Determining the right strategy and implementing it effectively, with the right support and engagement, is therefore vital. It can bring not only short-term wins, but also long-term benefits, positioning a company for success as its industry and technology continue to evolve at speed.
Laying the foundations for success
In our experience, a key driver of success in firms’ data transformation efforts is the right tone and sponsorship from the top, and the resulting support from across the organization. Indeed, we often warn of the opposite: a “them-and-us” approach, where data is cast as only the responsibility of IT or data teams.
In light of this, a shared understanding of the advantages of a transformed approach to data, right up to and including the C-suite, is vital. As well as ensuring that resources are available, this can help ensure the right teamwork and culture are in place. Indeed, when we conducted research in collaboration with University of Oxford’s Saïd Business School, we found that transformation programs were 2.6 times as likely to succeed when the human aspects were right. The full research can be found here.
In particular, leadership teams must have a clear vision of the strategic benefits they’re pursuing. In the wealth and asset management sector, we see six areas that are common priorities for transformation, with data playing a crucial role in each:
- Client-centric operating models
- Digitized distribution
- A reimagined investment proposition
- A focus on growth areas
- Transformation of business models
- Leveraging of inorganic growth opportunities
But while companies need speed and agility from their data in these areas, and a strong sense of confidence in it, these elements are too often missing or inadequate. In our survey of the wealth and asset management sector, 45% of respondents felt they lacked a trusted data layer in their company. When asked “How happy are business users with the state of data in your firm?” the average score was 4.8 out of 10, while for “How difficult is it to respond to new data requests from users?” the average score was 5.1.
Viable options for progress
Thankfully, there are proven options for progress that can deliver tangible benefits and cultural change. Naturally, firms need tailored approaches reflecting their specific circumstances and ambitions, but it is worth outlining three standout strategies that we frequently recommend:
- Modernizing data operating models using managed data service providers and potentially a data mesh.
- Implementing innovations, such as live data shares and self-service data product marketplaces.
- Creating a pilot project to unlock paths to broader transformation.
Modernizing data operating models using managed data service providers and creating a firm-wide data mesh
Data transformation operating models can bring big benefits to businesses in terms of performance and preparedness for the future, while also reducing costs substantially. Two key strategies are particularly notable in this field, and typically help facilitate each other: using managed data services and creating a firm-wide data mesh.
Managed data services can help firms transform their sourcing and management of particular data domains, such as instrument or benchmark data. Indeed, in our survey of executives, data management was the most widely cited area for potential outsourcing among asset managers, with 48% considering it.
There are a range of benefits. Most obviously, outsourcing unburdens the internal data function. However, managed data services can also provide trusted data at a lower cost of ownership.
Furthermore, and at a broader level, they can be invaluable in creating a wider transformation to a firm-wide data mesh. This approach decentralizes data management, allowing organizations to share, access and manage data at scale, while being agnostic to specific technologies. Benefits include more agility, better data for decision-making, improved data quality, and more cross-functional collaboration. It has four guiding principles:
- Domain ownership: Operational data ownership should be moved to domain teams, away from the central data team.
- Data as a product: Data should be provided to, and meet the needs of, users beyond the domain team.
- Self-service data platform: A dedicated data team provides a domain-agnostic data platform, enabling domain teams to seamlessly use data from other teams.
- Federated governance: The organizational rules and practices to facilitate an effective data ecosystem and ensure adherence to industry regulations.
Perhaps unsurprisingly, data mesh requires organizational, cultural, architectural, technical, operational, and infrastructural changes.
