6 Indicators Your Data Platform Is Not Ready to Scale
March 13, 2024
Read Time: 5 minutes
Data & Governance
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The ability to scale a data platform is crucial for institutional asset managers and private market fund managers seeking growth and innovation. A scalable data platform ensures that as your organization expands, so does your capacity to handle new and unique data sources, increased data volumes, user demands, analytical requirements, and more.
However, not all data platforms are able to flexibly evolve with an organization.
Carefully evaluating and choosing the right platform is essential to ensuring your firm can adapt to changing industry demands.
Digital transformation is propelling managers to address complex challenges impacting investment performance, fees, and operational costs. Barriers from outdated legacy systems, however, are holding some firms back.
Many firms run on antiquated on-premise technology stacks that cannot scale and often require engineers or vendor support teams to make even minor changes.
A fit-for-purpose platform offers the compute and storage power to rapidly scale activities and host powerful analytical tools. Firms also gain the ability to ingest the increasing volumes of data they need to gain a competitive edge. Equally important, modern systems enable all relevant stakeholders — employees or clients — swift access to the tools necessary for their jobs.
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As digitalization brings benefits to other sectors of the financial industry, institutional asset managers and private fund managers are pressed to stay competitive. While many institutions are eager to modernize their technology ecosystem, transitioning to a modern system poses its challenges. With a shortage of resources and expertise, firms managers need knowledgeable partners to effectively implement advanced data management platforms.
Here are six indicators you’ll want to evaluate to understand if your data platform can grow with your business:
One of the primary signs your data platform is not prepared to scale is the presence of performance bottlenecks. As your business grows, the volume of data your systems are processing and analyzing increases exponentially. If your platform struggles to handle the data surge, you may notice degradation in performance.
Common symptoms include slow query execution times, delays in data processing, and increased latency. Users may experience frustration due to sluggish response times and critical business processes may be hindered. To address this issue, evaluate your platform’s architecture, consider the elasticity of public cloud resources, and invest in upgrades if necessary.
A scalable data platform should provide seamless access to data for users across your organization. Aggregating and mastering data in a centralized repository creates a single view of the truth that can be used by all parts of your business and shared with downstream users.
If your platform struggles to provide timely and reliable access to data, it may not be ready to scale. Limited data accessibility can impede decision-making processes and hinder collaboration among teams.
Common indicators of limited data accessibility include restricted user access, slow retrieval times, and frequent data unavailability. To enhance accessibility, evaluate your platform’s data connectivity capabilities, pipelines, and distribution methods. Understanding what percent of the tool offers your end-users access to self-service capabilities will also go a long way in enabling your teams to interact with their own data. Lastly, consider if your existing data governance framework can ensure secure and controlled access to sensitive information.
As your business evolves, so do your data requirements. An inflexible data model that can’t adapt to changing business needs is another clear sign your platform is not ready to scale. Modifying or extending the data model is a complex and time-consuming process that can hinder agility and innovation.
Indications of an inflexible data model include difficulties in adding new data sources, modifying existing schemas, and integrating diverse data types, such as ESG, alternative data, or unstructured data.
A scalable data platform should seamlessly integrate with various data sources, applications, and third-party systems. If your platform struggles with data integration, it can lead to siloed information and a lack of real-time insights.
Signs of inefficient data integration include prolonged release cycles to add new sources or change the mapping, data inconsistencies, delayed synchronization, and difficulty collating disparate datasets. A well-integrated data platform ensures data flows smoothly across the organization to support informed decision-making. Upgrading legacy systems can also optimize total cost of ownership and enable organizations to develop innovations that drive operational efficiencies and revenue.
An effective data platform requires the right tools to meet the demands of a growing user base. And frequently, those users bring varying degrees of technical expertise. Modern systems offer built-in flexibility that lets users create new applications, gather their own data, or run analytics to complement and optimize the existing processes most useful to their team.
What it means is that processes once the domain of highly trained experts are now directly in the capable hands of teams that need them most. Teams can create and manage their own data models, track data lineage from the point of ingestion, or explore datasets to understand their relationships with other data. These new capabilities empower users to swiftly create applications that help boost productivity, agility, and collaboration.
Perhaps the most telling sign your data platform is not ready to scale is the absence of a comprehensive modernization plan. Scaling a data platform involves strategic planning and foresight to accommodate future growth seamlessly. Without a plan, your platform may struggle to adapt to diverse investment opportunities, new data sources, or increased workloads, leading to system failures and performance issues.
Key indicators of a lack of scalability planning include reactive rather than proactive responses to increased data volumes, unplanned downtime during peak usage, and difficulties adding new functionalities. To overcome this challenge, develop a roadmap that outlines how your data platform will evolve to meet future demands. Consider leveraging cloud-based solutions that offer elasticity and scalability on-demand, allowing you to scale resources as needed.
Knowing the signs your data platform is not ready to scale is crucial for ensuring your organization’s long-term success. A proactive approach will enhance performance and reliability and position your business to thrive in an increasingly data-centric environment. Investing in optimization, flexibility, and modern tools will let you build a robust data platform that can grow and evolve alongside your business.
Migration to a cloud-based platform offers enormous benefits to institutional asset managers and private markets fund managers. Many have already made the leap. Modernization, however, is not an easy task and requires careful planning and expertise to ensure data is migrated correctly. Without that, weaknesses within the data held on-premise may simply be relocated into the cloud environment, potentially replicating some of the drawbacks that prompted the switch in the first place.
To learn how Arcesium can support your business as you evaluate your investment management data processes, explore our capabilities for institutional asset managers and private markets.
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