어디에서 주워온 글인지는 확실히 기억이 안 나는데, 읽다보니 나름대로 괜찮은 내용인 듯 해서 적어두는 포스트. CIO의 잘못된 접근 방법 다섯가지를 들고, 그에 대한 올바른 접근방법을 제시하는 형식이다.
전문을 번역... 할 짬이 없어서; 요약문만 적어보도록 하면 :
It may be a truism that your BI reporting tools are only as good as the information you feed them (that is, "garbage in, garbage out"), but that doesn't mean that the right actions are a given. Since most organizations still take an isolated view of data, data governance remains a difficulty, says Ian Charlesworth, principal analyst with IT consultancy Ovum. Data is all too often siloed in different business units and is entered, treated and viewed differently, making "one version of the truth" impossible.
Know your data.
The first step of data governance is to establish a clear view of your data; find out what you have, how reliable the information is, what data is beneficial but previously unused, which data is corrupted and which IT projects are duplicating information. And be sure to communicate to stakeholders the cost of not having data governance and the value of creating it.
You procrastinate until you can do a complete overhaul.
An all-or-nothing approach is almost guaranteed to fail. For starters, bringing all data under control in one fell swoop is not realistic given time and money constraints, and in organizations where such an overhaul is possible, user resistance is almost a given.
Start small, think big.
Instead of all or nothing, prioritize the most crucial aspects of data governance, in keeping with your overarching vision. For example, Charlesworth recommends focusing on four key areas.
Data governance policies are set—no worries ever again.
Data governance is often begun in conjunction with a specific data warehouse or BI project. However, if you think of data governance as a "project," your efforts are doomed. Successful data governance depends on a long-term commitment from the business at large to both the technological and cultural foundations.
Establish a culture of data governance.
Ongoing training and key milestones that measure data governance's benefits can help keep quality control on users' radar. Successful data governance also depends on dedicated sponsorship from someone in top management. Charlesworth says the CIO is often the perfect person for the job due to a CIO's likely combination of forward-thinking and a focus on efficiencies around process, money and technologies. Some companies even create a C title specifically for the position, such as chief data officer or chief data steward.
You let red tape suck the life from your efforts.
Charlesworth says many data governance efforts fail to show positive change, and instead stall in meetings and bureaucracy. But if you don't focus on action and demonstrable wins, users won't feel the positive benefits firsthand, making user commitment unlikely.
To get user buy-in and commitment, you must create, demonstrate and internally market the positive changes won through data governance. For example, one measurable benefit to focus on initially could be improving validation of order entries to reduce errors.
You make ROI the be-all and end-all.
Can you accurately isolate investment benefits and attribute them to a particular project? In today's multifaceted, complex business environment, this is not likely, says Charlesworth. Calculating ROI on a particular investment assumes that everything else in the business either stood still or had no influence on the benefits, he says.
Create a clear picture of success.
Charlesworth recommends looking to other metrics such as internal rate of return (a measure of an investment's efficiency or the rate of growth a project is expected to generate) and economic value added (estimate of true economic profit). However, the most important thing isn't the calculations per se, it's the discussion around defining success—what it looks like and how you know when you have it, says Charlesworth. This is especially important in terms of measuring value of data governance at various phases and levels of granularity to make sure you stay on track, and, if not, making corrections. His examples of such metrics include a data quality dashboard that displays the accurateness of data processing, data consistency and reuse of rules/measures, and project-specific metrics such as standardization of product master data elements.
전문을 번역... 할 짬이 없어서; 요약문만 적어보도록 하면 :
1. 좋은 BI 도구만 구입하면 모든게 해결될 줄 안다.이렇게 적어놓고 보니 알만한 사람들은 지겹게 듣는 소리고, 모르는 사람은 이게 뭔 소린지 오해의 소지가 생길 것 같기도 한데.. 자세히 알고 싶은 분께서는 아래의 원문을 참고해 주시어요.
니 데이타를 알아라(Know your data)
2. 완벽한 조사가 가능할 때까지 모든 작업을 미룬다.
작게 시작하고, 크게 생각하라.
3. 데이터 거버넌스 정책이 정착되면 더 이상 걱정할 일이 없다.
데이터 거버넌스 문화를 수립하라.
4. 필요없는 일에 노력을 낭비한다.
정보의 빠른 전달이 성공을 가져온다.
5. ROI야말로 궁극적인 요인이다.
명확한 성공의 그림을 그려라.
http://www.cio.com.au/index.php/id;1712255683
Despite the idea that business intelligence is a crucial tool for getting and keeping customers, adequately measuring company performance, and delivering flexibility, challenges remain. One of the most important: data governance.
Although data governance is crucial to successful BI and data warehouse efforts, it isn't easy. To the rescue: five dirty data practices you may be guilty of, and five ways to clean them up.
Dirty Data Practice No. 1
You think buying the coolest business intelligence tool is all you need.It may be a truism that your BI reporting tools are only as good as the information you feed them (that is, "garbage in, garbage out"), but that doesn't mean that the right actions are a given. Since most organizations still take an isolated view of data, data governance remains a difficulty, says Ian Charlesworth, principal analyst with IT consultancy Ovum. Data is all too often siloed in different business units and is entered, treated and viewed differently, making "one version of the truth" impossible.
Clean It Up
Know your data. The first step of data governance is to establish a clear view of your data; find out what you have, how reliable the information is, what data is beneficial but previously unused, which data is corrupted and which IT projects are duplicating information. And be sure to communicate to stakeholders the cost of not having data governance and the value of creating it.
Dirty Data Practice No. 2
You procrastinate until you can do a complete overhaul. An all-or-nothing approach is almost guaranteed to fail. For starters, bringing all data under control in one fell swoop is not realistic given time and money constraints, and in organizations where such an overhaul is possible, user resistance is almost a given.
Clean It Up
Start small, think big. Instead of all or nothing, prioritize the most crucial aspects of data governance, in keeping with your overarching vision. For example, Charlesworth recommends focusing on four key areas.
- Create data quality processes and procedures, and where possible embed these at the point of data creation or capture. For example, create a data validation routine in an order entry system or establish a corporate standard for name and address nomenclature.
- Assign a data steward. This person should be someone from within the business who can champion and enforce data quality practices throughout the business. This person should have an intimate knowledge of how and where the data will be used by the business, and who can act as a liaison between the business and IT.
- Create a master data management solution. For starters, this means assigning unique identifiers to core information assets across the business, such as service codes, customer definitions and so on.
- Integrate metadata. Metadata gives important information to both IT and the business, puts complex information into layman's terms and relays vital information about underlying data syntax, semantic correctness and so on.
Dirty Data Practice No. 3
Data governance policies are set—no worries ever again. Data governance is often begun in conjunction with a specific data warehouse or BI project. However, if you think of data governance as a "project," your efforts are doomed. Successful data governance depends on a long-term commitment from the business at large to both the technological and cultural foundations.
Clean It Up
Establish a culture of data governance. Ongoing training and key milestones that measure data governance's benefits can help keep quality control on users' radar. Successful data governance also depends on dedicated sponsorship from someone in top management. Charlesworth says the CIO is often the perfect person for the job due to a CIO's likely combination of forward-thinking and a focus on efficiencies around process, money and technologies. Some companies even create a C title specifically for the position, such as chief data officer or chief data steward.
Dirty Data Practice No. 4
You let red tape suck the life from your efforts. Charlesworth says many data governance efforts fail to show positive change, and instead stall in meetings and bureaucracy. But if you don't focus on action and demonstrable wins, users won't feel the positive benefits firsthand, making user commitment unlikely.
Clean It Up
Deliver quick wins.To get user buy-in and commitment, you must create, demonstrate and internally market the positive changes won through data governance. For example, one measurable benefit to focus on initially could be improving validation of order entries to reduce errors.
Dirty Data Practice No. 5
You make ROI the be-all and end-all. Can you accurately isolate investment benefits and attribute them to a particular project? In today's multifaceted, complex business environment, this is not likely, says Charlesworth. Calculating ROI on a particular investment assumes that everything else in the business either stood still or had no influence on the benefits, he says.
Clean It Up
Create a clear picture of success. Charlesworth recommends looking to other metrics such as internal rate of return (a measure of an investment's efficiency or the rate of growth a project is expected to generate) and economic value added (estimate of true economic profit). However, the most important thing isn't the calculations per se, it's the discussion around defining success—what it looks like and how you know when you have it, says Charlesworth. This is especially important in terms of measuring value of data governance at various phases and levels of granularity to make sure you stay on track, and, if not, making corrections. His examples of such metrics include a data quality dashboard that displays the accurateness of data processing, data consistency and reuse of rules/measures, and project-specific metrics such as standardization of product master data elements.