I recently spent time at Strata+Hadoop World 2016 in New York. I attended this event and its predecessor, Hadoop World, off and on for the past six years. This one in New York had a different feel from previous events including the most recent event in San Jose at the end of March. Perhaps because of its location in one of the financial and commercial hubs of the world, the event had much more of a business orientation. But it’s not just location. Past events have been held in New York also, and I see the business focus as a sign of the Hadoop market maturing.
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
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
Some followers of Ventana Research may recall my work here several years ago. Here and elsewhere I have spent most of my career in the data and analytics markets matching user requirements with technologies to meet those needs. I’m happy to be returning to Ventana Research to resume investigating ways in which organizations can make the most of their data to improve their business processes; for a first look, please see our 2016 research agenda on Big Data and Information Optimization. I relish the opportunity to conduct primary market research in the form of Ventana’s well-known benchmark research and to help end users and vendors apply the information collected in those studies.
Topics: Big Data, Predictive Analytics, Analytics, Business Analytics, Business Intelligence, Information Management, Internet of Things, Operational Intelligence, Uncategorized, Unicorns, IT Performance Management (ITPM)
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
As a technology, predictive analytics has existed for years, but adoption has not been widespread among businesses. In our recent benchmark research on business analytics among more than 2,600 organizations, predictive analytics ranked only 10th among technologies they use to generate analytics, and only one in eight of those companies use it. Predictive analytics has been costly to acquire, and while enterprises in a few vertical industries and specific lines of business have been willing to invest large sums in it, they constitute only a fraction of the organizations that could benefit from them. Ventana Research has just completed a benchmark research project to learn about how the organizations that have adopted predictive analytics are using it and to acquire real-world information about their levels of maturity, trends and best practices. In this post I want to share some of the key findings from our research.
Topics: Data Scientist, Predictive Analytics, Sales Performance, Social Media, Supply Chain Performance, Analytics, Business Analytics, Business Intelligence, Customer & Contact Center, Workforce Performance, IT Performance Management (ITPM)
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.
For most people involved with business intelligence (BI), these are exciting times. Using BI to improve business processes continues to motivate organizations to invest in BI. The focus on BI also empowers business analytics and can be rented in the cloud computing model of accessing software. New technologies are adding dimensions to BI and creating both excitement and confusion for enterprises implementing them. We offer a variety of accomplished research that can help organizations overcome the hype and understand how to use these technologies to improve business decision-making, and we’re planning new research in 2012 on these topics.
Topics: Mobile Business Intelligence, Predictive Analytics, Sales Performance, Social Media, Supply Chain Performance, Business Analytics, Business Intelligence, Business Performance, Collaboration, Customer & Contact Center, Financial Performance, Information Management, Operational Intelligence, Workforce Performance
Revolution Analytics recently announced the winners of its “Applications of R in Business” contest. Revolution Analytics has built a business around supporting R, an open source statistical software package, and extending it with features it licenses to customers. I served as a judge in the contest. Since I was in the midst of analyzing the data for our predictive analytics benchmark research, I was interested to see how the contestants applied predictive analytics techniques to specific business problems.
Topics: Predictive Analytics, Sales Performance, Social Media, Supply Chain Performance, Business Analytics, Business Intelligence, Business Performance, Customer & Contact Center, Financial Performance, Workforce Performance
When it comes to technology, debates about whether a particular name suits its category are rampant. Here is a link to one such argument about the term “big data” from Curt Monash, an analyst whom I respect a great deal. This debate rages in the Twittersphere also, as in this comment from Neil Raden, another analyst I respect, suggesting that “big data is a marketing term … imprecise by design.” Another term I’ve encountered resistance to recently is “predictive analytics.” See: (“Revolution Analytics Hosts Contest on Business Predicting the Future“).
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