Why organisations need data driven operating models

by | 11 Oct 2022 | Data, Superannuation

In August 2022 I kicked off a series of articles on the 3 megatrends driving data management in superannuation with a summary of the trends. This is the second article in the series in which I expand on the first of the trends, data driven operating models.

More and more financial services organisations are understanding the power of data and what proper data utilisation can do for their clients and members.

They understand that by using data well, and collecting the right data, they can offer more personalised services at the same time as running more efficient businesses and improving their overall bottom lines. It’s a win win!

Chris Stevens is a financial services executive and until recently was chief operating officer at Mercer. Chris has been heavily involved in the push for organisations to adopt data driven operating models. I have great respect for Chris who can also see where the market is heading.

“I think the evolution of the market is one where data driven insights are becoming critical in the way you do business, whether you’re a superannuation fund wanting to engage with your members, whether you’re a technology provider that is looking to provide great outcomes to your clients, the use of data-driven insights is maturing very rapidly,” he told me recently.

At Novigi, we are getting an increasing number of inquiries and requests for help from organisations wanting to do their best with data and this interest is showing in our growth. In the last quarter of the 2021-2022 financial year, we grew by 25% quarter on quarter, and for the first quarter of this financial year we expect to grow by at least as much again.

Of course I’m happy that Novigi is doing well, but more than that, I’m excited that organisations are realising the benefits of a data driven operating model.

What is a data driven operating model and why do it?

Basically, the data driven operating model is about creating central data management capability and using that to then drive a range of key transformational outcomes.

These transformational outcomes that organisations want to achieve are:

  • Data driven discovery and innovation, which relates to the use of data and analytics in support of innovation in products and services;
  • Radical personalisation or taking the concept of segmentation to an extreme;
  • Massive data integration i.e. the combination of massive data sets and disparate systems that previously had not been considered or analysed together; and
  • Enhanced decision making, which results from having more data at your fingertips for an organisation’s leadership to make decisions with.

We actually borrowed these outcomes from the McKinsey Global Institute’s The Age of Analytics: Competing in a Data-Driven World report which outlined six models. But the four explained above are the ones which we think apply specifically to financial services organisations.

Organisations want to get better at data and adopt a data-driven operating model because they have realised it will ultimately lead to a better bottom line. They just need a bit of hand holding in the implementation.

How can organisations prepare?

Chris says there are a couple of things that organisations need to do to succeed with the implementation of a data driven operating model (and we strongly agree!).

“I think there’s going to be a recognition that you need to invest in technology, infrastructure tools, and architect an ecosystem that supports whatever the business strategy is. Typically, that leverages a growth aspiration, it relates to great client experiences, and obviously, how efficiently you run your business,” he says.

“The other key is really bringing in the right skills, or enhancing the skills you’ve got, in relation to the sort of capability that can leverage the insights that you get from the data.” Organisations need to put in the right data management frameworks which is essentially a three-part process.

  • Organisational model The first part is recognising that data is an asset with a financial value and attributing it appropriate importance within an organisation. Larger organisations might consider a chief data officer to be responsible for data and the company’s data office, and smaller organisations might look to giving an existing position responsibility for data.
  • Data management body of knowledge (DMBOK) Once an organisation has established responsibility for data within the firm then DMBOK is a widely used model that explains 11 knowledge areas the data office is responsible for. These are data governance, data architecture, data modelling and design, data storage and operations, data security, data integration and interoperability, documents and content, reference and master data, data warehousing and business intelligence, metadata, and data quality.
  • Data management platform (DMP) In terms of the infrastructure and technology to actually support the organisational model and DMBOK, this is where a data management platform for an organisation comes in. Platforms might accommodate data lakes (pools of raw data) and data warehouses, support data analytics tools, machine learning, AI and integration between applications. All of these functions and tech can be rolled up into one platform.

Where are organisations at?

Chris points out that historically, and perhaps especially in some smaller organisations, the data focus to date has been more on data strategy relating to client engagement, which is not necessarily an enterprise data strategy.

“It might not lead to a collection of data assets that are organised efficiently that extend beyond just the current engagement site into their own operating model, or operating risk management outcomes,” Chris says.

I would definitely agree. While most organisations in the financial services arena recognise the importance of data and what it can do for them, many are struggling with how to develop a strategy. Some do have client-engagement focussed data strategies, but some also have no real organisational data strategy, and they really should.

As Chris highlights, it really does need to be a bottom-up approach and organisations need to establish appropriate data management frameworks which include the three factors we’ve outlined above.

Novigi can help at all of these stages, from establishing the organisational model, right through to finding the right data management platform. This is our bread and butter and what we are good at.

I like how Chris puts it below.

“You want to have your own capability, but I think it’s also good to lean on some organisations that have a proven record in implementing those [strategies] into organisations and that’s a good way to have a natural handover from developing a strategy to implementation and continuity of knowledge and experience because that business partner gets to understand your business.”


Ash Priest is the Chief Executive Officer at Novigi

For more information about anything you’ve read here, or if you have a more general inquiry, please contact us

We love sharing our knowledge and insights, and stimulating discussion about data and technology in financial services. 

Browse our most popular articles

Pin It on Pinterest

Share This