How to Tackle Investment Data Management and Content Automation Initiatives in Tandem
Today, nearly every segment of the economy is seeing an increase in wage pressures and tighter labor markets. As a result, asset management executives are looking to efficiently allocate resources amongst already capacity-constrained teams and business units. This means manual, time-consuming tasks should and must be automated.
Deloitte’s 2022 Investment Management Outlook report showed 45% of survey respondents see operational efficiency as a top driver for digitization. Similarily, Synthesis Technology’s 2021 Asset Management Martech survey also showed operational efficiency as a top driver in terms of Martech stack changes over the next two years (82%). And while most asset management firms have some automation in place, they still are not investing enough in data management solutions. Only 24% of responding firms reported having a data management solution for product data, and even fewer (13%) reported having an integrated product data management and content automation system.
The lack of efficient data processes negatively impacts managers’ efficiency, bottom line, and competitiveness. For example, taking a month after quarter-end to update and distribute marketing and sales materials is costly, puts marketing and sales teams at a disadvantage, and may lead to investors and prospects looking elsewhere.
Managers also face increasing data governance and compliance scrutiny from the due diligence requirements of potential investors and consultants as well as internal stakeholders.
Golden source data – does it really exist?
While COVID didn’t create the need for clean, efficient data processes, the pandemic accelerated the timeframe for addressing these issues.
Despite the urgency of the matter, as asset management firms’ technology teams assess their data needs they often get caught up in an extremely broad and lengthy analysis. These analysis projects often balloon as concerns expand in scope to take on considerations about the larger tech stack, thinking about replacing legacy systems or how they can better integrate across departments.
A common mistake is to try to solve every data problem before improving the processes that rely on the data. For example, automating content like factsheets and pitchbooks often gets stacked behind the data overhaul project, which just delays the initial content automation initiative. Over the years, investment marketers have joked with us about the forever promised, but never delivered “golden source data warehouse.”
But it’s really no laughing matter.
The marketing teams charged with delivering up-to-date content to support sales cannot wait for the perfect data warehouse before beginning to make progress on their content automation goals. The renovation of core data processes must coexist on a timeline with marketing making progress on their operational goals, too.
While data clean-up and management is extremely important, managers can‘t get so caught up in creating a “golden source of data” that they lose sight of the marketing business objective — to get error-free, compliant materials published and distributed on a competitive timeline without burning out their content production teams.
It is possible to do both at the same time: Start your technology team down the path of improving data processes while marketing teams also move forward on streamlining content automation and data integration. Here’s how:
Three steps to tackling the data management problem while keeping content automation goals moving
1 – Start with the biggest thorn
Prioritize your list of pain points and begin at the top. Consider which single piece of data requires the most total time of manual updates across your firm, or which piece of content requires the longest and most manual-intensive production time. You might also consider how many business units rely on each of the items on your list so you can determine where streamlining would offer the biggest reward. However, keep in mind that some data problems are truly intractable. There may be just too many dependencies or too many parties that must come together to fix them right now. Prioritizing these problems will bog down the effort. If an item is not truly addressable today, push it down the list below things that are also important but addressable in the near term.
2 – Build a scalable model
Identify a core and heavily used piece of data to build a solid model around. Many firms start with their performance data as these systems are often the most mature, although not necessarily the easiest to clean up. Set a goal for this model data type such as a simultaneous delivery to all use cases on a specific schedule and with a goal for quality measurement. The idea is to make that data type a non-issue for the consumers of it. It just works. Once you’ve established an efficient process for that narrow set of data, you can expand the model to include, for example, portfolio holdings, sector composition, and other data points.
3 – Choose smart and adaptable endpoint tools for the data
If a business process or tool relies on perfect data delivered in a narrowly defined or inflexible format to be successful, it is likely doomed. If the data format is imperfect, the target system must be able to be adjusted. If the data is inaccurate, the target system should have facilities to help catch errors. If the data source and format needs to change over time, the target system should be adaptable to that. If data needs to be fixed or transformed by the target system, that should be done in a compliance-focused manner with strong feedback loops and accountability. The more data-adaptable the content automation solution, the more likely it will succeed.
Keeping an eye on industry best practices
Firms must stay abreast of what other managers are doing to make their data processes more efficient. Conferences, roundtables, or industry peer groups like the IMEA or SME Forum can be extremely helpful, as members can share lessons learned, success stories, and solutions. Participating in research like this one conducted by Ignites Research and SME Forum or the Synthesis Martech Stack Study can help managers understand how other firms are approaching and solving data challenges.
In our experience working with asset managers on data and content automation, we’ve found that firms often find themselves mired in a data problem that doesn’t really need to exist. The firm has chosen a goal or deliverable that adds little value, hardly anybody else is doing it, and almost nobody will notice if it goes away. Firms should be willing to ask themselves – and their vendor partners and peers – what can be skipped or simplified without measurably hindering progress. Remember that intractable data problem that we suggested be demoted on the priority list? Perhaps it doesn’t need to exist at all.
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