An In-depth Look at Current Data Management Practices

The 2015 FIMA West Benchmark Report

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Table of Contents

Executive Summary ....................................................................................................................................................................................2

Research Analysis ....................................................................................................................................................................................3-7

Conclusions and Recommendations ......................................................................................................................................................7

Appendices .................................................................................................................................................................................................. 8

Appendix A: Methodology ....................................................................................................................................................................................8

Appendix B: Demographics .................................................................................................................................................................................8

FIMA West .................................................................................................................................................................................................... 9

WBR ............................................................................................................................................................................................................... 9

Executive Summary

Since the Dodd-Frank Wall Street Reform and the Consumer Protection Act in 2010, data governance for financial institutions has become an ever increasing set of processes that continues to drive huge transformation. Due to this strict regulatory environment, government agencies are now responsible for regulating most facets of a financial organization’s process. Any part of the process that is required for effective data and information management is fair game. As we move through 2016, regulations for the financial sector are here to stay. There is no going back in time. The complexities of data will only continue to increase.

Sell-side and buy-side firms share many of the same concerns when it comes to data governance. The majority of sell-side firms including, investment banks, commercial banks, and brokerages have kept the concept of data governance front of mind, dedicating their attention towards strengthening their reference data management solutions for greater compliance, reporting, and enterprise risk management.

The majority of buy-side firms though are not as established with their data governance initiatives, and realize a serious reevaluation of data strategies is a necessary next step to remain compliant with regulatory demands.

It may seem like a daunting task, but driving change is a must. And the cost savings and efficiencies to be gained are significant. But we face two key obstacles:

Breaking Down Siloes Except for country-specific siloes where regulatory differences make them potentially necessary, we have to take a sledgehammer to the siloes. They cost us time, money and are rarely in the best interests of our business.

Alignment With only 2% of respondents suggesting their data organizations are aligned with business objectives. While regulations affect global institutions differently than a smaller regional firm, it is clear we have some serious heavy lifting to do.

Interestingly, these two obstacles to progress can be seen in just about any industry, financial or otherwise. Both siloes and alignment are problems faced across every industry Worldwide Business Research serves. We are fighting human nature. We are fighting ourselves!

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Less than one year

1-2 years

3-5 years

5+ years

1

2

3

4

5

7%

2%

24%

9%

33%

17%

36%

35%

37%

How long has the data management function been in operation at your firm?

How aligned is your data management strategy with business goals?

For financial institutions, the concept of data integrity and data governance isn’t new. For 36% of institutions within the industry, a data management function has been in operation at their firm for over 5 years, 33% for 3-5 years, 24% for 1-2 years, and 7% for less than a year. Although having a data management operation may seem like right steps were taken, there are various components that go

into an effective program, and if those processes are not in place it can be detrimental to the state of the firm. Additionally, to remain compliant, these organizations need to be up-to-date with the regulatory environment surrounding their data governance initiatives. As the regulatory environment changes, all steps need to be reviewed.

(1=Significant Alignment, 5=Little Alignment)

Research Analysis

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44%

29%

24%

3%

How do you plan to institutionalize the data strategy in your organization?

Alignment is a vital component in any change management initiative and especially imperative to a firm that is working to improve existing data management strategies. Currently, only 2% of institutions denote that their data management operation is aligned with business goals. While the majority of respondents (72%) indicate that their current data management strategy is not parallel to their current business objectives.

Think about that for a moment…how can our data management strategy, which is so critical to how our business functions, not be aligned with the business goals? The questions that come to mind quickly are: Why not? What steps do we need to take to ensure this? Who are the stakeholders to help me make this transition? And it’s clear we want a more top down approach, just by looking at the graph. Why isn’t it happening? It just may be a story of complexity.

Top-down approach business driven

Function-based data strategy

CDO to drive the global data strategy

Defined as per usage of downstream applications

Research Analysis

None, entirely in-house

Only non-critical data functions are outsourced

Specific functions like data validation, cleansing, mapping and transformation

Managed services for Enterprise reference data management

Reference data utility

25%

20%

2%

0%

What is the level of outsourcing across your reference data management functions?

53%

5

Individual systems based on business needs

Centralized reference data management

Hub and spokeframework

Multiple distributed systems

16%

22%

29%

33%

To what extent is your data management function centralized vs. siloed?

The majority (53%) of firms have indicated that they do not outsource across their reference data management functions. While less than a quarter (20%) of institutions are outsourcing for specific functions like data validation, cleansing, mapping and transformation, the bulk of organizations are managing everything entirely in-house. To begin to achieve economies of scale and reallocate resources that can be applied to revenue-generating activities, firms will need to turn to third party suppliers for reference data management support.

In which stage are we in the outsourcing journey?

Have we chosen to outsource non-critical components? Or are we taking conservative steps now and are looking to outsource more in the future? Perhaps with more alignment with corporate goals, we can unload more non-critical activities to third-parties, freeing up our time internally to focus on more critical data transformation projects.

And what about managed services? We could categorize that with outsourcing. Some are predicting that managed services will be the norm within ten years. Given the significant cost savings and efficiencies here, it will be interesting to see how managed services develop in the next 3-5 years.

Research Analysis

Many firms are still organized in silos, especially when it comes to data management, with 33% reporting that they have multiple distributed systems across their organization. The problem with distributed systems is that they are complex and difficult to understand and operate. Data inconsistencies, reporting issues, and multiple data sources of client information make it hard for departments of one organization to work together.

29% of financial firms report that they operate off of a hub and spoke business model. Some argue that this model is the future of data management, allowing their organizations to analyze and process more forms of data at a lower cost. 22% of financial institutions have adopted a centralized data management program, while 16% of firms indicated their data management function is siloed due to having individual systems based on business needs.

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58%

30%

12%

How do you manage a meta-data repository or data dictionary for your reference data?

58% of us indicate multiple data standards due to diverse data sources. Immediate questions that come to mind are whether or not multiple standards are optimal? What should be our preference here?

Given 30% have a standardized data model might suggest that less standards are optimal, and a single global standard might be most optimal. But there could be legitimate needs for a multiple standard approach.

Multiple data standards due to diverse data sources

Standardized data models

Global architectural standards

Research Analysis

Existence of multiple data sources and processing of data in silos

Replacement of legacy systems

Lack of enterprise-wide governance model

Inconsistent client and counterparty data

Instrument data

66%

45%

45%

32%

14%

What are the key challenges associated with adopting standardization in reference data management?

(Results reflect respondents who selected all applicable answers.)

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Research Analysis

When it comes to the challenges firms face trying to adopt standardization in reference data management, various reasons hold them back. While survey respondents were able to identify multiple challenges, the most significant (66%) was the existence of multiple data sources and processing of data in silos. The second and third most selected, was the replacement of legacy systems (45%) and the lack of

enterprise-wide governance models (45%), garnering the same response rate.

The reason we don’t have enterprise-wide governance models is likely a function of alignment with business objectives. [See Chart 4 above]. If the hub and spoke model is the future, and alignment is so critical here, then we better get moving.

Data is the biggest asset a financial institution has. And make no mistake, data management is complex. Even for senior corporate officers inside the banks. These complexities might be slowing down our most important data transformation initiatives. For those of us who feel the transformation is too slow for our liking, we have to simplify the story with key stakeholders who may have difficulty seeing past these complexities. We’re talking saving millions of dollars potentially and eliminating redundancies. It’s a great story. Perhaps we have to learn to tell it better.

Siloes aren’t helping. We have to take a sledgehammer to the siloes that are breakable. Clearly, with state and country regulations, siloes can never be fully torn down. But no matter the industry, siloes are a serious obstacle to progress. The hub-and-spoke approach with the CDO at the center, and a single standard may be the key to putting us in a position to show we can add significant value beyond reporting. And that is, after all, what we’re all hoping for.

Conclusions and Recommendations

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Appendix

Methodology

Demographics

The results analyzed in this report were gathered from responses to an on-site benchmarking survey delivered at FIMA West 2015 and prepared by report author

Chyna Dama. 46 financial executives responded to the survey.

How many employees make up your data management organization?

17% 1-5

30% 6-15

13% 16-25

5% 26-35

35% Over 35

New business models are emerging and buy-side firms want to be on the cutting edge of data governance. Although our research findings indicate that 35% (16 respondents) of financial institutions have 35 or more employees that make up their data management organization, there are far less employees on the buy-side compared to larger investment banks. For 30% (14 respondents), 6-15 employees make up their data management organization.

Balances of power are slowly shifting, and the drive to increase efficiencies has led to a restructuring across organizational landscapes. Firms are gradually growing their departments, bridging the gap and bringing together back office technologists, risk and compliance, and settlements, front office traders, M&A, advisory and sales representatives. As these departments continue to grow, having the right technologies and processes in place (so that they are understood by more than one department) is paramount to the growth of any financial institution.

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