To celebrate the addition of talkRA content to BillingViews, we found this from a year ago. Still completely relevant.
We are pretty sure that Analytics is the answer. We are less sure what the question is and we should acknowledge that, actually, Analytics is not the answer all the time. As the technology improves and improves our ability to automate processes, we tend to believe that technology will solve all our problems and tend to engage technology before we engage our brains.
This example from Jeff Jonas’ blog makes the point very well. A conversation between him and potential customer identifies the objective of using analytics is to find bombs. Using this example (and pointing out that you will only find bombs using analytics if someone writes ‘bomb’ on the side of the package) he “recommend(s) first qualifying the available observation space to determine if it is sufficient to deliver on the mission objectives.” If the available ‘observation spaces’ – data sources – do not provide sufficient data to get the job done, then it is worth considering how you might widen those observation spaces. He also points out that systems with multiple data points are a much richer source than those that harbor just one.
The bottom line is that if you cannot figure out the solution yourself or with your team, using the available information, then do not invest in a monster system. The point of analytics is that it can speed up and scale what can be achieved by the human brain – nothing more. With Analytics being the word of the year and probably that of next year, we need to take a clinical view of what these tools can actually achieve and not believe too much of the magic. The excellent revenue assurance blog, TalkRA recently dissected the announcement of Subex’s Zen, using his analytical brain to work out exactly what Zen could achieve. The answer was not the ability to find a needle in a haystack, nor a magic wand to find profit in products. The answer was that it would now take an analyst a quarter of the time it used to take to complete a job – if it was used properly.
Frankly, in the world of Bigger Data, that is magic enough.
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