June 23-26, 2013
INFORMS Healthcare 2013
October 6–9, 2013
2013 INFORMS Annual Meeting
June 10-14, 2013
Predictive Analytics World
September 8-14, 2013
2013 ASE/IEEE International Conference on Big Data
“Data science begins with data. Nothing gets built without data. Data science continues with science. Accurate, persuasive and effective prediction requires patterns. The process of discovering that pattern is science. Any product worth building requires a reliable pattern to exist in the data.”
– Christopher Berry, co-founder and chief science officer of Authintic, in his article on recommendation engines in the current issue of Analytics.
Special ArticlesStudy: Top CPOs drive higher profit margins
IBM recently released a study that indicates companies with high-performing procurement organizations are driving better bottom line results. According to the study, these organizations report profit margins of 7.12 percent as compared to just 5.83 percent for companies with low-performing procurement organizations.Read More
Special ArticlesRetail stores set for digital makeover
When shopping in a store with mobile-equipped store associates, 65 percent of U.S. shoppers and 75 percent of European shoppers expect the associates to be able to check product pricing on that mobile device, according to new Forrester data based on a survey of more than 4,000 U.S. and 13,000 EU respondents. Additionally, 38 percent and 60 percent, respectively, expect the associate to use her mobile device to answer technical questions about a product.Read More
Industry NewsShopperTrak acquires interior analytics provider RapidBlue
ShopperTrak, the world’s largest counter and analyser of retail foot traffic, has aquired RapidBlue Solutions Oy, a leading European provider of retail data and analytics.Read More
New Frontiers for Analytics
By Keith Collins
Our customers around the world tell me that there is more data than ever, leading to greater expectations for decisions and less confidence in making those decisions based on intuition alone. Analytics has never been more important.
David Mohler (CTO of Duke Energy) says the network is key to the smart grid. As major home devices become IP-enabled, the network will be flooded with data that contains huge potential for both customers and society. Interesting questions arise about how to define layers of optimization from the home to the transformer to the distribution grid, including determining the minimum data necessary to automate appropriate actions at each stage.
Eric Bibelnieks (Group Manager of Guest Data and Analytical Services at Target) shared his belief that high-performance computing brings tremendous opportunities to optimize across problems that were historically rendered independent, suboptimal activities because of their complexity and scale.
Eric Williams (CIO at Catalina Marketing) manages what might be the world’s largest single database table to tailor unique offerings for any American who uses a grocery loyalty card. The volume of data is amazing, but even more astounding is the revenue the Institute for Operations Research and the Management Sciences (INFORMS, publishers of Analytics magazine) for undertaking a needs assessment of the broader analytics practitioner market and planning to address those needs. Extending beyond the operations research (O.R.) audience is critical, because we have found that solving problems requires practitioners to move beyond their core discipline to call upon a full range of analytical practices. We look forward to the report about new areas of applications across industries and across analytics.
We are glad others have joined the bandwagon, but SAS is hardly new to analytics. In fact our roots were planted in 1966 by a consortium of academic statisticians who needed to analyze agricultural data. Increasing volumes of available data demanded both sophisticated statistical analysis and powerful data management capabilities, strengths of SAS® software that endure today. As those students graduated and moved into industry, they realized the value that these capabilities also offered to their commercial problems. By 1976, they formed a company so that they could put SAS software to work on business problems.
Over time, SAS has moved from selling software tools for statistics, O.R., data mining and econometrics to more directly developing solutions for our customers’ business problems. We still make and sell these tools, now stronger than ever, but we are also combining underlying algorithms in particular ways to find answers that were previously considered unattainable. One of our motivations is that the power of analytical techniques is now accessible to users who might not be experts in the specific techniques. There simply are not enough experts, and the opportunities are so compelling that a broader audience wants to try analytics.
A good example is our work in operations research. We first released SAS/OR® software in 1982, and in recent years we have completely revamped our offerings in optimization and simulation. Drawn by a broader market understanding of the power of O.R., we have been applying underlying O.R. algorithms to specific problem classes, such as marketing optimization, and even in specific industries, such as revenue management and price optimization in the hospitality industry. This evolution has involved gaining industry expertise to understand questions such as what kind of data is available, what is and isn’t easy to measure, and what regulatory and/or business process constraints apply. All these questions vary by industry.
This evolution also means combining different analytical approaches. The tight margins in retail make maximizing product profitability at the end of a season or the end of product life critical. Profit maximization requires knowing which products to mark down when, by how much, and in which locations. Attaining this information requires estimating demand as a function of price; this is not an easy estimation problem because available data is typically not from demand, but from sales. Because the problem is so closely tied to the optimization, the best solution is an integrated approach, such as the one offered in SAS® Markdown Optimization for the retail industry.
As we bring our approaches closer to the business and more accessible to a broader audience, we are also maintaining the analytical rigor through R&D investments. We realize we can’t develop software for every problem, so we staffed an O.R. Center of Excellence with individuals who have both doctoral-level training and also years of experience applying O.R. techniques to problems across industries. They focus on model formulation, model solution and implementation. They have helped customers solve problems in industries such as manufacturing, retail, consumer packaged goods, banking, media and entertainment. They draw on their own experience as well as deep expertise across the Advanced Analytics Division. Due to positive customer response, we are currently exploring additional Centers of Excellence dedicated to industries (such as manufacturing) and to applications (such as data mining).
I see three other exciting frontiers: high-performance computing, visualization and text analytics. In the world of practical large-scale computing, I see a trend towards distributing computation over many inexpensive computers rather using than a single machine with many cores. We are taking advantage of this paradigm by developing high-performance versions of select analytical solutions. Communities such as INFORMS are key to providing computational advances in this area, which is why SAS collaborates with external researchers in addition to our own R&D staff.
Surging interest in analytics requires new ways to convey these insights to laypeople. Business visualization displays analytics in compelling ways to a wider range of data consumers than ever before, because a picture can truly be worth a thousand words. Users across an organization can have a single, consolidated view and can also focus on views based on their roles. Within a business intelligence framework, these insights can be centrally and securely administered and then delivered to end users via the Web or even mobile devices. And using JMP® software from SAS, the visualization can be interactive and exploratory, so you can quickly get to the most important insights your data has to offer.
Most experts estimate that 70 percent of data is in unstructured, nonnumeric format, so SAS has stepped up investment in our tools for text mining, content categorization and sentiment analysis. We have also combined techniques to solve specific business problems; for example, our new SAS® Social Media Analytics enables organizations to gain detailed insight into chatter across various social media sites and then attribute these threads to specific parts of their business.
Our employees keep us abreast of these emerging trends so we can maintain our own competitive edge. In addition to participating in ongoing training and study, they attend conferences and are active in various professional organizations. For example, Kathy Lange is president-elect of the INFORMS Roundtable, and Bob Rodriguez will be president-elect of the American Statistical Association (ASA) in 2011. In addition to these roles, many other employees are in leadership roles or on committees of INFORMS, ASA and other professional organizations. We challenge our employees to translate new approaches they encounter into innovative ways to solve emerging problems, because I’m quite confident that the problems are only going to get harder. And SAS will be here with solutions.
Keith Collins (firstname.lastname@example.org), senior vice president and chief technology officer at SAS, is responsible for driving corporate technology strategy through a focus on customer- and partner-facing activities.