Long before the term “big data” hit the lexicon, analysts were using statistical models to make projections about the probability of events to come. These days are different because of organizations’ ability to collect many more data points, from many sources, at rapid speed—all feeding a growing number of use cases for predictive analytics that create both opportunities for new insights and challenges for finding them.
Below, find highlights from Data Informed’s coverage of predictive analytics, including tips of developing and implementing predictive models, articles on notable implementations and discussions with statisticians, scientists and business people in the field.
More data and simple algorithms work because having more data allows the “data to speak for itself,” instead of relying on unproven assumptions and weak correlations, writes Garrett Wu of WibiData in this opinion piece. Read more.
Agile analytics development goes beyond the famous Agile Manifesto for software projects. It requires a mindset and management approach that allows for fast failures and builds consensus quickly, writes Anand Krishnaswamy of ThoughtWorks in this article. Read more.
Predictive analytics enable better business decisions, but there are many pitfalls that can seriously skew the results. Enterprises can avoid such pitfalls through a careful analysis of the data combined with modeling techniques that do not place too much emphasis on variables that may be noise, writes Venkat Viswanathan of LatentView. Read more.
While many executives have supported the notion of anonymizing personal data when using it to gain insights into consumer behavior, few have come to personify the evolution of the practice as much as Jeff Jonas, an IBM Fellow and Chief Scientist of the IBM Entity Analytics Group. Read more.
PMML, the predictive model markup language, is a mature standard that can speed up the deployment of predictive analytics models from an analyst’s desktop to the data warehouse or analytics database. Read more.
To Lars Hård, founder and chief technology officer at Expertmaker, data in all its structured and unstructured forms represents signals that machine learning systems pick up to refine results for future interactions. The data, and those interactions, also figure into Hård’s work of applying principles of evolutionary biology to both business use cases, like shopping assistants, and medical research. Read more and listen.
Predictive models are hot, and new tools are emerging to open them up to new users. But it takes more than data and tools. To achieve results, businesses must estimate the impact of a model, integrate it into existing systems, and changing existing processes to take advantage of results, Paul Maiste of Lityx writes. Read more.
To make a positive impact with analytics requires calls for embedding the results of analytics systems into business processes managed and used by people—including many who have no experience with statistics or data management. That reality means that analytics professionals need to calibrate their approach to suit an organization’s existing culture to win widespread adoption, says Srikanth Velamakanni, the CEO of Fractal Analytics. Read more.
The payoff from investments in predictive and prescriptive analytics comes when front-line workers use the findings to make better business decisions. This article describes what UPS is doing to activate its workforce. Read more.
After consulting at McKinsey & Company, Omer Artun developed a deep appreciation for data-driven decisions. The founder of AgilOne, a cloud-based analytics service, discusses how predictive analytics can sort through noise to find insight. Read more and listen.
The improved tracking is a result of computing advances that have enabled National Hurricane Center’s predictive models to become faster and capable of resolving increasingly smaller features in the atmosphere. Also, “the number of data sources keeps increasing,” says James Franklin of the NHC. But, perhaps more importantly, the NHC has gotten smarter about how it incorporates that data. Read more.
Executives at a conference organized by PROS Inc. discussed lessons learned by pricing and analytics professionals in the trenches of pricing analytics, as they battle to impose data-based insights on pricing decisions traditionally reached through more informal means. Read more.
Supply chain executives have done the best they can with systems that peaked 10 years ago. It’s time for companies to seize five areas of opportunity–mobility, the internet of things, big data, predictive analytics and cloud computing–to create business value, writes supply chain expert Lora Cecere. Read more.
An estimated 22 military veterans take their own lives each day—one almost every hour, according to recent research by the U.S. Department of Veterans Affairs. Yet predicting who is likely to commit suicide remains a challenge for mental health professionals. That’s where computer scientist Chris Poulin and a semantics-based prediction tool enter the picture. Read more.
Researchers are applying predictive analytics to design diagnostics and evaluate treatments. The National Institutes of Health awarded IBM and two health care organizations a $2 million grant to develop predictive analytics for primary care doctors to identify patients at risk of heart failure as much as two years ahead of time. Read more and listen.
The U.S. restaurant chain Hooters is embroiled in a multimillion-dollar commercial indemnity lawsuit stemming from the sale of the company. What’s unique about the case is that the Delaware judge presiding, has recommended the use of a controversial data analysis tool, known as predictive coding, to resolve the dispute. Read more.
The Orlando Magic is gaining a reputation for being at the forefront of using data analytics to retain season ticket holders. It’s studying the behavior of these valuable customers this year – based largely on their ticket usage – to determine who is likely to renew for next year, who is not, and who could go either way. The Magic can then tailor its communications to help retain those it projects might leave. Read more.
The Internet of Things is more about bits than atoms, and this is especially true of the industrial Internet. The industrial Internet’s things—jet engines, gas turbines, oil rigs—are components of high-tech systems and also of business processes. GE is leveraging its status as a major builder of industrial technology, user of information technology, and seller of operational and engineering services to foster and guide the development of the industrial internet. Read more.
IBM’s Predictive Asset Optimization offering brings together capabilities such as maintenance-specific IBM SPSS visualization and analytics from IBM’s business analytics division, data handling technologies from the company’s information management division, as well as asset management expertise and consulting services. Read more.
In the oil and gas industry, prescriptive analytics—which tell what will happen, when, why and what to do about it—can help locate fields with the richest concentrations of oil and gas, and make the fracking process more efficient and safer for the environment, Atanu Basu of AYATA writes. Read more.
Synthesizing big data, mathematical sciences, business rules and machine learning, oil companies like Apache Corp. are using Ayata’s software to predict future outcomes and prescribe decision options to respond to these outcomes. Read more.
Helping to keep the RAF aircraft airborne and fighting were advanced predictive modeling techniques, deployed by a team of personnel from defense contractor and aircraft manufacturer BAE Systems, targeted on identifying likely maintenance needs. Read more.
Stephen Gold, an IBM vice president, says that Watson represents the arrival of “cognitive analytics” that augment applications that describe, predict and prescribe what users should see in the data they collect. Read more and listen.
Autometrics has built a business around sensing demand for automakers by tracking the Web browsing habits of potential car buyers and reporting them to the manufacturers on a daily basis. The major car companies can use the demand data to adjust production schedules, tweak marketing campaigns and minimize inventory, greatly increasing profit margins in a very competitive industry. Read more.
Dean Abbott, a predictive modeling expert, discusses how companies are getting value from their real-time Web analytics now, what has happened in the past to make it possible, and where the industry is going. Read more and listen.
Startup company Sideband Networks has announced a hardware/software product that can monitor and analyze network data traffic in a virtual environment in real time while comparing it to previous network activity patterns so that network admins to react faster to incidents with predictive alerts. Read more.
Lex Machina has developed an analytical reference for IP lawsuits by painstakingly collecting and analyzing data on court cases. It sells risk advisory consulting services to clients, which include law firms, corporate general counsels and technology companies. Read more.
The Obama for America campaign was about facing off against Mitt Romney for the White House. It was about the U.S. economy and jobs, taxes and the national debt, America’s standing in the world and immigration. But behind the scenes, the Obama campaign was about creating an analytics culture so that everyone—from tens of thousands of field workers to more than 100 data analytics experts—collected data, measured outcomes and refined marketing, communications and fundraising programs to achieve results. Read more.
A San Francisco-based executive search and technical recruiting firm, Riviera Partners matches seasoned techies with some of the Bay Area’s most highly sought-after employers like LinkedIn, Zappos and Dropbox. Read more.
BloomNation is powered by an algorithm that makes predictions based on the many data points generated by each flower. Read more.
Home page image of crystal ball via ThinkStock.