These are the six factors which collectively determine the likelihood of getting insight from an analysis. These are:
a. Relevance – is it the right data?
b. Quantity – is there enough of it?
c. Quality – is it good enough?
a. Have any?
b. The right ones?
a. Have any?
b. The right ones?
a. How much?
a. Have a specific question to answer or more generally an issue(s) to address
a. Have the desire and drive to want to address the issue – personal or corporate.
Like all sort of such indices, this not a science, nor is it an art, more a framework for thinking about where best to target effort, in order to increase the changes of valuable insight.
There are three ways to apply this. Firstly as a simple checklist. Secondly as an index into which to score against the six factors, and thirdly as a more sensitive algorithm, with some weighting for each of those six factors.
In order to achieve any insight at all, each of these factors needs to have some effort directed toward them, however small or implicit. Equally, in order to maximise insight, then each of these factors needs to (a) maximised while (b) being balanced with the others.
The simplest way to use this is a checklist. The key thing here is to check that there is some effort on each of these dimensions. Also that the effort is sensibly balanced across these dimensions. If there is zero effort on one any of these then that can mean the analysis is doomed from the start. After all there’s no point buying tools if there’s no capacity to use them, or having load of great data but not having the expert skills necessary to undertake an analysis using the tools. Can line up all of the data, tools, skills and capacity, but without any issues or inclination then this can be missing purpose and drive, and be victim of the ‘So what? Syndrome’.
B. Analytical Insight index
This next alternative is simple enough, simply score 0, 1 or 2 for each of these six factors (see below). Then multiply these six scores together. There you have it. The scoring is as follows:
0 – no effort
1 – some effort (or basic requirement)
2 –maximium effort (or enhancement)
So Insight Index = data x skills x tools x capacity x issue x inclination. Minimum product of zero (0x0x0x0x0x0) and maximum of 64 (2x2x2x2x2x2=64).
Now here’s the key point….if any of these six numbers are zero, the product of multiplying these together is zero. Which basically means that if some effort (or even maximum effort) is put into five of these dimensions then that effort counts for nothing if the 6th factor is zero (no effort). In the simplest of terms, everything might be in place except the data, so no chance of an analysis let alone insight.
And if each of the six factors are scored 1. So the product is 1. If each of the six factors scored 2. The product is 64, the maximum value for this analytical insight index.
The more one looks at this the more it becomes clearer that it is quite difficult to get a reasonable value. In fact of the 729 possible combinations, and the most frequent outcome by a big margin is zero, and just over 90% of time. These 729 calculations (combinations of 0, 1, 2) only produce a small number of mathematical products: 0, 1, 2, 4, 8, 16, 32, 64.
This is purposefully designed to focus on the integers, around the way a single zero can annul all other effort and 1s is a basic requirement, and 2 an enhancement.
There’s an even simpler approach which is simply adding the six scores together.
C. Analytical Insight Index – weighty version.
So here’s where this can get a bit more sensitive to specific circumstances, and can be used to reflect both the strengths and barriers for each of these six factors. This is about weighting each of the factors to some degree.
So based on:
Insight Index = Data x Skills x Tools x Capacity x Question x Inclination
This can be abbreviated to the six factors
II = D x S x T x C x Q x I
And each of these factors has its own weighting.
II = (d)D x (t)T x s(S) x (c)C x (q)Q x (i)I
The basic idea is to give higher relative weighting to those areas which are likely to be the biggest barriers, and hence more important areas into which to put effort.
As a starter, here’s my general take on a set of weights, based on my take of where I’ve seen the challenges over 20 years of analysis projects. I have a favourite cooking analogy to help with this too.
1. Data. Weight = 2
There’s typically no shortage of data, and applying some tests of relevance should still provide something reasonable and relevant.
In the cooking analogy, these are the ingredients. Might be expensive, fresh, free range organic through to those ingredients past their use by date.
2. Tools. Weight = 3
There are some basic spreadsheet type tools available to all, and the more expert data crunching tools can often be found in a corporate environments. Important to have the right tools for the job in hand.
In my cooking analogy these are the implements, and there is nowhere like the kitchen for the potential for the widest range of often specialist tools. Often a few tools are the most flexible ones. And some tools are just not fit for some purposes, not physically possible to stir the contents of a saucepan with it’s lid. The of course there are those whizzy tools that only get used once because they are so specialised or just more trouble than they are worth.
3. Skills. Weight = 4
Data and tools are not enough to be able to extract some insight. There’s a great temptation to jump in. Can be a bit like jumping into a swimming pool of data, with armbands, and the flailing around. There needs to be some skills with provides (a) the approach to analysis, and (b) effective communication of the messages. These are quite different to simply being able to use some tools.
In an analogy along culinary lines, if the data and tools (and capacity – see below) are the ingredients and equipment, then it’s the skills and experience of the chef which creates a potential culinary masterpiece.
4. Capacity. Weight = 5
Probably the magic ingredient. The fairly dust to sprinkle on the other factors. Capacity should probably be seen in the context of the money spent on data, tools and training.
In the culinary analogy this is all about the preparation, cooking and presentation on the plate. To extend this analogy further, data needs a minimum level of analysis to be robust, the way chicken needs a minimum level of cooking, anything less and there can be significant consequences.
5. Question. Weight = 3
There’s not usually shortage of questions, and there’ll probably be some relatively clear high priority ones to provide an early focus.
In cooking terms this about the basic need to consume food on a regular basis to provide energy to function. Quite functional really, this is probably a sandwich for lunch at the desk.
6. Inclination. Weight = 3
There’s a mix or personal and corporate here. In corporate terms, unless there’s an Executive Team which is in denial, then there’s going to be a desire to be effectively informed about the data which is relevant and important to a corporate purpose. Great of course if this is directly supporting a specific strategy or business plan objectives.
In the cooking analogy this is about the degree of appetite, or even hunger for that insight which informs improvement. This is maybe even about trying new foods, recipes, experimenting. This is about that food being part of a broader experience, a great meal shared with friends, that food being a means to a broader end.