The concept of Enterprise Data Management, stripped of all the buzzwords and hype, boils down to the basic processes of acquiring, validating, maintaining, and disseminating important enterprise data with minimal time and cost whilst achieving the “right” level of accuracy, often referenced as quality, as demanded by business use. There are more than enough tools available to implement the mechanics of EDM. The principal challenges are:
- Selecting tools fit for the purposes, budgets, and cultures of each individual firm;
- Implementing the selected tools efficiently, effectively, and completely;
and, most importantly
- Maintaining enterprise-level executive governance and oversight.
The concept of EDM… boils down to … acquiring, validating, maintaining, and disseminating important enterprise data with minimal time and cost whilst achieving the “right” level of accuracyMick Cartwright, Director, Asset Management, Accenture
The traditional approach to EDM has been mainly a derivative effort, the result of implementing functional, purpose-specific systems, without an enterprise-wide data focus or sufficient executive level direction. That legacy has led to the current tangled web of integration layers that contributes to the erosion of confidence in the data and diminishes the ability to respond to change. Restoring confidence and improving the firm’s capacity to respond to change quickly and economically are significant goals for any EDM implementation. To do so requires the involvement of all stakeholders, especially executive management.
While many EDM tools and much of the work will be technical in nature and performed within the IT function, the scope and ownership of the data must address the entire enterprise and all aspects of the business. Any notion that data ownership resides in the IT organization is at odds with how and why the data exist in the first place. While IT certainly bears technical responsibility for processing, storing and distributing data, the initial provenance of almost all enterprise data derives from the business mission. The sources are customer-facing business activities and supporting operational and administrative actions. This reality is the reason it is critical that all EDM activities include strong representation from the business and administrative sides of the firm alongside IT and executive management.
… it is critical that all EDM activities include strong representation from the business and administrative sides of the firm alongside IT and executive management. Mick Cartwright, Director, Asset Management, Accenture
The data governance organization (DGO)
The need to integrate stakeholders and their expertize from across the firm in service of EDM has given rise to the notion of the data governance organization (DGO), a senior management level construct. The DGO has the tasks of codifying the firm’s data needs as well as designing and enforcing policies to satisfy those needs. DGO activities include:
- Establishing the data “command and control” framework
- Empowering the owners of data to take responsibility for the quality of the data
- Soliciting and sustaining representation of all interested parties
- Standardizing the semantics and understanding of the data elements
- Designing a framework to govern new and existing data related projects
- Clarifying which parties are responsible for which data management activities
Critical to the long-term success of enterprise data management is that data governance become embedded as a part of the firm’s culture and operational approach. Governance is not a ‘one and done’ project rather it must be a way of doing business.
EDM success: An ongoing challenge affecting data quality and bottom-line results
IT role and challenges
When the firm’s data needs and strategy are well-defined, the IT organization will advise, recommend, and implement an appropriate data and application architecture. That framework must support the requirements of the entire data lifecycle, from acquisition through validation, storage, and distribution, with a careful eye on security, service levels, and cost. Throughout the path, organizations should consider metrics to monitor that they are meeting or exceeding self-imposed KPIs and SLAs. More than 70% of the executive-level respondents to a recent survey co-sponsored by SimCorp and WBR Digital agree the proposed architecture must address these “Top 3” data challenges:
- Data analytics – typically an area of Big Data focus
- Risk management – typically regulatory and financial
- Cloud services – infrastructure and cost effects
There are many technical design options to consider when proposing solutions for these architectural challenges. Some of the possibilities include:
- Maintaining and extending existing legacy “big iron” systems that were the “all-in-one” solution of times past
- Implementing “front-to-back” systems to replace a “best-of-breed” architecture
- Continuing with existing “best-of-breed” solutions that serve specific business silos such as trading or performance measurement
- Adopting newer approaches such as Platform as a Service (PaaS) offerings
In addition to evaluation of the purely technical aspects of the various options, there is also a decision to be made between “in-house” solutions and outsourcing. Evaluation must include careful consideration of operational support, oversight required, risks, and costs as well as the ability to evolve quickly in response to new business opportunities.
In the evaluation process, it will become apparent that there are several solution characteristics that represent trade-offs along some continuum. This implies that the choices may not be binary “either/or” but rather “more or less” of some such characteristic. Examples of those kinds of choices include:
- Fully integrated vendor solutions vs. best-of-breed
- Agility and flexibility vs. purpose specificity
- Support services vs. in-house talent
- Performance vs. cost
Decisions will differ from firm to firm depending on their characteristics/preferences and external factors. As an example, a firm with a main business focus on trusts and custodial activities may make different choices than a firm that specializes in automated algorithmic trading. In addition to the internal data needs firms such as these might have, each may experience external regulatory and economic pressures differently.
EDM challenges: As it applies to asset management
Amongst the many types of financial institutions, asset management firms are perceived to be lagging behind other financial sectors in the progression from the basics of EDM to more advanced levels. Yet, in addition to the shared challenges posed by the sheer increase in data volume and the characteristics of unstructured “Big Data” compared to rigidly structured operational data, many asset management firms have evolved their current EDM practices without the benefit of a DGO and with purpose-specific multi-vendor best-of-breed approaches.
… many asset management firms have evolved their current EDM practices without the benefit of a DGO and with purpose-specific multi-vendor best-of-breed approaches.Mick Cartwright, Director, Asset Management, Accenture
It is not uncommon for a single asset management firm to have:
- Multiple trading systems for various instrument types
- Separate performance measurement systems
- Different accounting platforms
- Several data repositories and reporting applications
All these are tied together by an increasingly difficult-to-change layer of data integration approaches. In a multi-vendor environment, there may be a great deal of data used in common across the systems (security master data for example) but each vendor’s system may contain subsets of such a data domain and may implement the data representations differently. Further complicating the data landscape, the values for even standard data fields may have been provided by multiple disparate sources with different opinions of the correct values.
Either in response to, or with foresight of such a situation, a handful of major vendors incorporate multiple functions into an integrated solution. The promises in this approach are uniform data content, less integration need, improved quality and consistency, and a lower total cost.
Taking the notion of integrated solutions even further, some vendors offer a complete managed service option. This kind of option may offer “soup to nuts” support for hosting infrastructure, software, data acquisition, processing, archiving, and reporting.
Anticipating change
Change is the one constant all businesses confront. It may be initiated either internally as a conscious business strategy or externally as the result of environmental factors, such as regulatory requirements. Recognition of its inevitability is a key factor behind an increasing interest in data analytics intended to foster discovery of new business opportunities or identify ways to mitigate risks. Data for this kind of analysis typically is outside the scope of a firm’s traditional operational systems; it is the realm of “Big Data.” Disruptive characteristics of such data include lack of structure, enormous volume, and a need for extended time coverage. Often the data is organized on the way out, during consumption, rather than on the way in, so standing notions of EDM on their head.
Among the key challenges, these characteristics imply for DGOs is the need to implement a robust metadata layer to catalog the available data content as well as the means and permissions to access and secure it. The same characteristics also have major implications for the storage and access infrastructure IT will implement.
Effective EDM and data governance are inseparable and no longer optional
Now more than ever before organizations cannot afford the reputational or regulatory risk associated with usage of bad data. Internal and external consumers should be able to trust the data and information delivered to them, while the producers of the same must continuously strive to improve the efficiency and accuracy of their processes.
In an environment of tightening budgets, enterprise management needs to continue to invest in a long-term data strategy as well as to engender the culture of effective data governance that will meet and surpass the demands for timely, accurate, and available information. Occupants of the “C-Suite” increasingly recognize and prioritize EDM as a strategic imperative to help navigate the ongoing data-related challenges.
About the author
Mick Cartwright, Director, Asset Management, Accenture
Mick Cartwright is a Director with over 20 years’ experience in the Asset Management industry. He has worked closely on many EDM North American projects and helps clients frame their EDM strategy and implementation roadmaps within their broader operational context. His proficiencies include EDM Design and Project Lead, along with performance measurement and attribution, operations, systems integration workflows, data conversion, functional and unit testing and deployment strategies. Prior to joining Accenture, Mick successfully delivered several large implementations of Enterprise Data Management systems, in addition to numerous performance measurement and attribution system integrations.
