In 2017 Strata + Hadoop World was changed to theStrata Data Conference. As I pointed out in mycoverage of last year’s event, the focus was largely on machine learning and artificial intelligence (AI). That theme continued this year, but my impression of the event was of a community looking to get value out of data regardless of the technology being used to manage that data. The change was subtle: The location was the same; the exhibitors were largely the same; attendance was similar this year...
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Topics:
Big Data,
Data Science,
Machine Learning,
Analytics,
Business Intelligence,
Data Governance,
Data Integration,
Data Preparation,
Information Optimization,
Digital Technology,
Machine Learning and Cognitive Computing
All too often, software vendors view analytics as the end rather than the beginning of a process. I’m reminded of some of the advanced math classes I’ve taken in which the teaching process focused on a few key aspects of a mathematical proof or solution, leaving the rest of the exercise to be worked out by the students. In other contexts, you may hear people say the numbers speak for themselves.
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Topics:
Data Science,
Machine Learning,
Analytics,
Business Intelligence,
Collaboration,
Data Governance,
Information Optimization,
Digital Technology,
collaboration for business
We at Ventana Researchrecently published our research agendas for 2018. Analytics and business intelligence are evolving and so is our research on their use across practice areas. Earlier research has shown that analytics can deliver significant value to organizations; for example, ourpredictive analytics research shows that 57 percent of organizations reported achieving a competitive advantage and half created new revenue opportunities with predictive analytics. Waves of investment in...
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Topics:
Machine Learning,
Analytics,
Business Intelligence,
Collaboration,
Internet of Things,
IOT,
Artificial intelligence,
natural language processing,
Natural Language Query
Ventana Research recently published the findings of ourbenchmark research on Data Preparation, which examines the practices organizations use to accomplish data preparation. We view data preparation as a sequence of steps: identifying, locating and then accessing the data; aggregating data from different sources; and enriching, transforming and cleaning it to create a single uniform data set. Using data to accomplish organizational goals requires that it be prepared for use; to do this job...
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Topics:
Big Data,
Analytics,
Data Preparation
I recently attended SAP TechEd in Las Vegas to hear the latest from the company regarding its analytics and business intelligence offerings as well as its data management platform. The company used the event to launchSAP Data Hub and made several other data and analytics announcements that I’ll cover below.
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Topics:
Big Data,
SAP,
Machine Learning,
Analytics,
Data Preparation
The Strata Data Conference is changing and it’s changing in a good way. At the recent Strata Data Conference in New York, Mike Olson, chief strategy officer at Cloudera, which co-sponsored the event, commented that at prior events we used to talk about the “Hadoop zoo animals,” meaning the various components of the Hadoop ecosystem of which I have written previously. Following last fall’s Strata event, I observed that the conference was evolving to focus on the use of data. Advancing that...
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Topics:
Big Data,
Machine Learning,
Analytics,
Hadoop,
Artificial intelligence,
Strata Data Conference
There’s been some speculation in the market that Hadoop may be disappearing. Some of this speculation has been driven by vendors that have recently downplayed Hadoop in their marketing efforts. For example, the Strata+Hadoop World conference is now known as the Strata Data Conference. The Hadoop Summit is now known as the Dataworks Summit. In Cloudera’s S-1 filing with the SEC for its initial public offering, the term “Hadoop” appears only 14 times, while the term “machine learning” appears 83...
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Topics:
Big Data,
Analytics,
Hadoop,
data lakes
Recently Hortonworksannounced some significant additions to its products at the DataWorks Summit. These additions reflect the fact that the big data market continues to evolve, as I have previouslywritten.
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Topics:
Big Data,
Machine Learning,
Analytics,
Hadoop
Natural language generation (NLG), the process of generating text or narratives based on a set of data values, can reach a broader audience. NLG narratives can be used for a variety of purposes, but in this perspective I focus on how NLG can be used to enhance business intelligence (BI) processes. In the case of BI, NLG can be used to explain what has happened and why it is happening, and even what actions to take. The NLG narratives can be understood by a broader range of business users than...
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Topics:
Machine Learning,
Natural Language,
Analytics,
Business Intelligence,
NLG
Many organizations continue to struggle with preparing data for use in operational and analytical processes. We see these issues reported in our Data and Analytics in the Cloud benchmark research, where 55 percent of organizations identify data preparation as the most time-consuming task in their analytical processes. Similarly, in our Next-Generation Predictive Analytics research, 62 percent of companies report that they’re unsatisfied because data needed for access or integration is not...
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Topics:
Big Data,
Analytics,
Collaboration,
Data Preparation,
Datawatch