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  • “A long, long time ago, I can still remember, how the music used to make me smile.  And I knew if I had my chance, I could make those people dance, and maybe they’d be happy for a while.”   Don McLean never did reveal what “American Pie” meant, though others have provided interpretations.   Thank you Don.  Perhaps you thought that meanings could be projected based upon individual experiences. 

    In early ‘94, I happened to stumble upon OLAP as it was morphing from an elitist mainframe technology into one that could be put into the hands of we common folk.  I’d never heard of Arbor or Essbase when I applied to that ad in the Dallas Morning News, but saw it was client-server and wanted some of that.  I found out later on that I was going to be the first dedicated Essbase consultant hired by Comshare – and that Essbase was not the same thing as Sybase.   Take a job on something no one’s ever heard of?  Sign me up!

    One of the joys of being the first guy in, is that you get to be a jack of all trades.  A little training here, an implementation over there, and oh – by the way – we need some help selling new deals.

    Implementations were their own kind of fun.  The early adapters generally were large companies that would at times create labs to explore this new OLAP toy.  One of my first clients, Shell Oil in Houston, actually paid me to benchmark Essbase calculation performance on Windows NT vs. OS X.  Paid to play.  How cool was that?

    But things were about to get a whole lot better.  As a techie that avoided the dark side of sales, what lie ahead was beyond the reaches of my limited imagination.   We didn’t know it, but we were about to become showman. 

    The glory was astounding.  As we were evangelizing OLAP, we developed quite the shtick.  We’d always get our prospect companies in Texas to give us a small extract out of their GL (in that mostly pre-ERP era).

    When I received 3.5” diskette with their data extract in my hand, it was sort of like receiving a tech toy from Amazon today, but exponentially better.  You just knew.  What was about to come could not be stopped, an uncontrollable destiny with only one outcome – and you could hardly contain yourself.

    So, you’re thinking – data on a diskette – how much could it be?  There was a secret to the shows that we put on.  We demoed everything live.  So all of the data needed to run on circa ’95 laptops running both the client and server versions of Essbase on Windows NT.  Everything crashed all the time.  Nobody seemed to care much... 

    When show time came, the choreography included setting up the audience with a teaser, showing them their data on the ‘back-end’ in a graphical format.  That was the perfect opening act, as hardly anyone had seen their business rendered in such a manner before.  Then we’d hit them with the punch line – the show’s climactic moment, that magical ‘slice and dice’ in Excel.  The ‘oohs’ and ‘awes’ filled the air, and unconstrained joy would permeate the room.  Conquering heroes, we were, as we pivoted the data from a row to a column, then drilled down to resounding applause.   My buddy David and I would be jumping in front of each other, quite literally, to deliver the best lines.  We may have been rehearsed, but we weren’t scripted.

    Audiences would clap, then just gush with unbridled excitement, as if every prior data frustration they had ever experienced had been somehow mystically lifted from their shoulders.   Some of the best testimonials that I’ve ever heard came from some of those early OLAP audiences, guttural praise, straight from the heart.  One of my all-time favorites was from a manager in the Lubes Division of Shell Oil in Houston, as he witnessed in front of we showman and a dozen or so colleagues, “This gives us answers to questions that we never even knew how to ask.”

    We got the deal.  It worked every time.  Even when our laptops would freeze up during the demo, and we’d have to re-start, we’d get the deal.  We always got the deal.  The early adapters willed OLAP into prime time. 

    The people smiled.  We made the people dance.  They were so very happy – for a while.  Then the novelty of that magical, pivoting two-step eventually ran its course, of course, as that is what happens to all new game-changing technologies.  The world went on and grew up in a hurry.  Oracle has Essbase these days, and their sales model is well, um … different.

    In our showman days, Decision Support Systems (DSS) was the collective term used to encapsulate all of what we provided, as the then progression from Executive Information Systems (EIS).  Since then, we’ve added Business Intelligence (BI), which in the day, was staunchly defended as different from DSS.  Then with a little help here and there from Hyperion and Gartner, along came Business Performance Management (BPM), which was the same as Corporate Performance Management (CPM), which is the same as what we now primarily have settled on as Enterprise Performance Management (EPM). 

    Add Data Analytics, Data Science, Big Data, Data Mining, Predictive Analytics, Data Visualization since then, just to name a few. We were telling everyone 22 years ago that coding was not necessary for OLAP – even the users in Finance could handle it.  The semantic layer is still alive and well, but there’s also been a renaissance in coding.  We’ve good coding schemes and data dreams and IoT not so much on the QT.  We’ve got Hadoop and R and Machine Learning and Python and Pig.  We’ve got Spark on the Apache.  We’ve got a zillion ways to zap every bit of data on the planet into sub-atomic data particles.  And we’re just getting started parsing data into oblivion. 

    I wonder sometimes if we’ve not come full circle?  We’re still mincing, slicing and dicing – but we’re just doing a great deal more of it – on a heck of a lot more data.  Would not all of the above ‘Big Data’ technologies, without too much of a stretch, settle themselves just dandy under the classification of Decision Support?   I’m just sayin’.

    Perhaps my slice of the pie is that I got to be a showman for a while. Or perhaps it’s just that I’ve had a bird’s eye view of seeing everything come full circle. Either way, it makes me smile.

    Robert Dayton

    February 8th, 2016 

  • The world's top brands have hired RM Dayton to align top analytic talent.

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    Conference - Collaborate 19 -- Technology Forum


    Collaborate will be held in San Antonio, Texas from April 7th-11th 2019.  Let us know if you'll be heading out to San Antone,.  Schedule 30 minutes on the calendar below, and let's talk shop about where business analytics is today, where it's heading, and how that might impact your company initiatives and career.

     

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    Conference - Predictive Analytics World


    The Predictive Analytics World Conference will be held in Las Vegas, NV from June 16th - June 20th, 2019.  We look forward to seeing you there to network with industry practitioners in business analytics.   Let us know if you'll be heading out.  Find 30 minutes on the below calendar, and we'll talk talk shop on business analytics over the java that you drink in Vegas.

     

     

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    Conference - The AI Summit 


    The AI Summit will be held in New York, NY from December 11th-12th, 2019.  We look forward to seeing you there.   Find 30 minutes on the below calendar, and we'll sync up in The City.

     

    Thank you!

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    Cross-Enterprise AI 101 for Analytics and EPM People


    Where does Cross-Enterprise Artificial Intelligence (AI) take the baton from Enterprise Performance Management (EPM) and other business analytics technologies?  

    In this e-learning session, we’ll briefly review the scope and breadth of traditional EPM and Business Analytics towards the objective of becoming an intelligent agile enterprise.

    We’ll talk through examples of advanced analytics applications typically beyond the scope of classic EPM, where massive integration of different ‘type of data’ silos are required, and different skill sets, such of those of a data scientist, are often deployed.

    You’ll then learn about the key components of a cross-enterprise AI platform, as well as several use cases highly adaptive for cross-enterprise AI. A prime objective of Cross-Enterprise Artificial Intelligence is to continuously sense what people want, and then deliver continuously adapting value.

     

    Register Here!

     

     

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    Cross-Enterprise AI vs. Intelligent Automation: What's the Difference?


    Where do Intelligent Automation (IA) and Cross-Enterprise AI fill the gaps in enterprise software technologies, such as Enterprise Resource Planning (ERP), Enterprise Performance Management (EPM), Business Intelligence (BI) and Business Analytics technologies? Where do they help people by allowing more time for the creative, spending less on the mundane?

    You’ll learn about the key components Intelligent Automation (IA), introducing how Robotic Process Automation (RPA) can provide with more time for creativity, realizing more value out of your existing technology investments.

     

    We’ll also review the key components of a Cross-Enterprise AI platform, highlighting the differences between Intelligent Automation and Cross-Enterprise AI.

    Learn how Cross-Enterprise AI and Intelligent Automation unleash business value in new ways, transforming core processes and business models in various industries.

     

    Register Here!

     

     

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    Ignite Business Analytics & EPM with IA


    How can Intelligent Automation (IA) supercharge Enterprise Performance Management (EPM), Business Intelligence (BI) and Business Analytics technologies via Robotic Process Automation (RPA)? 

    In this e-learning session, we’ll briefly review the scope and breadth of traditional EPM and Business Analytics towards the objective of becoming an intelligent agile enterprise..

     

    We’ll talk through examples of where massive integration and data silos typically exist in BI, EPM and Business Analytics solutions.

    You’ll then learn about the key components of RPA, as well as several use cases highly adaptive for Intelligent Automation.

     

    Register Here!

     

     

  • Eliminate the Fluff

    I have been working in Business Analytics for about 20 years, and for a good part of that, focused on aligning teams of consultants for projects. A lot of thought goes into putting these teams together.  The most important attributes to consider are technology expertise and domain knowledge. 

    Consultant profiles span a myriad of technologies, so what someone looks like ‘on paper’ from a technology expertise perspective can be deceiving. Technical expertise can be the most difficult to assess from a customer's point of view.  You're buying into the fact that the consulting firm may have done this successfully in the past, though perhaps with a vastly different set of characters.   

    After three or more projects, one generally has enough domain expertise so they no longer have to fake it.  In the ‘full-service’ consulting model, juniors would be put on the team as billable consultants, learning both technical skills and domain knowledge on the client’s nickel.

    Before founding RMDA, I worked on a projects with an experts in EPM that involved Enterprise Performance Management (EPM), Business Intelligence (BI) and Business Analytics. Each resource billed around from between $150/hr at the low end to $225/hr+ on the upper end.  So in in just one month, for a team of just three resources, the client was billed around $100,000. 

    The Less is More Paradigm

    During the build of the project, it took effort to make sure the right hand know what the left hand was doing. Precious time and money at $200/hour could be spent, for example, on naming conventions. This overhead adds cost and risk to the project. 

    With a 'less is more' mindset, RMDA challenges this resource alignment method and dares to ask, "why not?'  Why not reduce the footprint related to business analytics to fewer, pinpointed resources?  

    The paradox is, you can leverage the underlying technology investment in the 'to be' state better, while retaining more of the enhanced, prized intellectual capital you wanted to attain in the first place.

    Combining an agile approach with experience in the business analytics domain as practitioner's, transformative results can be delivered without the layers.  

    RM Dayton knowledge worker engagements consist of fewer, but laser focused resources, without the fluff - an approach that has earned the honor of being one of The Silicon Review 50 Most Admired Companies of the Year. Because you don’t have four or five experts sitting around, when one or two pinpointed resources would do, you save time and money while reducing project risk. 

    We can do this, as RMDA is not focused on 'all' IT, but rather, is focused solely on Business Analytics, Big Data and Data Science.

    Robert Dayton

    October 20th, 2017

    © 2017 RM Dayton Analytics, Ltd. Co.

    Related article on CIO Review - Enhance Customer Engagement from the Inside Looking Out.

      

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    Appointment Request - Phone Chat with Dayton Analytics


    Thank you for scheduling time on your calendar.  Please request to schedule 20 minutes at one of the available times listed on the calendar below.  If something comes up, please use the confirmation email that you'll receive to reschedule.  

     

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    Regardless of the ‘how’ resources are hired, one thing is for certain: knowledge workers must represent the interests of the company – what RM Dayton calls an ‘inside looking out’ mindset.   With the a ‘less is more’ philosophy, we dare to ask, ‘why not?’ Have 20 resources on a project? Why not cut that to 10 or fewer, laser-focused, where you retain the prized intellectual capital?  

    A business leader at one of the best-known brands in travel and hospitality, remarked after a top gun resource started, ‘he’s really good, but he knows about many things we are not currently doing.’  

    Bingo.  Over one year later, the resource is indispensable. A predictive model of performance was redesigned from scratch: adding new functionality while reducing error and the time to update and run the model by 2000% compared to the previous version.  They’ve made new forays into strategic business simulation, where the model ‘looks at’ business questions in a unique way, to provide answers largely immune to trend and extrapolation models.  Then there’s that Deep Learning Model, a long short-term (LSTM) recurrent neural network (RNN) model that will predict spatial, temporal and agent patterns of behavior. 

    The power of one.  Never underestimate it.  Just one person can make an insurmountable difference. 

    Companies that previously understood perhaps only a small percentage of its customers well, at discrete points in time, are getting to the point where they know more about more of their customers on a continuum.   Clearly, being on the Big Data sidelines is not an option.

    About a dozen years ago, businesses of all sizes began using offshore resources in droves to reduce costs.  Initially, companies did save money with a labor arbitrage.  Today, some companies are re-thinking this decision, deciding to re-shore, rather than to have much of their prized intellectual capital locked up in minds of behemoth offshore-based IT outsourcing organizations. 

    Whether companies plunged heard first into the gushing Big Data torrent or dipped their toes in with a POC, these IT outsourcing agreements and resources were largely in place.  Naturally, as a convenient convergence of time and circumstance, the hammers were handed to these resources to a large degree to go build some Big Data solutions.

    Some business leaders now wonder whether a commodity approach to their most-prized intellectual capital is truly in their best interest – and in the long run, they wonder, what are the true costs of outsourcing prized strategic and operational knowledge?

    This introspection is pronounced for big data analytics initiatives, where customer engagement is the goal, and success or failure may well foreshadow the ongoing viability and competitiveness of the organization in certain industry verticals.

    The approach of more low-cost resources seemed to work well for outsourcing call centers and technical support - you maybe really did need more bodies just to handle the volume.  But for transformative development leveraging Data Science and Big Data, the right elbow reflex of the non-focused IT outsourcing firms – who want to sell you more – may not be the right prescription. 

    RM Dayton knowledge worker engagements consist of fewer, but laser focused resources, without the fluff - an approach that has earned to be one of  The Silicon Review 50 Most Admired Companies of the Year

    RM Dayton Analytics is singularly focused on Big Data, Data Science and Business Analytics.  A simple, yet powerful, mindset provides for a more effective and efficient approach to providing a world class experience for our customers.   

    Combining an agile approach with the firepower of those who dare to ask, ‘why not?’, the firm is able to deliver transformative results, providing strategic and operational advanced analytics intellectual capital at scale and speed, from the inside looking out.   RM Dayton has earned several accolades, including 20 Most Promising Enterprise Performance Management Solution Providers 2016, 100 Most Promising Big Data Solution Providers 2017 and 50 Most Admired Companies of the Year 2017.

    June 1st, 2017

    © 2017 RM Dayton Analytics, Ltd. Co.