The Gartner BI & Analytics Conference is next week. It is one of the most well-attended events in the big data calendar each year with some great presentations and an all-star cast of analyst and analytics vendors.
As we have written before, we see the big data ecosystem as having three parts: storage, analytics and visualization. It is our opinion that analytics is under-innovated and under-appreciated relative to the other components – something that can be determined by looking at the number of companies that have gone public in storage and visualization compared to the number of companies that have or are slated to go public in analytics (but to be fair, increasing #s are being bought).
The market has joined our opinion over the last 12 months. While we noted this trend last year in some detail, we wanted to follow with a new data point. When users register, they express their primary areas of interest. Analytics leads BI this year – as it did last year. What is more interesting is that if you combine all the storage categories they still don’t make this list. Storage is a solved problem from a technology perspective. Sure there are implementation challenges (Hortonworks stock chart is evidence of that) but from a technical perspective, everyone from IT to LOB understands the game.
To see this from another perspective, take a look at Gartner’s slides on what the attendees are looking to spend on in the coming year. Analytics tops the list and again and it’s not just run of the mill analytics but hardcore, predictive analytics. This underscores what smart people like Gartner’s own Svetlana Sicular has been saying – you need better analytics to make the investments in storage and visualization pay off.
Better analytics doesn’t mean the same old hypothesis driven approach. That approach is broken. It doesn’t scale. It can’t deal with complexity.
Machine Learning approaches are a big step forward, but they too depend on knowing the right questions to ask, depend on applying the right ML algorithm on the right part of the data set – something complexity precludes.
All of this has us excited to get to Dallas and start demoing. For those who are going – we can’t wait to see you there.
We will be in booth 620, hosting a private breakfast on Tuesday for healthcare execs and have our collaborator/customer Adam Ferguson of UCSF doing a case study at 9:45 on Tuesday. Want to meet with us there? Drop us a line at brand@ayasdi.com.
The Gartner BI & Analytics Conference is next week. It is one of the most well-attended events in the big data calendar each year with some great presentations and an all-star cast of analyst and analytics vendors.
As we have written before, we see the big data ecosystem as having three parts: storage, analytics and visualization. It is our opinion that analytics is under-innovated and under-appreciated relative to the other components – something that can be determined by looking at the number of companies that have gone public in storage and visualization compared to the number of companies that have or are slated to go public in analytics (but to be fair, increasing #s are being bought).
The market has joined our opinion over the last 12 months. While we noted this trend last year in some detail, we wanted to follow with a new data point. When users register, they express their primary areas of interest. Analytics leads BI this year – as it did last year. What is more interesting is that if you combine all the storage categories they still don’t make this list. Storage is a solved problem from a technology perspective. Sure there are implementation challenges (Hortonworks stock chart is evidence of that) but from a technical perspective, everyone from IT to LOB understands the game.
To see this from another perspective, take a look at Gartner’s slides on what the attendees are looking to spend on in the coming year. Analytics tops the list and again and it’s not just run of the mill analytics but hardcore, predictive analytics. This underscores what smart people like Gartner’s own Svetlana Sicular has been saying – you need better analytics to make the investments in storage and visualization pay off.
Better analytics doesn’t mean the same old hypothesis driven approach. That approach is broken. It doesn’t scale. It can’t deal with complexity.
Machine Learning approaches are a big step forward, but they too depend on knowing the right questions to ask, depend on applying the right ML algorithm on the right part of the data set – something complexity precludes.
All of this has us excited to get to Dallas and start demoing. For those who are going – we can’t wait to see you there.
We will be in booth 620, hosting a private breakfast on Tuesday for healthcare execs and have our collaborator/customer Adam Ferguson of UCSF doing a case study at 9:45 on Tuesday. Want to meet with us there? Drop us a line at brand@ayasdi.com.
