Data Governance Trends

1. SGA Outlook April 2020 DATA GOVERNANCE Outlook 2020

2. CONTENTS 1 3 Introduction 2 4 The modern data governance 3 5 Data governance trends - 2020 4 10 Summary www.sganalytics.com SGA Outlook | April 2020 2

3. INTRODUCTION Besides helping in realizing value from the data and information stored within the business, Data Governance also ensures security and compliance. With appropriate deployment of people, processes, and technologies, the goal of data governance is to guarantee correct, understandable, trustworthy, complete, discoverable, and secure data to enable efficient business decisions. The goal is to turn d ata into information, and information into insight. – Carly Fiorina, Hewlett-Packard Co. With time, organizations have realized that the manual approach to data management needs to be discarded, and that it is the convergence of business and data that will rule the world in the future. Decision makers now understand the need to move away from traditional ways and adapt to the tools that are data governance-oriented, scalable, and bespoke to suit business needs. No large, global, heterogeneous, multi- business- and product-line enterprise can ever hope to clean up all its data – it’s always a continuous journey. The key is knowing what data sources feed your BI applications and how confident you are about the accuracy of data coming from each source. – Boris Evelson, Research Analyst, Forrester The emergence of technologies such as streaming data, big data stacks, and data lakes has promptly overwhelmed data governance tools. Regulatory requirements such as MiFID, GDPR, CCAR, Solvency II, and BCBS 239 have put an increased emphasis on data governance, which has made businesses realize the importance of data lakes, replicating “Amazon Marketplace” for their processes, wherein stakeholders can effortlessly request, search, and access their company’s data assets. www.sganalytics.com www.sganalytics.com SGA Outlook | April 2020 SGA Outlook | April 2020 3 3

4. THE MODERN DATA GOVERNANCE Nowadays, data governance frameworks and technologies address varied challenges by harnessing the power of machine learning and artificial intelligence to collect, validate, and analyze data. In the past, using traditional tools to solve data management problems was a complicated and cumbersome task. Key focus of modern data governance To reduce manual effort considerably and ensure that data search is simple and efficient This goal can be accomplished if organizations improve data access, data quality, and utilize machine learning capabilities to enable business users to self-service their data requirements. Organizations that understand and acknowledge that the business data they possess is a gold mine should adopt sound data governance practices to successfully leverage such data. www.sganalytics.com SGA Outlook | April 2020 4

5. DATA GOVERNANCE TRENDS - 2020 We are in the middle of uncertain times owing to the Covid-19 pandemic. However, businesses need to continue to gather data, glean insights, and make sound business decisions. As a matter of fact, the demand for accurate, complete, and efficient data has increased now more than ever as better decision makers with a sense of urgency will be the winners in this current business scenario. The adoption of social distancing and work from home by key data management stakeholders demands better data security as data is accessed and managed at home offices through unsecured public networks. While businesses that have embraced cloud-based data strategies will be at an upper hand, they will need to re-assess their data architecture, data storage and data lake concepts, and data distribution strategies to ensure data privacy and confidentiality. The key trends highlighted below will change the dynamics of data governance in 2020 and beyond: Self-servicing of data needs In an ever-changing and highly competitive world, data is the only key differentiating lever for growth among businesses. With the advancement in technology and automation, business stakeholders are in the pursuit of self-service data access to do away with the old paradigm of data distribution. The agenda is to provide users the flexibility to deep-dive into data sets and do their own data provisioning from a consolidated certified source such as a Data Lake. Quick access to data helps make critical business decisions on time. Moreover, governed self-service simplifies the data democratization process across the organization and connects the whole organization to a central data pipeline that provides valuable insights to help achieve business goals. Data Regulations DATA REGULATION POLICIES HIPAA EU E-Privacy Directive Regulation PIPEDA EU-US Privacy Shield AdChoices (self- regulatory) FERPA Japan Personal Information Protection Act Hong Kong personel data ordinance COPPA GDPR Source: mediatrust.com www.sganalytics.com SGA Outlook | April 2020 5

6. Data privacy is a topic of growing concern for businesses as it is creating hindrances in data collection and undermining information extraction. As soon as industries started reaping the benefits of big data through advanced technologies, data privacy and security began having critical concerns. Leading tech giants such as Facebook and Google, ride-hailing companies such as Uber, and many others have fallen prey to data privacy issues. These pressing issues have given birth to data regulations such as General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), etc., and it is predicted that many countries will formulate similar regulations in the coming times. Data generation is expected to become more complex than before. While the amount of data that organizations gather and operate upon is scaling up continually, as per SAS almost all (98%) organizations are facing challenges in complying with GDPR and other data regulation policies. While GDPR is one of the top priorities in the current paradigm, an overarching strategy that includes people, policies, and processes is essential to manage and protect data. The best fit for this situation is data governance as it produces waivers on how and where data is stored and sent, who can access the data and to which level, and what actions can be performed on the data by whom and when – under what circumstances and using what methods. Data governance facilitates compliance efforts while addressing the needs of data stakeholders. On the other hand, some organizations and thought leaders also believe that strict rules posed by regulatory acts such as GDPR can benefit them in the long run. In consonance with Deloitte’s GDPR benchmarking survey across a sample of organizations and industries in EMEA, 61% of respondents believe that GDPR has its benefits while 21% anticipate additional significant benefits such as competitive advantage, improved reputation, and business enablement. Moreover, a study by SAS reveals that organizations are looking for clear guidelines to handle their data. Typically, 71% of them trust that their data governance strategy will be revised as a result of GDPR, and 37% stated that the regulations would improve their general IT capabilities. Therefore, companies will continue to upgrade and re-adjust their data governance and data privacy processes to not only comply with data compliance regulations, but also establish a competitive edge. Edge computing The need for real-time data processing and the evolution of the internet of things have led to the innovation of computing at the edge. The untested distributed computing paradigm aims to mitigate the gap between computation and data storage to improve response time and save bandwidth. As per Statista’s forecast, the edge computing market is predicted to advance massively in the upcoming years. Forecasts predict an increase in revenue from USD2.8 billion in 2019 to USD9 billion by 2024. The key drivers of the growth of edge computing are the amount of data being generated, shorter response time, increased security risks, and the convergence of IT/OT. APP APP DATA DATA DATA APP APP APP APP APP Network DATA DATA APP DATA DATA APP APP APP APP Cloud computing APP Edge computing Source: mediatrust.com www.sganalytics.com SGA Outlook | April 2020 6

7. Although data governance outlines change management plans, data policies, and other restrictions on data handling to ensure security, an extreme data governance policy might stifle innovation. Edge-computing can help organizations in dealing with this data governance issue and ensuring a smoother data flow throughout the enterprise. Even better, edge-computing pledges to reduce operational costs and storage needs and offers customized solutions. Major players in the digital ecosystem such as Microsoft, Cisco, Amazon, IBM, etc. are hailing edge-computing as the future of data management and intelligent decision-making. Data Monetization Leveraging internal business data to create a new revenue stream can be a game-changer for organizations. Transforming data into revenue i.e., data monetization, is not a novel idea in the digital landscape. In fact, organizations have now started to explore effective measures to productize and commercialize their data to increase revenue. According to a Data Monetization Market Statistics by Allied Market Research, the value of global data monetization market, which was USD44,869 million in 2016, is expected to reach USD370,969 million by 2023 and this growth will be induced by the fast-growing enterprise data and technological advancements in big data and analytics solutions. On the flip side, data monetization is as effective as the organization’s data management infrastructure. Enterprise-wide information governance is the key to data efficiency. Data is certainly a powerful asset for an organization, but its value is bound to change with economic cycles and democratization. This impacts the transactional integrity around the bend and demands a befitting data-cleansing strategy. Data governance caters to de-duplicating, standardizing, and securing the organization’s data and endeavors to enable businesses to build new revenue streams using internal operational data. FIVE key steps You should take to monetize your company's data assets 05 01 03 Repeatability and Scalability Decision Architecture Data Science and Decision Theory Monetization strategy should be developed from your decision architecture and linked to your corporate business which in turn aligns with your strategic objectives. Determine the right analytics structure for performance, integrity, and scalability of your monetization strategy. Power your monetization strategy with data science and decision theory which would help you derive insights from your data to address a particular business problem or opportunity. Leaders should focus on making decisions based on data rather than solving a problem based on past experiences. Business leaders should develop monetization strategies that are automated, repeatable, and scalable throughout their organization. By building analytics capabilities organizations can get deep insights about their performance and help business leaders take crucial decisions. Monetization Strategy Analytics Structure 02 04 Source: tdwi.org www.sganalytics.com SGA Outlook | April 2020 7

8. Machine learning and AI to drive Data Governance As businesses further realize that the future belongs to those who make data-driven decisions, there is a significant upsurge in the consumption of data analytics and business intelligence across almost all industries. Besides, breakthrough technologies such as artificial intelligence and machine learning coupled with big data vow to overthrow guess works and make educated business decisions. This has led to an abundance of data like never before and has consequently compelled organizations to construct potential data management frameworks to instill trust in their data. Additionally, machine learning can assist in monitoring compliance parameters and promptly recognizing violations or breaches across databases. An interdependence exists between AI, Machine Learning, and Data Governance. While data governance is vital for employing AI and Machine Learning successfully for business needs, the advanced technologies are equally necessary to augment master data management and scale data governance processes efficaciously. Machine learning and AI to drive Data Governance Information Sharable Information (Data in Industry Context) Raw Data Described Data (Data in Organizational Context) Data Dictionary Reference Repositories External Reference Repositories Data Repositories Instance Data Business Metadata Technical Metadata Semantic Metadata Question-focused Metadata Industry Standards Partner Standards Regulatory Standards (ERP; EDW; Data Lakes; etc.) ISO 8000 ISO 22745 ISO 11179 Industry Standard RDBMS; Graph Dbs; NSQL... Data Glossary Dictionary Supply chain partner ISO 11179 Industry Association Concept Systems Government (Compliance) File Based Storage ISO 11179 Reference Data ISO 11179 ISO 22745 Data Operations Data Quality Rules MDM Rules Source: tdan.com Scalable data management frameworks Organizations are floating in an ocean of dark and dirty data at present. Disciplined data modeling can ensure better data governance operations and support cross- enterprise data sharing while paying heed to data quality, data archiving, data masking, and master data management. Apart from promising to sanitize the data environment of an organization, data governance intends to provide value to all types of data; structured, semi-structured, and unstructured. Even more so, data governance embraces data unification to surpass the difficulties posed by siloed and fragmented data. Without a systematic way to start and keep data clean, bad data will happen. – Donato Diorio, Founder & CEO, DataZ With an efficient and comprehensive data-operating model design, organizations can establish metrics to track and measure the progress of their data governance strategies and do a timely check if they are heading in the right direction – after all, data governance is an on-going process and cannot be achieved overnight. Besides, organizations can collaborate, articulate, and analyze the design of the system before investing heavily in it as data modeling is more agile. www.sganalytics.com SGA Outlook | April 2020 8

9. Data Cataloging Data Cataloging Analytics Environment BI/Analytics Wrangling Other Apps Data Source Metadata Source Relational Data Sources Search REST API ETL Relational Plugin Arch SMART DATA CATALOG Metadata REST API Discover/ Fingerprint Ratings & Reviews Curate Spark HDFS/Hive Business Glossaries Search Curation Glossary Amazon S3 Microsoft Azure Data Security Execution Environment Spark Hadoop Cloud Source: waterlinedata.com In today’s innovation-driven economy, the success of an enterprise depends on its ability to leverage its key business asset – data. Data governance is a strategic priority for any business that is on the lookout for factual insights to make critical business decisions and establish cutting-edge competition in the market. Data cataloging is one of the essential steps for a successful data governance process. Data is worthless if it lacks context and cannot be governed properly if its relevance is unidentified. Data cataloging helps in defining and classifying an organization’s data and provides visibility regarding where it resides – in infrastructure systems, applications, on-premises, or in the cloud. Data catalogs provide context to data in order to enable data scientists and data stewards to discover and understand relevant datasets so that business value can be derived. Furthermore, data cataloging enables broad and deep metadata connectivity, data quality monitoring, and lineage and impact analysis, which are some of the key factors that determine the success ratio of a data governance framework. As specified by Informatica, among the companies using a data catalog, 73% are more likely to be satisfied with their data-sharing abilities in contrast to the non-catalog users. Executive Sponsorship CEOs and decision-makers are beginning to focus on data governance as it helps reduce risk and encourages data-driven decisions. The power of data vis-à-vis revenue generation and the need for data consistency to enable informed decision-making has drawn the attention of company executives toward data governance. With data governance, an authoritative central repository of data is created, which stacks all crucial business data, helping CEOs and senior leaders to achieve their business goals and to establish consistent policies. The CEO of NewVantage Partners, Randy Bean said, “I have long had an aversion to the term, ‘Data Governance’.” As one senior executive puts it, “it sounds like ‘Data Government’, and that can’t be good!” Bean soon realized that proper management of data is essential for organizations that aspire to derive insights and value from their company’s data assets. He realized that data is a shared asset and must be governed efficiently to enhance data ownership and responsibility. www.sganalytics.com SGA Outlook | April 2020 9

10. SUMMARY Digital transformation is redefining the way we do business. It is essential to incorporate efficient data governance tools as they ensure data integrity, actively monitoring ever-evolving compliance standards and data security. A competent data governance strategy effectively manages data across multiple storage and access points while fulfilling the requirements of the end users. Organizations are increasingly getting involved in understanding the dynamics of data governance as they acknowledge that data-driven decisions will eventually determine the winner in their space. Corporations are starting to actively incorporate data governance by gamifying the practice for their employees. This aims to increase enterprise-wide data literacy. This is certainly a great time to reassess one’s data strategies as one looks ahead. The time to start is now. DATA GOVERNANCE AND SG ANALYTICS Having worked for Fortune 500 companies, data management professionals at SGA excel at collaborating with the clients to standardize, secure, and integrate data through appropriate data governance models and strategies. Our perspective ensures the establishment of data-centric views, enterprise-wide data management, and data quality. By combining our research and data analytics expertise, we have helped organizations in boosting return on investments and in revamping their marketing activities. Enable data consistency and improved data quality Optimize business planning and decision making with accurate data to increase revenue Enhance operational efficiency, and regulatory compliance Source: sganalytics.com www.sganalytics.com www.sganalytics.com SGA Outlook | April 2020 SGA Outlook | April 2020 10 10

11. About the Author Jayaprakash Mallikarjuna VP - Client Services Passion for generating actionable insights from data is something that drives Jayaprakash. In his 17+ years of professional journey, his key focus has been to enhance customer expe- rience and drive value for businesses. At SGA, he leads large data/platform management engagements. Prior to SGA, he spent over a decade at Thomson Reuters in the investment research space and then with a Sustainability research provider start-up. Being an ardent chess player and a runner helps him to strategize paths to the end-goals with steady speed. Watch and enjoy our corporate videos THE CEO SPEAK: Success Mantra of a Research & Analytics Firm Sharing Insights Click on the above title to watch the video RESEARCH ANALYTICS TECHNOLOGY New York Seattle London Zürich Pune Hyderabad About SG Analytics For over a decade, SG Analytics has been one of the leading global research & analytics firms with offices in USA, UK, Switzerland, and India, servicing scores of customers across the globe. We are the partner of choice for Fortune 500 companies across several sectors. We have been recognized as the “Best Employer” by the World HRD Congress in 2018. For further information, please visit our website: sganalytics.com Join the conversation This document makes descriptive reference to trademarks that may be owned by others. The use of such trademarks herein is not an assertion of ownership of such trademarks by SG Analytics (SGA) and is not intended to represent or imply the existence of an association between SGA and the lawful owners of such trademarks. Information regarding third-party products, services, and organizations was obtained from publicly available sources, and SGA cannot confirm the accuracy or reliability of such sources or information. Its inclusion does not imply an endorsement by or of any third party. www.sganalytics.com Copyright © 2020 SG Analytics Pvt. Ltd. SGA Outlook | April 2020 11