Historically, data warehousing projects have required potentially multi-million dollar investments, and time horizons of years, rather than months, to deliver. Adding to the project risks has also been the capacity for notorious failure rates. Leading to many Super Funds to think – why even bother? Especially in a low engagement industry with sparse data, particularly if you compared them to what a bank knows about their customers with such detailed transactional data. What factors and fears are holding Super Funds back? What do Super Funds really need to know to move forward in this important space?
Here we take a detailed look at the devil’s advocate question for many Super Funds in Australia today – “what’s the justification for spending $6 million on a cutting edge data warehouse?”.
We all know that data is gold. A myriad of publicly available McKinsey and Harvard Business Review articles describe and quantify the benefits of data led organisations all the time.
But what is this devil’s advocate question all about?
Super Funds have been talking about doing more with their data for years now. So, what’s holding Super Funds back?
The Essential Questions
Over the last year, we have spoken to more than 20 Super Funds about their data and analytics mapping where are they at now; what are their plans and what makes them and their various committees on risk nervous.
Every time we talk to decision makers and project managers at Super Funds, we are mapping those questions that underline the fears and missing information that are holding Super Funds back from helping them find the insights they need to build better relationships and make smarter decisions:
1. We can’t see the end state!
Can you actually show us what a good analytics solution looks like?
Reading between the lines:
We don’t want a presentation telling us what we should do. We have to convince our Execs, and many of them can’t really visualise what they are signing off on. Can we actually see a relevant Super Fund analytics solution in action?
2. What business value will we really gain?
We have read all the headlines: Artificial Intelligence (A)I, machine learning, predictive analytics, self-service, etc. What are the actual problems we will be able to solve? What are some examples of actionable insight we can promise to deliver?
Reading between the lines:
We need some help making our case, we don’t know what we don’t know. Are Super Funds really able to get meaningful insight from their data or is it all theoretical? Will our data actually be able to support this, or is it too sparse and possibly poor quality?
3. Is analytics really a priority?
Our Super Fund has been growing for years without one of these official data analytics systems. We deliver excellent returns and win awards. How do we convince our board that we need to do anything further with our data?
Reading between the lines:
We have heard horror stories about complex, expensive data projects that don’t deliver real value. We know we need to do more with our data, but what’s the best way to go about this? Is it really a $6M exercise?
Tackling the $6M question
Do modern data warehouse and analytics projects really cost $6M? Is it really a $6M dollar question?
It actually isn’t. In days gone by, data warehousing probably was expensive, and more often than not may have suffered from notoriously high failure rates.
The choice now isn’t binary: do nothing or drop 6bricks with Global Consulting Co. We are seeing companies, and Super Funds (small and large), delivering real business value from small agile data analytics initiatives. And within weeks. And for a dollar value that can potentially fit on your corporate card.
The Data Analytics Playbook
So, what’s the data analytics playbook to making this happen? Is there even such a thing?
We think so. Well, we actually know so.
Step 1 – What’s the end state we can realistically aim for?
Let’s look at what good analytics look like.
This is like divining what is possible. Is good analytics a mythical unicorn? We see Business Intelligence software companies demonstrating their wares with fictitious data based on a VCR manufacturer trying to understand gross margin and north American regional sales.
This isn’t relevant or helpful.
To be relevant and helpful we avoid fictitious demonstrations of data and products that bear no resemblance to the Australian Super Funds sector. We know what good data looks like for Super Funds and we take funds through analytic pathways that explore relevant questions. This includes which of my members are leaving my Super Fund, where are these members going and where should I focus my efforts. We show how different users across the fund can intuitively ask questions about their parts of the business.
Then we move onto more advanced topics involving robots. Once you have your core data in one place, it adds up correctly and is ready for analysis, you can begin exploring how advanced analytics can be tailored to your biggest risks and opportunities. It’s called machine learning, with an emphasis on the learning process that improves over time courtesy of testing and refinement. We like to show how machines can scour very large complex data sets to identify signals in the data that even skilled analysts may miss. It isn’t AI, but it is more like augmented intelligence.
So, Step 1 of the Data Analytics Playbook is to have a clear idea about what good analytics for Super Funds looks and feels like. What is right sized and relevant now, and what should a fund aim for in the medium term?
For our Super Fund clients they can now combine the theory with a clear and tangible picture of what their data analytics capability can look like and it helps them with their business case. This breaks down fears about change management and the ability for their people to use their tools. And we think it shows many ways in which teams in Super Funds can self-sufficiently ask questions of the data to extract their own insights. Insights that have actionable business value.
Step 1 also leaves no doubt about actual analytics use cases – what can we do with data that provides tangible business value?
Step 2 – Why are we doing this?
Before starting your data and analytics initiatives, you need to have a clear idea about why you are doing this. What are the goals, and why will this help your fund achieve its mission? I have written about this before and called this ‘Mission Focused Analytics’.
Answering the Why directly addresses the devil’s advocate quandary: does a successful Super Fund really need to do anything if they have been operating well historically?
My first thought is that it isn’t really my job to convince people that data is valuable. Our job is to help you plan your data and analytics initiatives, and then accelerate their execution.
We are seeing increasingly thirstier Boards and Executives asking harder questions. We are seeing leaders from other industries coming to Super where they enjoyed the benefits of mature data and analytics capabilities. We are seeing new CEO’s leading Super Funds in new competitive directions.
Probably more importantly, I meet people in Super Funds that wonder what impact they are having in the work they do. What happens weeks and months after engaging with members? Do they take any positive action?
And what about the expectations of our members? They are interacting with companies, banks, insurance, companies and streaming services that have gone big on data to really understand their customers and deliver amazing experiences. These customers, your members, are bringing these elevated expectations with them when they interact with your Super Fund, even if it’s only twice a year.
Finally, what’s a Super Fund really all about? Managing member’s superannuation is a means to improving our retirement outcomes. Whilst investment returns are a major focus, they are one driver of improved retirement outcomes. I love seeing the work of HESTA in this area: Georgie Obst, GM Campaigns and Customer Growth, summed it up beautifully at the recent CMSF19 conference, that in measuring the ROI of their data and analytics program, “At HESTA, our north star is a member’s Retirement Readiness Score. And everything we do should aim to improve that score.”
The Data Analytics Playbook Continues
In this blog I talk about some of the big fears that we think are holding Super Funds back in their data and analytics initiatives and we map out how to address these.
This leave the final question, ‘How do we get started? How do we avoid the traditional pitfalls? How do we move fast, prove value, and ensure we are moving in the best direction for our fund’s unique situation, membership, and business performance challenges? We call this XYZ – stay tuned.
If you would like to learn more about working with Laneway Analytics, and how our unique way of working might suit you, please send a short, clever, personalised email to email@example.com.
To build your own Data Analytics Playbook, or find out more about how easy it is to set up a Proof of Concept and kick off your data analytics journey contact me at firstname.lastname@example.org