Many businesses are close to being overwhelmed by the unceasing growth of data they must process and analyze to find insights that can improve their operations and results. To manage this big data they find a rapidly expanding portfolio of technology products. A significant vendor in this market is SAS Institute. I recently attended the company’s annual analyst summit, Inside Intelligence 2014 (Twitter Hashtag #SASSB). SAS reported more than $3 billion in software revenue for 2013 and is known globally for its analytics software. Recently it has become a more significant presence in data management as well. SAS provides applications for various lines of business and industries in areas as diverse as fraud prevention, security, customer service and marketing. To accomplish this it applies analytics to what is now called big data, but the company has many decades of experience in dealing with large volumes of data. Recently SAS set a goal to be the vendor of choice for the analytic, data and visualization software needs for Hadoop. To achieve this aggressive goal the company will have to make significant further investments in not only its products but also marketing and sales. Our benchmark research on big data analytics shows that three out of four (76%) organizations view big data analytics as analyzing data from all sources, not just one, which sets the bar high for vendors seeking to win their business.
Topics: Big Data, Predictive Analytics, SAS, Event Stream, Operational Performance, Analytics, Business Analytics, Business Intelligence, Business Performance, CIO, Customer & Contact Center, Data Management, Information Applications, Information Management, Location Intelligence, Operational Intelligence, Discovery
With much fanfare and a rarely seen introduction by CEO Ginni Rometty, IBM launched IBM Watson as a new business unit focused on cognitive computing technology and solutions, now being led by Senior Vice President Mike Rhodin. The announcement is summarized here:. Until now IBM Watson was important but had neither this stature in IBM’s organizational structure nor enough investment to support what the company proclaims is the third phase of computing. As IBM tells it, computing paradigms began with the century-old tabular computing, followed by the age of programmatic computing, in which IBM developed many products and advancements. The third phase is cognitive computing, an area in which the company has invested significantly to advance its technology. IBM has been on this journey for some time, long before the IBM Watson system beat humans on Jeopardy!. Its machine-learning efforts started with the IBM 704 and computer checkers in the 1950s, followed by decades of utilizing the computing power of the IBM 360 mainframe, the IBM AS/400, the IBM RS/6000 and even IBM XT computers in the 1980s. Now IBM Watson is focused on reaching the full potential of cognitive computing.
Topics: Big Data, Sales Performance, Social Media, Supply Chain Performance, IT Performance, Operational Performance, Analytics, Business Analytics, Business Collaboration, Business Intelligence, Business Performance, CIO, Cloud Computing, Customer & Contact Center, Financial Performance, Governance, Risk & Compliance (GRC), Information Applications, Information Management, Location Intelligence, Operational Intelligence, Workforce Performance, Cognitive Computing, Discovery, Exploration, IBM Watson
Like every large technology corporation today, IBM faces an innovator’s dilemma in at least some of its business. That phrase comes from Clayton Christensen’s seminal work, The Innovator’s Dilemma, originally published in 1997, which documents the dynamics of disruptive markets and their impacts on organizations. Christensen makes the key point that an innovative company can succeed or fail depending on what it does with the cash generated by continuing operations. In the case of IBM, it puts around US$6 billion a year into research and development; in recent years much of this investment has gone into research on big data and analytics, two of the hottest areas in 21st century business technology. At the company’s recent Information On Demand (IOD) conference in Las Vegas, presenters showed off much of this innovative portfolio.
Topics: Predictive Analytics, IT Performance, Analytics, Business Analytics, Business Intelligence, Business Performance, Customer & Contact Center, IBM, Information Applications, Data Discovery, Discovery, Information Discovery, SPSS
Business analytics can help organizations use data to find insights that lead to new opportunities and address issues unrecognized before. One player in this market is Datawatch, known for its tools for information optimization and harvesting value from big data including content and documents. I assessed the company earlier this year, and recently our firm recognized its customers’ achievements with 2013 Ventana Research Leadership Awards for Information Optimization with Phelps County Regional Medical Center and Governance, Risk and Compliance (GRC) with The Fauquier Bank.
Topics: Big Data, Sales Performance, SAP, Supply Chain Performance, GRC, Office of Finance, Panopticon, Operational Performance, Analytics, Business Intelligence, Business Performance, Customer & Contact Center, Financial Performance, Information Applications, Information Management, Operational Intelligence, CEP, Datawatch, Discovery, Information Optimization, SAP HANA
Teradata recently gave me a technology update and a peek into the future of its portfolio for big data, information management and business analytics at its annual technology influencer summit. The company continues to innovate and build upon its Teradata 14 releases and its new processing technology. Since my last analysis of Teradata’s big data strategy, it has embraced technologies like Hadoop with its Teradata Aster Appliance, which won our 2012 Technology Innovation Award in Big Data. Teradata is steadily extending beyond providing just big data technology to offer a range of analytic options and appliances through advances in Teradata Aster and its overall data and analytic architectures. One example is its data warehouse appliance business, which according to our benchmark research is one of the key technological approaches to big data; as well Teradata has advanced support with its own technology offering for in-memory databases, specialized databases and Hadoop in one integrated architecture. It is taking an enterprise management approach to these technologies through Teradata Viewpoint, which helps monitor and manage systems and support a more distributed computing architecture.
Topics: Big Data, MicroStrategy, SAS, Tableau, Teradata, Customer Excellence, Operational Performance, Analytics, Business Analytics, Business Intelligence, CIO, Cloud Computing, Customer & Contact Center, In-Memory Computing, Information Applications, Information Management, Location Intelligence, Operational Intelligence, CMO, Discovery, Intelligent Memory, Teradata Aster, Strata+Hadoop