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.
It has been more than five years since James Dixon of Pentaho coined the term “data lake.” His original post suggests, “If you think of a data mart as a store of bottled water – cleansed and packaged and structured for easy consumption – the data lake is a large body of water in a more natural state.” The analogy is a simple one, but in my experience talking with many end users there is still mystery surrounding the concept. In this post I’d like to clarify what a data lake is, review the reasons an organization might consider using one and the challenges they present, and outline some developments in software tools that support data lakes.
Topics: Big Data, Data Science, Predictive Analytics, Social Media, Business Analytics, Business Intelligence, Data Governance, Data Lake, Governance, Risk & Compliance (GRC), Information Management, Uncategorized, Strata+Hadoop
On Monday, March 21, Informatica, a vendor of information management software, announced Big Data Management version 10.1. My colleague Mark Smith covered the introduction of v. 10.0 late last year, along with Informatica’s expansion from data integration to broader data management. Informatica’s Big Data Management 10.1 release offers new capabilities, including for the hot topic of self-service data preparation for Hadoop, which Informatica is calling Intelligent Data Lake. The term “data lake” describes large collections of detailed data from across an organization, often stored in Hadoop. With this release Informatica seeks to add more enterprise capabilities to data lake implementations.
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)
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.
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)
SAP has launched its Enterprise Information Management (EIM) 4.0 release as part of its “Run Better Tour.” It includes a broad range of information management components spanning data integration, data quality, data profiling, metadata management and more. The launch was done in conjunction with SAP Business Intelligence (BI) 4.0, which got much bigger billing at the event –to the point where one might call this a stealth marketing campaign. However, the event did identify three themes intended to highlight EIM capabilities: event insight, trusted data and text processing. The goal here was to communicate the integration SAP has achieved within and between its BI and EIM products. IBM announced a similar advance with its InfoSphere products and Informatica has also invested heavily in integrating its information management products. Our Information Management benchmark research validates this approach, finding that incompatible tools create a significant obstacle to organizations’ quest for consistent sets of data.
Topics: Data Quality, SAP, Social Media, Analytics, Business Analytics, Business Intelligence, Business Technology, CIO, Complex Event Processing, Data Governance, Data Integration, Information Management, Information Technology, Operational Intelligence, IT Performance Management (ITPM)