That data is important hardly needs to be said anymore. Terms that were once buzzwords — “big data”, “machine learning” — aren’t so buzzy anymore. Modern business leaders understand the importance of managing data to create value and competitive advantage, and to minimise risk.
As the importance of data has grown, the technical solutions supporting data management have evolved. Modern data management solutions offer a wide range of features, but being clear on what business problems these features address can be challenging — both for the business leaders considering buying them and the data nerds who advocate them.
We’ve compiled the following to illustrate how modern data management solutions apply to superannuation and wealth management business problems (see Figure 1).
Figure 1: Data management solution architecture
Data storage and warehousing
Business problem #1: our data is spread across many internal and external systems, our administrator has a bunch of it, and I need it in one place for reporting and analytics purposes.
Business problem #2: Depending on where we look, we are reporting different results, and our data doesn’t provide the full picture.
Having data scattered across multiple systems is a problem most, if not all, industries face. In superannuation and wealth management this is compounded by the sheer number and centrality of third-party vendors. A superannuation trustee might find that key data elements are held by their administrators, insurers, custodians, and investment managers, among others. A modern data management solution needs to include data in its raw form (typically using a data lake, especially for unstructured or semi-structured data), data in its ideal form for business intelligence and reporting (using a data warehouse) and data integration (extract, transform, load — ETL) capability required to populate them from source systems.
Data in a data warehouse should be stored in a canonical data model that is independent of the source systems that feed it — for a superannuation fund this should be a system agnostic superannuation-specific data model that represents entities like members and contributions the same way regardless of what registry system or CRM the fund uses. Ideally, a data management solution should come with standard data integrations to the systems most commonly used in your industry.
Business intelligence and data analytics
Business problem #3: we need to reduce the cost and time it takes us to produce reports for the regulator.
Business problem #4: we need visibility over metrics related to member engagement and retention.
Business problem #5: we want to be able to predict whether a member is likely to leave the fund or change status (e.g. move to retirement products).
Business intelligence and data analytics is often one of the key drivers for data storage and warehousing. All financial services providers must produce reports for one or more regulators in identical formats. A data management solution tailored to the industry’s needs should be able to automate the generation of this reporting. Likewise, similar kinds of financial services organisations have similar business goals. Out-of-the-box reporting on member engagement and retention should satisfy say 80% of a superannuation fund’s intelligence needs in this area, with customisation through configuration able to provide the remaining 20%.
Data management solutions should also support artificial intelligence and predictive analytics use cases. Predicting member churn with the data available to a superannuation fund has long been possible. If funds have their data mapped to a common superannuation data model, there’s no reason this kind of use case can’t be provided as part of an off-the-shelf offering from a data management solution provider.
Application integration
Business problem #6: we have contradictory information in different systems.
Business problem #7: a member making a change through our mobile app won’t see it reflected on our member portal for quite some time.
Application integration and data integration are subtly different capabilities. Data integration is concerned with getting data into data lakes and warehouses, while application integration seeks to keep your applications and systems in sync.
Modern data management solutions combine APIs, process automation, and orchestration to prevent contradictions between your registry system, CRM, and call centre software, ensuring that changes made in one place are reflected across all channels in real-time.
Data governance
Business problem #8: we have data owners and stewards as per CPG 235, but they don’t have the tools to meaningfully manage the data they’re responsible for.
Business problem #9: we don’t have a good handle on what data we have, and the security implications of that data.
Business problem #10: we have a lot of data, but employees still find it difficult to identify and access data that would help them do their jobs.
Organisations in superannuation and wealth management have made substantial strides in data governance over the last decade. Many, if not most, now have a data governance framework in place and can point at data owners and stewards. However, in many instances, accountability and responsibility for data remains theoretical, as owners and stewards lack the necessary tools to manage the data under their remit.
Data governance tooling includes data catalogues, business glossaries, data classification, data profiling and data access management functionality. Integrating these into a broader data management solution encourages the discovery of data, supports an understanding of what data an organisation holds, who is accountable and responsible for it, what kinds of security controls apply to it, who can access it and how.
This is often essential for complying with regulation. For example, under the TFN rule (Privacy (Tax File Number) Rule 2015), TFN recipients must take reasonable steps to securely destroy or permanently de-identify TFN information when they are no longer required by law to retain it. A thorough understanding of the data stored is necessary to ensure that this obligation is met.
Data quality
Business problem #11: we know that our data is neither accurate, complete, nor timely.
Business problem #12: employees generating regulatory and internal reports spend a disproportionate amount of time fixing issues with the underlying data.
If the quality of your data is poor, the value of the rest of your data management solution will be severely limited. Modern data quality tools automate the identification of data quality issues, manage the workflow for their rectification, and even automate this process where it makes sense to. Data quality tools like Investigate DQ offer data quality rules built for industry specific business logic, to identify issues with data on an automated and regular basis proactively that would previously have required a human with substantial business knowledge to spot. Measuring and tracking the quality of data provides transparency on the current state and equips teams to be able to prioritise and address quality issues with obvious downstream impacts.
Master data management and data virtualisation
Business problem #13: We have issues answering fundamental questions like: how many members do we have? What is this member’s current email?
Business problem #14: We can’t compile a single view of a member for a call centre operator to reference when on a call.
Master Data Management (MDM) ensures that an organisation has a single, accurate view of its critical data entities, such as members, products, and accounts. MDM tools consolidate data from various sources, remove duplicates, and standardise data formats, providing a consistent and accurate data set.
Data virtualisation complements MDM by allowing users to access and query data without needing to know where it is physically stored. This capability enables a unified view of data across multiple systems and databases, enhancing data accessibility and usability.
By implementing MDM and data virtualisation, superannuation funds can achieve a single, reliable view of their data. This means that call centre operators can access comprehensive and accurate member information in real-time, improving customer service and operational efficiency. Furthermore, having accurate member data helps in answering fundamental questions, ensuring that strategic decisions are based on reliable information.
A modern data management system not only helps to address these business problems but is also an essential platform to collect, unify, discover, understand and trust data that underpins streamlined processes and reporting.
This article was produced as part of The Quarterly – Data and Technology in Superannuation, Q4 FY24
For more information about anything you’ve read here, or if you have a more general inquiry, please contact us.
Key Contributors:
Kevin Fernandez is General Manager, Market Strategy and Propositions at Novigi, and is based in the Melbourne office.
Sophie Bowen-James is an analyst in the Market Strategy and Propositions team at Novigi, and is based in the Sydney office.