From Strategic Intent to Real Wins | Top 5 Considerations for Data

The strategy to execution gap is real. Of course, there’s heaps of information out there on how to move from strategy to execution using tried and true practices – like building a talented team, creating a road map, putting in place a governance structure, lots of communication, and so on.

But data is different. Why is it so hard to get traction with data?

• It’s one of most pervasive assets, making organisational boundaries irrelevant. And this makes attributing value difficult.

• A lot of organisations have been burnt in the past with over-promises and under-delivery. So trust and confidence in data delivery – and often the data itself – is low.

• Data is often just a footnote mention (or worse, a warning) in the corporate strategy.

• There are very few really quick wins in data. And often these quick wins create a loss of focus while the team works hard to deliver the next ‘quick win’ on time.

Luckily, I’m a pragmatic optimist and over the years I’ve compiled a Top 5 list of considerations for data that will help translate into real data wins for your organisation. This list is a combination of my experience and also lessons I’ve learnt in the past from smart data people.

1. Don’t be Everything to Everyone

This is a really easy trap for data people to fall into… because heck, every problem is a data problem… and yes, data is going to magically bestow superpowers to everyone in the organisation!

So our Data, Analytics and AI strategies touch on every possible value proposition across the typical defend, extend and upend value propositions. And then we’re going to do everything from self-service and democratised data; to reporting and dashboards; to insight and analytics; to AI, ML, LLM and intelligent automation. And of course, we’re going to do it for everyone from enabling functions like finance, people, risk and legal, to core business operations.

Instead, pick one thing to be known for that’s important to the Board, CEO and therefore the whole organisation. Below are a few examples but the true test is that it’s about a 12-18 month horizon and something you can easily describe to a non-data person.

• For a high growth ambition enterprise with constraints on finances, use data to scale in areas that are always in the need of more budget and FTE.

• For an organisation wanting to increase digital experiences and interactions, use data to build pathways to switch to digital, overcome barriers and increase use.

• For a company preparing for privacy reforms and evolving cyber threats, focus on data lifecycle management.

• Another company may want to increase AI readiness or the next big thing, create an organisational goal to increase data maturity.

2. Data isn’t a Program or Project

Data doesn’t have a start or end date – it’s not temporary! Data needs to be a sustainable, ongoing capability – not just achieving or building one thing.

It’s important to recognise that people were using data way before you arrived, and guess what, they probably have done some pretty smart things with it.

And the language around building a capability is different from a project or program. It speaks to establishing, maturing, best practice, improvements and risk buy-down.

3. Invest in Data (All the Data)

Any investment in the data itself is no regrets. Technology will come and go and at the end of the day, no one sees it… Some might say they’re loaded down with legacy technology. But with the current pace of change, whatever you start building today is likely to be legacy by the time you’re done building it.

Data is a representation of reality in the organisation. The closer you get to the data and what it’s used for, the closer you get to real outcomes.

Investing in data means investing in data quality, information architecture, metadata, most efficient delivery for the ultimate use, data lifecycle management, and setting the rules for use (instead of by user).

4. Get the Funding and Delivery Model Sorted

If you don’t get this right, the rest doesn’t matter. The key here is putting in place the things that will speed up delivery. This includes principles, guardrails, appetite statements and a decision model. If you aren’t creating a need or friction for a decision model, then you’re likely not getting close enough to the data and the organisation-level value proposition.

It’s important here to not just figure out how to get work done, but done done. What’s the ongoing support model? This is critical with data because data needs to keep pace with business changes to remain valuable.

5. The X-factor

No, not the game show… Instead this is the one thing that only you know is a real challenge for your organisation. It’s something you know in the pit of your stomach you can’t ignore. It could even be something hidden that needs to be surfaced or voiced. Below are a few examples:

• Do you need to be mythbuster or a hypebuster?

• Do you need to teach senior leaders when and how to bring you to the table?

• Is there one person who really needs to be on board? Or is there one person who’s just always on board and there needs to be more supporters?

• Has there been the haves and have-nots in the past that needs to be addressed?

• Does the organisation need to learn how to learn?

• Has data become boring and needs to be made more fun?

Now imagine you’re that freshly minted CDO and you’ve come into the organisation where data is a footnote in the corporate strategy (if you’re lucky) or it’s warning from a consultant. How will you take that high level strategic intent and translate it into something everyone can get behind? Use these top 5 considerations to get started and don’t forget to celebrate those real data wins!