Masterminding Data Governance

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1 Why Data Governance Matters The Five Critical Steps for Data Governance Data Governance and BackOffice Associates Masterminding Data Governance 1 of 11 A 5-step strategic roadmap to sustainable data quality and business value.

2 Why Data Governance Matters Virtually every company applies quality control to such core business processes as production, supplier management, inventory and financial reporting. Yet data, the asset that underpins the business, is often produced and consumed without equivalent attention to its quality, or visibility into its impact on the business. In today s highly digital business environment, data governance has emerged as a key differentiator for organizations to transform data into business value. As the foundation for sustainable data quality, data governance can enable your organization to mitigate risk and strengthen compliance. It equips your business and IT teams to better access and utilize information to reduce cost, drive revenue and innovation, and speed time to market. Data governance can be a complex endeavor. An effective program requires management support and stakeholder collaboration. It demands the definition and enforcement of standards and responsibilities. A framework for continuous monitoring and optimization, and the right technologies and tools, are prerequisites for success. In this ebook, BackOffice Associates offers practical guidance for your organization to craft, implement and sustain a data governance program that helps ensure your data is business-ready, today and well into the future. Why data governance? + + Improve data quality and business outcomes + + Reduce risk and strengthen compliance + + Pursue revenue and cost-reduction opportunities + + Speed time to market + + Enable rapid innovation 2 of 11

3 The Business Impact of Data Governance 40% of BUSINESS INITIATIVES FAIL TO ACHIEVE TARGETED BENEFITS because of bad data 2 $2 billion REVENUE A YEAR impact with a 10% INCREASE IN DATA USABILITY. 3 $66 million REVENUE A YEAR impact with a 10% INCREASE IN DATA ACCESSIBILITY. 4 Data governance is not a new concept. For many years, organizations have recognized the theoretical importance of data governance as a framework to apply controls and accountabilities to information. Often, however, data governance has been regarded as something for IT to worry about. Many business leaders fail to appreciate the bottom-line business impact of data governance. In fact, 44 percent of North American companies don t have a data governance policy in place, according to a study by Rand Secure Data, a division of Rand Worldwide. Yet 46 percent of companies surveyed said they intended to implement a formal data governance policy within the next 12 months. Rapid increases in data volumes, velocity and variety, and the introduction of cloud platforms for various business functions has impacted data governance. Rising customer demands and the hyper-speed of global business put a premium on the ability to harness the power of data across a distributed environment in a consistent and seamless manner. Data governance is also vital in sustainable data quality. Some organizations may undertake a onetime data quality initiative, only to find that new quality issues creep in as data sources and processes change. Data governance s well-defined structure helps avoid the multi-million dollar impacts that poor data quality can have on large organizations. Too often, data quality and governance are implemented only after a critical error damages a company with revenue loss, non-compliance, customer dissatisfaction, brand damage or other consequences. Smart companies don t wait for a painful catalyst to take action they leverage the wealth of best practices and data technologies on the market. Whether launching a new data governance program or formalizing loose governance processes, five key steps can help you shape and implement an effective data governance program: + + Conduct a thorough data assessment + + Map maturity and a business case + + Select a data governance strategy + + Implement, manage and evolve + + Track key metrics and measure ROI 3 of 11

4 Conduct a Thorough Data Assessment A holistic data assessment reveals hidden data issues that undermine the business while helping guide strategic direction for a data governance initiative. In a data assessment, a cross-functional team of IT and business stakeholders is charged with examining source data at the transactional, operational and strategic level, focusing first on the most critical business areas. Done properly, the exercise will give management a complete picture of how business units treat and interact with data, including important nuances. It will also baseline adherence to existing standards and identify areas in which standards are lacking, contradictory or non-existent. Several areas that a data assessment may examine include: + + Duplicate and inconsistent data in the same or multiple systems + + Multiple sets of payment terms that affect finance, sales and purchasing teams + + Data discrepancies in charts of account that can undermine financial reporting + + Existing responsibilities and processes to test and audit systems Understanding the state of data at a technical level positions your organization to gauge overall data maturity and build a business case for data governance. Figure 1. A data assessment examines transactional, operational and strategic data. Transactional Data Operational Data Strategic Data Vendors Customers Materials Recipes Contracts Credit Limit Storage Type Quality Plans Bin Location Routing Plant Plans Payments Service Usage Travel Records Deliveries Tax Amount Activity Records Sales Invoices Orders 4 of 11

5 Map Maturity and a Business Case Establishing data governance requires an examination of your organization s structures and processes, the maturity of your overall data management environment and identification of priorities and pain points. Do you utilize data stewards, a chief data officer or a Center of Excellence? To what extent have you established data ownership and responsibilities to stakeholders? It s also a good idea to consider data governance from the perspective of specific areas in which it s most important, including analytics and reporting, information security, compliance, regulatory controls and finance, to help pinpoint priorities. Some key objectives to pursue include: Build management support. The best data governance programs enjoy strong executive support. Data governance champions should strive to communicate the business value of data governance to the leadership team. Identify priorities and pain points. Zeroing in on high-payback areas from the start mitigates ongoing issues and can provide swift validation of the value of data governance, positioning your team to grow the program. Formalize a business case. To the extent possible, benchmark the financial impact resulting from a lack of data governance and quantify the expected payback in cost avoidance, productivity, new revenue and other measures. 5 of 11

6 Select a Data Governance Strategy Companies implementing a data governance initiative should assess the different levels of data governance and carefully decide which one is the right fit for their organization. Data governance typically exists at four levels: No data governance. This is the Wild West model. Every user is trusted to enter in their data accurately and on time, all while minding corporate standard operating procedures and compliance requirements. The reality? Despite rigorous training, most users do not follow standard operating procedures. Based on the resulting lack of control and accountability, this is the least efficient and most risky model. Center of Excellence (COE). This model tasks a central group with the responsibility of creating and verifying all data requests before posting them to a system. The intention is to have a central core entering an agreed-upon single version of the truth. However, in many cases this model results in slow data-entry times and costly downstream effects. Passive data governance. Users enter data into a system, and then a toolset or reporting mechanism iteratively identifies data-related errors within that system. Errors are automatically reported back to their authors for correction and quality metrics are delivered to management. This model enables measurability and ongoing optimization. Active data governance. All data required to support the configured business processes is collected prior to posting into target systems and validated automatically through a collaborative environment. This eliminates the possibility of business-process interruptions due to omissions, duplicates, inconsistency, content errors or a lack of standards. The no data governance model is too risky. Although a COE is valuable as a central coordination point for data governance, tasking the COE with granular responsibilities can increase the time required to collect, validate and enter that data into a system. This model also proves difficult to scale with a growing application infrastructure footprint. 6 of 11

7 Select a Data Governance Strategy continued Accordingly, many companies implementing their first data governance strategy look to the passive data governance model to introduce automation and create accountability and data ownership at the user level. For some companies, including those operating in strictly regulated industries like pharmaceuticals, active data governance is advisable to control and validate data prior to entry into a system. Figure 2. Efficiency and error-resolution time improves with more advanced data governance programs. MANY BUSINESS-PROCESS INTERRUPTIONS FEW HIGH AUTOMATION LOW LOW NO DATA GOVERNANCE COE PASSIVE Data Governance DATA QUALITY (BUSINESS-READINESS ) ACTIVE Data Governance 7 of 11 HIGH WEEKS HOURS ERROR RESOLUTION TIME

8 Implement, Manage and Evolve Data governance is not a set-it-and-forget-it discipline. An effective program requires sound metrics, ongoing monitoring and adaptation to evolve the system. Best practices and lessons learned should be fundamental principles that guide data governance. Change management is an important element of data governance in giving you the flexibility to see changes in data values and how data is produced and consumed across multiple functional areas. Change management goes hand in hand with proactive impact analysis when revising data values, accommodating new business processes, and adding or removing data sources. Several best practices to follow in implementation and management include: Focus on reusability and automation. As the data governance program evolves, it s not necessary to start from scratch when extending the discipline to new functional areas. Look to reuse the rules and code developed at the outset across similar projects, and implement workflow-driven automation to reduce cycle times and accelerate value. Empower data stewards. As a key role in data governance, it s important that data stewards have a good mix of IT and business skills, and that they re equipped with the tools and authority they need to excel. Web-based applications to identify and remediate data errors and supply real-time metrics are especially valuable. Embed transparency and accountability. Your data governance processes should be highly transparent to stakeholders in terms of data itself, quality metrics and controls over data. Accountability for processes and decision-making should be clear and thoroughly defined. 8 of 11

9 Track Key Metrics and Measure ROI Forrester ROI Study Download the Forrester study on three-year ROI of 218 percent with BackOffice Associates solutions. During implementation, you will have established metrics, or key performance indicators (KPIs), that gauge data accessibility, completeness, timeliness, compliance, consistency and more. Baselining metrics at the outset enables you to measure results months and years down the road. As well, your systems should supply real-time visibility and alerting to problem areas, with ad hoc reporting to drill down into what s working and what s not. Once a data governance program is in place, some organizations tail off their efforts to regularly monitor and report on key metrics. Systematic measurement enables you to continuously optimize your data, and should be a focus area to help you make the most of your data governance efforts. Many organizations also neglect to quantify return on investment (ROI) from data governance, though ROI calculations can help build credibility and deepen adoption. In one real-world example, a company had initially opted for a manual approach to data quality that required users to have deep knowledge of SAP master data and data entry specifications. The process was difficult, labor-intensive and prone to error, prompting the customer to engage BackOffice Associates to introduce a data governance solution. The customer is saving $750,000 a year in personnel costs alone, while gaining new control and visibility into critical information on an ongoing basis. The ROI of data governance can be substantial. For example, a Forrester Research study quantified ROI at an average of 218 percent over three years, with an initial payback period of 4.7 months, among organizations using BackOffice Associates services and solutions for data quality, stewardship and governance. ROI was realized in such areas as: + + Cost avoidance for internal personnel to complete data migration, quality and governance projects + + Cost savings from data governance and improve data quality + + Time savings from improved and automated reporting + + Savings from reduced maintenance costs on legacy systems 9 of 11

10 Data Governance and BackOffice Associates BackOffice Associates Solutions BackOffice Associates helps to establish a repeatable and scalable data governance solution by providing best practices methodology and tools that enable the maintenance and monitoring of error-free information. Our governance solutions are deployed as lightweight web applications, greatly simplifying access and adoption. We are an experienced leader in providing data governance solutions, advanced end-to-end workflow process capabilities and actionable reporting automatically delivered to the responsible data owner for remediation. We deliver tracking metrics on data quality improvements and issues with details by business area, group and user. We also provide advanced functions to show how long a particular object has been in error status. The BackOffice Associates solutions feature metrics dashboards with workflow error notifications delivered to users via and a closed-loop data remediation method to quickly and easily correct errors. These solutions are all delivered through a simple web interface. Our process automation ensures proper tracking and closure of issues across multiple systems, helping you achieve and maintain successful data governance projects. To learn more, please visit our website. + + Data Assessment + + Data Quality + + Data Migration + + Passive Data Governance + + Active Data Governance 10 of 11

11 Contact us for a data assessment. BackOffice Associates, LLC NA EU +44 (0) APJ & ME BackOffice Associates, LLC. BackOffice Associates, Boring Go Live and all associated logos are trademarks or registered trademarks of BackOffice Associates, LLC in the United States of America and elsewhere. SAP as well as their respective logos are trademarks or registered trademarks of SAP AG in Germany and several other countries. Oracle and Java are registered trademarks of Oracle and/or its affiliates. All other products, company names, brand names, trademarks and logos are the property of their respective companies. 1. Rand Secure Data, 2013 Data Governance Survey, January Gartner, Measuring the Business Value of Data Quality, January 7, Sybase and the University of Texas at Austin, Measuring the Business Impacts of Effective Data, November Sybase and the University of Texas at Austin, Measuring the Business Impacts of Effective Data, November Forrester Research, The Total Economic Impact of BackOffice Associates Data Stewardship Platform, July of 11 Copyright 2014 BackOffice Associates, LLC. All Rights Reserved.

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