The IBM Data Governance Unified Process: Driving Business Value with IBM Software and Best Practices
By Sunil Soares
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The IBM Data Governance Unified Process - Sunil Soares
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Introduction by Steve Adler
Recently, I applied for a car loan from my bank. The online application process was fantastic and saved so much time that otherwise would have been spent in a branch office or on the phone. I got an acceptance quote and interest rate within 60 seconds. Minutes later, I called the bank to complete the process. Unfortunately, I made a mistake when I classified the loan as a refinance—it was actually a lease buyout. I couldn’t change the form online once it was submitted, and the call center representatives couldn’t do it either. So, I had to cancel that application and do it again. Again, it took just a couple of minutes to fill out the form and get a quote back, and, once again, I was on the phone talking to the representative.
A few days later, I went into the branch to complete the transaction. The branch manager was charming and helpful and, after signing about 30 different forms, I walked out with my car refinanced. Three days later, the leasing company called to say that two forms were missing from the application that had been forwarded by the bank. I called the bank, but the representatives there were clueless. The leasing company must be wrong,
they said. I called the leasing company, and back and forth it went, until I agreed to go back to the bank and re-sign the forms. Then I started getting email messages from the bank, informing me that my original online quote was still approved and pending my action—even though I was sure the bank told me it had cancelled that quote.
This kind of normal, everyday Data Governance problem besets every business. I’ve seen a lot worse. Most people just call them mistakes, but they can lead to lost business, increased risk, and certainly extra cost. Whether you have a formal Data Governance program or not, your organization has Data Governance problems like these, and many others as well. You know it, and your customers know it.
Once you recognize this problem, the choice is pretty simple: you can either deal with it or ignore it. Since you are reading this book, you’ve decided not to ignore it. Good.
Your next decision is how to deal with it. Mistakes are a part of life. Your business makes them because people run your business. The data didn’t get wrong on its own. You need to change the way the people who run your business think about data, what they do with it, and how they build businesses that use data in the first place. To do that, you need a system, a Data Governance program that helps bring people together, to coordinate, collaborate, and communicate.
This book has some important tools to get you started the right way toward building a Data Governance program, which can fix the simple, and complex, errors and omissions your organization makes every day.
You already made the most important decision, in buying this book. Now finish the book and start your program, because time is not on your side. In the few minutes it took you to read this, someone somewhere in your organization has dropped a few forms, miscoded a new account, or sent duplicate bills to a customer.
The clock is ticking....
Steve Adler
Chairman,
IBM Data Governance Council
1
Introduction to Data Governance
Data Governance is the discipline of treating data as an enterprise asset. It involves the exercise of decision rights to optimize, secure, and leverage data as an enterprise asset. It involves the orchestration of people, process, technology, and policy within an organization, to derive the optimal value from enterprise data. Data Governance plays a pivotal role in aligning the disparate, stovepiped, and often conflicting policies that cause data anomalies in the first place.
Much like in the early days of Customer Relationship Management (CRM), organizations are starting to appoint full-time or part-time owners of Data Governance. As with any emerging discipline, there are multiple definitions of Data Governance, but the market is starting to crystallize around the definition of treating data as an asset.
Traditional accounting rules do not allow companies to treat data as a financial asset on their balance sheets, unless it has been purchased from an external entity. Despite this conservative accounting treatment, enterprises now understand that their data should be treated as an asset similar to plant and equipment.
Treating data as a strategic enterprise asset implies that organizations need to build inventories of their existing data, just as they would for physical assets. The typical organization has an excessive amount of data about its customers, vendors, and products. The organization might not even know where all this data is located. This can pose challenges, especially in the case of personally identifiable information (PII). Organizations need to secure business-critical data within their financial, Enterprise Resource Planning, and human resource applications from unauthorized changes, since this can affect the integrity of their financial reporting, as well as the quality and reliability of daily business decisions. They must also protect sensitive customer information such as credit card numbers and PII data, as well as intellectual property such as customer lists, product designs, and proprietary algorithms from both internal and external threats. Finally, organizations need to get the maximum value out of their data, driving initiatives such as improved risk management and customer-centricity.
Data is at once an organization’s greatest source of value and its greatest source of risk. Poor data management often means poor business decisions and greater exposure to compliance violations and theft. For example, regulations such as Sarbanes-Oxley in the United States, the equivalent European Sarbanes-Oxley, and the Japanese Financial Instruments and Exchange Law (J-SOX) dictate a balance between restricted access and the appropriate use of data, as mandated by rules, policies, and regulations. On the other hand, the ability to leverage clean, trusted data can help organizations provide better service, drive customer loyalty, spend less effort complying with regulations and reporting, and increase innovation.
Organizations must also consider the business value of their unstructured data. This unstructured data, often referred to as content, needs to be governed just as structured data does.
A good example of unstructured data governance is setting records management policy. Many companies are required to maintain electronic and paper records for a given period of time. They need to produce these records quickly and cost-effectively during the legal discovery process. They also need to be in compliance with the established retention schedules for specific document types. Several organizations use the term Information Governance
to define this program. Although we use the terms data
and information
interchangeably, we will stick with the more commonly used term Data Governance
throughout this book.
Here are some benefits that organizations can derive by governing their data:
Improve the level of trust that users have in reports
Ensure consistency of data across multiple reports from different parts of the organization
Ensure appropriate safeguards over corporate information to satisfy the demands of auditors and regulators
Improve the level of customer insight to drive marketing initiatives
Directly impact the three factors an organization most cares about: increasing revenue, lowering costs, and reducing risk
Founded in November 2004 by Steve Adler, the IBM® Data Governance Council is a leadership forum for practitioners such as Data Governance leaders, Information Governance leaders, chief data officers, enterprise data architects, chief information security officers, chief risk officers, chief compliance officers, and chief privacy officers. The council is concerned with issues related to how an organization can effectively govern data as an enterprise asset. It focuses on the relationships among information, business processes, and the value of information to the organization.
According to findings published by Adler for the IBM Data Governance Council in the whitepaper The IBM Data Governance Maturity Model: Building a Roadmap for Effective Data Governance, these are the top Data Governance challenges today:
Inconsistent Data Governance can cause a disconnect between business goals and IT programs.
Governance policies are not linked to structured requirements-gathering and reporting.
Risks are not addressed from a lifecycle perspective with common data repositories, policies, standards, and calculation processes.
Metadata and business glossaries are not used to bridge semantic differences across multiple applications in global enterprises.
Few technologies exist today to assess data asset values that link security, privacy, and compliance.
Controls and architectures are deployed before long-term consequences are modeled.
Governance across different data domains and organizational boundaries can be difficult to implement.
What exactly needs to be governed is often unclear.
Data Governance has strategic and tactical elements, which are not always clearly defined.
Data Governance is about decision rights and influencing human behavior. This book is a practitioner’s guide based on real-life experiences with organizations that have implemented similar programs. It highlights specific areas where IBM software tools and best practices support the process of Data Governance.
2
The IBM Data Governance Unified Process
The benefits of a commitment to a comprehensive enterprise Data Governance initiative are many and varied, and so are the challenges to achieving strong Data Governance.
Many enterprises have requested a process manual that lays out the steps to implement a Data Governance program. Obviously, every enterprise will implement Data Governance differently, mainly due to differing business objectives. Some enterprises might focus on data quality, others on customer-centricity, and still others on ensuring the privacy of sensitive customer data. Some organizations will embrace a formal Data Governance program, while others will want to implement something that is more lightweight and