Teradata recently held its annual Partners conference, at which gather several thousand customers and partners from around the world. This was the first Partners event since Vic Lund was appointed president and CEO in May. Year on year, Teradata’s revenues are down about 5 percent, which likely prompted some changes at the company. Over the past few years Teradata made several technology acquisitions and perhaps spread its resources too thin. At the event, Lund committed the company to a focus on customers, which was a significant part of Teradata’s success in the past. This commitment was well received by customers I spoke with at the event.
Predictive analytics is a rewarding yet challenging subject. In our benchmark research on next-generation predictive analytics at least half the participants reported that predictive analytics allows them to achieve competitive advantage (57%) and create new revenue opportunities (50%). Yet even more participants said that users of predictive analytics don’t have enough skills training to produce their own analyses (79%) and don’t understand the mathematics involved (66%). (In the term “predictive analytics” I include all types of data science, not just one particular type of analysis.)
Qlik helped pioneer the visual discovery market with its QlikView product. In some respects, Qlik and its competitors also spawned the self-service trend rippling through the analytics market today. Their aim was to enable business users to perform analytics for themselves rather than building a product with the perfect set of features for IT. After establishing success with end users the company began to address more of the concerns of IT, eventually creating a robust enterprise-grade analytics platform. This approach has worked for Qlik, driving growth that led to an initial public offering in 2010. The company now generates more than half a billion dollars in revenue annually, making it one of the largest independent analytics vendors. Of which based on their company and products was rated a Hot Vendor in our 2015 Value Index on Analytics and Business Intelligence and one of the highest ranked in usability.
Throughout the course of our research in 2016, we’ll be exploring ways in which organizations can maximize the value of their data. Ventana Research believes that analytics is the engine and data is the fuel to power better business decisions. Several themes emerged from our benchmark research on incorporating data and analytics into organizational processes, and we will follow them in our 2016 Business Analytics Research Agenda:
Topics: Big Data, Predictive Analytics, Analytics, Business Analytics, Business Collaboration, Business Intelligence, Cloud Computing, Information Applications, Information Management, Operational Intelligence
I want to share my observations from the recent annual SAS analyst briefing. SAS is a huge software company with a unique culture and a history of success. Being privately held SAS is not required to make the same financial disclosures as publicly held organizations, it released enough information to suggest another successful year, with more than $2.7 billion in revenue and 10 percent growth in its core analytics and data management businesses. Smaller segments showed even higher growth rates. With only selective information disclosed, it’s hard to dissect the numbers to spot specific areas of weakness, but the top-line figures suggest SAS is in good health.
In our definition, information management encompasses the acquisition, organization, dissemination and use of information by organizations to create and enhance business value. Effective information management ensures optimal access, relevance, timeliness, quality and security of this data with the aim to improve organizational performance. This goal is not easily met, especially as organizations acquire ever more data at an ever faster pace. In our business analytics benchmark research of more than 2,600 organizations, almost half (45%) have to integrate six or more types of data in their analyses. More than two-thirds reported that they spend more time preparing data than analyzing it. To assist in dealing with these sorts of issues and others, we’ve laid out an ambitious information management research agenda for 2012.
Topics: Data Quality, Master Data Management, Social Media, Analytics, Business Analytics, Business Intelligence, Cloud Computing, Complex Event Processing, Data Governance, Data Integration, Information Applications, Information Life Cycle Management, Information Management, IT Performance Management, Operational Intelligence
I recently attended Teradata’s Third-Party Influencers Meeting (Twitter Hashtag #TD3PI) in Las Vegas where the company updated industry analysts and consultants on upcoming product plans and the status of two product lines it added via acquisition in the past year. Randy Lea, Teradata’s VP of product management and marketing, provided a company overview recapping highlights and defining the corporate strategy building on its work in 2010 that my colleague assessed. Teradata focuses on three markets: its core business of data warehousing, which it identified as a $27 billion market, the $15 billion business applications market represented by its Aprimo acquisition, and the big-data analytics market, a $2 billion market addressed in part by its Aster Data acquisition. I mention Teradata’s estimates of market size as they may indicate the emphasis and investment the company will make in each segment. Lea referred to Teradata’s independence from NCR, for almost four years, as a good thing. Independence, he said, has allowed Teradata to focus, to release products more frequently and to acquire companies that enhance their market position. Judging by the performance of its stock (NYSE: TDC), the financial markets seem to agree independence has been good – even with the recent market downturn its stock is up 75% in the last year.
Topics: Predictive Analytics, Sales Performance, Social Media, Supply Chain Performance, Business Analytics, Business Intelligence, Business Performance, Cloud Computing, Customer & Contact Center, Financial Performance, Workforce Performance
Last week I attended the IBM Big Data Symposium at the Watson Research Center in Yorktown Heights, N.Y. The event was held in the auditorium where the recent Jeopardy shows featuring the computer called Watson took place and which still features the set used for the show – a fitting environment for IBM to put on another sort of “show” involving fast processing of lots of data. The same technology featured prominently in IBM’s big-data message, and the event was an orchestrated presentation more like a TV show than a news conference. Although it announced very little news at the event, IBM did make one very important statement: The company will not produce its own distribution of Hadoop, the open source distributed computing technology that enables organizations to process very large amounts of data quickly. Instead it will rely on and throw its weight behind the Apache Hadoop project – a stark contrast to EMC’s decision to do exactly that, announced earlier in the week. As an indication of IBM’s approach, Anant Jhingran, vice president and CTO for information management, commented, “We have got to avoid forking. It’s a death knell for emerging capabilities.”
Topics: Big Data, BigData, BigInsights, BigSheets, EMC, Analytics, Apache Hadoop, Business Analytics, Business Intelligence, Cloud Computing, Cloudera, Customer & Contact Center, Greenplum, Hadoop, IBM, Information Applications, Information Management, InfoSphere, IT Performance Management, Location Intelligence, Operational Intelligence
Earlier this week EMC announced it will create its own distribution for Apache Hadoop. Hadoop provides distributed computing capabilities that enable organizations to process very large amounts of data quickly. As I have written previously, the Hadoop market continues to grow and evolve. In fact, the rate of change may be accelerating. Let’s start with what EMC announced and then I’ll address what the announcement means for the market.
Topics: Aster Data, Big Data, BigData, EMC, Social Media, Teradata, Apache Hadoop, Business Analytics, Business Collaboration, Business Intelligence, Cloud Computing, Cloudera, Customer & Contact Center, Greenplum, Hadoop, Information Management
In various forms, business intelligence (BI) – as queries, reporting, dashboards and online analytical processing (OLAP) – is being used increasingly widely. And as basic BI capabilities spread to more organizations, innovative ones increasingly are exploring how to take advantage of the next step in the strategic use of BI: predictive analytics. The trend in Web searches for the phrase “predictive analytics” gives one indication of the rise in interest in this area. From 2004 through 2008, the number of Web search was relatively steady. Beginning in 2009, the number of searches rose significantly and has continued to rise.
Topics: Predictive Analytics, Predixion, R, Revolution Analtyics, Sales Performance, SAS, Social Media, Supply Chain Performance, Analytics, Angoss, Business Analytics, Business Collaboration, Business Intelligence, Business Mobility, Business Performance, Business Technology, Chief Information Officer, Cloud Computing, Customer & Contact Center, Financial Performance, IBM SPSS, Information Builders, Information Technology, KXEN, Netezza, Oracle, Tibco Spotfire, Workforce Performance