Final Copy - Data Governance Vs BI Governance Whitepaper
Data Governance vsBI GovernanceData Governance has been a focus for enterprises for severaldecades and has only increased in importance as the sheer volume ofenterprise data has exploded. However, effective governance ofBusiness Intelligence at scale has been an elusive goal as it requiresa focus that is broader than that of Data Governance. Business usersin the enterprise consume data through BI Applications, not directlyfrom the data source. Whether users are consuming dashboardspublished through a BI Tool, or advanced visualizations using R andPython, good governance is impossible if the data visualization layeris ignored. After all, what is the value of high quality, well-curated dataif it is delivered to users in an inconsistent manner, or if it lacks theproper context to ensure that business users are interpreting itcorrectly.Give your data theaudience it deserves44 Tehama StreetSan FranciscoCA 94105 1-800-993-3467www.metricinsights.com 2021 Metric InsightsEffective BI Governance requires organizations to establish processesfor the governance of both data and analytics. To be successful at anenterprise scale, these governance processes must be supported byeffective technologies. This whitepaper outlines how a BI Portalprovides the key technical foundation to support a comprehensive BIGovernance strategy. The critical governance role of a BI Portal ispresented for both the scenario where it is the sole BI governanceplatform, and where it works in conjunction with a Data Catalog.
What is Data Governance?Data governance is the set of processes and technologies that are used to ensure effectivemanagement and utilization of data. Analysts and Data Stewards in an organization use DataGovernance Tools to simultaneously enforce corporate governance policies and to promote correctusage of data. Typically, metadata is extracted from Databases, ETL processes, and some BI Toolsand is consolidated within a Data Governance Tool where it is enriched with additional governanceinformation.Data Governance Tools provide capabilities inthe following functional areas:dataset and track these issues throughresolution.Data QualityCertificationData profiling is performed to alert analystswhen data pipelines appear to have issues withdata quality. For example, if today’s data loadcontains only half the number of rows in atransaction table, as is typical for a table, theanalyst or data steward should investigate theissue before reports are distributed withincomplete data. Data Governance tools alsoallow analysts to flag qualitative issues with aThe Data Governance tool identifies whichdatasets and visualizations have been certifiedand tracks ownership and certification changesover time. Certification details may beextracted from the underlying BI Tool metadatafor reports or certification can be performeddirectly within the governance tool. 2020 Metric Insights
Usage StatsIn some tools, usage statistics are collectedfrom the underlying BI tools and presented tothe analyst in the Data Governance tool. Thesestats identify the level of engagement ofbusiness users with each reporting asset andare used by Analysts to determine whichreporting assets are gaining traction within theuser base and which content isunderperforming.Data ClassificationEffective governance requires that datasetsand reporting be classified based on datasensitivity, the presence of PII data, as well asother key metadata. This classificationmetadata is required to inform the properusage of data to meet compliance,confidentiality, and privacy regulations. A datagovernance tool provides the ability to extendthe basic metadata collected from sourcesystems with this required data classificationmetadata.analysts will use a different set of rules tomeasure the same metric. This inconsistencypresents the business with a conflicting set ofnumbers and leads to a lack of trust in thedata. A Glossary that contains approveddefinitions for all key metrics along withestablished ownership of these definitions is acritical component of any data governancetoolkit.Life Cycle ManagementAll BI Assets, whether they are tables ordashboards and reports, must be managedthrough their lifecycle. Before a new dashboardis published to users, it must undergo aprocess to certify that it uses data andtransformation rules that are consistent withestablished metric definitions. Over time, asbusiness rules and data sources change,tables and reports that were previouslyconsidered a “gold standard” can becomeobsolete and must be updated or retired. Aneffective data governance tool provides amechanism to manage the lifecycle of all keyBI Assets.LineageBefore working with a dataset or a report,analysts must first understand the source ofthe underlying data. Lineage diagrams providea visual map of the sources for a givendashboard or dataset. They establish thenecessary context for an analyst who is tryingto find an existing BI asset that has the correctinformation to help answer a specific businessquestion.GlossaryIf an organization does not have a consistentset of definitions for key enterprise metrics andbusiness terms, invariably over time different 2020 Metric InsightsThe critical functions provided by a datagovernance tool are invaluable to analysts anddata scientists as they make decisions aboutwhich existing BI assets to use in an analysis.However, these tools are inadequate inaddressing the full governance needs of theorganization because they fail to support theneeds of all information consumers in theenterprise. A typical business user will notmake use of a data governance tool as part oftheir day-to-day work and will therefore notbenefit from the wealth of information that itcontains. As a result, many organizationsstruggle to achieve significant ROI from thesubstantial ongoing investment required tomaintain governance data in these tools.
What is BI Governance?BI governance extends the scope of traditional data governance to reach all information consumers inan organization by leveraging governance metadata to drive user engagement and improve overalldata literacy. A BI portal can be used as the foundation of effective BI Governance as it acts as thesingle pane of glass through which everyone in the enterprise accesses analytics. A BI Portal cancreate a tightly-knit relationship between analytics consumption and governance. This goal isachieved by integrating governance metadata into the workflow that is used to find and consumeanalytics. Instead of locking governance metadata away in a data governance tool that only a smallnumber of analysts will access, it is made broadly available to all users through the portal. Dataquality, usage statistics, and other important metadata is presented together with each visualization inthe portal enabling all users to benefit from the context provided by this critical information.In addition to enabling broad access to datagovernance metadata, a BI portal also providesthe following key BI governance capabilities todrive user engagement:details. This information helps users searchthrough the available content and identify thecorrect visualization or dataset to use inanswering a specific business question.SearchAccess RequestThe ability to effectively search across allavailable information assets in an enterprise isone of the most important usage-promotingfeatures in a BI Portal. Search provides theopportunity to present the user criticalgovernance metadata such as dataclassification, ownership, lineage, and usageDiscoverability is an essential component ofeffective BI governance. A BI Portal provides amechanism for users to search and discovercontent that they do not have access to viewand then request access to this reporting. Thiscapability ensures that duplicative content isnot generated simply because a user is not 2020 Metric Insights
aware of existing reporting. A wellimplemented access request mechanismprovides a self-service environment for accessgovernance that balances security withdiscoverability. The appropriate Analyst or DataSteward should be informedimmediately of any data anomalies in acritical data pipeline so that these issuescan be investigated and resolved. After data quality has been validated,business users must be informed of anyanomalies in business metrics.PersonalizationA BI Portal drives engagement by providing apersonalized experience to users in whichrelevant content is grouped into folders andglobal filters can be applied to customize thedashboard consumption experience. Thepersonalized experience can also extend togovernance in that users can be preventedfrom downloading or printing reports thatcontain sensitive information unless theybelong to a special “privileged” group.CollaborationThe collaboration enabled by a BI portalsupports good BI governance practices. Issueswith data quality or delays in the data pipelinecan be set to automatically trigger notificationsto users ensuring that business users do notrely on information that is incorrect or out-ofdate in making a critical business decision.Expert Analysis captured from importantvisualizations can provide business users withcritical context to properly interpret the dataand the collaboration features within the portalsupport discussions around the data.AlertingIn order to have effective BI governance,alerting must be deployed for both Analystsand Business Users: 2020 Metric InsightsDistributionThe distribution capabilities of a BI Portalensure that BI governance extends to businessusers in a number of ways: Users can be notified when criticalreporting is delayed due to data pipelineissues or quality alerts. Distribution of dashboards and reportsvia email can be initiated only after allrelevant data quality checks havepassed ensuring that users do notreceive reports with bad data. Analysts and Data Stewards can beautomatically notified of items pendingcertification. Notifications can also begenerated if certified content has beenchanged in the underlying BI tooltriggering a recertification process.MobileAll BI content consumed through the portalshould be available for consumption via bothdesktop and mobile. This provides a “singlepane of glass” experience and promotes digitalliteracy by making analytics available tobusiness users on any surface.
Leveraging Data Catalogs with BI GovernanceSome organizations find that they can support their essential data governance requirements bysimply deploying a BI Portal. In other cases, however, the more advanced governance capabilitiesoffered by Data Catalog tools such as Collibra and Alation are required. These tools provide AI-basedautomated data classification, PII data masking, and other capabilities that are outside the scope ofthe governance functions found in BI Portals.A challenge for organizations who elect to deploy a Data Catalog in their environment is that themetadata captured in these tools is not readily available to information consumers such as regularbusiness users. A BI Portal addresses this challenge by automatically synchronizing essentialmetadata from the Data Catalog into the BI Portal and making this information available to all userswho access content from the portal. For example, if a visualization is certified through a workflow inthe Data Catalog, synchronization ensures that certification and all related details automaticallybecome visible through the portal. Similarly, custom metadata and glossary terms maintained in theData Catalog are automatically pulled into the BI Portal so that this information informs the searchand analysis activities performed by all users in the enterprise. 2020 Metric Insights
SummaryTraditional data governance focuses only on the data and stops at the shores of visualizations. Thisgovernance approach is inadequate because most information consumed in an organization isthrough BI and reporting tools. By failing to extend governance to the tools that business users utilizeto consume data, governance efforts fall short in delivering data literacy and user engagement.Effective BI governance that encompasses both data and visualizations can be achieved byleveraging a BI Portal. A BI Portal can be used as a stand-alone governance solution or it can bedeployed alongside Data Catalog tools. In both scenarios, the governance and engagementcapabilities of a BI Portal deliver information with the necessary context to foster trust and a correctinterpretation of the data.44 Tehama StreetSan FranciscoCA 94105Give your data theaudience it deserves 1-800-993-3467www.metricinsights.com 2021 Metric Insights 2020 Metric Insights
Analysts and Data Stewards in an organization use Data Governance Tools to simultaneously enforce corporate governance policies and to promote correct usage of data. Typically, metadata is extracted from Databases, ETL processes, and some BI Tools and is consolidated within a Data Governance Tool where it is enriched with additional governance .
This section of the FHWA Data Governance Plan provides the organizational framework for how Data Governance will be managed within the Agency. 1.1 IntroductIon This document is intended to provide a brief introduction to Data Governance and how it will be implemented within the Federal Highway Administration (FHWA). The Data Governance Plan
We propose the Big Data Governance Framework to facilitate successful implementation in this study. Big Data governance framework presents additional criteria from existing data governance focused on data quality level. The Big Data governance framework focuses on timely, reliable, meaningful, and sufficient data services.
PART III Globalism, liberalism, and governance 191 9 Governance in a globalizing world 193 ROBERT O. KEOHANE AND JOSEPH S. NYE JR., 2000 Defining globalism 193 Globalization and levels of governance 202 Globalization and domestic governance 204 The governance of globalism: regimes, networks, norms 208 Conclusions: globalism and governance 214
of an organization, data governance teams are required to manage the firm's data assets. Key Components of Data Governance Typically, data governance focuses on the people, processes, policies, and supporting technologies that drive a data-driven culture, as outlined in Figure 1. Key components of any successful data governance program include:
STANDARD DATA GOVERNANCE PROGRAM. This paper focuses on the development of data governance (Area V in example above). We start by considering a standard data governance program. Typical Data Governance goals include seven components: 1. Improve decision-making and coordination 2. Reduce internal issues 3. Protect data stakeholders 4.
This white paper offers data governance professionals a visual reference and guidance on using The DGI Data Governance Framework to configure their data governance program. 7 Data Governance in Other States State governments nationwide are realizing data are an enterprise asset, much the same as desks,
and resources Data Governance for GDPR Compliance: Principles, Processes and Practices November 2017 43 This white paper provides an overview of data governance as it pertains to the GDPR, and how Microsoft services and products can help implement a data governance programme. Data governance is a broad topic and GDPR compliance is a complicated .
ANSI A300 standards are intended to guide work practices for the care of trees, palms, shrubs, and other woody landscape plants. They apply to arborists, horticulturists, landscape architects, and other professionals who provide for or supervise the management of these plants for property owners, property managers, businesses, government agencies, utilities, and others who use these services .
