Top 5 Challenges in Developing Data Governance Plans
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Watch the companion webinar presentation at: http://embt.co/1zjVzEi These days, it seems that many companies are assigning their business and data professionals an urgent task to establish a corporate data governance plan, but many of them aren’t sure where or how to start. In order to implement data governance in your company, you have to deal with multiple challenges, such as: Making a business case for data governance Getting management and executive buy-in (including funding & headcount) Developing a realistic timeline – and realize that it’s an ongoing process, not a time-limited project Implementing a robust infrastructure to establish the framework for your environment Mitigating risks as the program evolves Before you can create an effective data governance program, you have to start with the foundation and treat your data as a corporate asset. When you have a consistent standard for metadata, you can provide a solid structure across your enterprise that will improve data quality, enhance data security, and reduce risk. Join this webinar to learn about a realistic approach to defining, establishing, and measuring success for your data governance initiative.
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Top 5 Challenges in Developing Data Governance Plans
- 1. 1 1 Top 5 Challenges in Developing Data Governance Plans Francis McWilliams Solutions Architect Francis.McWilliams@embarcadero.com
- 2. 2 Top 5 Challenges in Developing Data Governance Plans 1. Management Sponsorship 2. Scale Correctly 3. Roles and Responsibilities 4. Enforce Data Governance rules 5. Begin Business Glossary work early
- 3. 3 Management Sponsorship 1. Obtaining Management Buy-in 2. Benchmarking 3. Top-Down and Bottom-Up 4. Communicate!
- 4. 4 Scale the milestones • Keep it realistic - Especially in the beginning • Baby Steps – Bite size pieces • Think Ongoing process
- 5. 5 Data Governance Roles and Responsibilities 1. Overseeing Body / Committee 2. Business Element 3. IT Support Team
- 6. 6 Roles 1. Data stewards: The people who know your data 2. IT SME and Data SME and Business SME 3. Enterprise Architects
- 7. 7 Business Glossary Metadata Context vs Content • Start Early • Data Stewardship - Continued
- 8. 8 Enrich using metadata • Required to paint the full picture • Lack of information results in poor decision making • Provide clarity over time
- 9. 9 Apply naming conventions • Enforce consistency • Help provide meaning to physical implementations • Provide clarity over time
- 10. 10 Data Governance is not a project 1. Ongoing process 2. Build for permanency
- 11. 11 What not to Do • Buy in and not commitment • Cart before the Horse • Ocean Boiling • Goldilocks Syndrome • Too many people / Overload • Failure to Implement • Didn’t Plan for a journey
- 13. 13 Thank you for attending! • Learn more about the ER/Studio product family: http://www.embarcadero.com/data-modeling • Trial Downloads: http://www.embarcadero.com/downloads • To arrange a demo, please contact Embarcadero Sales: sales@embarcadero.com 1 (888) 233-2224
Notas del editor
- What is Data Governance? Data governance is an umbrella term for an emerging discipline that encompasses a number of different practices for data quality, data management, business process management, and risk management. The goal is to ensure that data serves business purposes in a sustainable way. MDM Institute defines data governance as “the formal orchestration of people, processes, and technology to enable an organization to leverage data as an enterprise asset.” The Data Governance Institute goes a step further, stating that “data governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models, which describe who can take what actions with what information, and when, under what circumstances, using what methods.” According to Wikipedia, “data governance encompasses the people, processes, and information technology required to create a consistent and proper handling of an organization’s data across the business enterprise. Goals may be defined at all levels of the enterprise and doing so may aid in acceptance of processes by those who will use them.” These definitions point to the need for exercising control over how data is used in an enterprise. Successful data governance programs can help ensure that organizations have consistent policies and processes for defining, managing and using corporate data. But many data governance efforts miss the mark and fall short of those goals. Effective data governance serves an important function within the enterprise, setting the parameters for data management and usage, creating processes for resolving data issues and enabling business users to make decisions based on high-quality data and well-managed information assets. But implementing a data governance framework isn't easy. Complicating factors often come into play, such as data ownership questions, data inconsistencies across different departments and the expanding collection and use of big data in companies. The Need for Data Governance Data governance enables organizations to access the full value of data, the currency of today’s networked economy, while protecting that data from risk. Data is growing across all areas of the enterprise: on the average, at the rate of 1.5 to 2.5 times a year. Laws regulating the use of data and associated compliance issues are also growing. For instance, in the U.S., the 2002 Sarbanes-Oxley Act can result in fines and even prison sentences for noncompliance to external reporting requirements
- In this session, Francis will share:: In general, it’s well understood that enterprise data governance needs to be a joint business and IT endeavor. What gets organizations in trouble is how they actually go about implementing governance programs.
- data governance best practices, roles and responsibilities - any justification for investing in data governance should focus on advancing business goals and enhancing the bottom line. It’s important, to zero in on the business problems that data governance can fix and be sure to show what those problems are costing the company. Sometimes the justification for a data governance program is clear-cut: Poor data quality is leading to a disastrous online experience for customers. But other times the process of identifying the business problems and quantifying their cost to the organization can get murky. Benchmarking You cant manage what you cant measure – Deming Initial metrics You can't manage what you don't measure. It is an old management adage that is accurate today. Unless you measure something you don't know if it is getting better or worse. You can't manage for improvement if you don't measure to see what is getting better and what isn't. To begin, we'll define a few of the terms. We are using "measure" as a verb, not a noun and "benchmark" as a noun, not averb. Measure: The verb means "to ascertain the measurements of" Measurement: The figure, extent, or amount obtained by measuring" Metric: "A standard of measurement" Benchmark: "A standard by which others may be measured" So we collect data (measurements), determine how those will be expressed as a standard (metric), and compare the measurement to the benchmark to evaluate progress. For example, we measure number of lines of code written by each programmer during a week. We measure (count) the number of bugs in that code. We establish "bugs per thousand lines of code" as the metric. We compare each programmer's metric against the benchmark of "fewer than 1 defect (bug) per thousand lines of code". Measure those activities or results that are important to successfully achieving your organization's goals. Key Performance Indicators , also known as KPI or Key Success Indicators (KSI), help an organization define and measure progress toward its goals. They differ depending on the organization. A business may have as one of its Key Performance Indicators the percentage of its income that comes from return customers. A Customer Service department may have as one of its KPIs the percentage of customer calls answered in the first minute. A Key Performance Indicator for a development organization might be the number of defects in their code. Business users often don’t understand precisely why something is a problem or how that problem translates to increased costs or reduced revenues. A good way to identify business problems and ultimately arrive at their cost is to start by asking business workers to describe their biggest pain points when it comes to data quality. For example, the problem could be an application or system failing to deliver reliable results. Then ask the business users why it is a problem for them. Next, ask why the organization should address the problem, and so on. Eventually you will arrive at a deeper issue that directly affects the bottom line. For example: Poor data quality is leading to a disastrous online shopping experience, and X amount of revenue is being lost each month. Once the business issues are clearly defined the process of assigning an approximate dollar value to the problem gets easier. For example, if a salesperson spends three hours performing manual processes because of poor data quality - and that salesperson usually sells something every 1.5 hours -- then the company has just failed to close two deals. It all comes down to increasing revenue or decreasing your costs. Top-Down and Bottom-Up Multiple departments come across department/enterprise start at the top. Single department bottom up should work Organizations should begin the process by building a “coalition of the willing” -- representatives from each department who recognize the value of data governance and want to help, Communicate! It is important that you communicate your metrics both up and down the organization. Your boss wants to know what's going on, but your employees need to know also. They are not motivated to improve unless they know how they are doing. In addition, most of the suggestions on how to improve will come from them. Post team and individual results, either on line or just by hanging charts on the wall. Use pie charts, line charts, key driver charts, and other graphs to quickly, easily, and visually communicate the metrics. Review your metrics and use them to guide your decisions. With your metrics in place, you can tell which strategies are working and which aren't. If you make a change, you use the metrics to tell you whether the change improved things or not. When the metrics show improvement, share that success with everyone. Tell your staff. Tell your boss. Tell the guy you meet in the hall. And don't forget to reward the people who were responsible for the success, even if it's just a verbal pat on the back. Measure what's important. Publish your metrics and benchmarks. Reward people for exceeding their goals. And then start over.
- Trying to fix everything at once. A significant trap that many data governance efforts fall into is trying to solve all of an organization’s data problems in the initial phase of the project. Or companies start with their biggest data problems, issues that span the entire enterprise and are likely to be very political. It’s almost impossible to establish a data governance program while at the same time tackling data problems that have taken years to build up. This is a case in which you need to “think globally and act locally.” In other words, data problems need to be broken down into incremental deliverables. “Too big, too fast” is a sure recipe for disaster.
- Keep it realistic - Especially in the beginning Dividing up the data governance roles and responsibilities into three major categories, including the overseeing body, the business element and the IT support team the overseeing body is based in the business side of the company and charged with handling the day-to-day aspects of running the data governance program. For example, it makes sure the approved business glossary is readily available and that data quality standards are continually being written and enforced. Business users understand the data they create better than anyone and should therefore be largely responsible for ensuring proper data governance. That‟s why the second category of data governance roles and responsibilities is all about the business. The business level of the data governance organization consists of the data owners, or the data governors, as they are sometimes called. These are the people who make the call when questions about data policies and procedures arise.
- Data stewardship. Data stewardship is typically not a full-time role. They are Subject matter expert in a specific area. And then there are the data stewards, These are the folks who are out in the business; they have their day jobs, but they are the people that their peers tend to turn to with questions. They know the data. The data stewardship level is where the real work gets done because stewards are responsible for reporting any problems to the overseeing body - a task that often leads to new data quality policies and procedures. The third level consists of technical personnel who can explain data quality issues that crop up For example, as the result of extract, transform and load operations, They can also diagnose issues that exist within individual systems or applications. On the IT side, Enterprise Architects can also be your best allies - They get what you are trying to do.
- Content vs Context Anyone considering a data governance program begin work on the business glossary as early as possible. A business glossary contains all the mutually agreed-upon definitions of business terms and uses metadata to expose those definitions to the entire enterprise. Business glossaries are typically built out gradually. It’s all about getting people on that same page with the terminology, If you don’t do that early on, the whole effort is that much harder .You have to have a common business language, he said. If you’re not speaking the same language, [data governance is] pretty challenging, if not impossible. "What is a 'Case'?“ Data stewardship adds another dimension - and more challenges - to data governance efforts. Whether an organization hires full-time data stewards or delegates stewardship responsibilities to existing employees, business units sometimes are reluctant to accept the new arrangement for maintaining data definitions and enforcing polices on data use. In an ideal environment, all users adopt a stewardship-minded approach and take responsibility for handling data in a way that both meets their immediate business needs and serves the company's overall requirements for data quality and consistency. But data stewardship processes need to be attuned to an organization's corporate culture in order to help foster internal adoption and compliance.
- Lack of information results in poor decision making – not for business decisions around customers, products, employees, etc, but also in terms of data security breaches Provide clarity over time – some of the helpful metadata is providing information on stewardship
- Create a Rosetta stone, which helps interpret data meaning over time
- Not building sustainable and ongoing processes. Even if the initial investment in time, money and people is adequate, many organizations don’t establish a budget, get resource commitments or design data governance processes with an eye toward sustaining the governance effort over the long haul. Trying to fix everything at once. A significant trap that many data governance efforts fall into is trying to solve all of an organization’s data problems in the initial phase of the project. Or companies start with their biggest data problems, issues that span the entire enterprise and are likely to be very political. It’s almost impossible to establish a data governance program while at the same time tackling data problems that have taken years to build up. This is a case in which you need to “think globally and act locally.” In other words, data problems need to be broken down into incremental deliverables. “Too big, too fast” is a sure recipe for disaster.
- Buy-in but not commitment. IT often regards data governance sponsorship as business executives writing a check and putting people on a governance committee (see below). While that is in fact a great first step, the business needs to do more. People from the business side need to create the data definitions, business rules and key performance indicators (KPIs) for a data governance program; achieve agreement on them across an organization; enforce usage and compliance; and ensure that the definitions, rules and KPIs are updated on an ongoing basis as business needs evolve and change. The reality is that in the vast majority of cases, data governance tasks are merely tacked on to the already overloaded schedules of business managers instead of being made a priority, with other responsibilities correspondingly getting taken off their to-do lists. Without a real business-resource commitment, data governance will take a back seat to the daily firefight and will never be implemented effectively. Cart before the Horse One thing most organizations have gotten right on the enterprise data governance efforts I’m familiar with is creating a governance steering committee and a separate governance working group. The steering committee should have business representatives from across the enterprise, and the working group typically is made up of the data stewards who do the real governance labor. What organizations often get wrong is the timing: They form these panels and assign people to them before they really understand the scope of the data governance program and the roles and responsibilities of the participants. A guaranteed way to stall a data governance initiative in its tracks and lead the business to lose interest is to prematurely organize the management framework and then realize you need a do-over. Trying to solve world hunger or boil the ocean. A significant trap that many data governance efforts fall into is trying to solve all of an organization’s data problems in the initial phase of the project. Or companies start with their biggest data problems, issues that span the entire enterprise and are likely to be very political. It’s almost impossible to establish a data governance program while at the same time tackling data problems that have taken years to build up. This is a case in which you need to “think globally and act locally.” In other words, data problems need to be broken down into incremental deliverables. “Too big, too fast” is a sure recipe for disaster. The Goldilocks syndrome. In the story of Goldilocks and the three bears, the little girl keeps encountering things that are either one extreme or another, which is precisely what happens on many data governance programs. Either the program is too high-level and substantive data issues are never really dealt with, or it attempts to create definitions and rules for every data field in every table in every application that an enterprise has – with the result being that the effort gets bogged down in minutiae. There needs to be a happy compromise between those two extremes that enables the data governance initiative to create real business value. Committee overload. The good news about governance steering committees and working groups is that they get people representing various business units and departments involved in the governance process. The bad news is that they tend to get a lot of people involved in the process. Often, the more people on each committee, the more politics comes into play and the more watered-down governance responsibilities become. To be successful, try to limit the size of committees to between six and 12 people and make sure that committee members have the required decision-making authority. Failure to implement. If the data definitions, business rules and KPIs are created but not used in any business processes, a data governance effort won’t produce any business value. The governance process needs to be a complete feedback loop in which data is defined, monitored, acted upon and changed when appropriate. Creating definitions and rules without implementing them is like getting blueprints drawn but never building a house. Similarly, ongoing communication about governance initiatives frequently doesn’t take place. That can result in business users going back to their old habits and the data governance program losing momentum. Not building sustainable and ongoing processes. Even if the initial investment in time, money and people is adequate, many organizations don’t establish a budget, get resource commitments or design data governance processes with an eye toward sustaining the governance effort over the long haul. Credit to: Rick Sherman is the founder of Athena IT Solutions, a Stow, Mass.-based firm that provides data warehouse and business intelligence consulting, training and vendor services. In addition to having more than 20 years of IT experience, Sherman writes on IT topics and is a frequent speaker at industry events. He blogs at The Data Doghouse and can be reached at rsherman@athena-solutions.com.