Analytic-Driven Action, one step into the uncharted abyss… What Now?


 Hello everyone. A topic that has been running through my mind is what impact does “analytic-driven action” have on our analytic, reporting and operational business systems. First there is plenty of stochastic prose about terminology used in BI/BA space today. I think it’s important you are aware of my interpretation of the terminology, so I put a write up at the end of the blog.

Otherwise we’ll continue.

My interests today are around predictive analytics and real-time analytics. Let’s assume we’re using super fast equipment returning data in milliseconds or we’re leveraging predictive tools that allow us the ability to take on the proactive steps in engaging customers, partners or employees in real-time. If we have the ability to leverage new found intelligence to effect action (aka analytic-driven manipulation of the user experience) my question is what do you do with it and how do you set up your systems so that you can do not just one data-driven action, but essentially blend analytics and operational systems in an iterative social activity?

What I am talking about here is a perfect puree of business, sales, support, and analytics systems. In other words we’re talking 100’s of millions if not billions of dollars of transformation for you average Fortune 100 company to put this into action. So we have to make an enormous, profitable market while we’re at it. And, hopefully change the world.

There are a few ways I’d like to evolve the thinking on this topic to make it worth the effort. In this blog I have laid out a few areas to consider in forming an approach. First topic: if we’re going to do “Analytic-Driven Action” we need to include additional functionality beyond our CRM and ecommerce toolsets. Let’s also consider:

–          True Engagement – Sending a coupon based on my GPS co-ords is shallow and intrusive.  Engage me, know me, interact with me, and provide more than I know about myself and my surroundings. As Guy Kawasaki  likes to put it “Enchant me”.

–          Cross System Integration – You want to immerse and manipulate me, then you’ll need to bring multiple sources of information together to create data that expands my accessible knowledge about items in my locale, and about the other people around me who resemble me in a demographic way.

–          Process Management – We’ve all been on that phone call where 30 people join a weekly cadence and no one’s done anything… Our systems are similar we can’t shepherd important customer/client/constituent interaction without being able to take an action and follow through. We’ll need workflow technology to deliver on our promises.

–          Authorized Personal Profile – The privacy answer can’t be “no systems” or “no privacy”. We need concentric circles of privacy, where we allow access to our general profile, our personal relationships, and our financial/health data. Allow us to let people into our circles and establish a social contract of privacy that is democratic and individually controlled, yet universally leveraged.

–          Minimum Collective Knowledge – Recently I did an informal survey as I ran on the treadmill at my local YMCA. The Y had 4 choices in 24/7 news running (CNN, Fox, MSNBC, CNBC) That day I ran about an hour and in that time, I would tell you unofficially, these 4 stations only overlapped on about 10% on the news they covered, (overlap of all 4 on same topic was about 4%). There were interesting stories across the spectrum, but really quite different. If you didn’t invest in churning between channels, you really miss part of the contemporary experience. Ultimately, we’re living different lives side-by-side. If the polarization of our politics is any example of that, then I am concerned with the specialization of information creating more distance between us and our commonalities waning.  Yes this is a societal issue, but it has relevance in this discussion. We can’t get so specialized that we forget the value in a common dialog and the structures that compose that. If we get out of “broadcasting” activities how is that lessened need supported and/or replaced.

When we talk about Analytic-Driven Action, we often reference the use cases of “fast trades on the trading floor”, or “proximity offers to mobile user” as examples.  These are two early versions in this field of thought, but they don’t test out the conceptual model to my satisfaction.  Let’s find something more interesting.  Life’s two most important activities are to “live” and “reproduce”.  I want to keep my “G rating”… so I’m going to choose “Iive” as the verb for my use case.  We all want to live and we are all motivated to take action that will ensure we live while shopping, exercising, taking a business trip, etc. In my mind, living is made up of primarily 4 things:

  • Historic vices & virtues – what you’ve done right & wrong in the past. This has little to do with your future, but you may already have been impacted by it. I put stress in the “vice” category…
  • Current vices & virtues – what you do today really matters for your current health
  • Environmental factors – Is there a piano hanging over your head, or a hurricane off the coast?
  • Defects – Genetics… Some of us have defects that will appear, and some will be fatal.

So what could we do to improve a person or group’s chances of living?  A quick brain dump of marginal ideas rendered the following list:

  • Remind someone they haven’t exercised in two days, and provide suggestions on optimal time to exercise with close locations based on weather and schedule
  • Point them to 5 people within a mile that also would like to play a pick-up game of basketball
  • Report that you’re current hydration, heart rate and temperature for people you age has lead to ___% instances of hypovolemic shock in the last year.
  • Offer a note that someone similar to them has just sat down for coffee alone and wouldn’t mind a conversation.
  • Identify other close-proximity people in a traumatic situation, so that the team can group to survive.
  • ID tha someone 1 mile away is looking for an expert in your expertise. (service is healthy…)
  • Verbal “play by play” steps to perform CPR, merged with a launched 911 call with GPS co-ords auto supplied, notifying a doctor that is 200 yds away.
  •  See that you are late for you meeting automatically sending an email to the attendees with an estimated arrival and/or launch a bridge call to connect everyone to get started.
  • Suggest products that are located within walking distance (when you have a free 30 mins in your schedule) that people of similar profile have purchased for health reasons, and accept alternative requests from the user. “No I don’t want a Buns of Steel Video, but my wife did need a cartridge for the Brita filter, is there one close?”

   How do systems have to change?

So maybe I went on too long about ideas and I am sure there are tons of other, better ideas. My point is that let’s not do thinly veiled commerce with “did you know there’s a Tony Romo around the corner…” type activity, but let’s actually impact people’s lives in a positive way. Analytics can’t crunch through numbers in real-time, then feed little digital billboards to us and call it innovation. Instead engage with the ability to inspire and interact.  This requires something more than real-time analytics, it requires our operational systems to respond in similar ways. Not that everything will run in real-time, but the systems need to interact on demand, kicking processes off that then come back with answers or start execution of other events and deliverables.  Additionally, we need the ability to integrate with other sources of data and operational systems that are owned by others.  The “supply chain” revolutionized the retail store, thus this model will revolutionize our economy and ultimately society all by surrounding the individual and his/her tribes.  We need to apply various flavors of analytics in conjunction with operational systems with the individual in mind, instead focusing on short-money targets. Otherwise we will miss a grand opportunity to change the way we do business in the future.

=Terminology Reference Section=

Here’s my little reference section on terminology. Is there a difference between all the current buzzwords? I believe the answer is yes and there is a difference worth noting. Here’s a reasonable definition I support (http://bit.ly/Of0tXt). I have also provided my 2 cents on the differences of BI, BA, RTA, & PA in this section.

Business Intelligence (BI) was, is and will be a function within corporations that generates reports on internal operations, sales tickets and possibly soft targets like customer feedback. It is the reporting function that sits upon primarily relational databases today and has been mostly rear facing. The primary data management functions within BI are to aggregate and slice views out of the big chunk of data (i.e. June Revenue, Inventory Turns, Customer wait time, etc.). Companies read this intelligence and react to change their business. I do believe this term will evolve to incorporate the entire world of business analytics, if for no other reason companies like SAP with their BusinessObjects, Sybase, and HANA products will continue to use the term BI in their product suite. That’s ok, but in most companies today it’s their reporting system and different than analytics.

Business Analytics (BA) is different. I am NOT saying it’s new and that some companies haven’t been deploying business analytics/data analytics in their overall business intelligence investments, but most haven’t considered its impact at the same level that they do today. Business analytics is the leverage of statistical analysis to explore business interactions and data correlations. These tools were once relegated to the engineering or science lab, but now are being employed in BA. BA uses linear regression, logarithmic regression, k-means, hypothesis testing, scatter plots to iteratively push millions of bits of data through analytic pipes hoping to get new insight at the end. “We can say with 95% certainty that customers with yellow cars will likely to… in this scenario…” It’s more like archeology than a hardened business process.

Real-time Analytics (RTA) So what is the difference in real-time analytics and predictive analytics? These two are arbitrarily inter-changed in the press today. Probably because it is not profitable for companies to split hairs over such definitions and thus the lines blur, but in my mind they are distinctly definable and different. The term real-time comes from real-time systems. R-T Systems are required to not only compute, but compute at a given speed. While getting my masters, I worked at a company writing code for real-time systems, in my case NTSC (20 frames/sec). It is difficult to ensure that code written on one system runs consistently at the same speed on another system. As you can guess they don’t, you have to come up with algorithms that create the consistency.  So to me real-time analytics not only refers to systems that can deliver fast results, but that the analytics has some form of temporal requirement.

Predictive Analytics (PA) is not a time based activity, it is primarily leveraging tools like linear regression to determine the “Y” deviation as you plot x.  If you have thousands of examples of x,y plotted out and you know your “x”, you can predict your “y”.  For example: “If you are 40 years old, then you have a 50% or better likelihood of you will ___ your____”.  We’re using vast historical knowledge to predict behavior.

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MAKING SENSE OUT OF ANALYTICS… It’s time to progress this!


Big Data”.  A phrase destined to live a long life because it’s catchy and amorphous enough to mold itself into the current conversation. There is a great deal of literature, lesson and lore now floating around on this topic. We know Hadoop customers store petabytes of unstructured content, we know there are approximately 5 billion cell phones in the world producing both data and mobile consumers, we know Facebook is this important global mind meld of  puppy-dog pictures, farmville, and patriotic prose. Today, I want to involve you in what has been speed walking through my brain, how do you take something as esoteric as analytics and apply big data to it?

If you’re not a data scientist already, go load the analytics package “r” on your laptop. Then, create yourself a list, an array or a matrix of data and run some of the analytic functions contained within the package (i.e.  t.test(), lm(), kmeans()) . If you do this,  you’ll quickly learn that:

a)      This is super deep complex activity that combines statistics and programming

b)      Not suited for the majority of brains in the IT industry today

c)        Manipulated, massaged, converted, and carefully transformed by humans from one analysis to the next making decisions as you go.

d)      And… the data for each analysis isn’t the most aggressive volume of data (in no. of gigabytes) you’ve seen before.

If this is true, how do we answer the following questions:

1)      How does the data get so big?

2)      How do we apply Big IT to Analytics?

HOW DOES DATA GET BIG?

Ok, let me baseline you on linear regression.  Linear Regression (LR) is the gateway drug to predictive analytics. LR is the assessment of existing data that shows a linear pattern as you traverse a set of variables. This is called “Best fit” or Least Squares Regression Line. With luck your data will provide a best fit that is so linear, when you predict one variable, it gives you an algorithm (or coefficients) to predict the other.  In the link I reference (here), is an example of tracking two variables “age” and “height” across a sample.  The result is an algorithm where we could enter an “age” and get a suggested “height” back, thus providing predictive capabilities.

There are other algorithms beyond LR. Shopping baskets are processed as key value pairs (KVP). Combining each pair combination looking to trends in buying patterns. KVP is in demand today, and it’s a culprit in creating large amounts of data. A classic example is predicting what consumers buy. Everyone always talks about groceries because everyone buys groceries and they buy lots of things each trip.  Being able to predict what people will purchase, would allow grocery stores to better serve, while reducing costs to help their razor thin margins. This is why there are so many grocery store examples…

However, I’d prefer a new target for our discussion. Its beach time, I’m heading to a North Carolina beach destination soon, so let’s do beach shop souvenirs. If you’ve been to a North Carolina beach you know it’s all about lighthouses, pirate legends, and casual fun.

In our example there are:

–          Pretty T-shirts

–          Rebel/Pirate T-shirts

–          Surfer/Cool T-shirts

–          Sea shells

–          Bow covered flip-flops

–          Pirate gear

–          Cheap surf/water gear

–          Sun tan lotion

–          Postcards

–          Lighthouse gifts

–          Boat-in-a-bottle gifts (BiaB)

Review my list, I think we can predict a few type of shoppers.  The contemporary “southern belle”, the “pirate on the inside” and the “surfer dude wannabe”. I would speculate that these three shoppers would tend to buy like in these patterns:

–          Southern Belle – Pretty T-shirts, bow flip-flops, sea shells, sun tan lotion, postcards, lighthouse gifts, and BiaB

–          Pirate Pete – Pirate/Rebel T-shirts, pirate gear, and BiaB

–          Spicoli Dude – Surfer/Cool T-shirts, Sun tan lotion, surf gear

Note, I speculate on my mental library of personal observations, KVP speculates based on data.  To come to a more definitive conclusion, we would use KVP to process a day’s worth of transactions. If we ran these tests, you would be able to appreciate the vast number of combinations to consider. If our goal is to identify correlation or causality in product purchase relationships (say a person who buys a shell, likely to also buy lighthouse at a 95% confidence), you have to consider all the combinations of purchase relationships across a large number of receipts. This mean comparing: one to one combinations, 2 to 1 combinations, up to N to 1 combinations where N is the number of items purchased (data scientists…yes this is a simplification, be kind with your technical assessment of it…). Now apply those combinations across 100’s shoppers in a given day, across a chain of stores, across the summer season.  Now imagine you’re a global retail giant. What rubik’s cube of potential value this could be, and how much data gets generated in the process. It’s the combinations of assessment that make the data growth sky rocket off the charts.  Now think about how you manage it, communicate it, leverage it, and do you ever throw it away?

HOW DO WE APPLY BIG IT?

I know the word Big IT sounds like something we’re trying to get away from, right? Scale up is dead, scale out is hip. However to me Big IT is the lessons we learned about mission criticality, scale, consolidation/virtualization, and service levels that run all our companies today. We have a hoard of global IT professionals keeping the lights on and analytics has to make the jump from academia and departmental solutions to Big IT to get to Big Data. If we really want to know infinitely more about us (US defined as myself, myself with others, others without myself, only men, only women, only women in Europe, only teens who play football, I think you get it…) we need the IP and assets we’ve developed in the last era of scale up.  We need the connectivity, we need the structure of  things like ITIL, we need hiccup tolerant approaches that run on systems management tools, not hundreds of IT resources flipping out blown out components  they bought at a school auction. The “science project” has to become big business.

So how do we apply something that is as complex and focused as analytics to IT? I think its organizational changes that provide a vehicle for architectural changes.  Companies need to consider a “Chief Strategy Officer” (CSO) role as a binding force. Some companies may make this an engineering position or a marketing position based on their primary culture, but the role should exist and that role needs to set an analytics strategy for the company.  First they should define an analytics mission statement. “What are we going to do with analytics within the company?” Then they need to answer the basic questions about what we know of: our employees, our customers and our processes. Additionally they need to ask what in the big “datascape” do we want to bring into our analytics engines to accomplish our mission.  With this they can set an architectural strategy that leverages old tech and incorporates new tech to meet the mission objectives. Otherwise the company is locked in silo’d perspectives and can’t get to the bigger order items, it’s hard to construct this monster bottom-up.  Many of the companies I talk to who are starting enterprise programs, still seem to be searching for the how to bring it together. The answer is, just like the CIO organized IT, companies need a c-level resource to define the charter.

With the correct organizational structure, a company can then look at an architecture that can index the outside bits for later use, adjudicate the data flow to peel off the useful content into higher functioning data stores and then apply analytics packages to distill insight. Techniques like machine learning will help automate the processing and allow the super smart operators to become more productive. And, the programmers will write applications to get the global-mobile user in active participation by both creating and consuming the information within the process.

EMC-Cisco Put Big IT Power in SAP HANA Solution


 In case you didn’t put your eyes on this important piece of information, there was a press release last week that dropped the green flag on the HANA race: “Cisco and EMC Deliver Premier Infrastructure Solution Running SAP HANA for Big Data”. The product is a stack comprised of a Cisco UCS blade server, Cisco Nexus switches and an EMC VNX 5300. I won’t go into the configuration details here, but I will mention the lack of need for FusionIO cards, the multi-protocol flexibility (file & block support), and the 5 9’s availability that this stack provides. This configuration, which simplifies interfaces, is what SAP is calling a “scale out” configuration providing disaster tolerance in addition to meeting performance requirements. I think this is an interesting entrant to the market for several reasons. Let’s first look at it from EMC’s current messaging they unveiled at EMC World just a couple weeks back.

“Cloud Transforms IT and Big Data Transforms Business”

For a couple of years now EMC with its partners Cisco, VMware and VCE have been setting the measurement for private, hybrid, public clouds with the Vblock architecture. Let’s face it; it can be difficult to inject new technologies into an industry segment like SAP because of the mission criticality of the associated landscapes. In the beginning, many were skeptical if this venture would fly, because it was disruptive and it required not only SAP customers, but the ecosystem to get behind it to create a cloud economy of sorts. History shows that it was well received; many of the global integrators and service providers set up standards on Vblock, built hardened procedures for deployment, and created demonstrations of their best solutions on the platform. Now many of those initial buyers have started a chain of customers who are going into production, signs are good for the Vblock as a critical platform in the realization of SAP on the Cloud. As the tagline states: “Cloud transforms IT”, I was Emcee at a CIO luncheon during EMC World where we had 3 customers speak about their virtualization/cloud journeys with SAP and it was interesting to not only hear the positive comments, but to hear the way they talked about what is next. The tone and terms they used painted a sense freedom. They were thinking bigger, more aggressively than those who are stilling managing the world on physical architectures. For those who have dipped a toe or have dived in head first into the cloud will tell you, the water’s warm, come on in.

Ok great if you give me the latitude to believe my glowing report above (I know I’m biased, but it is true). Then let’s get on to the 2nd component of the tagline: “Big Data Transforms Business”. It’s hard to dispute this statement out right. I think everyone gets the concept of exploiting the inherent growing atmospheric datascape around us will generate new insights in sales and services for companies, and reduce their costs to up sell and please their customers. BUT…

It’s a big BUT… Very few in the industry have figured out how to orchestrate the change. Analytics is esoteric, super brain work that is done with statistical nuance. How do you volumize, programmatize it, is the fresh and challenging question. Most analytics programs have been the realm of Line of Business (LOB) departments who aren’t necessarily versed in big IT. They don’t understand how to go big with big data.

SAP, the market leading provider of business software, has earned the necessary IP clout to make big moves in analytics. Yet traditionally they were not deep in big IT. If I were to put words in their mouths… “IT is a necessary evil that costs too much and adds complexity to our customers’ missions”. With that said, they have been investing for a few years now to understand how innovations like “cloud” can be a catalyst to change. Add to that Oracle’s investment in proprietary vertically integrated stacks and SAP has formed a new level of interest in what’s going on below the deck… So much so, I believe SAP has made the biggest move in database technology in a decade. With the creation of HANA and the purchase of Sybase, SAP has created the general market’s first mainstream in-memory (lightening fast) database which works (will work) in conjunction with Sybase ASE to provide a cheaper platform than their rival Oracle (per their claims). This gets interesting if you understand how much of their existing install base currently uses Oracle, and may eagerly consider a reduction in that investment. Now SAP is defining the terms of the next battle. You could easily say that SAP is the best-in-class business software vendor with a dark horse position in the database market.

Now take “Big IT” best-in-class providers Cisco and EMC who have a significant joint SAP install base, and have compatible market offerings, and the collective opportunity between these companies becomes positive ballast pressing on the tipping point of SAP’s HANA/Sybase go-to-market.

The green flag has dropped, the race is on. I think the Cisco/EMC offering will hit a market sweet spot that demands a refined balance between cost, risk mitigation, and performance. EMC, with its partner ecosystem, can live out the tagline: “Cloud Transforms IT and Big Data Transforms Business”.

If you need to connect on this topic, please let me know, I’ll make sure you get to the appropriate owners in the ecosystem.