Data governance Vs Governance & data
Data Governance is somewhat easier to define using wiki on 17th March 2019
Data governance is a data management concept concerning the capability that enables an organization to ensure that high data quality exists throughout the complete lifecycle of the data. The key focus areas of data governance include availability, usability, consistency[1], data integrity and data security and includes establishing processes to ensure effective data management throughout the enterprise such as accountability for the adverse effects of poor data quality and ensuring that the data which an enterprise has can be used by the entire organization.
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. It provides all data management practices with the necessary foundation, strategy, and structure needed to ensure that data is managed as an asset and transformed into meaningful information[2].
These goals are realized by the implementation of Data governance programs, or initiatives using Change Management techniques.
Data governance is a data management concept concerning the capability that enables an organization to ensure that high data quality exists throughout the complete lifecycle of the data. The key focus areas of data governance include availability, usability, consistency[1], data integrity and data security and includes establishing processes to ensure effective data management throughout the enterprise such as accountability for the adverse effects of poor data quality and ensuring that the data which an enterprise has can be used by the entire organization.
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. It provides all data management practices with the necessary foundation, strategy, and structure needed to ensure that data is managed as an asset and transformed into meaningful information[2].
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. Some goals include
Increasing consistency and confidence in decision making
Increasing consistency and confidence in decision making
- Decreasing the risk of regulatory fines
- Improving data security, also defining and verifying the requirements for data distribution policies[3]
- Maximizing the income generation potential of data
- Designating accountability for information quality
- Enable better planning by supervisory staff
- Minimizing or eliminating re-work
- Optimize staff effectiveness
- Establish process performance baselines to enable improvement efforts
- Acknowledge and hold all gain
These goals are realized by the implementation of Data governance programs, or initiatives using Change Management techniques.
Let's compare to the finance thinking ( financial governance)
The obvious differences are easy to pick out, but here are the keys; we have a finance plan, a budget, reporting, governance, audit and it is integral to everything we do. Massive energy goes on consistency.
Data is apparently the new business (but data is data and not oil) but we leave #data-governance as subset technical issue and the business driver. It is subtle but read the language of data governance again above (and on the web) and see if you think it leads strategy and finance or is servant to it. Finance has a grip and appear unwilling to let go or being the driver for strategy.
Aspects that are currently out of scope of data governance (which should worry us)
- data business plan and how it create margin
- consistency of data across the eco-system
- suppliers and partners approach to data (audit)
- external audit and validation, verification
- impact on society and brand of data right not data wrong
- ethics, bias, automation, decision making and politics
- consistency of data policy and management (privacy policy, T&C, contractor terms and employee terms)
- opt-in and opt-out philosophy
- sharing data / data portability, data mobility
- reporting on data ( as in b/s, p/l and cash flow)
- Standard Data Practices (in progress)
What would governance look like if data was the driver and not finance?