Map Rock Problem Statement – Part 1 of 5


Map Rock addresses the need to manage competitive fitness in an increasingly complex world through superior development and management of versatile strategies. There, that is the 25-words-or-less, Twitter-able, sound bite “Problem Statement” for Map Rock. I somewhat facetiously refer to this series of blogs as the “Map Rock Problem Statement” when it really is a “Problem Essay”. So out of professional courtesy to everyone, before I begin here is the Elevator Pitch:

The Problem Map Rock is trying to solve: Take Performance Management to the next level by “bringing the Strategy Map to life”. Performance Management initiatives would benefit from Business Intelligence systems that focus on presenting relationships rather than primarily returning data, sums and calculations, via what are still just reports. Business Intelligence packages many data points into aggregated “information” (data to information), but eventually there will be so many pieces of “information” that it again becomes data. Additionally, data from which we can calculate relationships exist is a number of formats and in a great number of isolated sources. What is needed is a system that can integrate these sources, sort the information into a hierarchy, and maintain the validity of the information.

Current Solutions and Where Map Rock Fills Some Holes: The Business Intelligence areas such as Data Warehouses, Performance Management, and Predictive Analytics as it stands today has added tremendous value to the decision-making capability of enterprises, but hasn’t lived up to its full potential. The vision of a Centralized Data Warehouse is elusive due to factors such as the complexity of integrating semantics across dozens if not thousands of data sources. Performance Management fails as it only tells us what is wrong with KPIs that we aren’t even sure is what we should be measuring. Additionally, KPIs are disconnected allowing workers ample room for gaming the system, which actually makes things worse. Predictive Analytics falls short in that the models make predictions based on historic patterns that are severely prone to skewing by one-off events. Simply removing the one-off event as an outlier could fail to detect what is really the birth of new trends.

Map Rock’s Added Value: New initiatives such as Self-Service BI, Master Data Management, Metadata Management, Semantic Webs, and of course, Big Data are significant steps in the right direction. But at the same time, these initiatives can further complicate matters if they are not united. For example, Big Data in itself for the most part mostly adds to more data points – sometimes simply more isn’t the answer. What is required is a way to integrate the heterogeneous array of technologies attempting to help us make better decisions from a higher level. Additionally, we need to smooth out communication between the wide communication chasm between where BI leaves off (ex: the OLAP cubes and Predictive Analytics models) and the human brain.

Oh good, you’re still here.

If Map Rock sounds intriguing enough from the elevator pitch, I will be posting a marketing-oriented blog on February 8, 2013 on how to inquire on a demo. Please look out for it. Otherwise, I offer this essay on all that is behind Map Rock. It will take many more than 25 words or a one minute speech to lay out the primary concepts underlying Map Rock which I will do by discussing:

  • Embedding the concepts into the well-known Performance Management framework.
  • Building on top of the efforts of what is traditional BI and Predictive Analytics.
  • The strategy of building a “pidgin” to bridge human and machine intelligence versus a genuinely AI system.
  • The fundamental place of competitiveness, strategy, and imagination in a complex world.
  •  Understanding the difference between complicated and complex systems for insight into why the result of current BI projects are still often only marginally helpful, or at worst, we still make a lot of bad decisions.

This series of blogs is the “Why”, the reasoning behind Map Rock, not the “How”. This blog isn’t intended to be the “marketing” blog.  I look at this article more as Map Rock’s “Federalist Papers”, from which the more consumer-friendly and poignant United States Constitution is derived. Actually, for Map Rock there is a journal of about 500 pages (in Word) dating back to 2003 which I’ve condensed down to a these approximately 20 pages. This theme of “why” is actually in itself very “Map Rock” as “why” is really a set of relationships, and relationships is what Map Rock is all about. We can be taught how to do something, but if we don’t know why, we will be lost when (not if) conditions for that “how” change.

The slogan driving the development of Map Rock is: The better we understand relationships, the more effective we can be at manipulating our surroundings. Humans have an enhanced ability to learn; that is to assimilate and process relationships throughout our life. When we understand why something happens, how are things related to each other, we can then engineer a solution to achieve a desired state in a system even if the starting points are different each time. A “solution” is a set of manipulations to pieces of a system. Over the last couple hundred thousand years, we’ve done very well in taking us from a relatively weak, “jack of all trades” animal to the apex of the apex.

About ten years ago I read the great book, The Ingenuity Gap, by Thomas Homer-Dixon. In a nutshell, the thesis is that eventually the increasing complexity of the world will overtake humankind’s ability to engineer our way towards our dreams and out of the messes we individually and collectively get ourselves into. The world is becoming more complex by magnitudes, but the innate intellectual capacity of humans is rather constant, or at best improved incrementally through superior education techniques.  I thought then that the popularity of this book would open the door for my thoughts around what would eventually become SCL from a “solution looking for a problem” to a “solution to a recognized problem”. Ten years later, we’re somewhere in between, but I optimistically think leaning toward the latter side.

That means I still have a significant “solution looking for a problem” issue to overcome – which by the way isn’t necessarily a bad thing. The big obstacle I feel stems from society having grown too comfortable with the seductive simplicity of the sound-bite, non-competitive, tips and tricks, best practices, bullet point, PowerPoint, quick fix, instant gratification, elevator pitch, Tweet quips, risk averse, single-function, multi-tasking, lowest-common-denominator culture that we’ve made for ourselves.

Don’t get me wrong. Believe me, I partake in and greatly appreciate all the ease and convenience the sound-bite culture provides. In fact, innovation in large part is about making the mundane of life as quick, effective, and painless as possible. But I feel the art of “American Ingenuity” (which can exist anywhere in the world where there are the conditions for innovation) and the appreciation for it is slipping through our fingers and I don’t believe it’s something that is easily re-learned or re-taken.

Innovation is about delayed gratification. It involves thinking deeply and widely, allowing for and learning from mistakes, and being allowed to be a little bit playful and crazy. It’s what differentiates humans from other creatures that do live solely by simple rules. When it comes to the chores of life, of which there are more imposed on us every day at home and work, I’d like them to be as simple and painless as possible. But when it comes to creating new things and competing, at the risk of sounding sadistic, we need to embrace the opposite. “Embracing” in this case means instead of rejecting complexity, we face it and tame it. Towards that goal, I think of my development of Map Rock over the past few years as having fought an epic battle with a grizzly bear that I’ve now tamed. Maybe we’re not yet BFFs, but at least we can have a working relationship, which is a start.

The complexity of life is growing at an accelerated rate at this time for many reasons which I’ll list later. Complexity means there is an unpredictable aspect to the outcomes of all movement involved in a complex system. In the course of all this movement, things are naturally destroyed and new things are created. But we humans have attachments to things and have a natural tendency to seek stability valiantly resisting the relentless change.

No, the world hasn’t come screeching to a halt due to the growing complexity of human activity. Life on Earth is still much too powerful to come to an end from our yet comparatively puny efforts. Life endured all sorts of much bigger catastrophes over a few billion year span. Humans are innovative and resilient creatures. The question is, how can we mitigate the risks and capitalize on the constantly changing conditions? Maybe we think we are handling it just fine. But maybe there is a boiling frog problem. Maybe we haven’t reached a scalability tipping point where drastic change can come very abruptly. Any more clichés? Hahaha.

At the end of the day, my intent for Map Rock is to help answer these three powerful questions:

  • How could this have happened?
  • What could possibly happen?
  • How can I make this happen?

Coming up:

  • Part 2 –  I describe Map Rock’s target audience and the primary business scenario for Version 1. It is not just a tool for quants, wonks, and power-users.
  • Part 3 – We delve into a high-level description of the Map Rock software application, where it fits in the current BI framework, and how it differentiates from existing and emerging technologies. This is really the meat of the series.
  • Part 4 – We explore strategy, complexity, competition, and the limitations of logic.
  • Part 5 – We close the Problem Statement with a discussion on imagination, which is how we overcome the limitations of logic, and how it is incorporated into Map Rock.
  • Map Rock Proof of Concept – This blog, following the Problem Statement series will describe how to assess the need for Map Rock, readiness, a demo, and what a proof-of-concept could look like.

Related Blogs

It may be beneficial to peruse material I’ve posted over the years that are collectively the soul of Map Rock. In a sense, almost all of my posts have something to do with Map Rock, but these posts strike me as the most relevant at this point. Map Rock is the manifestation of all these concepts. However, I will write this “Problem Statement” with the assumption that none of the posts were read.

Please keep in mind that these blogs were written over a few years (2005 through 2012) and may be a bit, or more than a bit outdated, at times as things have moved on over the years and my thoughts on the subjects have evolved as well.

Find and Measure Relationships in Your OLAP Cubes The first two blogs listed here set the direction for my efforts leading to Map Rock. This one really represents the foundation of Map Rock, the ability to “cast a wide net” for correlations or even lack of correlations. The main idea is to look for relationship measures to begin with, as opposed to looking for aggregate measures as is normal browsing an OLAP cube.

Bridging Predictive Analytics and Performance Management Performance Management usually centers around the Scorecard, a report on the Key Performance Indicators. It is just a report, the nerves reporting pain. But imagine if the pain in your nerves didn’t report to the brain with an awareness of pain from other parts of your body, an awareness of what is going on, a catalog of things it can do to alleviate the pain, etc.

Undermined Predictive Analytics This blog was meant to be a reminder that it’s a jungle out there. There is a big difference between data mining people as they just go about their daily business and when there is actually an intelligence involved or when people know they are being watched. In business, a big problem with performance management is that workers are clever in gaming the system.

Cutting Edge BI is About Imperfect Information There is no “one number” answer and practically all answers must to preceded by a series of “it depends” questions.

Why Does a Lt. General Outrank a Major General? This blog attempts to illustrate the role of strategy and tactics at different levels of jobs. But as companies trend towards decentralization of responsibility, the delegation of coming up with the “how” to people at all levels, what emerges is that modern information worker, the commando. That commando, who is often a player/manager, must be strategically, tactically, and operationally proficient.

Data to Information to Data to Information One of the main notions of Map Rock is that it’s the relationship between data that provides the really juicy, meaty insights. More data, as in Big Data, isn’t in itself the answer. A focus on Big Data still sidesteps tackling the challenges of embracing complexity.

Why Isn’t Predictive Analytics a Big Thing? At the time of the writing of this blog, Predictive Analytics was still frustratingly rather fringe. Since 2009, it is perhaps still not a household word but almost a “officehold” word. I positioned Predictive Analytics then similarly to how I’m positioning Map Rock; as a bridge between the chasm left by most BI implementations and the human brain.

Predictive Analytics is Science for the Masses The first feedback I usually get on Map Rock is that it is a quant’s tool. It is a tool intended to make non-quants a little bit “quantier”. It amazes me how people casually tell me they have “non-thinker” roles, as if thinking is reserved for scientists and quants. Who has never strategized about something? Who as a kid hasn’t schemed about something like getting a Red Ryder BB gun for Christmas? I’ve encountered so many people who say they know nothing about data mining but yet provide fantastically profound arguments for why Barry Bonds may or may not be better than Babe Ruth.

Where Do Rules Come From? A major factor of the evolution of SCL was triggered by a comment made by a friend of mine way back when I first began developing it. He said that the really hard part was encoding the rules. He is absolutely correct. That lead to the path on finding sources of rules that already exist or are as naturally produced as possible (ex: clickstream analysis gleaning insight from something people already do) and integrating these rules.

Exponentially Growing Complexity. There are many powerful trends adding to the complexity of our lives. It’s important to recognize them.

Things Quickly Become Complex. A short true story of how complexity slapped me in the face.

About Eugene

Business Intelligence and Predictive Analytics on the Microsoft BI Stack.
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2 Responses to Map Rock Problem Statement – Part 1 of 5

  1. Pingback: The Magic of the Whole is Greater than the Sum of Its Parts | Soft Coded Logic

  2. Pingback: Planning a 1-Day Symposium in Boise on the Utilization of Graph-Centric Data Technologies in Business Intelligence | Soft Coded Logic

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