The Guiding Principles of an Effective Data Strategy
Data-driven asset managers are those that maximize the value of data and treat it as a strategic asset—using it for innovation and critical business decisions. These asset managers are characterized by a strong data foundation, cloud-based optimization, and an insights-driven culture. And to identify and seize growth opportunities, they take proactive steps to embed AI, machine learning, and advanced analytics at their core.
Margin compression across the asset management industry is increasing the need for operational efficiencies. At the same time, firms are pursuing market and product expansions to compete and grow. Effective and efficient data management is the key to solving these two, sometimes competing priorities.
Generating and Capturing Data is Easy; Leveraging Insights is Difficult
Whether they’re transitioning their entire operating model to support investment accounting, or simply trying to keep pace with rapid change in the industry, asset managers know that immense potential value is locked up in their data. But many are struggling to unleash it.
Though data creation is accelerating, too few asset managers are using data today to gain a competitive advantage. Becoming truly data-driven involves linking a data strategy to clear outcomes and prioritizing data as a strategic asset. By converting raw data into valuable insights, data-driven asset managers can improve their ability to analyze investments, manage portfolios cost-effectively and most importantly, deliver alpha to their investors. To compete and win in the future, asset management leaders need to commit to a strategic, data-driven culture.
Whether they’re transitioning their entire operating model to support investment accounting, or simply trying to keep pace with rapid change in the industry, asset managers know that immense potential value is locked up in their data. But, again many are struggling to unleash it. Let’s go through the reasons why in this blog post.
What are the Reasons Firms Struggle to Leverage their Data?
All too often, a firm’s operational, and cultural challenges are hampering its efforts to realize an effective data management strategy. In addition, firms have to evolve quickly and adopt new processes and technology to deal with the massive amounts of data that have become available and that key stakeholders in the firm want to collect and analyze. The impact of this datafication is nothing short of revolutionary. Consider the following, in 2000, only 25% of all the world’s stored information was digital. Today, less than 2% of all stored information is non-digital.
So, what’s holding your organization back?
- Lack of an enterprise-wide data strategy, C-level sponsorship, and the right workforce skills.
- Poor data quality requires tremendous time and effort to produce transparent, trusted, and integrated data that is accessible at speed.
- Fragmented data and a slow data supply chain due to legacy technologies and outdated governance practices.
Gartner defines dark data as: “The information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetizing).”
The average information worker spends as much as 36% of their time searching for data and more than 40% of those searches are fruitless, according to a report by IDC.
BNY Mellon conducted a survey of executives at asset management firms who suggested the top use cases for deploying data analytics are performance and risk management (45%) and asset selection and allocation (43%). Identification of new client profiles (37%) also makes a strong showing and, even in the front office, operational efficiencies are still important (41%). In other words, data analysis touches on the use cases by which investment managers are able to deliver more value to investors, optimize their costs and expand their investor base.
It is clear that data can support more robust investment analyses, decreasing costs to generate alpha or generate beta. With the proliferation of data sources, the volume and complexity of data are increasing exponentially. Effectively integrating new types and volumes of data can help managers identify opportunities, make more informed investment decisions and monitor performance.
New sources of data are fundamentally reshaping the role of central portfolio management, these changes flow through to every other part of an asset manager’s business model as well. The BNY Mellon survey continues suggesting, sales and marketing (74%) and distribution (66%) also are likely to see impacts from data capabilities.
What is the Solution to Collecting, Managing, and Analyzing Data More Effectively?
As always, we try to avoid doing any commercials in our blog posts, however, we want to use RyanEyes software to illustrate the critical points of collecting, managing, and analyzing data.
Let’s start by looking at one of the top use cases for data analysis and insight. Portfolio management rises to the top of asset managers’ data priorities for good reason. First, data can support more robust investment analysis and increased speed and efficiency to generate alpha. With the proliferation of data sources, the volume and complexity of data is increasing exponentially. Effectively integrating new types and volumes of data can help managers identify opportunities, make more informed investment decisions and monitor performance.
These needs drive the rapid development of tools to visualize increasingly granular information such as predictive analytics dashboards and artificial intelligence-driven heatmaps. Meanwhile, cloud-based technologies support the scaling requirements of data growth as new database capabilities allow for the inclusion of both structured and unstructured data types.
Let’s examine accounting data closely, for example. With the advent of cloud-based accounting systems, your data is located in a proprietary database in your cloud provider’s environment. They leave it up to “you” to figure out how to collect that data, join it to other cloud-based accounting data sources typically handled by your fund administrator and then join it with in-house systems that help you make investment decisions. You need a tool that works well with all three and helps you along the way of making sense of so many different data points including:
- Leveraging a hybrid solution that will allow you to join your data with these multiple sources and then store it in an accessible format that can with you wherever you take your firm.
- Setting up “Watchers” around your data so that when you process your data you are flagged instantly with things that have gone awry rather than finding it out after the fact and potentially after you have made a wrong decision based on incorrect or incomplete data.
- Seamlessly integrating your collection of data without interfering with the workflow.
Retain Control over the Acquisition of Data
Fund managers and administrators are collecting and gathering data from a diverse group of sources including:
- Web Scraping
- Connections to databases like Bloomberg and other research hubs
It is critical to handle all of this incoming data with a clear plan, control, and process. Once this data is processed, analyzed, and transformed into useful insights it becomes the competitive advantage of the firm and part of its intellectual property. The following are the cornerstone aspects of a data management plan:
- Data Management plan
- Types of Data
- Security/ Protection
Ryan Eyes monitors all incoming data from these diverse sources, normalizes the data, and aggregates it in our data warehouse, Collimate. With accounting data specifically, it is critical that firms retain a copy of the data at a granular level. Too frequently firms collect redundant data which creates issues with reconciliation and confusion at the reporting level.
Ryan Eyes has also built specialty connectors to Bloomberg and Eze Castle in order to seamlessly bring data into a data warehouse. Ryan Eyes has links to counterparties like Morgan Stanley, Goldman Sachs, SS&C, Credit Suisse, Wells Fargo, and BNP Paribas to name a few. The value is that firms don’t have to build in-house systems that aren’t always effective or reliable.
RyanEyes cuts the time to develop the links between your standard data providers and bespoke data sources by 50%. Further, it allows the business rather than IT to control how and where that is done. The value is that portfolio managers and fund administrators don’t need to manually collect data from counterparties, normalize the data, and aggregate it into existing databases. For most firms, the time savings can be dozens of hours a month.
Preserve any Data Outside of Core Applications to Retain Control and Access
As firms transition to the cloud, it is critical not to “orphan” your data in applications. Previously, with an onsite software and system implementation you might retain access to your data, with cloud applications, the data retention strategy is a variety of techniques.
In addition, many firms are leveraging and integrating with multiple, separate databases in the cloud. Make sure your database is accessible outside of the cloud application where it resides. Further, normalize any data that is stored in non-standard formats such as files, bespoke databases, or cloud-specific repositories. An accounting repository allows them to take data and easily get back to the data, thus the portability of data as we suggested prior.
“Orphaned Data” – Data Held hostage by a cloud provider that requires you to keep paying a monthly subscription. Often consulting firms set up this solution leaving owners of the data without access.
With the advent of online systems, too often, your Cloud provider treats “YOUR data as “THEIR data” meaning, they make it difficult for you to control the data exclusively. Previous in-house solutions allowed firms to control the data. Cloud data is more difficult to integrate with other firm data. The solution is a “data suitcase” where you can take your data to other service providers as you see fit.
Use Cloud Data Stores Effectively from Multiple Perspectives
As we suggested earlier, asset management firms like so many organizations are moving to cloud providers to improve the security, flexibility, mobility, collaboration, and cost-effectiveness of their data management strategy. Asset management firms in particular benefit from the integration effectiveness which allows for integration tools that span across multiple providers including;
- Azure databases
Cost-effectiveness requires that you have monitoring tools in place to let the business know the ongoing charges of keeping a server online. Cloud providers charge firms by the amount of data that is transmitted and the CPU usage.
- Have schedules and alerts that allow business users to access Cloud resources only when they need to use it.
- Allow for a business user to start or stop cloud resources from outside of the cloud provider – with sufficient approvals in place.
For example, RyanEyes allows you to enable a business user to start a Cloud service (AWS instance of a server) only after they have received approval from a manager. Which gives the firm the benefit of scheduling downtime across applications.
Harnessing the Value of Data Insights to Generate Alpha
A strong data management program is key to transforming data into an asset and successfully delivering on a firm’s goals and complex projects. Asset managers often struggle to take a firmwide view of data, so their data management approach supports only specific non-integrated requirements. That approach creates a siloed understanding of data which then causes a fracturing of the data structure and differing points of view on reporting requirements across functional domains.
In the two images we provided, you can see how technology evolved and empowered asset managers, fund administrators, and others to collect, manage, and analyze more data than ever before. However, as we suggested, these folks have suggested that they are not able to harness the potential insights that the data could offer.
How Ryan Eyes Software Provides Transparency to the Handling and Management of Data
- Document retention – Critical, understanding when the document processes fail and points along the chain when it fails. You need metrics to understand when the processes fail – RE follows these processes.
- Metrics on how often your staff executes procedures is critical information. You want to retain control over that.
- Understand how information is processed, validated, and ultimately certified as being valid. Job workflows allow you to track where the data is coming from and set up tasks for handling the data.
- Spreadsheet processes – how to track them and mitigate the damage they may cause to an organization. Often spreadsheets are lost within firms and folks don’t understand who is using the spreadsheets or where the data is coming from. Ryan Eyes has processes that allow users to make the process transparent.
To circle back there is an explosion of data, in part because of investments that companies have made in data tracking and manipulation capabilities. Individual securities performance, trading patterns of investment professionals just as recently as five years ago these things were nearly invisible or at least much harder to measure and to manage.
Data acquisition, management, and analysis tool have evolved and are significantly more powerful; in addition, the ability to store volumes of data for interpretation has increased exponentially. That said, as we suggested in the introduction to this post, firms, portfolio managers and fund administrators are still struggling to manage the data effectively.
Across the asset management industry legacy, inflexible technology stacks (on-premises databases and software installs, for example) are common. Legacy systems and data issues can hinder asset managers’ time to market, operational readiness with new products and their capabilities to integrate new third-party data sources.
Ryan Eyes allows fund managers to focus on analyzing the data to leverage their core competencies more effectively and not feel the need to create their own solutions, spreadsheets, and systems to generate alpha and reduce operational costs.
To Learn More About Choosing a Data Management Provider for Your Organization – Contact Us, We Are Happy to Help – 1 (347) 759 0105.
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