Convergence

In the 1990s there was a lot of buzz around "Convergence" in the Telecom/Internet space. This referred to the merging of technologies used for textual Internet data, telephone/voice calls, and video conferencing. The ambition was to have one unified communications network for all types of media. A lot of technologies (e.g. Asynchronous Transfer Mode) were specifically developed for that purpose. Yet, ultimately things simply converged into an already existing protocol, TCP/IP. As systems became faster, production cheaper, and the necessary support maturity for the established network infrastructure became common place, it was pragmatic to have the communication need merge into existing technologies. 

Consider the following evolutionary timeline between the dawn of computing and modern applications, starting from the bottom of the stack. Notice the tilting from "Technically focused" into the "User oriented" space over time. Early in the evolutionary cycle development was driven by what was technically possible, progress was largely limited by technology constraints at the time. Higher up in the evolutionary time line, we notice the shift more toward optimization for user value. The development goals are driven by the business goal, not the technical feasibility.

If we consider the evolution "through the ages" among interrelated aspects, we get a better sense for the concept of convergence:

If we follow the trajectory of what has become Business Intelligence, we see a similar trend to innovation not being so much in the individual functional tool or technical infrastructure space, but in the maturity of their integration. Along those lines, we can project where Business Intelligence may go.

What is today commonly referred to as "Business Intelligence" will eventually evolve into the following concepts:

Cloud Services
Abstraction of the underlying Information Technology & Software infrastructure from the user.

Semantic Web
Smart tagging of content, more meaningful search results, sets the stage for collective intelligence.

Social Media
Collaboration portals & market places for dynamic interaction and trade. Exchange of knowledge, virtual goods, services.

Crowd Sourcing
The effect of dynamic and self-motivated work distribution based on individual interest, capability and availability.

Collective Intelligence
The iterative, incremental and recursive knowledge evolution as a result of crowd sourcing. More powerful than any algorithmic search engine.


Also, notice the evolution of the network effect. Originally, things evolved in a single stack. Then more vertical stacks developed more or less in parallel, with little integration. And ultimately, the various concepts play into each other, in reciprocal and recursive fashion, causing the effect of emergence (complex behavior arising from comparatively simple parts).

The network effect and emergence are not to be underestimated in the evolution of what is known as "Business Intelligence" today. Ultimately they will contribute to various different technologies, methods, and cultural paradigms to converge into the next level of intelligence, business being just a part of it, but with much dependent on and impacted by.

Future, Agility & Pragmatism

Another shameless pitch for our sister blog "What the Future may hold":

Why is it so hard to predict the future?

I am cross-posting this because this question is very relevant to the Business Intelligence and Analytics space.

If you are in the business of predicting your business outlook, it is of little value if you don't frequently check course, to make sure you're on target of your prediction, or better yet, if you anticipated target has moved, and you may need to adjust your strategy.

This is where Agility & Pragmatism in Business Intelligence comes in.
Check your course often, adjust as needed

Collective Intelligence

A few posts back I was struggling to find a term for the phenomenon of what I called then "macro intelligence". Turns out (credit where credit is due), there has already been coined a term for that: collective intelligence

Diagram courtesy of Wikipedia.com - Author: Olga Generozova. The diagram is based on the types and examples of collective intelligence discussed in the books 'The wisdom of crowds' and 'Smart mobs'

The momentum in the media for this phenomenon only came from the proliferation of what we call "social networks" on the internet now. But in a way, the trend has been there all along, just the media has become more efficient and finally put the potential of high-velocity collaboration into the lime light.

Collective Intelligence was the concept I so ardently tried to wrap my head around, when I aspired to integrate Business Intelligence with a more holistic approach to approach broad solutions toward complex problems.

Cross Reference

Since Business Intelligence and Agility have a mutual relationship, allow me to reference a pertinent article from my blog about Agility here.

After all...

• Business Intelligence implementations (on any level, whether technical, organizational, or cultural) benefit from an agile approach (evolutionary, incremental, iterative).

• And Business Agility can be increased and supported by mature Business Intelligence practices.

So they go hand in hand, as these two blogs go.

The Human Side of Business Intelligence

For those who wonder whether there are more tangible recommendations coming in this series of thoughts about Business Intelligence, I would like to clarify a few things...

Intelligence is about empowerment, self-motivated learning, not being told what to do. As such, this is not a "how-to" guide. This forum is supposed to stimulate thought, hence the popular format of asking questions, instead of claiming static/universal answers. It is a journey of discovery, and not finding the goals for you.

I feel that there are plenty of Web sites, discussion forums, articles, and blogs already that address the marketing side and technical aspects of "business intelligence".

The big opportunity for evolving more maturity in the B.I. space, the way I see it, is in the common sense department.

If B.I. is to provide intelligence to business, then the following is relevant:

• businesses are operated by people, humans.
• businesses serve people, humans

Therefore, BUSINESS intelligence is foremost about the intelligence of HUMANS.

Short of strategy consultants, professional coaches, and program managers, I don't see that reality addressed much in the general B.I. hype.

To address this space, the human side of business intelligence, is my mission in this blog.

A Menu of Topics

I have a couple of topics in the pipe line, which I would like to throw out there in summary. I am a big friend of interaction, feedback. Intelligence, after all, encompasses learning, and learning relies on feedback loops.

I appreciate any input on priorities, additional topics, scope, depth, relevance on what I have in mind so far for future articles in this series...

Why Scorecards without Discussion are useless

Numbers (quantitative data) without context (qualitative information) don't explain anything.

A good business leader will use scorecards to initiate conversation with business stake holders, inquire context, stimulate exchange on background and ideas to respond to realities. Reflexive response to numbers without context is NOT business intelligence.

Social Business Intelligence

Self-serve analysis tools integrating different users' interests, sharing the patterns with each other, surfacing correlations of interest and overlapping functions, increasing transparency and accountability. The dawn of B.I. Wikis.

The Power of Reflection

Reflexive behavior has its purposes. But it needs to balance out with deliberation, intentional delay in response, to consider. Just like any good diplomat knows, sometimes delaying action is a good response to a crisis, for example to avoid escalation, going down the wrong direction.

The non-functional requirements
The following aspects are a case in point that "Business Intelligence" is not mainly about technology, software, or databases, but starts with people.  A business, as we know it today, is still largely comprised of and driven by people, humans with individual personalities, preferences, and attitudes. Thus, it is important to recognize the non-functional pre-requisites necessary to support any Business Intelligence effort.

More on the LEGO principle and how to apply it to B.I. 

• Build complex solutions by combining the simple
• How do you keep control over the emerging complexity?
• Self managing parts emerge in responsibility for the whole

All these dependencies and assumptions 
Why the distinction between "structured" and "unstructured" data? 

Streaming Business Intelligence
No more "ETL"! The usage of data will have to be driven at demand-time, not design-time.

The historical strata of B.I. / Decision Support

At any given time we have to support and integrate that which we built in the past with that which we need today, and with that where we may go in the future.

Abstraction, a Quality of Intelligence

"Intelligence is a term describing a property of the mind including related abilities, such as the capacities for abstract thought, understanding,communication, reasoning, learning, learning from past experiences, planning, and problem solving." [http://en.wikipedia.org/wiki/Intelligence]


Why is abstraction relevant? It helps us narrow down the myriads of information coming in from the world into our limited sensory and memory capacity. Abstraction helps us recognize patterns, without getting bogged down too much into infinite detail. It helps manage boundaries in information processing. The distinction of similarities and differences is vital for human understanding. It is the spirit out of which the Ying/Yang philosophy evolved.


Abstraction fosters re-use and adaptability.


In the marketing realm around "Business Intelligence" much emphasis is put on silver bullets, such as "the tool" or  "the method", solving all your B.I. needs. Amazing how these claims of solving needs are made before even knowing the individual customer these are marketed to.


I guess to a certain point, these solution providers determined that there are common patterns to address, and they created and market a common solution for these kinds of problems. The purpose of a tool is to abstract a specific problem, so the tool can be re-used across any particular instance of a need. Generic vs. specific.


Yet, the abstraction of B.I. tool providers are still very specific. They assume the problem space to be very narrow. "Your company needs a Data Warehouse". Why? For what business purpose? Isn't there a deeper business opportunity than just slicing & dicing numbers across dimensions? Numbers without context are useless as metrics. Can your tool help me establish context between what is good, and what is bad, for my business?


Will any technology, software, database be capable enough to tell me what I should do, from a business perspective, instead of just spewing out numbers, or information without context?


So, to put the call for more Abstraction in Business Intelligence into context, let's step back for a little bit, and re-evaluate what we are trying to do in the bigger picture of our businesses. Why do we have a B.I. initiative? Have our B.I. programs become a self-fulfilling prophecies? Let's ask ourselves if there isn't a more generic way to address the challenges, and focus on more  adaptable approaches, instead of getting bogged down in the granular details of technology and present-day tool capabilities.

The A.B.C. of Intelligence

Gotta love how marketing terms fall into place...

A.I. - Artificial Intelligence
B.I. - Business Intelligence
C.I. - Customer Intelligence

And in combining these, we can describe a phenomenon at the hight of any economic bubble:

Artificial Business Customer Intelligence

Cheers! :-)

A Culture of Business Intelligence

QUESTIONS


Along my mission to help refocus Business Intelligence off and away from that mindless culmination of technology, tools, and thrown together (or even over-engineered) data, let's review some of the, seemingly, common-sense aspects of Business Intelligence....

How about asking tough questions.
What good are answers that we have no questions for? (play Watson ;-)

How about an intrinsic shift in business stakeholders thinking.
One of the hallmarks of human intelligence" is "thinking", so why should this logic not apply to business as well?


How about more accountability (transparency about what's being done and why; ownership, clear expectations and costs associated)


How about more commitment, and less lip service 
(business intelligence starts with all business stake holders, regardless of B.I. initiative, tools, data)

How about awakening genuine interest ("what's in it for me?") , instead of mandating, top-down. This will foster agility. 
Goal-seeking agents versus puppets on a string (that doesn't scale). Self-motivated behavior is more effective than central planning (this insight courtesy of a 70 year study in the Soviet Block)


How about being honest about the risks and rewards, and keep a healthy balance between them.


How about minimizing that which does not add value, or can't be tangibly justified.




ANSWERS


There is a reason why topics about organizational or process Agility interlace with the Business Intelligence thread.


How would you feel if you had only one sink in your house. And from the faucet of that one sink would run water only once every 6 hours. And it would take about 5 hour delay between you opening up the spigot, and when liquid would actually flow out of it. And once it would flow, you realize it's dirty, soapy waster water, instead of fresh drinking water.


Now if you are involved with an existing "Business Intelligence" infrastructure at your company, compare the above analogy to it. Not far fetched, eh?


In the developed world, the water utility company typically provides fresh water, 24x7x365, by default. It's not that the commercial water provider fails a lot, and only responds to complaints, and then only fixes those household water supplied where the highest fine gets ordered.

Again, compare this conceptually to you Data Warehousing/B.I. infrastructure. Okay, I guess fires can be fought with waste water just as well ;-)


So why does this 100% availability & quality service fulfillment work?

1. customer expectations (in those societies that have had infrastructure stability for a long time)
2. adverse consequences (legal, financial, reputation)

And again, compare this to your organization, in terms of Business Intelligence... 

(1) represents the consumer of your B.I. service. 

(2) represents the B.I. service provider.

If, in the business world, 

• the B.I. consumer's expectations would be so ingrained, as to accept nothing but the most timely and accurate information, available whenever demanded (and not dependent on supply schedule), and if 
• the B.I. provider would have a stake in the adverse consequences to the business, when the B.I. deliverables do not measure up to the rightful expectations (timely, accurate)

do you think Business Intelligence might be a more successful undertaking?

Compared to today's most common B.I. approach of...

• the supply side (B.I. provider) defines the SLA, based on what's "doable" (which is often heavily deluded based on the provider's limited capacity, or competence)
• the demand side (B.I. consumer) not being aware of what's possible, and how much more empowered they could be (just as the competition that has figured this out!)

So what do you think would it take to adapt the "utility" mindset (a culture of reliability, continuity and service) to the Business Intelligence space?

Will "B.I. in the cloud" take care of this? 

And if so, do the people running "the cloud" know all this?

The Semantic Overload of "Business Intelligence"

Between head hunters, sales brochures, pundit articles, and social forums, it appears more and more that the concept, not just the term, of "Business Intelligence" has been hijacked. And since often perception is reality, this misconception is adversely affecting the quality of actual business intelligence usage, in turn giving it a bad reputation.

One of the major definitions floating around has been that "Business Intelligence" is...
"the processes, technologies and tools needed to turn data into information, information into knowledge and knowledge into plans that drive profitable business actions. Business Intelligence encompasses data warehousing, business analytic tools and content and knowledge management"

That is like saying "A company's profit is what its SAP application shows in the GL reports". Hmmm, no. Profit is actually real money in the bank, tangible currency. The description above is about software, process, and data.

Finance is about money, as opposed to "Financial Processes", "Financial Systems", "Financial Reports", etc.

• Processes are about streamlining tasks, making them more reliable, efficient and verifiable.
• Systems are about implementing processes, automating them, speeding them up, versus manual labor.
• Reports provide information, not money. Checks provide money.

Business Intelligence is a company's insight into its processes, how they perform; or in alternate terms: the awareness of its actions and how they influence its customers, market opportunities and business operations. It is conscious about relevant cause & effect relationships in its business handling.

Intelligence is cognitive capability.

Not a tool.

Not a system.

Not an architecture.

Not a process.

There are, however, business intelligence processes, architectures tools, and systems.

And there are plenty of good reasons to build, sell, integrate, and use them.

Just call those tools, systems, architectures, processes by name.

Why is this important?

Because so many companies struggle gain any value out of their B.I. investments, as they assume they are done, have "Business Intelligence" already, by mere acquisition, implementation, and casual use of B.I. tools, systems, processes. And then everybody wonders what went wrong; expectation missed by confusing intent.

The responsibility for the suffering of the silver-bullet syndrome is equally distributed between eager vendors, over-hyping their tools' or services' capabilities, and the naïveté of decision makers falling for the hyped promises. 

Business Intelligence is a mind set. A way of doing business. Not a the sales/implementation of of Information Technology, Software, or Databases.

Follow-up: Several days after I had written this, I stumbled over another blog along the same lines. The two articles were authored independently, but speak to the apparent state of affairs in 

Integrating Agility

So "agile" is one of those popular buzzwords, you can add it to anything, and it makes it sound cool....

Agile Software Development.

Agile Project Management.

Agile Business.

It's one more sad case of lost meaning by over-use. But oh well, we live in an overzealous marketing culture.

So let's step a bit. Semantics, after all, is a key pillar of better Business Intelligence and the future Web.

Agile software development seems at quite a mature stage, so we'll exploit it as a model for the other layers. The principles are sound, the opportunity is in more organizations adopting agile development methodologies, and embracing iterative & incremental delivery.

Agile project management is evolving right in line with agile software development. If you think of agile development as the physical (because tangible) part, let's consider the project part as the logical aspect. Lots of good thought has become established and is applied to more and more projects as we speak.

Agile business is less formally defined, and more driven by creativity, competition and new opportunities.

So where's the catch then? As is my passion, in integration.

If we can agree for a moment that the number one challenge these days in suceeding with any endeavor is complexity, and furthermore, that complexity at a high level is comprised of

• Scale (the sheer size of an undertaking, the volume of elements involved)
• Dynamics (change, speed, reciprocal interactions)
• Context (relationships, structural, logical, semantic, social/behavioral)

then we have a good basis to address the challenges more concisely, without getting bogged down by academic details (I am a pragmatist, after all).

So where was I going with this.... right, integrating agility between the development, project and business levels.

So if I understand things right, agility manifests itself in practice as

• cyclic revisiting of multiple phases (iterations),
• with incremental value added to (often intermediate) results,
• based on high levels of interactivity & feedback between the stake holders (developers, project managers, the business)

So, on the business end, this would manifest itself to something (simplified) as A/B Testing in Marketing.

On the project side, this would correlate to timelines, deliverables, and resource load/distribution.

At the development level, this is guided by tangible artifacts, and progress toward completion as well as quality tracking (testing)

The challenge remains, how do you integrate theses seemingly not closely correlated layers? I know, methodologies like Scrum imply that everybody is involved in the same cycle. The reality is that every role (of the various stake holders) has a different perspective, and therefore implicitly different priorities, and potentially diverging (tactical) goals.

So how do we synchronize goals of the different roles & perspectives? Perhaps we should borrow a page from John Zachman? Also, David Hay has expressed some intriguing thoughts along those lines. In another article, we shall explore some of their concepts & ideas as they are applicable and re-usable to the B. I. space.

Why am I bringing all this up? Because it's part of Business Intelligence.

Not the numbers, but the results!

Where is this all going?

So I can see you out there wondering, "where is this guy going with all this?"

My intent was not to educate, but to stimulate, thought. Make people interested in this space ponder the possibilities, reflect on the impact, of automating and scaling intelligence.

Brainstorming has a tendency to dump impromptu thoughts out there, with later iterations iteratively sorting through the incremental results and picking the most interesting and relevant bits from the pile, refining them to a more coherent picture. So in the spirit of evolutionary development of the world, and agile progression of methods, tools & technology, this series too evolves as we go along.


I've always been an integrator, not an inventor. I let brighter minds discover the ground-breaking stuff, and once it's ready for prime time, I will aspire to make it work together. It takes all kinds, the precisely focused genius to come up with new things, and then those with a more generic perspective, putting the pieces together for broader solutions. I see myself as the latter.

We are at a great juncture in the Business Intelligence / Web Analytics space. It appears that we are at the juncture where Analytics & "Intelligence" (at whichever questionable maturity level) starts becoming part of the fabric of the Web, soft of as layed out in the described evolution of the upcoming Web 3.0/4.0 of "Semantic Web" and "Metaverse" fame.


In the telecom industry of the late '90s, they called it "convergence". Back then it referred to (voice) telephone calls, video conferencing, text messages and other data traffic using the same network. An old hat by now, but someone had to come up with the vision.

It seems to become obvious now that we have a new convergence going on between...

• Cloud computing (technology advances, cost efficiencies)
• Artificial Intelligence (autonomous software agent-driven systems)
• Social networking (crowd-sourcing of quality control & investigative journalism)

Now, if you consider the 

1. Computing Cloud to be the infrastructure (just what it's intended to be),
2. the Artificial Intelligence parts the thoughts (processes, algorithms), and 
3. the Social networking the sensory input from the real world (the people, human intelligence, component), 

you have the classic Hardware->Software->Data pattern traditionally applied to computers, while at the same time the model of the human brain is mimicked here: 

• Nerve cells (infrastructure), 
• Thoughts (A.I.)
• Sensory Input (human-driven social networks)

Considering that what makes the human brain stand out is called "intelligence", then it's not far fetched to recognize this new global / social / media / software / service / network driven phenomenon as some sort of a macro-intelligence, just like the term "macro-economics" has become established for global trade & finance.

The pieces are coming together slowly but surely...

*.intelligence

So whatever we may call the larger *.intelligence movement (heaven forbid, humans actually measuring up to their claim of being the most intelligent species around? <chuckle>) ...

I am neither working for a spy agency, nor would I ever dare to claim to be in the know of it all. I also don't have scientific credentials. My judgment is largely casual observation / intuition / experienced based.

But that's the whole point. As an individual, humans tend to be notoriously ambiguous in their perception, and often off in their resulting judgment (due to the emotion component).

Yet, just as George Soros stated in  "The Alchemy of Finance", human emotions are relevant to consider because they are causes for downstream effects, which in turn affect other emotions (Soros called it "reciprocal" relationship).

So emotional judgment is real, and important to consider. You take the individual opinion, impulse if you will, and connect it on a massive scale (in a reciprocal fashion), and you get emergent behavior (that's from chaos theory: large crowd of agents with conscious individual activity, responding to its surrounding, mutually influential).

I am not the first one to link Artificial Intelligence to Analytics and Business Intelligence. But often I ponder in my day job about how we are doing reporting pretty much the same way like in the mainframe days, based on the turnaround cycles, except that now the reports are graphic instead of green/white-lined all-caps lines of letters. And I wonder whatever happened to the concept of computers automating and speeding up information processing & presentation.

So yes, a bit more agility, the wait times aren't 3 months anymore, but as the Twitter-based infographics from Egypt showed (within few days after the happenings), it is possible to provide impressive intelligence about a complex situation in a digestible format, in very short time. And Twitter wasn't even specifically set up to measure such things, it was more of a side effect, having a simple event based system with a very compact data model (author, hashtags, message, temporal sequence) used on such a broad, yes global, scale.

I hear the groans...."but you can't use stuff like this for financial reporting!"
Why not? I've seen many projects where, after lots of effort, they still struggled to reconcile the numbers and explained discrepancies away with system/business constraints. As 2008 has proven, (human-driven) financial accounting and controlling isn't all that it's shaped up to be, in terms of accuracy and dilligence.

If you have enough autonomous agents, automated of course, via computer software, roam your corporate environment, listening in to your transactions etc. they should arrive at least at the same accuracy as human effort does today, with less effort, and probably in a more traceable (i.e. accountable) way.

Isn't that what they invented computers for to begin with?
They are counting faster and more reliably than humans.

Food for thought...

More than B.I. ?

I have always been more of a strategic thinker. Of course the day job had to be more on intermediate, tactically focused projects, for quick gains, because that's where the money is, and one has to live.

Now as I've had the luxury to take some time off, to distance myself from the daily stress, I gained some interesting perspective.

As I have been catching up with the overall social media / networking phenomenon, and realized how mainstream it has become, it hit me how we are on to something far bigger than "business intelligence", "customer intelligence", or "operational intelligence".

Some of the real-time visualizations of the political trends in North Africa made me realize that we are on the way to Global Intelligence, more real-time than we ever managed to do on the comparatively measly scope of just business.

It is my firm conviction that traditional Business Intelligence, Analytics, Data Warehousing, as we know it, is going to be displaced by this new way of measuring, monitoring, understanding, judging, concluding, that I shall dub for now "global intelligence" (uh oh, sounds so clandestine, doesn't it ;-)

I'm sure someone out there has already another term for it. But hey, the telephone was also invented by different  people around the same time without them knowing of each other.

I am pondering whether I should re-brand my blog to cover the bigger phenomenon. What do you think?

What's the Purpose of all this?

What is the purpose of your business? The intrinsic driver of being in business?

What helps you to gauge whether you are on target with your business goals?

Your individual story will vary, but here is a generic example of what I'm after. Whatever the goal of your business is in terms of product or service offering, your business' ultimate purpose is most likely to...

• maximize revenue
• increase profit
• attract & retain customers
• minimize risk exposure
• achieve recognition (market leader, reputation/image)

Do you know what you want to get out of your Business Intelligence efforts, investments?

Would you like to...

• get a handle on how your business performs ?
• understand your customer better ?
• identify inefficiencies in your current business processes ?
• determine risk or exposure ?
• recognize growth opportunities ?
• test a new business model/process ?

Then relate your B.I. inquiries to your top level goals...

Business performance, in terms of 

• revenue
• profit
• customer satisfaction
• risk

Understand the customer better, in terms of 

• conversion
• retention
• life time value

Inefficiencies, in terms of 

• investment return
• customer churn 
• expense ratios

You see how these quick summaries of purpose could easily be used as a headline for any B.I. feature request? It's that straight-forward, once the awareness is there.

We all strive for results. But what is their ultimate purpose?

Semantics

BUSINESS. INTELLIGENCE.

What is Business ? 

• Markets, Opportunities, Demand
• Products, Services, Offerings
• Customers, Partners

What is Intelligence ?

• Learning (by observation / from experience)
• Understanding (making sense of surroundings, figuring out what to do)
• Abstract thought (modeling, generalization, recognition & application of patterns)
• Reasoning (rational thinking, judgement, sense, explanation of cause & effect)
• Communication (knowledge exchange, collaboration, influence)
• Planning
• Problem solving (overcome obstacles)
• Goal-oriented (act purposefully, solution-seeking, leverage tools)

Combined... Business & Intelligence

• Learn - about the opportunities, the customers
• Understand - how much demand, in what area, from where
• Reason - what offerings can serve the demand
• Establish Patterns - comoditize, automate solution (for efficiency, scalability)
• Communicate - with target audience, partners, suppliers, enabling organizations
• Plan - product development, distribution channels, support
• Solve - any logistical, legal, organizational challenges
• Goal - set it, achieve it!

These patterns of thought remain stable, no matter the latest market dynamics, technology fad, or "expert" opinions. These are common sense you can build on, model after.

Ask yourself at every step, with every function, every outcome of your B.I. initiatives:

do the results help you learn, understand, communicate, solve problems, achieve goals?

Can you correlate your efforts on B.I. projects, and your investments in B.I. infrastructure to improvements on the business side? 

Results do matter

Business, Intelligence, Results.

Actionable Insights.

How do you measure "actionable" ?

What works, and what doesn't?

What is the consequence of your B.I. driven actions?

Your B.I. tools give your insights.

Your business takes action as a response to these insights.

Then what? Did it change anything? How does it compare to before?

How do you know that the changes in business outcome were a result of your changes?

Would they have organically evolved anyway? 

You won't know the difference if you don't measure. 

And what do you measure? What is your base line? What do the numbers mean? 

Do you have the proper context to compare them? (trend?)

Are they comparable at all? (apples vs. oranges?)

Existential Questions:

- where are we? (Status)

- where have we come from? (Growth)

- where are we going? (Trend)

- what has gotten us here?
- where should we be going? (Goals)

- what will get us there? (Process)

Questions over questions, and to find the right answers is part of the B.I. puzzle.

Anybody can come up with numbers. But you need meaningful results.