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Enterprise Data Management Best Practice Guide

Discover 7 steps for creating a successful enterprise data management platform.
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Enterprise Data Management Best Practice Guide

7-Steps to Success

COVID-19 has exposed current data and data management challenges. These lessons shape the buy-sides' post-COVID-19 expectations of data management and analytics as well as best practices.  

A survey conducted by Gartner* in the early weeks of Covid-19 revealed that nearly 18 percent of companies were highly prepared for the impact of coronavirus. What has become apparent in the weeks since is that both data and analytics challenges facing most industries are critical and urgent -- and have catalyzed rapid shifts in priorities and a need for clear best practices regardless of perceived readiness.  

Today's buy-side managers require and rely on accurate and up-to-date information to inform their investment decisions. A failure to have access to a full and accurate data set compromised the ability to accurately evaluate investment opportunities, make critical trading decisions, and communicate with investors.  

How should your firm manage investment data? What are today's best practices for daily operational processes, and what benefits do systems and methods provide? The following steps empower firms to properly implement a successful enterprise data management initiative in your firm.  

  1. Conduct an Enterprise-Wide Data Management Audit The first step in evaluating your enterprise data management best practices is to conduct an internal audit of what you are already doing. It is necessary to understand the big picture, from data you already have, where it is coming from, how it is being processed, to how it is governed and stored. An audit like this provides a holistic view of current processes, highlights areas for improvement, and ensures everyone in the firm is on the same page when it comes to tackling the enterprise data management challenges ahead.
  1. Map out defined and measurable objectives Upon completion of your firmwide audit, it is necessary to evaluate and prioritize. Which areas does your firm need to resolve first? Where are these opportunities for obvious and quick wins? Here are four key areas to consider post-audit and ahead of selecting a new data management system.
  • Consolidation and Consistency A primary objective should be a consolidated view of data. This allows portfolio managers to track investment policy compliance and overall performance. Consolidation makes operational tasks more efficient and client reporting flexible.\
  • Automation Manual data entry has a significant impact on data integrity as it is prone to human error and often requires significant time and effort to manage effectively. Errors compound and operations teams end up spending excessive time tracking them down. Automation empowers managers to reclaim time and effort and focus on core competencies.
  • Agility and Speed Timely data is vital in making informed and strategic trades. It enables managers to correct and identify mistakes daily, avoiding month-end headaches. Reconciling and validating data to custody banks and the trading system aligns with the best practices, and the right system can perform daily reconciliations quickly but requires a consolidated data source with access to consistent data throughout the month.
  • Scalability A best of breed scalable solution is designed to manage changes in investment strategies, onboarding clients, and adding both new and complex asset types without taxing IT resources.  
  1. Focus on data quality Data quality is an important factor in successful enterprise data management, but one that is often neglected or underestimated. In short, the quality of the data you start with is the foundation for everything that follows. It is often said that quality data is 'fit for purpose,' and there are many types of fitness for purpose, largely because there are many stakeholders, steps, and systems involved in any end-to-end investment process.  
  1. Identify the data management advocates for your firm. For successful enterprise data management, it is imperative that business users and IT departments be aligned. To have a robust enterprise data management strategy, it must be clear who is responsible for what will lend itself to defining and becoming your data governance team. This group is often tasked with budget approval, setting governance goals and priorities, architecting the data governance model, selecting technologies, and evangelizing the data program.  
  1. Implement a data governance process Firms must ensure that there is a stringent data governance process in place. Doubt over conflicting data sources must be removed, allowing firms to achieve a single point of truth. There must be strict standards in place to ensure the integrity of data, and there needs to be accountability within the business in terms of who is responsible for the accuracy of certain data sets.  
  1. Select the right enterprise data management technologies We touched on the four key areas to consider around your data technologies as enterprise data management is not just about getting the right people; it is also about getting the right technology. Choosing the right tools can make or break your enterprise data management initiative. However, hard work does not stop once these technologies are implemented. Processes must put in place around these tools to promote usage – and, more importantly, correct usage – throughout the firm.
  1. Educate your users about enterprise data management Having a strong enterprise data management initiative in place is great. However, the real benefit of enterprise data management comes in its ability to transform your operations in the long run.

Many firms explore and know that an enterprise data management solution is a way to solve the data dilemma but may struggle with knowing where to start. As a consequence, enterprise data management tends to take a backseat to other high-priority business projects. The final push to enterprise data management may then come via a tipping point, like running out of data center space or exhausting cost-cutting measures. Or, it may come from a triggering event such as an acquisition, divestiture, reorganization, or security breach. Embarking on enterprise data management is a big undertaking, but the benefits are significant.  

Identifying, securing, and making data available to decision-makers and downstream systems can provide the intelligence and agility the firm needs to be competitive. Advancements such as data classification, cloud-based services, and multi-tiered storage, help IT organizations realize these benefits faster and more completely.  

The power of enterprise data management in helping firms identify, secure, and efficiently host their company's data so they can get back to helping the business move forward is the benefits are indisputable —to improve speed, agility, competitive differentiation, and compliance, among other things.

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