Using data as a strategic asset

Any organisation today is sitting on a pile of data. But there is no respite as there is a continuous pursuit of accumulating more and more data. There seems to be a data deluge, which has led to the creation of monikers like “data is the new oil” (which in itself is a misnomer). As sources of consumer interaction and engagement have increased, so has the volume of data. To what extent is this new data any actionable is a big question.

On one hand organisations have this deluge of data to deal with, while on the other hand they are under constant pressure to simplify their business operations. Having complexity in the source that can provide insights for organisational growth does not help in charting a simple strategy. In majority of instances, executives struggle to squeeze the data end of the funnel to get actionable strategies out at the other end. It is almost like a ‘drain-block’ scenario, wherein too much substance is being forced through too narrow a pipe, with the expected result.

Let’s take a step back from the ‘drain-block’ scenario. Also let’s give organisations the benefit of doubt that all continuous and active pursuits of data are meaningful (whether it is for the short-term or long-term). So, are there opportunities to distil these existing masses of data into digestible nuggets of ‘insights’ so that they can travel smoothly through the corporate strategy planning process?

The biggest challenge organisations (and more so those with a global footprint) face is the ‘disparity’ and ‘fragmented’ nature of data collection. This is equally true for both centralised and decentralised organisational structures, both in terms of hierarchy and decision-making. In most organisations, where there are separate business units responsible for different parts of the product / brand portfolio, data collection and its accumulation happens in a “the left hand does not know what the right hand is doing” manner. Whenever a organisation embarks on a harmonisation programme, the first level to align is the different ways of looking at the same thing. In most cases this disjointed and fragmented blocks of data have limited use and actionability, and even though collected globally, does not allow the organisation to shape a global strategy.

In a piece of analysis conducted and published in 2013 by McKinsey & Company, one of the key tenets behind an effective data-driven strategy was having the ability to creatively select the right data from the available pool. McKinsey also observed in its analysis — “Often, companies already have the data they need to tackle business problems, but managers simply don’t know how they can use this information to make key decisions.”

The availability of data, its unstructured and fragmented nature and its form has an impact on strategy creation and implementation at all levels.

  • Category: Lets assume there are six countries in the global organisation matrix who have gone ahead and done their own segmentation work. The objective was to identify local market opportunities for effective portfolio deployment. Without having the need to conduct a new one, these six disparate segmentations can be bought together (fused) through analytical techniques to create a global opportunity matrix for portfolio rationalisation
  • Brand: For example there is a global brand at different levels of maturity in different markets (leader vs. challenger vs. new entrant). Just like the segmentation example, if there are different brand health and equity measurement programmes in place, then comparable metrics can be identified to arrive at a brand’s global health check
  • Advertising: Global and local creative development strategies always come into loggerheads in global organisations. The debates don’t end at creative development but continue on to their effectiveness measurement. Disparities in measurement frameworks (and resultant metrics) make global creative strategies difficult to implement. But by going deep into local level insights, the creative development process can be developed with a localisation objective. Measurement frameworks, even though fragmented, can still be harmonised to find common truths

Some of the processes above have been simplified to showcase the opportunity for strategic use of existing data (in reality some of these processes can be quite complex and long-winded), but the end point will still be the same. It is always a more efficient and productive process when organisations maximise the potential of existing data (and not add to the stockpile).

A critical thing to keep in mind is the fact that ‘strategy is forward-looking while data is backward-looking’. Predictive analytics is still a fledging body and can only become more accurate when the inputs going into the models have been creatively and strategically selected.

Effective and creative use of existing data assets requires marketers to adopt new behavioural traits, all of which are supposed to challenge traditional mindsets towards data:

1) Control the habit of ‘asking’ or ‘commissioning’: A new strategic question does not require asking more ‘new’ questions or requesting for more data or commissioning new projects. One of the biggest success factors around gaining visibility and authority on social media is the ability to do excellent curation. The same principle can be applied to answer new strategic questions / challenges. Curate existing pieces of strategic work, conduct due diligence of recommendations given by your key strategic partners and dust off some of the reports that have been accumulating on your desk.

2) Challenge the definition of ‘new’: As mentioned before, adding to the data stockpile is often a result of questions that are perceived to be ‘new’, while in reality these are questions that probably has been asked in a different form by a different part of the organisation. Really, really challenge the definition of ‘new’ when a question comes attached with a new data request.

Let’s revisit the definition of “new”:

  • produced, introduced, or discovered recently or now for the first time; not existing before.”the new Madonna album”
  • already existing but seen, experienced, or acquired recently or now for the first time.“her new bike”

Constantly challenge the definition of “new” by reverting to the possibility that it can already exist and is just being presented in a different form.

3) Let go of your ‘data crutches’: In a lot of instances, answers to challenging or vexing strategic questions requires fresh interpretation of existing data or a new way of thinking. This should be the starting point. It should never ever start by a request for new data. Marketers should realise the fact that consumers and markets will never evolve or change with a speed akin to programmatic ad placement. Letting go of your ‘data crutches’ does not mean turning a blind eye to an asset that you can mine. It essentially means stop asking for new data whenever a question is posed to you

4) Push back on KPIs: The acronym Key Performance Indicators has the word ‘Key’ in it, which points towards focus. Marketers should relentless push back on any increasing list of KPIs (the moment they are more than 5, the acronym in itself loses value). Lesser the KPIs, lesser is the need for data and more importantly, lesser is the probability of getting paralysed by data anxiety

If you have had successful product launches in the past, analyse how many KPIs were used to monitor product performance across the innovation lifecycle. If you have had successful advertising campaigns in the past, understand the KPIs on which their success was measured. You will be surprised on how few they are. Apprehension and lack of confidence influences the use of ‘data crutches’, which in turn warrants the need for more KPIs. This leads to more data being collected and the cycle continues.

5) Instil an attitude that hastens ‘redundancy’: This can be the most controversial of all, and can only come through a thorough understanding of different types of data and their usefulness. Decision-makers should have confidence to shoot down unnecessary, time-consuming, cyclical, open-ended and fragmented data requests. Such kind of requests (whether added to new or existing questions) just adds to ‘mining’ time and does not inform or influence strategy.

A constant ‘redundancy’ process can be quite useful. Every piece of data collection exercise should go through a due-diligence process that identifies all possible uses. Anything for which it cannot be used shouldn’t be part of any request (either internally or from external sources). Decision-makers should also follow an ‘expiry date’ principle. Unless very strategic in nature, every piece of data should have an expiry date. If a similar request comes before an asset’s expiry date, then the new request should be immediately rejected.

For the data-insights-strategy funnel to work effectively, we need to minimise the amount of information flowing through the funnel. This can only happen through a relentless process of sieving and throwing out and letting only the ‘diamonds’ live within the organisation. Yes, tactical data is critical for making short-term decisions, but we need to make sure that it is used for what it is supposed to solve (and every piece of data request does not become a tactical one).

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