Data Governance is a core area that businesses need to adopt in the data-driven world. Data has been around since the earliest of times, from the first libraries in the ancient world that started to collect and store information.
The collection of scientific research information, from census information about human populations, weather and spatial data to DNA genetic data, have all been contributing to the need to store data for analysis. The breadth of the information that is available for analysis covers our entire planet and beyond, and the population as well as different species. With our life and environment becoming documented to the finest degree the need for categorisation, data labelling and data management has become engrained into our society. Where research led the way for documentation of classification for data, business is now at a crucial time of growth and expansion to enable innovation.
With all data there becomes a continual need for its management and a core starting place is data governance. The DAMA Dictionary of Data Management defines Data Governance as “The exercise of authority, control and shared decision making (planning, monitoring and enforcement) over the management of data assets".
The goal of data governance is to help an organisation to manage data as an asset efficiently and effectively. It provides the principles, policy, processes, framework, metrics and oversight that are required to drive the most business value. Data governance programs have a goal of creating sustainable data management, good data quality that is measured and defining policies and practices. A much-needed area that needs to be considered is that of culture and embedding that culture of data management into the business.
We start with understanding what data assets a business has from the core known data and dark data; data that is collected but not used. The proliferation of duplicate data around a business is key to document. Often the first thing that comes to mind with data governance these days is compliance with all the data breaches that keep occurring. The areas one thinks of here are:
- Regulations, such as GDPR
These require data inventories and audits to understand what personal data your organisation collects, where it is stored, how it is protected and who may have access to it. This is part of the picture that needs to be considered.
DAMA-DMBOK is an international guiding framework for the management of data. The framework includes areas such as:
- Data Strategy – defining, communicating and driving execution.
- Policy – metadata management, access, usage, security, quality
- Standards and quality – data architecture and data quality standards
- Data issue management – compliance, ownership, policy, terminology, data quality, data access
- Data management improvement projects
- Data asset valuation constantly define business value of data assets.
Consideration for the allocation of roles and responsibilities within an operating model helps guide the adoption of best practices.
In conclusion, managing data assets within a business requires it to be embedded in the culture of an organisation. Having high quality data leads to better business decisions. Having a core oversight function that is provided by a Chief Data Officer helps with keeping the day to day running of data in the fore front of everyone’s minds and you never know where the next innovation will come from.
- Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success - Book by Robert S. Seiner A Guide to everything you need to know about dark data
- A brief history of big data everyone should read
- A very short history of big data