Archive for the ‘Data Governance’ Category

Managing Data Governance in a Cloud-Focused World

The rate at which companies are amassing data is staggering. More than half of organizations today (57%) have production workloads running in the cloud and with the amount of new devices being introduced that create, consume and transmit data to the cloud, it has become critical to have some type of cloud governance program in place. However, one of the most challenging elements of such a program is how to manage an organization’s sensitive data. This data could encompass anything from bank account and credit card numbers to HR payroll data. Misuse or negligent handling of this information could cost companies tens of thousands of dollars per record lost in a potential data breach. Besides monetary consequences, we’ve also seen how disastrous a data breach can be to customer confidence. Cloud governance is nothing to scoff at!

When the stakes are this high, it is understandable that companies are reluctant to trust the cloud. Gartner predicts that “through 2020, 95% of cloud security failures will be the customer’s fault.” However, cloud providers have made significant improvements to their security offerings over the last five years. This means that with proper planning and preparation, you can still reap the benefits of cloud efficiency and agility while maintaining appropriate levels of security.

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Narjit Aujla

Three Fundamentals for Building a Solid Data Governance Program

Time and again, we talk with clients who are neglecting perhaps the most important feature in a solid data strategy: data governance. With the explosion of data resulting from an increasing adoption of digital initiatives and the undeniable fact that we are now living in a data-driven world, it is more important than ever for organizations to recognize the importance of protecting data as a key asset. From regulatory challenges in the U.S. driving a need for better data governance programs and a trend in hiring chief data officers to the imminent General Data Protection Regulation (GDPR) in the European Union, the pressure is growing on organizations across all industries to recognize the need for better maturity in managing and governing data assets.

Data governance as a practice has been around for some time, but many organizations continue to struggle to incorporate basic data governance processes into their overarching data strategies. Those who fail do not always do so from a lack of effort. Where to start and how to build a data governance plan is still a significant issue for most companies, and we have seen many firms have multiple false starts before they are able to gain the needed traction.

During a recent webinar we hosted, we asked the audience – primarily IT, audit, finance, and risk and compliance professionals ­– to weigh in on how well their organizations are doing with data governance. A full 39 percent of this group told us they have no idea whether their data governance programs are effective. Even more startling, just short of 20 percent admitted their enterprise has no data governance program in place.

These numbers may appear surprising, but they are typical of what we see across all industries – although certain groups, such as financial services, do have a higher maturity when it comes to data governance due to specific regulatory and compliance requirements that include anti-money laundering (AML) and Dodd-Frank regulations, and the fact that many banks have a global presence, making them subject to GDPR for their EU clients. Many organizations recognize the need for strong governance but often find it takes years to work through the complexities involved in establishing workable governance functions.

We understand the situation. We also know there is a way for organizations to build an outstanding data governance program that fits their needs, without the frustration. Here are just three tips to help get a data governance program started:

  1. Begin with an assessment of the organization’s current state. At Protiviti, we leverage multiple assessment models, including the Enterprise Data Management (EDM) Council’s Data Management Capability Assessment Model (DCAM) for financial services companies, and the Data Management Association (DAMA) International’s Guide to the Data Management Body of Knowledge (DMBOK®) across other industries. The DCAM framework includes eight core components ranging from data management strategy, data and technology architecture, and data quality to the rules of engagement for data governance programs. Whatever the model used, it should be matched to the organization’s needs and not applied generically.
  2. Establish a pragmatic operating model. Data governance programs must combine functional expertise, industry knowledge and technology in a well-organized and coordinated way that is planned, holistic, actionable, simple and efficient. We call that our PHASE approach, and it sets a solid foundation for future data governance by bringing together these three key components and identifying tactical steps to execute and operationalize data governance.
  3. Have simple guiding principles. We recommend that organizations:
    • Establish clear goals and purpose
    • Only put governance where needed
    • Keep the plan simple
    • Design from the top down, but implement from the bottom up
    • Be flexible
    • Communicate, communicate, communicate.

One of the most critical success factors in establishing a data governance program is to identify the value it will deliver to the organization. There is a risk this focus on value may get lost in compliance situations, where meeting a specific requirement is unquestionably the goal. Therefore, it is important for organizations to also ask: What real business problem are we addressing through our governance strategy? How will the organization be better off tomorrow than today as a result of our governance work?  What are our data problems costing us – both in opportunity costs (not being able to pursue something) as well as real monetary costs?  And how can we do all of this with a smaller spend, showing quick value?

As chief data officers join the executive suite in increasing numbers, the importance of maturing data governance is confirmed. Ensuring that the data governance team has a seat at the table for all major business decisions and key projects – both business and technology – is proving to be a best practice and a critical success factor for the future of the organization’s data strategy. Data governance is a process, not a project. By making it a core competency, organizations will be ready to take on the data-driven future.

Matt McGivern







Josh Hewitt



Categories: Data Governance

Data Strategy Webinar Series

Maximizing Data to Drive Your Organization Forward 

Get a jump-start on your 2018 professional development. We invite you to join subject matter experts from Protiviti’s Data Management & Advanced Analytics team for this informative five-part webinar series. Join us on Thursdays beginning February 1 and continuing through March 8 at 2:00 p.m. eastern to learn what’s new in Business Intelligence and Data Governance.

Register for one or all five!

Building a Comprehensive Data Strategy
Speaker: Jeremy Stierwalt, Director
Thursday, February 1 @ 2:00 p.m.

Data is arguably the most important asset a company possesses, and a data strategy allows the company to recognize that its data is a structured, comprehensive, cross-domain asset. The organization’s volume of data, where its data is stored, who in the organization is responsible for the data and where future data investments will be made are all components of this comprehensive strategic approach. Jeremy will analyze the anatomy of a data strategy and will also share success stories from Protiviti clients.

Key Learning Points:
– Learn what to include when defining the scope of a data strategy
– Discover how to develop a road map for the organization by defining key gap-closing initiatives
– Recognize how strong, experienced leaders can ensure success
– See what a Future State recommendation would look like
– Learn how to formalize a Data Governance process as part of the strategy development

Jeremy Stierwalt brings more than 20 years’ experience in all aspect of data management and governance, business intelligence, advanced and predictive analytics solutions. This includes vertical expertise in Financial Services and Insurance, Manufacturing, Consumer Products, Retail, Sports and Entertainment, and Professional Services coupled with Line-of-Business expertise in Finance, HR, Supply Chain, Sales and Marketing, and Procurement functions. Jeremy has held various leadership positions including membership to the Executive Leadership Team, and is a frequent speaker at National & Regional Conferences on topics related to data governance & management, big data, and analytical solutions.

6 Simple Steps to Solve Your BI User Adoption Challenges
Speaker: Steve Freeman, Managing Director
Thursday, February 8 @ 2:00 p.m.

In spite of significant Business Intelligence (BI) investments, just a relatively small percentage of companies consider their BI programs a success. Many continue to struggle with how to best adapt to changes in the business and evolving systems needs. Join Steve as he walks through the six components that make up the FRA2ME methodology, which allows every client to maximize their BI investment, turning business users into advocates and dramatically growing BI adoption throughout their organizations.

Key Learning Points:
– Explore the six steps to building FRA2ME methodology in your organization
– Learn how FRA2ME helps organizations delineate between information and insights
– Understand how a “Build, Adapt, Outreach” approach helps users adjust to change
– Discover how to have your end users saying “I don’t know how I’d do my job without this”

Steve Freeman, a Managing Director in Protiviti’s Data Management and Advanced Analytics practice, developed the FRA2ME methodology to help clients generate “raving fans” among end users. Responsible for Protiviti’s SAP Analytics practice, Steve also serves on the firm’s Financial Services practice leadership team. He has held numerous roles in sales and executive management in the Business Intelligence and Analytics space including: SAP BusinessObjects, Oracle, Verint and Cognos. A thought leader in analytics and end-user adoption, Steve’s expertise also centers on Customer Insight Analytics, Sales & Financial Forecasting, and Organizational Optimization.

Managing the New Currency: Developing a High-Performing Data Management Organization
Speaker: Don Loden, Managing Director
Thursday, February 15 @ 2:00 p.m.

Data has become so critical for success at any organization that it’s now being described as the new currency – impossible for a business to grow and thrive without it. Attendees will learn what successful companies are achieving with strong data management in place and will learn how to avoid common pitfalls that jeopardize solution adoption. Don will also outline why quality data and a trusted data platform are key components for both data and analytics success.

Key Learning Points:
– Learn why many data management efforts fail and how to align those efforts with the business
– See how to follow the crumbs to find the reward of user adoption
– Learn why a trusted data platform is critical for success

Don Loden is a thought leader in the data warehousing and governance space. With more than 15 years’ experience in ETL Architecture, Development, and Tuning: Data Modeling; Data Warehouse Design: Analytics Architectures; Business Analysis and more, he also speaks regularly at industry functions and has written numerous articles for publications focused on data warehousing, master data management, governance, SAP HANA, and data quality best practices. Don is also the author of two books on SAP technologies and strategic use cases. His sales acumen coupled with his expertise has helped the Data and Analysis solution grow substantially over the last several years.

Avoid an Epic Fail: Why Data Governance is a “Must Do”
Speakers: Josh Hewitt, Director
Thursday, February 22 @ 2:00 p.m.

Do you trust the data your organization uses for strategic, financial or regulatory reporting? Is there clear ownership and understanding of key data elements? Does identifying root causes of data quality issues often leave you scratching your head and scrambling for help? Data Governance needs to be viewed as a Program and not a one-time Project. Discover real-life lessons learned from our clients at varying levels of Data Governance maturity and will provide tips to ensure you can best prepare and improve your Data Governance capabilities along your journey.

Key Learning Points:
– Understand scoping and key functions of Data Governance programs
– Review different operating models
– Examine components of achieving higher maturity within people, processes and technology
– Learn how to build a business case and drive value through Governance
– Explore how Data Governance supports an overall Data Strategy and Advanced Analytics

Josh Hewitt, a Director, has more than 13 years’ experience in Financial Services and has a wealth of knowledge surrounding data governance/management, information technology, regulatory/compliance Risk Management and program/project management. Having been part of multiple data governance program planning and implementations, Josh offers a unique perspective on what has worked in other organizations and the best approach to the implementation of successful programs. He also has significant experience in data governance/quality tool selection and evaluation and a strong understanding of the benefits and pitfalls of many different solutions and can help Financial institutions evaluate, select and implement solutions.

Managing Data Governance in a Cloud-Focused World
Speaker: Narjit Aujla, Manager
Thursday, March 8 @ 2:00 p.m.

Companies like Amazon and Microsoft are opening the door for anyone with internet access to stand up rogue environments outside of corporate guideline. This produces a unique set of challenges from a Data Governance standpoint like standardizing KPI definitions, Master Data Management, and Data Security. Narjit will take attendees through the steps necessary to integrate the cloud movement into a Data Governance approach, while still enabling the usefulness and practicality of cloud applications.

Key Learning Points:
– Learn how metrics used by cloud shadow applications adhere to the Enterprise Data Dictionary
– Learn how to assess the risk of a cloud service to better protect its sensitive data
– Examine how IT manages authentication and data flow within a cloud application
– Learn how to create concrete and reasonable cloud data governance controls
– Understand what steps to take to ensure the cloud provider adheres to vendor requirements

As a manager in Protiviti’s Data Management and Advanced Analytics practice, Narjit Aujla has worked with companies spanning a variety of industries. Narjit specializes in Data and Analytics with a focus on Enterprise Information Management (EIM), front-end dashboard development, data modeling, and architecture strategy. He also works closely with SAP BusinessObjects Enterprise suite of tools including SAP Design Studio and SAP Web Intelligence in addition to other analytical tools such as SAP HANA Studio and SAP Lumira, Narjit has experience working with various database solutions, including Oracle DB2 and Microsoft SQL Server. He has also helped businesses refine their Data Governance strategies, identifying gaps in business process using data profiling tools such as SAP Information Steward.

Making SAP Information Steward a Key Part of Your Data Governance Strategy

Part 3 – SAP Information Steward Metadata Management and Metapedia

Part 1 in our series on Data Governance defined the concept of Data Governance and gave suggestions on how to go about implementing an initial program at a corporate level. Part 2 provided an overview of how SAP Information Steward can help you get started with a Data Governance program and detailed the Data Insight module of the tool. In Part 3, we will now turns towards the Metadata Management and Metapedia module of Information Steward to show how they can help in other areas of Data Governance.

Common Concerns
The following questions and comments coming from within an organization are ones that we hear often:

  • How were the values on this report calculated?
  • Where is this data being sourced from?
  • I can’t trust this report; some values look right but others seem way off base
  • What definition of Customer do you mean here? We define it differently
  • We view this set of material as Finished Goods, but some other plants view them as semi-finished. We sell these but that other plant is responsible for putting these materials into a large assembly

Mostly these conversations boil down to two main problems:

  1. Business users are completely blind to how the data they see in a report has been processed.  They don’t know where it came from or how it was calculated, and therefore they don’t know if it can be trusted.
  2. Common terms are being lost in translation across the enterprise. One group defines a term one way, and the rest of the company defines it another. As a result, communication has become challenging as conversations devolve into how to properly define certain terms, rather than solve the actual business problem that has come up.

To download full PDF and Continue Reading….



About Rich Hauser
Rich is a Manager in the Data & Analytics practice of Protiviti, specializing in Enterprise Information Management.  He has delivered customized Data Governance and SAP BusinessObjects solutions for customers of all sizes across a variety of industries.  With Protiviti, Rich utilizes SAP Data Services and SAP Information Steward.

Making SAP Information Steward a Key Part of Your Data Governance Strategy – Part 2

SAP Information Steward Overview and Data Insight Review

Part 1 in our series on Data Governance defined the concept of Data Governance and gave suggestions on how to go about implementing an initial program at a corporate level. The definition that we use is:

Data Governance is your organization’s management strategy to meet the data quality needs of final data users and consumers. It verifies that data meets your organization’s security requirements and ensures that it complies with any regulatory laws. It is the marriage of data quality, data management, and risk management principles. It is implemented via corporate policies, procedures, controls, and software.

Now that we know what it is and how to start a program, let’s discuss how SAP Information Steward can fit into a data governance initiative. SAP Information Steward is an enterprise-level data quality solution that allows you to profile data, perform impact and lineage analysis, construct a corporate dictionary, and define custom cleansing rules for incoming data. Each of these functions is performed by a different module of the software, which are: Data Insight, Metadata Management, Metapedia, and Cleansing Package Builder. Your initial data governance goal will determine which of these to utilize first. Data Insight is the data profiling tool and data quality monitor. Metadata Management is the impact and lineage analysis tool that can determine where a piece of data is used through the enterprise and what may affect the data. Metapedia is the corporate dictionary where business terms can be defined for the use throughout the organization. Finally, Cleansing Package Builder is the data quality tool that allows data area experts to define transformations and cleansing rules in order to standardize a particular set of data. This post will cover Data Insight in detail, while subsequent posts will breakdown the other modules of the Information Steward tool.

Data Insight allows you to profile data from a range of sources that include standard relational databases, SAP HANA, SAP ERP, SAP Master Data Services, and even flat files. Data profiling is simply the process of analyzing the data that exists in a source and collecting statistics from that analysis. It answers the question: “What does my data source actually contain?”, as there is often a disparity between what a source should contain and what it contains in reality. Data profiling is the starting point for data integration tasks, data warehouse projects, and many data governance programs. Without this starting point, one cannot properly calculate true measurements of the data quality improvements that are achieved through a data governance or data quality program.

To download full PDF and Continue Reading…

Richard HauserAbout Rich Hauser
Rich is a senior Business Intelligence consultant specializing in Enterprise Information Management. He has delivered customized SAP BusinessObjects solutions for customers of all sizes across a variety of industries. With Decision First Technologies, Rich utilizes SAP Data Services and SAP Information Steward.

Making SAP Information Steward a Key Part of Your Data Governance Strategy

Part 1 – Data Governance Defined

Data Governance: You’ve probably heard the buzz about this topic that is becoming a larger part of many IT conversations. But similar to the vague term “Big Data” these days, just what exactly is data governance? Is it a piece of software that one can simply buy off the shelf? And what are the benefits that one can expect to receive by implementing it?

Chances are that if you are reading this article you have some kind of data issue in your organization, and you are not alone. Data problems are costly in a variety of ways. For example, they can result in lost of potential sales due to incomplete customer demographic data. Or, they can cause an unnecessary expense, such as a sales return that resulted from an undeliverable address. Worst of all, a data problem can become systemic if the error is propagated throughout the enterprise via normal integration channels. A Data Governance program will be your strategy for finding data error, repairing these errors, and preventing them from occurring in the future. But more importantly, it will be an overarching set of policies and information management principles that apply to your entire enterprise.

It’s helpful to start by defining Data Governance. A quick internet search will retrieve a large number of definitions that all sound relatively familiar, but the common themes that run throughout all of them are:

  • A data governance strategy is a set of policies and procedures that manage the quality of information assets
  • Data governance is a program to manage information. It is an ongoing process that will change over time. It is not a one-time project (wishful thinking)
  • Data is an enterprise asset. Like any other asset, it requires an investment in order to be maintained and improved

Data Governance, in summary, is your organization’s management strategy to meet data quality needs of final data users and consumers. It verifies that data meets your organization’s security requirements and ensures that it complies with any regulatory laws. It is the marriage of data quality, data management, and risk management principles. It is implemented via corporate policies, procedures, controls, and software.

To download PDF and Continue Reading…

Richard HauserAbout Rich Hauser
Rich is a senior business intelligence consultant specializing in Enterprise Information Management. He has delivered customized SAP BusinessObjects solutions for customers of all sizes across a variety of industries. With Decision First Technologies, Rich utilizes SAP Data Services and SAP Information Steward.