Trends Driving the Future of Analytics

Posted by  Jen Underwood   in  , , , ,      5 months ago     8203 Views     2 Comments  

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Another sleepless night in San Jose, California without my beloved Diet Mountain Dew until tomorrow morning… Seems like a perfect time to share a few thoughts on awesome future analytics industry trends that I am monitoring. As I continue to grow and stretch domain knowledge across numerous vendors, I am also being pulled more and more towards futures analysis.

Reviewing futuristic innovations as an engineer at heart is incredibly fun, interesting and also requires expert alerting, filtering and skimming through massive industry, social and media noise to find golden nuggets. When you do find a fascinating innovation in infancy, the commercial applications for it may not be obvious. This is where you need to be creative visually yet remain totally practical “guestimating” what abstract concepts may become viable, commercial applications and what might fail. Two of my favorite “tech futures” thought leaders so far are Daniel Burrus and Ray Wang.

Here are few future technology trends that I feel will significantly influence analytics application designs soon if they have not already begun to do so.

– Embedded Analytics (already prevalent today and simply growing)
– Cognitive BI (the IBM-Watson effect, Siri, Natural Query, etc.)
– Automation of BI and Analytics
– Hyper-Individual Experiences
– Marketplaces
– Cloud (also widespread today and growing rapidly)

Let’s take a peek at a few of these trends starting with Cognitive BI and Automation. In future articles I will dig into a few others.

Cognitive BI and Automation

Cognitive systems in general will transform how organizations think, act, and operate in the future. “Smart machine models” will be designed to analyze unstructured data, video, images and human language via artificial intelligence and machine learning algorithms. A few players in this space include IBM, Data RPM, AlchemyAPI, Digital Reasoning, Highspot, Lumiata, Narrative Science, Cortica, Ersatz, Semantria, Numenta and nViso. GigaOM does a lovely job covering bleeding edge vendors like these.

The feature image above showcases Digital Reasoning’s Synthesis cognitive systems technical solution architecture design that is already being implemented by early adopters. You can see big data, advanced analytics, artificial intelligence and even a little Tableau sprinkled in. As a Tableau fan, I’d totally love to hear/see a joint Digital Reasoning and Tableau session – hint hint if Ellie, Thierry, Sean or anyone over there reads my blog.

We are already seeing cognitive technology in the BI world with Watson Discovery Advisor, Analytics (Project Neo) and Explorer, Oracle Endeca, Data RPM, Targit, Power BI Q&A and other analytics systems using natural language queries to automate information discovery and reporting. What this means…users on big data systems will no longer be limited to querying information stored in predefined views, tagged semantic models or static data models. Data is being accessed via automated indexing – think Google search for BI apps/reports.

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IBM Watson forever changed the technology world and how data can be used to uncover valuable insights. IBM’s Watson is an artificially intelligent computer system capable of answering questions posed in natural human language. Extensive research with regards to text mining and human language context was led by David Ferrucci and a joint team of IBM and major university research groups. The project was inspired in a restaurant where the show Jeopardy was playing the background. A few years later, Watson was featured on Jeopardy and won against the top players in the world. I will never forget that special day back in 2011 or the amazing behind-the-scenes television program IBM made sharing their Watson research journey, failures and successes. To build Watson, IBM used virtual teams and virtual reality meetings to get the best talent on the planet involved in that project.

Watson is significant because its advanced natural language processing can understand the complexities of unstructured data, that account for ~80 percent of the data in the world today – without tagging or modeling. It uses data science techniques to automatically generate hypothesis and evaluate a panel of responses based on relevant evidence. Essentially Watson is employing dynamic learning techniques, a little like neural network per se, that allows it to continue getting smarter based on outcomes with each iteration and interaction. Looking ahead, IBM is enhancing it to include foresight, planning, perception and extrapolation. That is truly mind exploding with regards to technological advancement. If this fascinates you, take a look at the IBM paper called The Era of Cognitive Systems: An Inside Look at IBM Watson and How it Works.

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Another really cool aspect of is this particular project is the Watson Question and Answer API (QAAPI) for developers. App or BI developers can integrate Watson capabilities, as a service, through the use of a REST API. IBM is not at all the only player with this technology but I feel that they are the market leader.

Taking Cognitive BI one step further, a company called nViso is transforming facial imaging, human non-verbal signals and emotional responses into insight. This nifty innovation will be used for measuring customer satisfaction and a plethora of other applications. Oh boy, the fun we are going to have making silly faces at cameras in the real world to toy with this one, confuse marketers or express our dissatisfaction more colorfully than ever before!

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More Automation

A former Microsoft peer of mine recently left the BI world for fear of automation. At first I thought he was overly paranoid but now I am beginning to see what he sees. There are increasingly more aspects of analytics applications being automated or simplified with technologies like Watson and Data RPM. I also saw something from esteemed Industry Analyst Neil Raden on Twitter about Microstrategy and automation – dying to know what Microstrategy is doing in this area.

As technology continues to improve and shifts to the cloud, the BI and analytics world will continue to evolve. There are already less data model building jobs and more what does the data actually mean jobs today. I forecast that trend to continue. I don’t know where ETL stands down the road. I am seeing ETL automation tools just like predictive automation and reporting automation with Q&A but I do remain a bit skeptical. Viable automation of BI does feel to be a few years out from becoming a reality. However, I am getting less skeptical over time and wondering what will be next, curiously exploring what is happening in the wonderful world of technology.

About  

Founder and Principal Consultant, Jen Underwood has almost 20 years of hands-on experience in the data warehousing, business intelligence, reporting and predictive analytics industry. Prior to launching Impact Analytix, she was a Microsoft Global Business Intelligence Technical Product Manager responsible for technical product marketing and field readiness for a $10+ billion market suite of analytics offerings spanning across Microsoft SQL Server, Office and SharePoint. She also held roles as an Enterprise Data Platform Specialist, Tableau Technology Evangelist and a Business Intelligence Consultant for Big 4 Systems Integration firms. Throughout most of her career she has been researching, designing and implementing analytic solutions across a variety of open source, niche and enterprise vendor landscapes including Microsoft, Oracle, IBM, and SAP. As a seasoned industry presenter, author, blogger and trainer, Jen is quite active in the global technical community. Recently she was honored with a Boulder BI Brain Trust (BBBT) membership, Tableau Zen Master (MVP) award, PASS Excel BI Chapter leadership role and a Dun & Bradstreet MVP. She writes articles for BeyeNetwork, SQL Server Pro and other industry media channels. Jen holds a Bachelor of Business Administration degree from the University of Wisconsin, Milwaukee and a post graduate certificate in Computer Science - Data Mining from the University of California, San Diego.

5 Comments

  1.   October 28, 2014, 12:05 PM

    Hi Jen,
    thanks for making an insightful post on Spotfire. I appreciate your effort in sharing your own knowledge on this.
    Do you mind to share a little bit more detail on the book you mentioned?
    Could you either provide the link to where we can purchase the book or the title to the book, please?
    I tried to google for the book and all I came up with were the ones you said not to get. lol

    Thanks!
    Iman

  2.   October 14, 2014, 3:11 PM

    hello,

    thanks for this post. i find it while searching for a tibco spotfire book. i am new to spotfire. i went to tibco but couldn’t find the book. can you give me the title of the book? or link where you got it from?

    thanks!!

  3.   October 3, 2014, 5:47 PM

    Jen,

    Thank you very much for such a thorough and fair comparison between Power BI and Tableau. It would save a lot of time and money for any one considering to deploy these platforms.

    I am a seasoned Microsoft BI Developer and specialised in Dimensional Model (Kimball Method). I don’t know much about Tableau. So it would be greatly appreciated if you can explain about whether Tableau can handle SCD (Slowly Changing Dimension) type 2 without first build it in a Relational Database using ETL. And also about data integration (Conforming Dimension) from few source system.

    Regards,
    Albert Benjamin

    •   October 4, 2014, 11:24 AM

      You are very welcome. The only front-end BI solution that I know has an SCD type 2 wizard per se is Birst. With Tableau, the ETL and data mart design is still critical for accurately reporting changes over time. Tableau “blends” are not a replacement for data integration or data marts. It is more like a Band-Aid for rapid personal exploration. I do have an article that is a related relevant read at http://sqlmag.com/blog/does-excel-power-pivot-replace-data-warehouse. Just swap out PowerPivot with Tableau and the exact same concerns, considerations and pains still exist. Hope that is helpful.
      Jen

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