Posts from Jane Zupan

Machine Learning-based Relevancy

Search engines index millions of pieces of information, structured and unstructured. But simply indexing information isn’t enough to give a user the results they need when they perform a search.

The Need for Relevancy

The goal of relevancy tuning is to help a user get the best results for a given query they are trying to run. Relevance is telling the search engine how to best sort the information in its index to ensure search results match search queries as closely as possible. It’s the process of bringing the most relevant information to the top of the result list.

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All About Machine Learning in Cognitive Search

Recent research shows that over 66% of employees are dependent on search in their daily work. But there’s a problem. Forty-one percent are frustrated with their existing search application.

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3 Questions for Matt Maccaux of Dell EMC

Matt Maccaux leads Dell EMC’s global Big Data Practice and he’s one of the featured speakers in our upcoming 2/22 webinar, Building a Modern Data Architecture.

We recently caught up with Matt and asked him 3 questions about his work in Big Data Services.

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3 Questions for Mike Moreno of Cloudera

Mike Moreno is the Sr. Partner Marketing Manager at Cloudera, and he’s one of the featured speakers in our upcoming 1/31 webinar with Forrester Vice President and Principal Analyst, Brian Hopkins.

We recently had a chance to catch up with him and get his take on the latest trends & happenings in Big Data & Hadoop.

Attivio: What challenges are you seeing among Cloudera clients?

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What is the Point of a Modern Data Architecture?

Good question. What is the point? The point is to create measurable business value from enterprise data. Of course, before measurable business value comes insight. The Modern Data Architecture (MDA) recognizes that insight can lie hidden in data of all types—structured or unstructured, messy or modeled, historical or realtime.

 

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Have You Thanked Your Data Steward Today?

The other day I Googled, “the problem with a modern data architecture.” Of course, at Attivio we’re big evangelists for an MDA, but it’s always interesting to see what the contrarians have to say. There were over three million returns, but none on the first two pages said a word about problems. Lots of articles about how to develop an MDA or how to optimize an MDA or why you had to have an MDA. You get the picture.

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4 Big Data Predictions for 2017

If you’ve been following the Attivio blog, you’ll know that we have not been shy about sharing our ideas on what we see in the Big Data market right now. But in this post, our CEO, Stephen Baker, shares our predictions for the industry in 2017.

Time will tell how accurate these predictions are, but these are the trends we see bubbling up as we talk to customers, analysts, and others in the ecosystem. 

  

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The Modern Data Architecture in Financial Services

An entire ecosystem of tools and data processing frameworks have grown up around Hadoop. Almost as soon as someone identifies a weakness or limitation—and there have been more than a few—someone else creates a fix. That's one of the reasons the Big Data ecosystem is so complex. And why many large companies hung back before jumping in. They wanted to see if any leaders would emerge from the chaos.

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CTO Will Johnson on Attivio Past, Present, and Future

The search market has come a long way from 2007 when Attivio was founded. We took some time to chat with Attivio co-founder and CTO Will Johnson about just how far Attivio has come and how the search market itself has evolved.

Attivio CTO Will Johnson

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The Missing Link between Big Data and Business Intelligence: Self-Service Data Discovery

If your organization is going to win on analytics, it needs to view all of its information as a strategic enterprise asset. This includes not just the 10% you know about, but the 90% of dark data that hides in information silos. There are big challenges on the path to surfacing all of your enterprise information for business intelligence. The biggest challenge is not in storing data, or in analyzing it, but actually finding the right data. But why is it so hard? Here are the top three reasons:

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