Your business needs to save money? Step forward data leaders!

I’ve shared a series of blog posts to help data leaders in a business that needs to save money. Some have focussed on opportunity areas for cost-cutting. Others have warned against false economies to avoid. Guest blogger, Tony Boobier, even shared on heroes & villains in a time of crisis.

Building on that general advice, I want to now encourage data & analytics leaders to be proactive at this time. Rather than hiding to avoid the eye of those cutting budgets, I recommend taking a step forward. Data leaders are almost uniquely well-placed to help at a time like this.

You may not feel like that during another round of recruitment freezes, budget reductions and other challenges from senior leaders. But, I hope this post will encourage you. Perhaps it can help you notice how much you & your team have to offer others. Rather than protecting your data fiefdom, let’s consider how you conserve the wider organisation & become that much trumped ‘trusted advisor’.

Why are data & analytics leaders well-positioned?

What do I mean by data or analytics leaders and their teams being well positioned? I’m thinking of three aspects that together make such a function almost uniquely positioned to help the organisation & senior leaders. Here are three reasons why they are so well placed to help a business save money.

Firstly, most mature data & analytics teams support the whole of the rest of their businesses. They provide data, reports and analytics (and perhaps models or data products) to many different functions. Hopefully, this includes customer-centric measurement & analysis, which cuts across product, channel or segment silos.

Secondly, such teams have the information needed to calculate the relative value of different activities. Hopefully, they are also experienced at not just the mathematics involved in calculating or forecasting profitability, but also the assumptions needed. Years of experience coping with dirty & incomplete data should have developed skills in approximation & hypothesis generation.

Lastly, data & analytics teams with an execution capability are able to put their recommendations into action. Whether they view such roles as Database Marketing, Decisioning, Engineering or Product Delivery – they should exist. So, unlike a Finance function, they are able to action recommended changes & measure whether or not a forecast saving was achieved (and at what cost in terms of impact).

How does data teams’ broad view save money?

Let me return to the first of those points. The breadth of data, view and focus of such a team. If data & analytics teams currently only support a few business functions or product lines, I would encourage them to expand. The power of data analytics (and reuse as identified by Bill Schmarzo) comes from viewing the organisation as a whole or from a customer perspective.

Given so many businesses are structured into product, channel or segment divisions, teams that can look across them have an important perspective. When combined with the skills that should exist within a mature analytics team, there is an opportunity to help senior leaders. I recommend data leaders proactively seize the moment and offer targeted advice.

From my own experience, examples of such recommendations could come from a range of analyses. Identifying potentially long tenure or high lifetime value customers who are price sensitive or approaching trigger events. Knowing who it is worth incentivising to stay could add value. Calling out marketing activity/spend that is underperforming can be a welcome candidate. Profiling those customers who could be directed toward more online (lower cost) service channels can also help. To help avoid gut reaction false economies, share such proactive targets for cost-cutting that make commercial sense.

How data teams’ commerciality can help

I’ve called before for investment to develop the commerciality of your analysts and data teams. Here is one example of how that can pay dividends. Wise data leaders, preparing in advance for tighter times, will ensure their analysts know how to calculate helpful metrics. ROI, NPV and Payback Period all help.

Further to this financial measurement capability, hopefully, analysts & data scientists are also building their statistical skills. Using resources like “The Art of Statistics” and “The Book of Why”, improves their ability to prove causation or at least improvements (or not). Applying statistical rigour to identifying (or designing tests to learn) which current activities are actually value-adding.

The third part of this benefit of your data function is the data that they have access to and familiarity with using. Both data & analytics teams should take opportunities like today’s economically challenging times to educate their businesses on what can & can’t be measured. There is a guardian role here. During a time when most business functions will be producing business cases & other arguments to defend themselves from cuts, data teams should shed light. Their expertise in domain data often means that they are better placed than Finance teams to challenge claims made, not just the validity of the calculations.

How decisioning teams prove value with test & learn

During much of the economic cycle, the behaviour of decisioning teams in protecting control groups (or fallow groups) can be seen as an overhead. However, during a downturn, they can prove their mustard. Reduced budgets provide an ideal opportunity for such teams to complement the above advice from analysts or data scientists.

I recommend that data leaders make some noise about the ability of this team to rapidly prove the savings that could be made. It is a time when your internal audience will be more interested than normal in uplift rates & value add. The classic benefit case of being able to achieve 80% of the sales through only 20% of the spend will be much more welcome than in richer times.

But this is also a time for decisioning and data teams to not be shy. Boldness should be shown in both pouncing on the opportunities for action & investment. With regards to action, data leaders should be reviewing what has been learnt but not acted upon previously. Often you will find past potential cost savings that were politically unpopular or seen as a lower priority. These are worth proactively recommending now savings are needed. Plus, given the experience senior stakeholders will be having of how a data capability can deliver savings, now is a good time to seek investment. It might seem counterintuitive, but whilst senior stakeholders most value your output is an ideal time to highlight what more you could offer with improved infrastructure or training.

Did you step forward as a data leader to help your business?

I hope that advice helps prompt your own more relevant and helpful ideas. I’d also be fascinated to learn more about what has worked in your organisation. What examples could you share of where you’ve been bold and proactively guided your senior leaders to save money wisely?

To help avoid the damage of the false economies that I mentioned in a past post, I believe data leaders need to be this proactive. If you have been keeping your head down so as not to have your budget spotted by the bean counters (or axeman), I hope I’ve encouraged you to be braver. Why not take some time now to think about how you could be more proactive & prove the value of your function at such a time as this? I look forward to reading how you’ve helped others save money.