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.
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.
I recently attended the SAS Analyst Summit in Steamboat Springs, Colo. (Twitter Hashtag #SASSB) The event offers an occasion for the company to discuss its direction and to assess its strengths and potential weaknesses. SAS is privately held, so customers and prospects cannot subject its performance to the same level of scrutiny as public companies, and thus events like this one provide a valuable source of additional information.
Topics: Big Data, Predictive Analytics, SAS, Analytics, Business Analytics, Business Collaboration, Business Intelligence, Business Performance, Information Applications, Information Management, Uncategorized, Visualization
Last week I attended Spark Summit East 2016 at the New York Hilton Midtown. It revealed several ways in which Spark technology might impact the big data market.
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
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.
In a move that may indicate the beginning of a new wave of activity in the business intelligence (BI) market, Oracle has announced its intention to acquire Endeca. Founded in 1999, Endeca originally focused on search capabilities for online commerce. Users selected a product attribute, and the software automatically revised the remaining selection criteria based on products matching the previous selection. We have been covering Endeca as part of the BI and information applications marketing. For instance, if the products only come in one color, the color attribute would be removed from the selection criteria and possibly replaced by other relevant criteria. Most of us take this behavior for granted as it has been adopted or imitated by many e-commerce sites and other Web properties.
Topics: Predictive Analytics, Sales Performance, Social Media, Supply Chain Performance, Business Analytics, Business Intelligence, Business Performance, Customer & Contact Center, Financial Performance, Information Applications, Workforce Performance
About 30 years ago, perhaps on this very day, I was sitting in front of an Apple II working on a VisiCalc spreadsheet. At the time, I don’t think I even knew who Steve Jobs was. I wasn’t in the software industry yet. I was working for a public accounting firm. The Apple II sat in a corner of the office “typing pool.” For those of you who don’t know what a typing pool was, there was no swimming involved – it was a group of full-time employees with dedicated equipment who did all the typing and word processing tasks of the office.
Topics: Mobile, Sales Performance, Social Media, Supply Chain Performance, Sustainability, Business Analytics, Business Collaboration, Business Intelligence, Business Mobility, Business Performance, Customer & Contact Center, Financial Performance, Governance, Risk & Compliance (GRC), Information Applications, Information Management, Location Intelligence, Operational Intelligence, Visualization, IT Performance Management (ITPM)