Organisations have too much data. On the top of it, there is a continuous active pursuit of accumulating more and more data. On the other hand, there is constant pressure and shareholder expectations for corporate strategies that are simple, effective, and are hard nosed for long-term survival. In a majority of instances, executives struggle to squeeze the data & insights end of the funnel to get actionable strategies 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.
PwC has written a great report that provides guidance on how Key Performance Indicators (KPIs) should be selected for corporate strategy reporting and performance evaluation. More at the link below:
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 and there are explainable reasons behind them (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 into the corporate strategy planning process?
The biggest challenge that organisations, and more so global ones, face is the aspect of ‘disparity’. In most (if not at all) decentralised global organisations, and even in centralised ones, data accumulation happens in a way that can be described with the age-old phrase, ‘the right hand does not know what the left is doing’. What is the outcome of these actions? Disparate sets of data that have some actionability at a local level, but their real potential (at a global level) remains unidentified because no one takes an initiative to “join the dots”.
Ask an agency or a consultancy embarking on a global strategy project or ask a client who is very serious about commissioning one on the single biggest challenge they are expected to face? The unanimous answer will have three words in it (accompanied with different degrees of emotion), “stakeholders”, “alignment” and “management”. Unfortunately, one of the consequences of addressing the three words above many a times means the creation and accumulation of more data.
In this 2013 article by McKinsey & Company, one of the key tenets behind building an effective data-driven strategy is selecting the “right data” and selecting it “creatively”:
An insightful sentence in the article reads, “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.”
This sentence strongly resonates with me in terms of my experience working with multiple global clients. We can think about it any levels of strategy formulation (and it does not necessarily needs to be corporate strategy):
- Category: Let’s assume that six different countries in your global organisation matrix who have gone ahead and conducted their own category segmentation studies (albeit with differences in approach and focus areas). You can create a cohesive, insightful and actionable global category strategy by combining these six segmentation studies, and without having the need to conduct a fresh one
- Brand: You have a global brand that is at different levels of maturity in different countries (in one market it is a challenger, in one market it is a leader and in a few others it has just entered). If there are multiple brand evaluation and health monitoring programmes in place in each country, you can easily bring together local insights to create a global brand positioning (which is consistent but has strong level of local insights in it)
- Advertising / Communications: First of all, and this can be a separate article in itself, there is a need to let go of the crutch of continuously monitoring the impact of your advertising (sometimes in media neutral time periods, sometimes in target groups that may not have been reached etc.). So if you have a global creative or even locally adapted ones, local level insights from a combination of media planning and research agencies is good enough to inform your creative strategy
I have simplified some of the processes above to allow for strategic use of existing data (in reality some of them can be quite complex and long-winded), but the end point will still be the same. It is always a significantly more efficient 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 the power of predicting. So till the time we have AI taking over the whole process of marketing and corporate strategy, it still makes prudent sense for organisations to maximise the potential of their biggest asset (data).
For those who are interested, our end is very near:
Effective and creative use of existing data assets requires marketers and strategists to adopt a few behavioural principles, all of which are supposed to challenge traditional thinking and reacting to it:
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. This link should provide some perspective:
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 and strategists should relentless push back on 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 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.
Here is a good comprehensive document from EY on how KPI selection, setting and monitoring should ideally be done:
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.
I will summarise this post using the earlier quoted ‘drain blockage’ analogy. 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).