The business intelligence market is bounded on one side by big data and on the other side by data preparation. That is, to maximize their performance in using information, organizations have to collect and analyze ever increasing volumes of data while the tools available are constantly evolving in the big data ecosystem that I have written about. In our benchmark research on big data analytics, half (51%) of organizations said they want to access big data using their existing BI tools. At the same time, as I have noted, end users are demanding self-service access to data preparation capabilities to facilitate their analyses.
IBM recently held its inaugural World of Watson event. Formerly known as IBM Insight, and prior to that IBM Information on Demand, the annual event, attended by 17,000 people this year, showcases IBM’s data and analytics and the broader IBM efforts in cognitive computing. The theme for the event, as you might guess, was the Watson family of cognitive computing products. I, for one, was glad to spend more time getting to know the Watson product line, and I’d like to share some of my observations from the event.
Data virtualization is not new, but it has changed over the years. The term describes a process of combining data on the fly from multiple sources rather than copying that data into a common repository such as a data warehouse or a data lake, which I have written about. There are many reasons for an organization concerned with managing its data to consider data virtualization, most stemming from the fact that the data does not have to be copied to a new location. It could, for instance, eliminate the cost of building and maintaining a copy of one of the organization’s big data sources. Recognizing these benefits, many database and data integration companies offer data virtualization products. Denodo, one of the few independent, best-of-breed vendors in this market today, brings these capabilities to big data sources and data lakes.
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, Operational Intelligence, IT Performance Management (ITPM)
My colleague Mark Smith and I recently attended data integration vendor Informatica’s annual industry analyst event. The company offered some impressive numbers regarding growth and profitability over the years, with 30 consecutive quarters of growth even during the recent recession. Through acquisition and its own research and development activities Informatica now has a broad portfolio of products. It includes data integration and supporting migration, replication and synchronization needs, master data management, complex event processing and other elements of the information management spectrum. As at last year’s event, the company retains a sharp focus on the data integration related portfolio, and its product roadmap addresses four key themes impacting that market: big data, cloud computing, social media and mobile technology. We also see these themes as significant technology trends, and our approach is outlined in our 2012 research agendas for information management and in the larger business technology innovation agenda. Thus it was interesting to hear Informatica’s take on them.
Topics: Big Data, Sales Performance, Social Media, Supply Chain Performance, Business Analytics, Business Intelligence, Business Performance, Cloud Computing, Customer & Contact Center, Data Integration, Financial Performance, Informatica, Information Management, Workforce Performance
Talend recently announced version 5 of its information management platform, which emphasizes unifying its various components. Through a combination of development activities, acquisitions and partnerships, Talend has been steadily building its portfolio of information management capabilities. In addition to its core data integration capabilities, it has added data quality, master data management, application integration and with this release business process management (BPM).
Topics: Big Data, Data Quality, Master Data Management, Talend, Business Analytics, Cloud Computing, Data Governance, Data Integration, Governance, Risk & Compliance (GRC), Information Applications, Information Management, Strata+Hadoop
Kalido recently introduced version 9 of its Information Engine product. The company has been around for 10 years but has had difficulty establishing its identity in the information management market. Kalido was perhaps ahead of its time, partly a vendor of data integration, partly master data management and partly data governance. As an example of the positioning challenge, its core product, Information Engine, while not a data integration tool, could in some cases provide sufficient capabilities to meet an organization’s data integration needs. Its real value, however, comes from authoring and management of information about the user’s data warehouse.
Informatica recently introduced HParser, an expansion of its capabilities for working with Hadoop data sources. Beginning with Version 9.1, introduced earlier this year, Informatica’s flagship product has been able to access data stored in HDFS as either a source or a target for information management processes. However, it could not manipulate or transform the data within the Hadoop environment. With this announcement, Informatica starts to bring its data transformation capabilities to Hadoop.
Topics: Big Data, MapReduce, Sales Performance, Social Media, Supply Chain Performance, Business Analytics, Business Performance, Customer & Contact Center, Data Integration, Financial Performance, Information Management, Workforce Performance, Strata+Hadoop
Informatica has announced version 9.1 for Big Data. I wrote previously about Informatica 9.1,the latest iteration of the company’s data integration platform, following its industry analyst summit. At that event in February, the company officials alluded to future plans regarding Hadoop and other big-data sources yet to be finalized. This announcement reveals those plans. Informatica will support three types of “big data”: big transaction data from relational databases and data warehouse system, big interaction data from social media, customer interaction systems and other systems, and big data processing, which means Hadoop, the open source software framework. Let’s look at each of these types.
Topics: Big Data, MapReduce, Social Media, Supply Chain Performance, Business Collaboration, Business Mobility, Business Performance, Customer & Contact Center, Data Integration, Informatica, Strata+Hadoop
The information management (IM) technology market is undergoing a revolution similar to the one in the business intelligence (BI) market. We define information management as the acquisition, organization, control and use of information to create and enhance business value. It is a necessary ingredient of successful BI implementations, and while some vendors such as IBM, Information Builders, Pentaho and SAP are in addition integrating their BI and IM offerings, each discipline involves different aspects of the use of information and will require it sometimes integrated and sometimes separate.
Topics: Data Quality, Social Media, Analytics, Business Analytics, Business Collaboration, Business Intelligence, Business Technology, CIO, Complex Event Processing, Data Governance, Data Integration, Information Management, Information Technology, Operational Intelligence, IT Performance Management (ITPM)