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Data as an asset

by Max Duhe, Matt Gracie, Chris Maroon, Tess Webre
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7 minute read 25 February 2019

Data as an asset Defining and implementing a data strategy

7 minute read 25 February 2019
  • Max Duhe United States
  • Matt Gracie United States
  • Chris Maroon United States
  • Tess Webre United States
  • Tess Webre United States
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  • Where to begin: Defining a data strategy with success in mind
  • How to implement a data strategy: Turning a document into a movement

It’s easy to be overwhelmed by the challenge of turning data from an afterthought into a core facet of business operations. But CDOs can take comfort in knowing that change doesn’t happen overnight. 

How do you get your organization to value data? Accurate data is the fuel that propels government organizations toward achieving their mission. Increasingly, government organizations agree it is time to view data as a critical strategic asset and treat it accordingly.1

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Managing and leveraging data typically falls to the chief data officer (CDO). In the Big Data Executive Survey of 2017, 41.4 percent of the executives surveyed believed that the CDO’s primary role should be to manage and leverage data as an enterprise business asset.2 However, many government organizations fail to invest in the resources necessary to realize the data’s inherent value.

It is easy to become overwhelmed by the challenge of turning data from an afterthought into a core facet of business operations. Organizations can become paralyzed because they don’t know where to begin. But CDOs can take comfort in knowing that change doesn’t happen overnight.

To unlock the value of an organization’s data, a CDO should develop and implement a clear data strategy. The data strategy can help organizations take a strategic view of data and use it more effectively to drive results. The best data strategies are generally tailored to the organization’s needs and help the CDO engage necessary stakeholders, plan for the future, implement strategic projects, develop partnerships across the organization, and emphasize successes to drive a strategic mindset.

Where to begin: Defining a data strategy with success in mind

A data strategy provides an organization with direction. CDOs can use the data strategy to organize disparate activities, consolidate siloed data, and orient the organization toward a cohesive and unified goal. The aim is to set the stage for treating data as an asset, resulting in improved decision-making, enhanced user insights, and greater mission effectiveness.

Tailoring a data strategy to an organization’s unique needs

Every organization is different; there is no definitive checklist for a data strategy. Successful data strategies come in many shapes and sizes, tailored to each organization’s strengths and weaknesses.

CDOs who are unsure of their organization’s strengths and weaknesses benefit from an assessment of their data maturity. Assessments are intended to provide a pulse check that CDOs can use to prioritize goals and initiatives within the data strategy to meet the organization’s unique needs. With this understanding of strengths and weaknesses, CDOs can tailor their strategy to build upon organizational data opportunities while being cognizant of limitations.

The aims of an organization’s data strategy should align with the overall mission and goals. For instance, the US Navy CIO’s data strategy emphasizes data analytics and data management to enhance combat capabilities.3 Similarly, the Department of Health and Human Services’ data strategy focuses on consolidating data repositories to create a shareable data environment for all relevant stakeholders.4

It’s all about the people

To be effective, a data strategy should also consider the human side: owners, stakeholders, analysts, and other users. Organizations that encourage staff to think about information and data as a strategic asset can extract more value from their systems.

For example, New York City’s first chief analytics officer, Michael Flowers, addressed several complex problems through a data strategy that emphasized engagement of all data owners across the local government. “Our big insight was that data trapped in individual agencies should be liberated and used as an enterprise asset,” he said.5 Flowers’ efforts led to the development of New York City’s data integration platform, which now allows different parts of the local government to share data with each other to collaborate and solve problems.6

Gaining buy-in across the organization is instrumental in developing a successful data strategy, as is understanding all relevant organizational needs. The CDO should engage all parts of the organization from day one. Without input from key stakeholders, the CDO may fail to incorporate critical organizational considerations into the data strategy.

Planning for the future

Nothing is stationary. CDOs should recognize that not only will their organization change, but so will various industry tools and technologies, as well as broader government policies and practices. It is imperative to plan and establish a data strategy that accounts for future changes. A flexible data strategy can open up the ability for the organization to continue to use data as an asset for the long term.

As an example, the City of San Francisco addresses this challenge by regularly revisiting its data strategy. The City reviews and revises its data strategy each year—including refining its mission, vision, and approach—all while adhering to a set list of core goals. This periodic review of its data strategy keeps the City’s approach to data use up to date while sustaining accountability for pursuing the City’s overall strategy.7

How to implement a data strategy: Turning a document into a movement

Implementing a data strategy is a daunting task. One common difficulty is that many organizations are hesitant to change legacy IT operations—especially for government, whose budgeting process can make even small changes difficult to implement. However, difficult does not equate to impossible. CDOs can nudge their organization toward alignment with the data strategy’s principles and goals.

Transforming strategy into action

Even the most brilliant strategy will not improve an organization’s use of its data assets if it sits on a shelf. Converting a data strategy from a piece of paper into a state of mind can be messy, and CDOs should be realistic about the pace of change, especially early on. The most effective approach in the face of organizational inertia can be to set realistic expectations and identify opportunities to show value early on.

Once the data strategy is developed, CDOs should identify and list key business issues that the data strategy is designed to address or solve. For example, will the data strategy enable the organization to meet upcoming regulatory or legislative deadlines? Are there existing modernization efforts underway that require a data conversion? Is there a particular weakness from the assessment that can be addressed by implementing a data governance council? CDOs can develop a list of projects by identifying specific ways the data strategy can address these issues.

It is important to prioritize issues that will add the most value to the organization. To establish the data strategy’s credibility and utility, it’s helpful to start with high-visibility projects that draw on key components of the data strategy and that support the CDO’s own key goals.

What defines a good opportunity will be different for each organization. Finding the right project requires the CDO to have a clear understanding of the organization’s wants and needs. For example, while developing the US Air Force’s data strategy, the CDO identified manpower shortages as a critical issue. The CDO prioritized this limitation early on in the implementation of the data strategy and developed a proof of concept to address it.8

Turning action into victory

A CDO can improve the chances of a project’s success by developing partnerships across the organization. The best partnerships are those that are mutually beneficial, where all parties are invested in the effort’s outcome. All parties should have skin in the game; this way, once the solution is deployed, everyone can declare victory.

An effective partnership can be maintained by simple, frequent, prioritized, and actionable communication. Simple and frequent communication keeps all parties informed about progress and minimizes negative impressions from minor setbacks. Delivering prioritized and actionable information enables all parties to act efficiently, and fosters a shared sense of ownership.

One effective partnership model can be for the CDO to share resources with partners across the organization. For example, a member of the CDO’s team could work with a partner for a limited time to implement a specific project. This could benefit both teams: The project team gets an additional resource, and the CDO can be confident that the work aligns with the overall strategy. If a team member cannot be spared, the CDO can provide the project team with tools, subject-matter expertise, or other assets. Not only does this increase the odds that the project will meet its objectives, but it also affords the CDO greater control over the project’s alignment with the data strategy.

An example of such a partnership is the US Department of Transportation’s (DOT’s) development of a national transit map in collaboration with several state and local transportation organizations.9 In this effort, the DOT provided technical assistance to local transit agencies, who also benefited from having their data made publicly available.

Turning a victory into a strategic mindset

Success breeds success, and CDOs should capitalize on every victory. For example, enhanced data availability and a strategic analysis enabled the CDO of the US Department of Health and Human Services’ Office of the Inspector General to detect hundreds of millions of dollars in fraud in 2017.10 CDOs can point to victories like these when making a budgetary case for additional resources, technologies, or capabilities. Additionally, a CDO can use victories to foster excitement and buy-in from the organization.

Every victory counts. After one victory, find the next. Find another project, turn it into a success, and publicize it appropriately. Engage the energetic participants, and continue to work on the less-than-enthusiastic ones.

The future starts now

The years to come will present many unique opportunities and challenges for data management. CDOs are uniquely positioned to guide organizations through the process of managing data and unlocking its value.

To be successful in this effort, a CDO must have a nuanced understanding of the organization’s current data culture, resources, and opportunities for improvement. Through this understanding, CDOs can develop and implement an actionable data strategy to achieve the desired future state.

Understanding how to create and tailor a data strategy will be a critical skill for CDOs. By carefully selecting and implementing a data strategy and capitalizing on victories, CDOs can position their organizations for success in making use of data as a valuable strategic asset.

Acknowledgments

Cover image by: Lucie Rice

Endnotes
    1. Executive Office of the President of the United States, “Leveraging data as a strategic asset,” accessed February 5, 2019. View in article

    2. NewVantage Partners, “Big data business impact: Achieving business results through innovation and disruption,” 2017. View in article

    3. Department of the Navy, United States of America, “Strategy for data and analytics optimization,” September 15, 2017. View in article

    4. Office of the Assistant Secretary for Planning and Evaluation, US Department of Health & Human Services, “Improving data for decision making: HHS data collection strategies for a transformed health system,” December 21, 2011. View in article

    5. William D. Eggers, Delivering on Digital: The Innovators and Technologies That Are Transforming Government (Deloitte University Press, 2016). View in article

    6. Ibid. View in article

    7. Edwin M. Lee and Joy Bonaguro, “Data in San Francisco: Meeting supply, spurring demand,” City and County of San Francisco, July 31, 2015. View in article

    8. Rusty Frank, “AF chief data officer: Data is the future of the force,” US Air Force, February 23, 2018. View in article

    9. Ben Miller, “US DOT calls on state, local agencies to help build national transit map,” Government Technology, April 20, 2016. View in article

    10. Frank Konkel, “How federal agents took down a $1.3 billion health care fraud scheme,” Nextgov, July 20, 2017. View in article

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The chief data officer in government: A playbook for CDOs

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Max Duhe

Max Duhe

Max Duhe is a consultant with Deloitte Consulting LLP. He has seven years of experience helping nonprofit and federal customers discover the value of their data through advanced analytics and visualization. Duhe holds a master’s degree in Public and International Affairs from the University of Pittsburgh where he concentrated on the national security risks of illicit nonstate actors. He is based in Arlington, Va.

  • maduhe@deloitte.com
Matt Gracie

Matt Gracie

Matt Gracie is a managing director in the Strategy & Analytics team at Deloitte Consulting LLP. He leads Deloitte’s NLP/Text Analytics practice that supports civilian, defense, national security, and health sector agencies gain insight from unstructured data, such as regulations, to better serve their mission. Over the years, Gracie has pioneered the engagement of various new technologies that are now commonplace in our society—from e-commerce to artificial intelligence. With over 30 years of experience in financial services and consulting, Gracie is a thought leader with global and national experience in strategy, analytics, marketing, and consulting. He is based in Arlington, Va.

  • magracie@deloitte.com
Chris Maroon

Chris Maroon

Chris Maroon is a senior consultant with Deloitte Consulting LLP. He has over four years of experiencing delivering data analytic, business intelligence, and systems engineering solutions for federal government clients. Maroon holds an MSc in Data Analytic Engineering from George Mason University and a BSc in Electrical Engineering from Villanova University. He is based in Arlington, Va.

  • cmaroon@deloitte.com
Tess Webre

Tess Webre

Tess Webre is a senior consultant with Deloitte Consulting LLP. She has over five years of experience implementing data management projects in the federal space, including analytics, data quality, master data management, and data governance. She holds an MLS from the University of Maryland. She is based in Rosslyn, Va.

  • twebre@deloitte.com

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