Good data takes serious organisational commitment – get started with these simple steps
Journal
Anyone that has tried appreciates that achieving good data across an organisation is not easy. The quality of data is key to an organisation's strategic decision making, agility, productivity, and survival. For regulated organisations in financial services sectors such as superannuation, banking, insurance and wealth management, data quality is becoming more important because the consequences and risks of making incorrect decisions are now far greater.
As an executive in your organisation, you play a key role in influencing and decision making.
How do you tell shareholders that business decisions and more importantly risks undertaken were managed and informed by data, analysis, and information in which there was a sub-optimum level of confidence?
How could you explain to regulators the information your organisation supplied has holes all through it?
The most significant factors for achieving quality data relies on strong data governance processes with sound data validations from the get-go, a well-designed data model, and metadata taxonomy. There also needs to be alignment between the business needs and goals and with the IT architecture design and deployment plans. Key business sponsors must also be engaged.
Steps to ensuring data quality success
Build a single point of truth or data quality definition, understand where to find data, what it means, and how to use it within business and technology teams.
Establish sound change management policies for the single point of truth or data quality definition, quality rules and data models.
Identify the golden / primary source of data and avoid keeping duplicate copies of data.
Establish data standards (i.e. standard formats for dates, customer reference numbers, allowed values, alphabets, codes etc.) and leverage reference metadata to reduce variations in values.
Keep data validation rules and processes close to data ingestion processes as much as possible.
Invest in good data quality tools that can be used across departments.
Setup automated schedules to detect data quality issues which can be then resolved as early as possible therefore reducing the compounding effect on data quality issues.
It may be necessary to establish a data quality team with a primary focus on building key performance metrics, tools, and controls to monitor data quality. The team can collaborate with data administrators and data governance teams to re-enforce quality standards and provide feedback into changes proactively. Using software automation will ensure data quality “scope creep” is kept to a minimum and managed accordingly.
Big data providers must take steps to ensure that data held within their organisation is accurate, up-to-date, complete, and relevant. Getting to the point where your data is good takes commitment, effort, and investment.
