Value of data analytics inportfolio managementEnhancing federal agency portfoliosthrough data analytics“What is Portfolio Management (PfM) and how can data quality and analytics serve as a driver forPfM solutions for the Federal Government”? PfM is the strategic layer of performance managementtypically undertaken to confirm the alignment of a federal agency’s investments to its vision, mission,and strategic goals. In the federal government, PfM focuses on enhancing the portfolio by unifyingFinancial Management, Enterprise Architecture, Capital Planning and Investment Control (CPIC),Budgeting, Project and Program Management, and Operations. Effective data managementpractices and analytics serve as a critical foundation to successful PfM.Federal government agencies directed to use same OMB memo highlights the importance ofPfM and data analytics data and analytics, requiring 2013 budget plansSince 1996, the U.S. Congress has directed the to explain how agencies will acquire, analyze,use of PfM as part of the CPIC process as a evaluate, and use data to improve policy andmeans for federal agencies to manage, control, operational decisions to set outcome-PfM andand report on investment of resources. data analytics to help identify the most cost- effective practices and programs.1In December 2010, former U.S. ChiefInformation Officer Vivek Kundra took PfMrequirements to the next level by unveiling a 25 -point implementation plan to reform federal ITfunding, acquisition, and management. Centralto this redefinition is specific direction to seniorleadership calling for greater responsibility anduse of PfM protocols to help evolve a moreeffective mission-aligned information technology(IT) investment mix.Along with similar emerging federal agencyguidance calling for greater accountability andanalysis of investment resources, agencies arefurther challenged by the federal budget for fiscalyear (FY) 2012, which includes more than $1trillion in deficit reductions (two-thirds from whichwill come from cuts).In August 2011, Office of Management andBudget (OMB) memorandum M-11-30 wasissued to federal agency heads and set FY13 1 M-11-30 MEMORANDUM FOR THE HEADSdiscretionary appropriations targets at a 5%–10% OF DEPARTMENTS AND AGENCIES, White house.gov,reduction from FY 11 appropriation levels. This http://www.whitehouse.gov/sites/default/files/omb/memora nda/2011/m11-30.pdf
As the acceptance of the need for effective advanced PfM COTS tool will be as effectivefederal PfM practice grows against the backdrop without good data quality and integrity. Data thatof increasing budget constraints, federal is both correct and measurable is a requisite toagencies are recognizing and taking action help achieving effective evidence-based decisiontoward an effective PfM and data analytics making for PfM.implementation. Deloitte’s PfM life cycleFinding effective methods and tools Deloitte’s PfM Analytics comprises practitionerAs agencies embrace PfM, they are learning the skills, innovative technologies, and leadingvalue of the way PfM analytics uses data to drive applications to continually gain PfM insights frombusiness strategy and measure performance. which to help drive business outcomes. ThePfM Analytics includes a range of capabilities — Deloitte PfM approach is a critical integrationfrom looking backward to evaluate what methodology that can evaluate strategy andhappened in the past, to forward-looking investment results to help leaders make timelyapproaches like scenario planning and predictive investment shifts. This approach can be used atmodeling to enhance client decision making. the IT, enterprise, program, or project level as an investment control mechanism. The Deloitte PfMStill, the PfM approach taken by many Federal model is a continuously improving and cyclicalagencies focuses mainly on PfM tools and process — as investments are made, theirprocesses. Agencies new to PfM may look for a performance is monitored on a recurring basis.quick fix and direct their attention solely on Portfolio source data is compared and used topurchasing commercial off the shelf (COTS) consistently reevaluate the same investments insoftware to provide the reporting and tools subsequent cycles and assess new opportunitiesneeded for PfM. More experienced agencies based on the goals and strategies of the agency.take a step back to first focus on the reporting Throughout these cycles, Deloitte takes a holisticneeds of the agency, and then look for the right - approach to PfM services so that the mostfit PfM methodologies and processes to meet effective data analysis methods are selected fortheir requirments. That choice provides the each client.transparency in funding and prioritizationnecessary to help make effective decisions. Translating the vision Deloitte understands that each client facesWhile tool selection and a tested methodology different challenges in reaching their goals, andare essential to effective PfM, a true PfM for that reason the first step in the PfMdifferentiator can be found in Deloitte’s methodology is to understand the client and theirapplication of analytics and effective data quality situation to tailor our approach to serve theirmanagement to support a PfM solution. This specific needs. This involves thoroughlydifferentiation is especially true as agencies face reviewing and understanding the agency’sincreasing amounts of data to process, mandates, vision, and data in order to determinecomprehend, and utilize effectively. PfM the required set of tools to fully serve the client.combined with data analytics creates a powerful PfM Analytics are used to assess the client’sevidence-based decision method. Good PfM current capabilities to gain a clearer picture ofpractice includes data management protocols, the gap between what the organization can doand the establishment of a data governance and what it should do. This step involvesprogram, which includes the process by which identifying the critical stakeholders and the gapsan organization manages the quality, in the data available to them; and whether theconsistency, usability, security, and availability of client is drawing on all available systems andan organization’s data. information so that the strategic and mission objectives are met. Effective PfM requiresWhen looking at their decision capabilities, stakeholders to provide and have access toorganizations are often unaware of the quality data and reports to help make informedmagnitude of the operational inefficiencies decisions.caused by missing or inaccurate data. Neitherthe leading PfM methodology nor the mostAs used in this document, “Deloitte” means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please seewww.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and itssubsidiaries. Certain services may not be available to attest clients under the rules and regulations of public 2accounting.
Data strategy and risk assessmentDeveloping the prioritization model DataUnderstanding where the agency is “now” in remediationterms of mission alignment results and data — Afterquality is critical to PfM Analytics and performingprioritization model development. Investment a broad,prioritization capability enables the agency to detailed“rack and stack” its investments based on an dataagreed upon set of weighted criteria that aligns assessmentto the agency’s mission. In many cases, the andagencies lack a commonly agreed upon set of identifying client data issues, anomalies, anddefinitions to support the understanding and exceptions, Deloitte performs data remediationmonitoring of performance metrics. Using to mitigate present and future data concernsanalytical insights and predictive models, by leading change initiatives within the clientDeloitte identifies a portfolio’s key performance organization. Data calls and cleansing mayindicators, which are used to develop scoring need to be performed.criteria to support the prioritization model Data validation — It is important to determinedevelopment. The model is built based on data accuracy and completeness. It isweighted value and risk criteria determined necessary to determine whether the availablethrough a collaborative effort between the data sufficiently captures the informationDeloitte team and client stakeholders. needed to correctly support the established scoring criteria. Data gaps, or missingCollect portfolio data, analyze, and report information, lead to inaccurate output of PfMPrior to analyzing portfolio data and producing reports and incomplete analytics. These issuesPfM reports, Deloitte performs data quality should be addressed before the agency canchecks necessary to help achieve effective PfM become analytics driven or fully depend ondelivery through a framework that is tailored to PfM reports to make decisions.provide an end-to-end data quality solution (asshown in the graphic below): Data quality monitoring — After data validation, Deloitte incorporates mechanisms to sustainThe PfM data approach not only assesses the and measure data quality. PfM and analyticshealth of the data, but also provides insights into are continuous improvement processes; it isthe root causes of data error. These insights can important to plan for change initiatives that arethen drive appropriate remediation, preparing the not a one-time fix. Deloitte works with thedata and building a sustainable process to align client to help establish data governancethe data with PfM strategic and tactical goals. standards and cultivate a change in the organization’s culture as it pertains to data, Data strategy and risk assessment — analytics, and reporting. Establishes the program framework, scope, and prioritization. Deploy and execute the portfolio The Deloitte PfM life cycle is an iterativeData quality analysis — Helps obtain an process. Throughout the engagement, Deloitte understanding of the data and determine will continuously refine the criteria and analytics, whether data correctly represents reality, so while communicating and generating reports as that data categories fully reflect values per new information becomes available. The their definition. We work to exclude data that creation of interactive dashboards can provide does not correctly reflect true portfolio more efficient reporting capabilities, data information, inaccurate reporting, and reduce visualization, along with measuring and ill-informed decision making. comparing quantitative and qualitative metrics. Continuous monitoring through ongoing data analytics and/or sampling will measure and track portfolio performance. Through data analytics and PfM, Deloitte will increase the transparency of the portfolio. Outputs of analysis become part of the organizational culture, delivering the insights people need — resulting in more 3