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The way it is, and all that jazz

What triggers a review of organisational strategy?

Well, typically organisations usually shy away from major strategy reviews when things are just ticking over quite nicely. The old axiom of “if it isn’t broke don’t fix it”, has a lot of power of persuasion, even in cases where the logical and coherent thing to do would be to continually review strategy.

Many companies reach for a new strategy when one fine day they are rudely awoken to the fact that they are not doing as well as they once did.

Basically, organisations will seriously think about strategy at times when stakeholders and shareholders start to kick up an almighty ruckus.

With this incentive behind them, organisations embark on the tortuous journey to a new organisational vision and strategy. Or do they?

For the sake of brevity I will exclude all of those organisations who look at the strategy process as a great big exercise to produce an organisational wish list, for as warm and fuzzy and socially responsible it might be. For as laudable these initiatives might be, they just don’t count as strategy in this paper.

So, in the process of arriving at a strategy, we need to address the vision, the challenges to the vision and coherence of the executable strategy. In order to address these core elements effectively we need to be able to access as much relevant and actual information and data we can in order to generate, test and categorise our hypotheses.

So, where is this information and data and how can we get it?

I will take a brief look at three major areas of information and data, which roughly correspond to:

  1. Operational awareness. Data associated with key business data objects.
  2. Market forces. Competitive data taken from disparate data sources.
  3. Strategic fit. Data and information taken from disparate data sources, unstructured data and held in people’s heads, email servers, document archives laptops, tablets and mobile phones.

Now it must be made clear, upfront and clear, that data in an organisation is subject to the vagaries of data quality assurance. Therefore, in a far from perfect world, we will usually be looking at the following issues and will have to work with them, no matter:

  1. Incomplete and missing data.
  2. Erroneous and compromised data.
  3. Overlapping and contradictory data.
  4. Data as noise.
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