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Effective Data Governance

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Effective Data Governance PERSPECTIVE EFFECTIVE DATA GOVERNANCE Abstract Data governance is no more just another item that is good to talk about and nice to have, for global data management organizations. This PoV looks into why data governance is now on the core agenda of next-generation organizations, and how they can implement it in the most effective manner. Why is data governance Variety of data and increase in demanding around data privacy, personal important and sandboxing culture information protection, data security, data lineage, and historical data. challenging now? The next-generation analytics utilize data from all kinds of social networks and This makes data governance top priority for Data has grown significantly blogospheres, machine-generated data, Chief Information Officers (CIOs). In fact, a Omniture / clickstream data, as well as survey by Gartner suggested that by 2016, Over time the desire and capability of customer data from credit management 20% of CIOs in regulated industries would organizations, to collect and process data lose their jobs for failing to implement and loyalty management. Alongside this, has increased manifold. Some of the facts the discipline of Information Governance, organizations have now set up sandboxes, that came out in various analyst surveys successfully [3]. pilot environments, and adopted data and research suggest that: discovery tools and self-service tools. Such Data to insights to actions: Need for Structured data is growing by over 40% • data proliferation and the steep increase in accurate information every year data consumption applications demands Today’s managers use data for decisions Traditional content types, including stringent and effective data governance. • and actions. In our experience, many unstructured data, are growing by managers feel that the data they are [1] More stringent regulations and up to 80% every year accessing is inaccurate or incomplete, compliance Global data will grow to 40 zettabytes and hence, both confidence and adoption • The growing regulatory and compliance (ZB) by 2020 [2] of business intelligence and analytics requirements are making effective data systems is low. Hence, data governance • 85% of this data growth is expected to governance a must have for industries initiatives need to generate good come from new types; with machine- like financial services and healthcare. The confidence in the data managers see and generated data being projected to regulatory requirements are now more use for decision-making. increase 15 times by 2020[2] What are organizations consumption applications like data vision, identify data to be governed, doing to establish warehousing, business intelligence support the implementation of DG effective data and analytics systems, data quality, policies, while actively participating metadata management, master data in change management and governance? management, data strategy, and data monitoring. IT teams are responsible Role of the chief data officer life cycle. for the management of data and measurement of data quality, as well (CDO) Collaborative Data Governance as collection and management of Gartner predicts that 50% of all Organizations metadata and master data. They also companies in regulated industries It is important for Chief Data Officers provide tools and technologies for will have a CDO by 2017 [4]. The to engage the right teams in the Data implementation of the related tools CDO will be responsible for Governance Organization, to align and technologies. enterprise data management, data governance and business goals. enterprise data governance, data Business teams are important to define External Document © 2018 Infosys Limited External Document © 2018 Infosys Limited Typical Data Governance Org Structure » Set strategy and direction for overall data products Steering Committee » Approve funding and resource allocation from BUs BU » Prioritize adoption of enterprise data capabilities Sr. Mgmt. » Ultimate Business Authority for the data they own Data Governance Business » Council Data Owners » Support data governance and standards teams » Develop investment recommendations for Exec. Management review / approval » Ensure data-related work is performed according to operating procedures Data » Determine usage permissions and policies Standards Group Data Steward » Liaison between business and technology around data issues and questions » Delivery / » Responsible for understanding data modeling, tracking usage, access, Execution Group Data Custodians Key SMEs and implementation » Ensure data is available / accessible, well-performing, and recoverable Figure 1: Role of Business Teams in Data Governance Articulate tangible benefits from governance strategy includes policy and implementations that drive common data governance initiatives and rules for data quality management, goals and integrate outcomes across these metadata management, master data disciplines enjoy benefits like improved In order to keep all the stakeholders management, as well as data security. The reliability, traceability, and authenticity engaged and ensure continuous implementation of each of these individual of data. This will also provide a common investments in data governance initiatives, disciplines presents the intended benefits. communication platform across all the it is important to articulate the value However, data governance strategies stakeholders. generated by data governance initiatives via the right metrics. A dashboard with relevant metrics can be an easy tool to assess the impact of data governance initiatives, such as improvement in data quality parameters, improvement Reliability Traceability Authenticity in dollar-value impact because of End-to-End data lineage Consistent consumption of improved data quality, and the ability governance policies, and traceability, enabling data from authoritative standards, and procedures better audit controls data assets that are to rationalize enterprise metrics to data Better accountability and Capability to respond to data-ownership improves changes rapidly through Better understanding of definition standardization. Such tools comprehensive impact play an important role in monitoring data being reported both analysis consistent usage externally and internally Improve quality, Robust quality controls and controlling adherence to the data Enable better quality consistency, and usability across the data life cycle governance policies. information delivery and of master and reference analytics data across segments Sum of parts is greater than the KEY BENEFITS whole – seamless data governance Accountability Business Better Stronger For Data Agility Compliance IT Agility Insights Organizations are now looking at data governance holistically. The Data Figure 2: Benefits of Seamless Data Governance External Document © 2018 Infosys Limited External Document © 2018 Infosys Limited Big data user ratings are replacing Effective Data Governance at requirements, tools and business traditional data quality (DQ) metrics Infosys processes, easy decision-making process, Ratings by data consumers are replacing stakeholder interaction, and business Infosys holistic service offerings help next- traditional means of measuring data access. generation organizations to implement accuracy for non-traditional data such Once an enterprise establishes the above effective data governance. as sentiments and opinions. Most of the process of enabling informed business leading data platform providers such as The goal of data governance initiative decisions as the collective aim of data Cloudera, Hortonworks, and Informatica is to manage data for delivering timely, governance, it becomes significantly easier (BDE) offer ways of capturing the usage trustworthy, and relevant information to define the goals and priorities for any of data from data lakes and user ratings to make informed business decisions. initiative. Good data governance paves the about the usefulness and quality of the However, data governance is not only way for enterprise policies, people, and information. about data, but also about enabling clear processes to launch that initiative. ownership, business rules, operational Enterprise Data Quality Master Data Metadata Data Data Strategy Data Management Management Management Protection & Diagnostics Governance Enterprise data Data quality Leverages Frameworks, Deploy data Data governance assessment, Infosys deep accelerators security across Assessment framework experience on backed up by the enterprise Toolkit to including monitoring, master data deep with identify processes, transformation management competency in optimization organization, framework, implementation industry-leadin scalable areas in data and Infosys and toolkit s, and unique g tools and solution, landscape, Data backed up by methodology. technologies. ensuring data redundancy, Governance pre-built protection and technology solutions and privacy, as a and data solution. accelerators. service. life cycle Setting data management life cycle management policies. Figure 3: Effective Data Governance @ Infosys The focus now turns to the tools and technologies to implement and ensure compliance with data governance requirements in accordance to business access. Infosys Data Governance Solution offers a robust framework and technology solutions that leverage partnerships with leading product vendors in this space. External Document © 2018 Infosys Limited External Document © 2018 Infosys Limited Infosys Data Governance Framework with measurable KPIs
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