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: Analytics, Big Data, Business Analytics, Business Performance Management (BPM), CIO, Customer Performance Management (CPM), Data Management, Event Stream, Information Applications (IA), Information Management, Information Management (IM), Location Intelligence, Operational Intelligence, Operational Performance Management (OPM), Predictive Analytics, SAS, Business 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: Analytics, Big Data, Business Analytics, Business Collaboration, Business Performance Management (BPM), CIO, Cloud Computing, Cognitive Computing, Customer Performance Management (CPM), Discovery, Exploration, Financial Performance Management (FPM), Governance, Risk & Compliance (GRC), IBM Watson, Information Applications (IA), Information Management (IM), IT Performance Management (ITPM), Location Intelligence, Operational Intelligence, Operational Performance Management (OPM), Sales Performance Management (SPM), Social Media, Supply Chain Performance Management (SCPM), Workforce Performance Management (WPM), Business Intelligence
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: Analytics, Big Data, Business Performance Management (BPM), CEP, Customer Performance Management (CPM), Datawatch, Discovery, Financial Performance Management (FPM), GRC, Information Applications (IA), Information Management, Information Management (IM), Information Optimization, Operational Intelligence, Operational Performance Management (OPM), Panopticon, Sales Performance Management (SPM), SAP, SAP HANA, Supply Chain Performance Management (SCPM), Office of Finance, Business Intelligence
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, Business Analytics, CIO, Cloud Computing, CMO, Customer Excellence, Customer Performance Management (CPM), Discovery, In-Memory Computing, Information Applications (IA), Information Management (IM), Intelligent Memory, Location Intelligence, MicroStrategy, Operational Intelligence, Operational Performance Management (OPM), SAS, Tableau, Teradata, Teradata Aster, Strata+Hadoop, Analytics, Business Intelligence
Business intelligence software is supposed to help businesses access and analyze data and communicate analytics and metrics. I have witnessed improvements to BI software over the years, from mobile and collaboration to interactive discovery and visualization, and our Value Index for Business Intelligence finds a mature set of technology vendors and products. But even as these products mature in capabilities, the majority lack features that would make them easy to use. Our recent research on next-generation business intelligence found that usability is the most important evaluation criteria for BI technology, outpacing functionality (49%) and even manageability (47%). The pathetic state of dashboards and the stupidity of KPI illustrate some of the obvious ways the software needs to improve for businesses to gain the most value from it. We need smarter business intelligence, and that means not just more advanced sets of capabilities that are designed for the analysts, but software designed for those who need to use BI information.
Topics: Big Data, Business Analytics, Business Collaboration, Business Intelligence, Business Mobility, Business Performance Management (BPM), Cloud Computing, Customer Performance Management (CPM), Discovery, Governance, Risk & Compliance (GRC), Information Management (IM), Location Intelligence, Mobile Technology, Natural Language, Operational Intelligence, Sales Performance Management (SPM), Supply Chain Performance Management (SCPM), Workforce Performance Management (WPM)