorys already employs 85 people and the prospects are as bright as its hip new offices in University Circle. "Size matters," insists Dr.
Sudow agrees. Three kinds of big data. In the past couple of years, marketers and pundits have spent a lot of time labeling everything ”big data.” The reasoning goes something like this: Everything is on the Internet.The Internet has a lot of data.Therefore, everything is big data. When you have a hammer, everything looks like a nail.
When you have a Hadoop deployment, everything looks like big data. And if you’re trying to cloak your company in the mantle of a burgeoning industry, big data will do just fine. But seeing big data everywhere is a sure way to hasten the inevitable fall from the peak of high expectations to the trough of disillusionment. We saw this with cloud computing. So where will big data go to grow up? Once we get over ourselves and start rolling up our sleeves, I think big data will fall into three major buckets: Enterprise BI, Civil Engineering, and Customer Relationship Optimization. Enterprise BI 2.0 Most “legacy” BI tools are constrained in two ways: Civil Engineering Customer Relationship Optimization.
Mission impossible? Data governance process takes on 'big data' "Big data" alluringly holds out the promise of competitive advantages to companies that can use it to unlock secrets about customers, website usage and other key elements of their business operations. But some caution should prevail: Without a proper data governance process, the zest to spearhead big data projects can unleash a mess of trouble, including misleading data and unexpected costs. Data governance's role in keeping big data houses in order is just starting to emerge from the shadows, though. Big data, which typically involves large amounts of unstructured information, is a very recent phenomenon that has found its way into many organizations under the IT department's radar.
As a result, governance of big data environments is at an incipient stage, and there are few widespread prescriptions for how to do it effectively, according to data management analysts. Looking for clues on big dataThe difficulty is that everything about the data governance process for big data is so new. What is Big Data and How Do We Use It? Big Data is a big opportunity—but are you ready? Best Practices For Managing Big Data. Big Data Runs Afoul of Big Lawyers - meshIP Blog. How to leverage “Big Data” to better manage the business of law. “Big Data” is a term that is capturing a lot of attention. Almost every industry is involved with some type of transaction that leads to mass amounts of data.
These large data sets are often referred to as Big Data because they are difficult to optimize: How do you capture, store or analyze the information in a meaningful way? Many industries see the value of analyzing this data as a way to spot trends. For example, the financial industry was an early adopter of Big Data, collecting and analyzing credit card data to better understand customer spending habits. This same approach can be applied to the legal industry. Together, both law firms and legal departments need information to help identify a balanced solution. What is the legal industry’s Big Data? Most discussions about the legal industry’s Big Data naturally are focused on e-discovery. Electronic legal billing and matter management programs also have become pervasive in corporate law departments.
News | TyMetrix. Big Data Meets BigLaw "What are the odds of winning this case, and what's it going to cost me? " Those are questions clients routinely ask their attorneys. Today, lawyers draw on experience and gut instincts for the answers. Sometimes, they are even right. It may not be long, however, before computers spit out answers with far more accuracy. Legal scholars, computer science engineers, and commercial companies are building databases and using algorithms to crunch massive amounts of historical legal data to identify the significant factors that influence particular legal outcomes. These experts say that such factors can then be used to predict what will happen in future scenarios. The trick, however, is getting usable data. But that's just the tip of the iceberg, says Katz. One company that is trying to capitalize on the potential of this technology is TyMetrix, part of Wolters Kluwer Corporate Legal Services.
Of course, such predictions are only as good as the size and quality of the data. Big Data Analytics Use Cases. Are you data-flooded, data-driven, data informed? Are you insight driven or hindsight driven? Are you a firm where executives claim – “Data is our competitive advantage.” Or sprout analogies like, “data is the new oil”. The challenge I found in companies is not about having a 100,000 ft view of the importance or value of data. Everyone is searching for new ways to turn data into $$$ (monetize data assets). In other words, what is the use case that shapes the context for “Raw Data -> Aggregated Data -> Intelligence -> Insights -> Decisions -> Operational Impact -> Financial Outcomes -> Value creation.”
However, despite the rosy predictions, many organizations will flounder in their Big Data efforts not because they lack analytics capability but because they lack clear objectives, experimental mindset or multi-year roadmaps in converting noisy data into useful signals. What are your Use Cases? So the first question is: What do you really want to achieve? Big Data, Little Data Fraud Use Cases.
Big ethics for big data. As the collection, organization and retention of data has become commonplace in modern business, the ethical implications behind big data have also grown in importance. Who really owns this information? Who is ultimately responsible for maintaining it? What are the privacy issues and obligations? What uses of technology are ethical — or not — when it comes to big data? These are the questions authors Kord Davis (@kordindex) and Doug Patterson (@dep923) address in “Ethics of Big Data.”
In the following interview, the two share thoughts about the evolution of the term “big data,” ethics in the era of massive information gathering, and the new technologies that raise their concerns for the big data ecosystem. How do you define “big data”? Douglas Patterson: The line between big data and plain old data is something that moves with the development of the technology. The impact, however, is where ethical issues live. The influence is a two-way street. Big data tends to be a broad category. Big Data, Big Returns – An Infographic From Informatica | BigData News.
The Risks and Rewards of 'Big Data' for In-House Counsel.