Less than a week apart, Google announced the acquisition of Looker and Salesforce announced the acquisition of Tableau. Both large companies have begun to realize they’ve never really addressed the need for analytics. They’re great at saving and organizing data, but neither has a suite worth anything to provide easier to use dashboards and analytics that are key component of business intelligence.
I have two brief articles on forbes.com discussing the announcements.
My annual (I’ve done it twice now, so that now fits) has been posted. I review some of the key stories I covered in 2018 and make some basic predictions for what will happen in the near-term future. Hint: Aritifical intelligence and machine learning are still early in the adoption lifecycle, but cloud technologies mean faster adoption curves than in the past.
There are a couple of recent examples of provider fraud in healthcare. One came from a conversation I had with Alteryx at the Tableau conference. The result of my investigation is my latest forbes.com article.
ThoughSpot is an analytics company and they made a number of announcements at their conference this week. One that caught my eye was about SearchIQ, a new product for natural language processing (NLP). It layers text and voice query on top of their platform. Read more in my latest article on forbes.com.
I’ve published another article in my Management AI series that’s a subset of my forbes.com column. This one is a high level introduction to natural language processing (NLP) and natural language generation (NLG).
Data discovery, as we defined it in the 1990s, has changed to take advantage of modern algorithms. It’s moved past basic data analysis and is better at finding relationships between different pieces of information. Discussed in detail in my forbes.com article.
Here’s an article on Forbes about Tableau buying Empirical Systems.
I’m playing with alternating between the publishing page and blog entries and am not sure which I like better.
Which businesses really need a data scientist? https://bit.ly/2JInbhj
My focus on business intelligence the last few years, my long term interest in artificial intelligence and the growth of machine learning came together to drive the content for my latest Forbes article.
It’s been a while since I watched a webinar, but since business intelligence (BI) and (AI) are overlapping areas of interest, I watched Tuesday’s TDWI webinar on Machine Learning (ML). As the definition of machine learning expands out of the pure AI because of BI’s advanced analytics, it’s interesting to see where people are going with the subject.
The host was Fern Halper, VP Research, at TDWI. The guests were:
Ms. Halper began with a short presentation including her definition of ML as “Enabling computers to learn patterns with minimal human intervention.” It’s a bit different than the last time I reviewed one of her webinars, but that’s ok because my definition is also evolving. I’ve decided to use my own definition, “Technology that can investigate data in an environment of uncertainty and make decisions or provide predictions that inform the actions of the machine or people in that environment.” Note that I differ from my past, purist, view, of the machine learning and adjusting algorithms. I’ve done so because we have to adapt to the market. As BI analytics have advanced to provide great insight in data discovery, predictive analytics and more, many areas of BI and the purist area of learning have overlapped. Learning patterns can happen through pure statistical analysis and through self-adaptive algorithms in AI based machines.
The most interesting part of Fern Halper’s segment was a great chart showing the results of a survey asking about the importance of different business drivers behind ML initiatives. What makes the chart interesting, as you can see, is that it splits results between those groups investigating ML and those who are actively using it.
What her research shows is that while the highest segments for the active categories are customer related, once companies have seen the benefits of ML, the advantages of it for almost all the other areas jump significantly over views held during the investigation phase.
A panel discussion then proceeded, with Ms. Halper asking what sounded like pre-discussed questions (especially considering the included and relevant slides) to the three panelists. The statements by the two SAP folks weren’t bad, they were just very standard and lacked any strong differentiators. SAP is clearly building an architecture to leverage ML using their environment, but there weren’t case studies and I felt the integration between the SAP pieces didn’t bubble up to the business level.
The reason to listen to this segment is Mr. Gualtieri. He was very clear and focused on his message. While I quibble with some of the things he said about data scientists, that soap box isn’t for here. He gave a nice overview of the evolving state of ML for enterprise. The most important part of that might have been missed by folks, so I’ll bring it up here.
Yes, TensorFlow, R, Python and other tools provide frameworks for machine learning implementations, but they’re still at a very technical level. They aren’t for business analysts and management. He mentioned that the next generation of tools are starting to arrive, one that, just like the advent of BI, will allow people with less technical experience to more quickly use models in and gain insights from machine learning.
That’s how new technology grows, and I’d like to see TDWI focus on some of the new tools.
This was a good webinar, worth the time for those of you who are interested in a basic discussion of where machine learning is within the enterprise landscape.