Data Management: Target Operating Model – Missing Link for a Sustainable Data Culture

Establishing a Data Management Target Operating Model is a critical step to achieving C-Suite authorization and accountability; creation of an enterprise-wide Data Management Ecosystem; and, creating a sustainable and repeatable data culture in the organization. These are the foundations for building a Data Management Function.

Target Operating Modela presentation of the vision of the ideal future state Operating Model of a business process or a collection of processes within the organization.  It forms the “rules of engagement” for execution.

Data Management Strategy – the overall strategy contains the roadmap for executing according to the target state design closing any gap from the current state.

The Target Operating Model must recognize the operating levels within the organization that the processes will operate; Enterprise, Function, and Domain.

Enterprise – The organization-wide operating level processes.

A Region operating level may be a subset of the Enterprise when a region has specific process requirements.

An External operating level may also exist when data needs to align to industry standards for sharing organization to organization.

Function – The Line of Business and Control Function operating level processes in the organization.

Domain – The operating level defined by a logical collection of data. A Function may be accountable for more than one Domain.

The Data Management processes operate at all three levels of the organization. The processes at the Enterprise level are typically owned and executed by the Office of Data Management led by the Chief Data Officer. This is the control function for the organization’s data. How and who executes the data management processes at the Function and Domain levels is defined by the Target Operating Model answering questions around centralized, distributed or hybrid execution.

No organization of any size can be fully centralized as it removes accountability for the data from the business processes that produce the data. However, striking the right balance in a hybrid model will allow you to gain the efficiencies of central execution for the appropriate data management processes. Turning those processes into an enterprise service where possible.

The relationship between the Data Management Target Operating Model and Data Management Strategy are not linear. However, the operating model is required first to establish the vision and rules of engagement. Then defining the strategy and the roadmap for execution may result in tactical adjustments and an evolution to get to “target”.

Without a fully developed Target Operating Model that is executable at all levels of the organization the Functions and Domains are left to design a model that works within their silo but will not embrace a cohesive enterprise-wide operation.

The Success

In the financial services sector, some organizations have formally created a Target Operating Model for the Office of Data Management. However, to the extent that their Data Management Function is not entirely centrally executed at the Enterprise level most have failed to define fully how the model applies to the Function and Domain levels of the organization.

Also, many of the operating models that exist have come primarily from the Technical side of Data Management due to the lack of engagement in the Data Management Function from the Business side. For more detail on this concept, see article ”Derailers to Data Management Success”.

The Problem

When an organization does not formally adopt a Target Operating Model for Data Management, disparate, tactical operating models will evolve to fill the void. These bottom-up models will not produce the desired success due to the following constraints:

  • Lack of an enforceable and auditable authority to the function
  • Lack of Business accountability and corresponding engagement
  • Inability to create consistency and integration of data management strategies and capabilities across the organization
  • Disconnect between regulatory and business objectives – inability to monitor the wellbeing of the data against these objectives
  • Inability to deliver a compelling vision and roadmap for the Data Management Function to senior leaders, audit, and regulators

The current state in many organizations is that there will be pieces and parts of an operating model at the various levels of the organization. These were developed bottom-up out of necessity and should be acknowledged and incorporated into the fully integrated Target Operating Model. It will be important to celebrate the success of the past even when improving for the future.

The Opportunity

Organic bottom-up adoption has not worked. A top-down approach is required for successful adoption. This means data is recognized by the CEO and the C-Suite as a critical asset to the organization. And like other assets, it must be managed and deserves the discipline of Business Process Management along with a formal Data Management Strategy. For more detail on this concept, see article “Data Management: Heroic Efforts, Tribal Knowledge, and Luck”.

The CEO should be the Champion for data across the organization. Increasingly data is being discussed not only in the C-Suite but also the Boardroom. Harnessing this newfound interest is an opportunity. The vision that comes from the Target Operating Model is the first step to adoption. Adopting the vision is required before jumping straight to a Data Management Strategy as the vision presented in the Target Operating Model is the cornerstone of the strategy.

The Challenge

Be careful what you ask for, the C-Suite and Boardroom focus on data can quickly unravel any progress an organization has achieved from the bottom up activities. The Chief Data Officer needs to provide the tools the C-Suite leaders need to leverage their focus and establish a foundation for success.

Because Data Management is a new business process, senior leaders may not understand the required business process and benefits like they do for other established business processes (i.e., Finance, Risk, Regulatory Reporting, etc.).

The Target Operating Model and the vision it presents is the cornerstone of the Data Management Function. Gaining agreement on the vision is a critical step in establishing the Data Management Strategy. This vision is the foundation for senior leader understanding of Data Management across the organization.

Without the “target,” the strategy becomes a pin-the-tail-on-the-donkey exercise. The organization is blindfolded without a vision. Creating the Data Management Strategy will be a competition among leaders resulting in mediocrity and confusion if a shared vision is not first adopted.

The Benefit

Board support and C-Suite adoption of the Target Operating Model, first, followed by the Data Management Strategy will set the stage for a culture of data sustainability across the organization. Three essential benefits will follow.=

  • Function Authority – Defining the Data Management Target Operating Model for the organization at the Enterprise, Function and Domain levels will support C-Suite Leadership adoption. The adoption will be the basis for authority and empowerment for the Data Management Function and specifically the Office of Data Management for consistent and sustainable adoption by all the stakeholders.
  • Stakeholder Integration – Adoption of a Target Operating Model at the Enterprise, Function and Domain level of the organization will support the creation of consistent, integrated strategies by the various entities at each level of the organization.
  • Operating Efficiency – Optimizing centralized versus federated execution of Data Management will reduce execution time and operating costs and remove barriers in the domain to domain interactions. The efficiency will enhance regulatory preparedness and reduce the cost of compliance.

The How

Approach and Scope

 

The diagram above defines the five phases of developing the Target Operating Model as the vision for the Data Management Function.

Phase 1: ACTUAL – Current State

It is critical to gain a common understanding of the current state of Data Management at the Enterprise, Function and Domain levels in the organization. This will define the requirements and constraints of the organization due to size, complexity, geography, and culture. The best approach is to assemble a comprehensive document review. Where no documentation is available interviewing key subject matter experts will help to fill in the gaps.

The first step is to establish an overview of the Organization and Data Management Function.

  • Organization Operating Model
  • Data Management Function History
  • Data Management Governance Model
  • Enterprise Data Management Standards
  • Function Funding Structure and Process
  • Recent Audit Results
  • Data and/or Domain Inventory

The second step is a review of the current organization and Data Management Function strategies.

  • Business Objectives (Enterprise and Function)
  • Data Management Drivers (Enterprise and Function)
  • Stakeholder Analysis(es)
  • Resource Plan(s)
  • Organizational Structure(s)

The third step is to conduct a Data Management Capability Assessment. As an Authorized Partner of the Enterprise Data Management Council, we recommend using the DCAM™ assessment tool and methodology. This provides a framework for evaluating the Data Management Capability at the Enterprise, Function, and Domain operating levels of the organization across the range of required capabilities.

  • Data Management Strategy
  • Data Management Program
  • Data Governance
  • Data Content
  • Data Quality
  • Data Collaboration

Phase 2: CONCEPTUAL – Industry Constructs

Industry Constructs based on standard Data Management best practices grounded in the DCAM™ defined capabilities are the benchmark for the Target Operating Model. The outcome of the current state analysis was a set of requirements and constraints that support a critical review of the Industry Constructs. The review will inform the design of executable models customized for the organization.

Phase 3: DESIGN – Target Operating Model(s)

The Target Operating Model is a collection of models that establish the “rules of the game” for the Data Management Function. As stated above, Industry Constructs inform the Target Operating Models design, customized based on size, complexity, geography, and culture of the organization. The components comprising the Target Operating Model should include:

  • Data Management Accountability Matrix – establishes relationship and accountability between Stakeholders in the Data Ecosystem.
  • Data Management Business Glossary – establishes common language and meaning for the Data Management Function.
  • Data Management Principles – establishes the core principles upon which the Data Management Function is designed.
  • Stakeholder Analysis – establishes a complete inventory of Stakeholders in the Data Management Ecosystem.
  • Data Management Capabilities – establishes the framework for the required Data Management Capabilities.
  • Data Management Function – establishes roles and their responsibilities aligned to functional capabilities.
  • Data Management Organization – establishes roles and responsibilities in the organizational structure.
  • Governance – establishes a framework for governing the data and Data Management Function at the Enterprise, Function and Domain levels.
  • Data Management Funding – establishes a framework for funding the Data Management Function and Projects.
  • Data Management Toolset – establishes a framework for the Data Management technology tools used across the organization.

Phase 4: EXECUTION – Strategy and Roadmap

Using the Target Operating Model as the vision of the Data Management Function the Data Management Strategy defines the roadmap for achieving the vision. The key is to integrate the strategies at the Domain, Function and Enterprise operating levels into one consistent execution of the vision all aligned to achieving the ultimate strategy of the Businesses. Use the “rules of engagement” to make a consistent, integrated, and efficient Data Management Function across the organization.

Phase 5: ADOPTION – Top Down Engagement

The Target Operating Model Adoption Plan requires a top-down approach leveraging the highest level of the Data Management Governance framework: C-Suite leaders. This will establish the authority of the Data Management Function. The Target Operating Model is the first stone in the foundation and supports the following stones – all keys to gaining senior leaders as targeted advocates for Data Management.

Alignment with Enterprise Technology Architecture and Roadmap – the Target Operating Model defines the partnership between the Business and Technology.

Alignment with Data Management Strategic Plan(s) – the Data Management Strategy sets priorities and roadmaps for operationalizing the Target Operating Model.

Alignment with Business Objectives and Data Management Drivers – the Data Management Strategy must deliver against the objectives of the Businesses across the organization.

Conclusion

The absence of an integrated vision defined in the Target Operating Model has been the missing link for achieving a culture of data sustainability across the organization. The CDO must have the vision to convert the Board, CEO and C-Suite leader newfound attention to data into active evangelists for Data Management. When adoption of the Target Operating Model vision comes first, building an integrated strategy across the organization is possible. Without an integrated vision and strategy, organizations will continue to solve and resolve data issues in silos. The culture of data sustainability turns the data of the organization into one homogenized asset. This is the framework that allows big data, AI, and machine learning to become a part of the data asset. The result is transformative for an organization in how they manage their business, meet regulatory requirements, and most importantly, how they innovate and differentiate for their customers.


Join the Conversation

Please provide your feedback on any points raised in this paper. Specifically, if you believe that you and your organization could benefit from engaging with other industry practitioners to share best practices and develop standard Data Management processes, we’d love to hear from you. Please be a thought leader and share your best practice for a sustainable data culture with industry standard process capabilities. Share this with other practitioners – let’s get the crowd moving.

The wisdom of many is greater than one.


Published: September 8, 2017
Last Updated: November 4, 2017
Author: Mark McQueen – Founder and Principal Consultant, FutureDATA

Thinking Partners:

Tejasvi Addagada – Dattamza
Pete Youngs – Ortecha
Eric Sutherland

About the Author

Mark is the founder and principal consultant of FutureDATA Consulting.  He is passionate about sustainable processes. During 20+ years at a Fortune 25 financial institution, he has witnessed the practice of Data Management take center stage in the C-suite. However, the derailers to success have been the lack of business accountability for the data and applying business process management discipline to the Data Management function (see Post: Derailers to Data Management Success).

FutureDATA is a network of highly experienced independent consultants. Our consulting services are Data Management capability assessment, capability process optimization, and capability execution based on the industry standard from Enterprise Data Management Council Data Management Capability Assessment Model (DCAM™). The Data Management Function capabilities include Data Management Strategy, Data Management Program, Data Governance, Data Content, Data Quality, and Data Collaboration.

FutureDATA is an Authorized DCAM™ Partner allowing us to leverage the industry standard DCAM™ toolset to build Data Management Capabilities with our clients. Additionally, we are contracted by the EDM Council supporting the implementation of their Best Practice Program.

We measure our success by empowering organizations with sustainable Data Management capabilities to deliver data as the foundation for knowledge, innovation, and competitive advantage.

Mark McQueen

2 thoughts on “Data Management: Target Operating Model – Missing Link for a Sustainable Data Culture”

  1. Hey Mark, Fantastic start to this topic. Some thoughts for consideration:

    Overall: I think you need to answer Why is this critical – to sustain and grow the value of our existing investments by establishing how our assets will be catalogued, mantained, updated, archived, and disposed (or something like that). Analogy to warehousing

    SAS had 5 models for data stewardship: Subject, Function, Business process, Systems, Project. Of these, it may be worth spiking out what an operating model for a project looks like as it (a) determines how to leverage / modify existing assets and (b) transitions to steady state.

    Note that ‘Enterprise’ could be bigger than an organization (thinking healthcare or even finance if you consider partners that are doing payments, confirmations, etc.)

    Is there a consideration for a hybrid model of a TOM? Leverage the insights from the bottom up to inform a top-down framework that enables coherence while supporting buy-in from the people doing the work. In particular where your top-down buy-in isn’t that strong at the outset?

    Should we make the point that Target should be a business-enabled outcome? That is, we aren’t trying to achieve IT’s cost savings targets. What is the big play for the business and how does IM support it?

    I think you should present a more balanced argument about centralized vs. federated vs. decentralized. For some organizations, some other models may work and centralized may be counter-cultural. We need to talk about what is necessary for success and how different models support that success – pros and cons.

    Is it worth talking about doing pilots first? that is do we need to work out the one TOM that will rule them all or build one out in a robust fashion, prove that it works and then do the next one. When designing the one, think big and act small.

    Is it worth spiking out governance at two levels – strategic (to establish the functions, frameworks, etc.) and operational (the day-to-day functions of quality & stewardship)

    Is there really a data management strategy – or is really the strategy a business and technology target state in which data is the central part? I’m trying to think about there is only one strategy and parts of the organization have an organizational strategy of how to achieve it.

    Will you have some basic (Level 1) TOM’s for people to chew on? Given that they can google some, it might be worth having a simple one and then indicate that it is necessary to drill down to achieve the aims

    Cheers,

    E

    1. Thanks for your feedback Eric. I am going to walk through and reply to each of your comments. Please push back where you think I have missed your point or not responded appropriately.

      In the opening paragraph of the article, I present why the TOM is critical to the organization at a very high but critical level. The example of “why” you provided I believe is specific to the Data Management Framework Construct. That is creating the full set of processes that allow you to manage the data. We may need to add more content in that construct. The same is true for your second point on “5 models for stewardship”. I think there is an opportunity to define stewardship and operating model in the context of a project but propose that falls outside the direct scope of the DM TOM. An organization has a model for project execution and governance; it is critical that data and data management requirements exist in that process. I will add this to the prospective article list and invite you to author the draft.

      Great point that the organization levels have ignored a higher level when the organization needs to interact with external ecosystem data. I will add this to the updated paper.

      Similarly, you make a good point that The TOM must acknowledge and embrace the existing bottom-up operating models. The current state in many organizations is that individual Functions or Domains, out of necessity, developed their operating model in advance of a fully developed organization TOM. I will add this to the updated paper. Politics in an organization requires that you recognize and celebrate the success of the past even when improving for the future.

      I do think that I have made the point that the business function that creates the data must be accountable for the data in partnership with Technology and all the control functions of the organization that consumes their data. However, since you have suggested this point, I will look to emphasize this in the updated paper.

      Your suggestion about central, decentralized and hybrid is a valid point. I will look to create a text box that can explore that a bit further. However, I also think this is a topic that warrants a separate but related paper.

      I do think the concept of pilots is valid. However, I would propose that the pilots have to be within the context of the overall TOM for the organization. The Data Management Strategy will also acknowledge that getting to Target may be a journey, not a big bang. I will add this point to the updated paper.

      Your point about “spiking out governance at two levels” should be considered as part of the Data Governance Construct but not detailed in this paper. I will review this as a potential addition to the commentary on the Construct.

      I believe there is a DM Strategy. It is the strategy for the control function for data. Very similar to the other control functions of the organization. Now, I make the point that the strategy needs to exist at all levels of the organization and needs to deliver against the business objectives of that level o the organization. That said, I will review whether I can enhance the point in the updated paper.

      While I agree, you can Google and find several hundred one-page slides of Data Management Operating Models. The whole point of this article and the proposed approach is that the TOM is much more than a simple slide. It needs to create the guardrails for data management across the organization.

      Keep me honest. I will alert you when I have made all the updates committed to above so you can re-review. Thanks.

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