Data-to-Decisions [D2D] is one of the broadest business research themes that Constellation Research covers. As such, the SuperNova Award entries cover a wide range of data collection, management and analysis for every aspect of organizational and personal decision making. The judges had quite a decision of their own to make, selecting the most innovative and disruptive uses of data to impact decision making from among 28 applicants representing just as varied use cases. D2D and the submissions received apply the most interesting data sets to the most advanced decision challenges facing enterprises, governments, communities and individuals, today. I want to congratulate the 11 finalists in D2D and introduce you to them. For it's you, the reader of this blog, those who are interested in disruptive, innovative decision making, the public who find this page, who will decide the winner from these finalists. Voting opens to the public, from 2013 September 9 through October 9 on the Constellation site. So, here are the innovators and organizations found to be the most innovative of the SuperNova Award D2D applicants.
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Ashish Braganza, Senior Manager of Global Business Intelligence, Lenovo
Lenovo, the world’s largest PC vendor, is a $US34 billion personal technology company serving customers in more than 160 countries. The challenge facing Lenovo to maintain that top spot. To do so, Lenovo created the Global Business Intelligence [GBI] team and tasked them to improve website conversion rate, and provide an eight-fold return on the investment in the GBI team. Through predictives, optimization and data, using Adobe Systems' Adobe Marketing Cloud [Analytics, Social, Media Optimizer, Target and Experience Manager Solutions] the GBI team was able to directly impact the corporate financial stream, creating a five-fold increase in click-through, boosting revenue per customer by 26 percent.
Brad Donovan, Manager, Agile Analytics and Innovation, GlaxoSmithKline
GSK employees serve US communities by discovering, developing, and delivering new medicines, vaccines, and other healthcare products to help people do more, feel better, and live longer. Unfortunately, a long established commitment to advanced statistics and what is now called data science, had led to a situation wherein the IT & Analytic communities could not respond when Marketing and Sales needed new insight. Teradata, IBM & SAS came on as an advisory council to verify or refute IT's objections to the new Agile Analytics & Innovation group using data directly from the EDW by way of Teradata Data Labs. This is a move from where the old Analytics team was seen as a bunch of PhD stats using SAS and pushing out one-time models. Using Teradata EDW, Data Labs and analytics partners, the Agile Analytics & Innovation group took data aggregation from 30 hours to 3 minutes, model execution from 40 hours to 1 hour, QC from 40 hours to 5 hours, but most importantly they took the predictives and models of the Analytics team from a "bright shiny object" to an actionable solution in production, accessible by thousands of business analysts throughout the company. In many ways, this is a reverse of the current trend towards creating data science teams, and shows how companies that are just moving forward with data science, can leverage and productionalize the improved inferences and predictions stemming from computational statistics, data mining and machine learning.
Bruce Yen, Director of Business Intelligence, Guess?
Established in 1981, Guess?, Inc. began as a denim company and has since successfully grown into a global lifestyle brand that directly operates 511 retail stores in the United States and Canada and 328 retail stores in Europe, Asia and Latin America. To be immediately responsive to customer and business needs, Guess sought to harness the power of big data in near real-time and thus compress the business decision cycle. Guess? Inc. extended the power of the HP Vertica Analytics Platform to power all of its BI & Analytics, but the real disruption came with a cutting-edge analytics iPad application, “G-Mobile,” designed for non-traditional Business Intelligence (BI) users. Via G-Mobile, Guess? extends analytics to the business front line — including designers, buyers, planners, sales executives, and allocators — so they can better manage the business with the right data at the right time. This reduced the load window by 50 - 62.5 percent, from 3-4 hours on the legacy platform to 90 minutes at most, accelerated speed-to-insight: on the legacy system, it might have taken 15 to 20 minutes to generate a merchandise report; it takes just five to 20 seconds on the iPad accessing data remotely, and improved merchandise allocation and distribution of inventory across retail locations due to complex queries, such as sales for all best-sellers, performed 60 to 80 times faster.
Dirk Zeller, Head of IT Consulting at Mercedes-AMG GmbH, Mercedes-AMG GmbH
The image of AMG as the successful performance brand of Mercedes-Benz is reflected in its impressive successes in the world of motorsport and its unique vehicles. Today it is a one hundred percent subsidiary of Daimler AG and is the group's technological spearhead in the high-performance car segment. The AMG brand promise of "Driving Performance" stands for state-of-the-art technology and pure driving excitement. The engine is the key component of every Mercedes-AMG vehicle, allowing us to deliver on the promise of ‘Driving Performance’. AMG have realized that a key function like engineering could benefit tremendously from real-time analytics to innovate and accelerate all engine testing processes which are usually time-consuming (e.g. up to 50mn of engine dyno time are wasted in case of a non-successful engine test-run) while the resources, especially dynometers are limited. Comparing current engine testing data with previous test-bench data to evaluate the performance of the engine was for example extremely complex and in some cases not even possible. Using SAP HANA, mobile, and predictive products, Mercedes-AMG – in collaboration with SAP AG Partner MHP - have built a highly innovative real-time quality assurance platform for the optimization of end-to-end testing processes in development and manufacturing. The solution delivers real-time analytics to engineers on any device to allow them get a 360° view of the performance of the engine during all testing phases – also leveraging high-volumes of polytechnical data coming from sensors connected directly to the enginesThis has reduced run-time for non-successful test-drives (before: 50mn ; after: immediate halt if parameters are out of range), saved between one to several days in engine testing capacity, which can now allow us to test more engines during the same period of time and/or allocate this time for other added value tasks, and decreased capital expenditures. The result is a highly scalable platform for the future. The same approach can be applied to other use-cases: test vehicle on test-track, test-vehicle on long-test runs, crash tests, interior testing, and even getting real-time information from the vehicle while being used by the customer, allowing for a future of predictive maintenance.
Karen Simmons, Senior Director, Enterprise Data Warehouse, Kelley Blue Book Co., Inc.
Founded in 1926, Kelley Blue Book delivers the most market-reflective values in the industry on its top-rated website www.KBB.com, including the famous Blue Book® Trade-In and Suggested Retail Values, and Fair Purchase Price. Kelley Blue Book undertook a significant initiative to create a single 360-degree view of online activity and behavior across disparate data sources throughout and from outside the enterprise, Web360. Kelley Blue Book leveraged technology from Informatica, IBM Netezza, MicroStrategy and SAS to launch Web360, thereby replacing a fragmented assortment of data integration tools, as well as proprietary data integration frameworks, with a unified platform approach to integration. The Web360 initiative enables the company to profile, cleanse, integrate and analyze large data sets with complex relationships. This resulted in improved analytics and intelligence, providing an enhanced and more compelling consumer experience, creating increased performance for advertisers who benefit from new and faster data-driven intelligence.
Oswaldo Mestre, Director, Division of Citizen Services, Office of the Mayor, City of Buffalo 311 Call and Resolution Center
The City of Buffalo 311 Call and Resolution Center, in conjunction with the ancillary programs within the Division of Citizen Services, not just a call center; 311 increases the City's effectiveness in responding to public inquiries, providing insight into the needs and concerns of residents, and promoting accountability by ensuring that services are being delivered in a consistent and timely manner citywide. Using data mostly from 311, the Division of Citizen Services’ Operation Clean Sweep identifies various areas of the city to send the Clean Sweep team of a city, state, county and federal government police and health and human service providers to offer educational outreach, along with beautification crews to address physical issues in the area. Using KANA LAGAN CRM system, 311 allows the City to track issues, get locations, categorize and store each department’s issues in one system, thereby enabling each department to prioritize and respond to issues accordingly.
Lance Henderson, CEO, Zamzee
Zamzee uses sensor technology and a community web site to influence families to healthier lifestyles. Using Bunchball’s Nitro gamification platform and avatar engine, Zamzee participated in research studies by Hopelabs showing that activity levels and associated health measures were all positively influenced through the gamification process.
Roman Coba, Chief Information Officer, McCain Foods Limited
McCain Foods Limited is an international corporation in the frozen food industry, known for frozen potato specialties, and also producing frozen pizza, appetizers, oven meals, juice and desserts. Using Teradata Enterprise Data Warehouse with MicroStrategy and IBM Infosphere, McCain Foods use Optimal Equipment Efficiency (OEE) in real-time and projecting it to all production employees in an easy-to-understand format, creating a cultural shift with the ability to access and analyze not just data, but data that is transformed into information that is intelligent and actionable. One unintended result has been the creation of an extremely competitive workforce as production employees can now see in real-time how they compare to other plants, which makes them compete to outdo other plants.
Ronald Baden, VP of Services, Host Analytics
Host Analytics provides cloud-based financial applications for planning, close management, reporting and analytics. Using FinancialForce PSA, Host Analytics went from using Microsoft Word documents and manual processes to using the FinancialForce PSA application’s detailed reports and dashboards allowed the company to better allocate resources based on project needs. This led to Host Analytics customers giving the company the highest combined overall ratings for reported measures in each of the Vendor, Product and Implementation experience categories, as well as receiving other customer service awards.
Russ Turner, Site Reliability Engineering - Manager, Domino’s Pizza
Domino's pizza restaurant chain launched a web ordering service in 2007. This created a big data problem of unwieldy amounts of machine log data. Domino's deployed Splunk Enterprise to deal with this data. The learning experience from the initial uses of Splunk led to this machine analytics solution being deployed across the enterprise and to use in areas other than the original IT and networking areas to to improving business decisions through visualizing sales trends such as orders per minute, numbers of transactions per store, types of pizza customers are ordering and the coupons they’re using to do so. Machine-to-machine data (M2M) data analytics allows Domino's to determine the types of devices–iPhones, Androids or Xbox’s–that are being used to place orders, or assess promotions in real time. All of this made Domino's IT team a legitimate source of business insight across all areas of the organization.
Tony Candeloro, Vice President Product Development, ARI
ARI provides global vehicle fleet management that drives the best results for each of its clients’ unique and complex needs. In the US, more than 450,000 fleet vehicles are covered by ARI’s maintenance management programs. ARI facilitates maintenance and repairs for these vehicles via a nationwide open vendor network. Currently, the network consists of more than 66,000 vendors with a controlled spend for parts and labor of almost one billion dollars. ARI’s commitment to its customers is to match them with a vendor that is best suited for their vehicle and repair type, to negotiate the best price on the parts and labor costs that get passed through to the customer, and to keep vehicle downtime to a minimum. By leveraging SAP HANA in-memory technology, SAP Xcelsius Dashboards and Infosol’s Info Burst Data Caching, ARI created a a neural data network that relates all vendors with given a radius throughout the US. This data drives dashboards that can analyze regional vehicle operating parameters, regional vehicle spend, and regional vendor operations to identify opportunities to leverage and target our clients’ total spend. Correlating this information with geospatial dashboards provides ARI with a clear picture to better negotiate discounts on behalf of our clients and to communicate with our vendors about the opportunities that ARI provides to their immediate markets.
Bottom Line
The Constellation Research Super Nova Award finalists are market leaders and fast followers, using technology from entrepreneurial firms such as Splunk and Concurrent, from established vendors such as HP, Informatica, SAP and Teradata, and innovative solutions from all manner of vendors in-between. If you wish to get ideas on how to solve similar problems, get the entire story, from the link for each finalist in the above summaries<>. Every organization, every individual, can use internal and external data to create inferences and predictions to gain better insight and increase performance. Learn from your peers, by reading these SNA applications. You can also learn the theory behind data-to-decsions.
