One of subdivision of IT governance is a data governance.it is represent the policies, objectives, and strategy for organizational data. It is responsible for data stewardship, even overseeing local data stewards. Also, it responsible for similar activities for particular data business units or subject areas. By mutual agenda and collaboration, the data governance makes and maintain enterprise view of its data. Indeed, it includes high-level oversight of daily management activities data. Often data governance happens over a data governance council, whose members come from an assortment of key business areas and IT, including data stewards (Brown, DeHayes, Hoffer Martin & Perkins, 2012).
In previous years, data governance has been seen unclear idea. Its activities placed on standby in many organizations. With the time passing, the idea begin to change. Companies are trying to let it part of their data management efforts as portion of smaller short-term data initiatives and portion of projects enterprise wide while it is still to be regard as mature aspect of data management. The cultural barrier was the biggest challenge companies have faced while trying to apply data governance concept. Even though some view improvement, this is still present. “Data management in the past years has been seen to be largely IT-based discipline, but it is now maturing, and we’re seeing more business engagement in the process of building the necessary infrastructure,” says Wrazen of Trillium Software (Mangado, 2010).
Companies may also have a better chance of success in data governance programs as more people gain experience in data management (Mangado, 2010).
Where Data Governance is Applicable?
Today there are types of data exist among organizations. For example, with the emergence of social media – which can generate online customer behavior data – such as records of the paths taken by users through a company website, call detail and data- usage records (also known as event detail records). Also from telecommunications corporations, and data files or transaction records or audio files captured by call centers (e.g. devices that control client contacts through telephone calls) Biometric data (such as fingerprints or retina scans) as well as medical images and health care data, while traditional mission-critical applications continue to generate terabytes of transaction data.
Collibra is company behind the data governance platform for all data citizens. It makes and sells the operating model and software. In addition, it offers service to speed client’s journeys for true data governance. Also , it runs the annual Data Citizens Conference and on-going Data Citizen City Tours in cities around the world, and run Collibra University and host the Collibra Community for its clients. In different areas like in Europe and North America and through partners in South America, Middle East and Asia Collibra bring true data governance to business through local offices.
Collibra Data Governance Center, data citizens can:
- Get up and running quickly with ready-to-use best practice models.
- Use semantic search capabilities to make finding and accessing the right data blazingly fast.
- Easily automate governance and stewardship tasks, define ownership structures, and establish workflows to involve the right people in data decision making.
- Deliver instant access to the right data through mobile access and capabilities.
- Collaborate with data stakeholders build and maintain context around data
- Use interactive data lineage diagrams to visually explore everything about the data, including business terms, policies, issues, relationships, and direction.
- Establish a data help desk to identify and resolve data, issues quickly and building confidence in data sources.
- Link data sources, business applications, data lakes, and metadata from across enterprise with pre-built integration templates
- Create a catalog of data so you know what you have, what it means, where find it, and whom you can call about it (Chalker, 2014). Health Catalyst: As the age of analytics grows in healthcare, health system executives find themselves more and more challenged to explain a data governance strategy that maximizes healthcare data’s value to the mission of their organizations. ―Data is the new oil,‖ Andreas Weigend, former chief scientist at Amazon.com recently said.
In order to achieve the greatest common goal, data governance is to be as lean as possible govern to the least extent needed, and this is the Philosophy of Data Governance for Health Catalyst.
Business factors need to evaluate prior to implementation:
Start strong: Data governance is a foreign concept for most organizations. Getting start will require educating stakeholders so that they buy in to the benefits of new behaviors. In addition, put in significant effort to set up the council, define an operational model and scope, and communicate expectations. When all stakeholders are prepared, the initial meetings are more likely to achieve real results.
Get some quick wins: Showing success will go a long way toward getting people excited and on board, and keeping them committed to the program. Look to demonstrate the value of governance to a widespread audience by resolving an issue or fixing a problem with broad visibility and impact in the organization. For some people in the company, the data governance program will involve giving up decision rights and conforming to new rules. Show benefits early on to help illustrate the positive side of that tradeoff.
Ensure executive buy-in : In addition to the council chair, name a visible executive sponsor and make sure he or she is committed to supporting the mission and vision for the data governance program. Typically, the sponsor leads a function that benefits significantly from better data, and can communicate the value proposition by delivering results that also drive value for their cross functional partners. Providing key executives and leadership with regular updates and provide them with success stories will be crucial in helping get the message disseminated across all functional teams.
Set up a proper foundation: The council requires to identify what they are governing. Including data models, metadata (data dictionaries), the organization has to make sure its own strong data management program. Business and IT leaders need to know who owns data, and have knowledge of the major issues, quality problems, and how they are affecting the business. A good foundation will ensure the long-term viability of the program, and an understanding of the baseline will help to chart progress.
Measure results: Once set a baseline; establish metrics to measure the council’s impact on the organization with regular basis. Track data quality, if possible, measure impacts to revenue and costs, and report the progress to the council at each meeting. Set up a maturity model to measure the qualitative performance of the council itself. Visible metrics will guarantee the group held accountable for results.
Execute for longevity: A successful data governance program will require time and resources – especially during the initial stages where the working structure and processes are still being defined and implemented. Provide council members with the necessary support so they can dedicate the time to make the program a priority. Over the long term, the council must stay on top of new data-related issues that the organization is facing so that the program can continue to show a meaningful impact.
In any case, the organization must have policy for a data governance, which outlines the role of business managers in data administration and the role of business managers in data administration (Brown, DeHayes, Hoffer, Martin & Perkins, 2012).
Benefits gained after implementing data governance:
Data governance processes used to improving policies regarding the data resource. Issues data quality, privacy, security, backup and recovery, and access are particularly important (Brown, DeHayes, Hoffer, Martin & Perkins, 2012).In addition, when we compare the cost of devices and equipment’s of data governance with benefit and data that gave we will find the benefit is more and it will worth.
Almost all published frameworks recommend that, a data governance should undertake the maturity assessment, establish the current state of data management and control, such as DataFlux, EWSolutions, Gartner, IBM, MDM Institute, ARMA International and CMMI Institute’s Data Management Maturity Model (Neera Bhansali, 2013), however, the studies focused on the maturity of organizational data management practices are rare(Aiken ,2016)
There has been a lot of argue around this thought in the previous times, but experts agree the right time for data governance has come nowadays. “Data governance should be seen as more than a data exercise,” says Wrazen. “It’s a continuous strategy, a system that’s always moving – it has no end line. Only time will tell us how well companies fully embrace this concept, making it part of long-term data management efforts. However, at this point, companies seem at least eager to give it to go (Mangado, 2010).
Aiken, P. (2016). Experience Succeeding at Data Management—BigCo Attempts to Leverage Data. Journal of Data and Information Quality, 7(1-2), 1-35.
Anthony Chalker (August 12, 2014). Retrieved from https://www.isaca.org/chapters3/Atlanta/AboutOurChapter/Documents/GW2014/Impl ementing%20a%20Data%20Governance%20Program%20-%20Chalker%202014.pdf
Carol V. Brown, Daniel W. DeHayes, Jeffrey A. Hoffer, E. Wainright Martin & William C. Perkins (2012). Managing Information Technology. Seventh Edition; Prentice Hall
Mangado, C. (2010). Data governance: From concept to reality. Inside Reference Data, 5(6), 20-21. Retrieved from https://search-proquest- com.sdl.idm.oclc.org/docview/759093676?accountid=142908
Neera Bhansali (2013). Data Governance: Creating Value from Information Assets. CRC Press.