Notes: I came by this bunch of slides I did for an internal presentation on business intelligence. Although dated and ugly, the points still hold: Always look beyond the buzzword and try to understand the roots of problems; the most difficult challenge in business intelligence is, time and again, not about technology, tools, cost, management, or any combination of one or more of them, but about sheer human judgement and a devotion to probabilistic thinking. The ones who are crazy enough to think they can just buy business intelligence from vendors are the ones who fail.
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Today I’m going to talk about Business Intelligence, what it means, what it implies, its miserable myths and its debunking, and what we need to go for it.
Let’s start by asking what we mean by Business Intelligence.
What do we mean by business as in business intelligence? Well, maybe something similar to what we mean by business as in business value, business strategy, and business success. Business is business.
But what do we mean by intelligence? Is it what we think we it means? Let’s investigate a little bit further.
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According to dictionary, intelligence means:
- The ability to acquire and apply knowledge and skills
- The available ability as measured by intelligence tests or by other social criteria to use one’s existing knowledge to meet new situations and to solve new problems, to learn, to foresee problems, to use symbols or relationships, to create new relationships, to think abstractly
Is that what we mean by intelligence as in business intelligence? Well, maybe. But let’s look at an example first.
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If you open Microsoft Excel, create a new spreadsheet, and input all the data you need, and Excel displays them properly and correctly as expected.
Is Excel intelligent?
What if, besides displaying your input properly, it also automatically calculates the average, median, and even standard deviation for you?
Does that make Excel intelligent?
And what if, besides all that, it automatically provides an estimated number for next month?
Can Excel be called intelligent if it does that? Let’s keep that example in mind…
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…and think about this: what do we expect from something intelligent?
Well, maybe it should not just be about presenting data; maybe it should provide meaningful and therefore valuable information; and maybe it should offer interpretation, estimation, and even prediction.
If something does that, it’s kind of intelligent, isn’t it?
Here’s another perspective: according to Wikipedia, Business Intelligence is the set of techniques and tools for transformation of raw data into meaningful and useful information for business analysis purposes. Although the definition feels kind of narrow, what I really like about it is the notion of ͞techniques and tools͟.
Techniques AND tools.
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Techniques and tools may be the two essential components of Business Intelligence.
Techniques are about how we interpret things: hypotheses, with a mix of many things including the models for estimation and prediction, the mathematical basis for analyzing, business domain knowledge, and data Science.
Tools are what we use to produce the expected results. We are all familiar with the names behind those tools: MicroStrategy, QlikView, Oracle, IBM, etc.
To truly understand what it takes to approach Business Intelligence, we need to start with the notion of Data, Information, Knowledge, Intelligence (adapted from DIKW).
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Data is descriptive.
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If we go further and do some calculations, we get information.
Information is diagnostic.
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Up to now, all we have are just facts. But if we explore further, and interpret the data and/or information in some meaningful ways, we can gain knowledge. For example, if we do a linear trend estimation here, we get trend that says something about how things are going in overall.
Knowledge is analytic.
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If we explore even further, to the final frontier, we may even get predictions, future prospects.
We don’t have Business Intelligence until we get here.
Intelligence is predictive.
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We humans create techniques.
Techniques create insights out of data.
Tools deliver insights.
There’s a major myth about Business Intelligence: the hype vs. the reality.
All kinds of companies are bombarding us on a daily basis about how their products and services, tools and platforms can bring us Business Intelligence. We bring you insights, they say. That’s a Hype mindset that says tools create insights. But in order to understand what they really mean, we need to know the reality of Business Intelligence.
There are at least three layers in Business Intelligence:
At the bottom we have Theoretical Layer: the theoretical and sometimes heavily mathematical basis that we use to create frameworks and establish models to describe, analyze, interpret, and predict. If we don’t know what curve-fitting or linear trend estimation is about, it’s very unlikely that we really know what a trend is about. Likewise, if we don’t know anything about various existing predictive models, it’s very unlikely that we really know when to use which.
In the middle is the Methodical Layer: the practical and applicable methods and techniques through which we put the theories into actual use.
At the top is the Operational Layer, or call it technical layer since it’s almost always about software tools. This is the layer of tools that implement the methods we come up with at the middle layer.
That is the Reality Mindset of Business Intelligence: tools are just the top layer, the tip of the iceberg. Techniques are the the two biggest layers in Business Intelligence: We humans create techniques. Techniques create insights out of data. Tools deliver insights. Many Business Intelligence tools provide a rich library of methods and models for us to use, but the tricky thing remains: we need to know when and where to use which.
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Here’s a summary of the values of data, information, knowledge, and intelligence. One of the implications is about what kind of questions we could ask before even start approaching business intelligence.
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Now let’s go back to the concept of Business Intelligence.
What do we mean by intelligence as in business intelligence?
Whatever it means, it happens in human brain, and is implemented by computer tools.
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It all comes down to try some and buy some. We need to try some techniques to see what could work, and then buy some tools to help us do that work. The work we call business intelligence. Tools can NOT.
And let me end my sharing with this quote I really love:
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Noah Fang 