Data Quality is about having the right data, in the right place, for analytics and operational purposes
Ultimately, data quality is a business function that should be supported by IT.
Ensuring quality data is an ongoing function that may include a combination of automated data quality checks and validations, automated data cleansing and matching, and manual data scrubbing or remediation.
Good quality data is the foundation for successful operations, analytics and planning.- ensuring that the data supporting operations, decision making and planning are fit for purpose - irrispective of when, how or why they entered the organisation.
While tactical data quality approaches have a role to play at a project level, we suggest that data quality adds most value when it provides a consistent approach to ensuring fit for purpose data across the enterprise. Tactical approaches should therefore be alligned to the enterprise data quality strategy, which may, in turn, be driven by your data governance policies.
We assist you to ensure that the data that powers your business is fit for purpose, through a combination of data quality implementation, education and the enterprise data quality platform.
Our goal is simple: to deliver “peak condition” information fit-for-your-business, however, wherever and whenever you need it.
Enterprise Data Quality demands a solution that can scale to the needs of your enterprise, yet can ensure correct and consistent data at every touchpoint.
Our data quality solutions range from:
- the delivery of a quick data audit in support of any data intensive project,
- to a data quality strategy alligned to your data governance goals,
- to the implementation of full scale data cleansing and data matching capcbilities in support of
Enterprise Data Quality means moving beyond a limited, application-centric view of data quality to ensuring consistent application of data governance policaies and standards across all applications in your architecture.
Our consultants have delivered data quality solutions for a range of data areas - including Product/Materials Data, Financial Systems, Real Estate Management, HR/Employee Data, Supplier Data, Customer Data and Name & Address Data.
We have worked in a number of industries - including banking, insurance, government, telecommunications, hospitality, mining and manufacturing
We understand the complexities of managing African data - including multiple languages, minimal standards and a lack of reference data and our methodologies address these complexities for best results.
Whatever your data quality problem, we provide a practical solution:
- Once off Data Audit or ongoing Data Quality metrics
- Once off Data Migration
- Address Validation and Geocoding
- Data Governance and Quality for Regulatory Compliance
- Automated data cleanisng processes and services
According to Bloor Research, "the fundamental purpose of IT is to provide information to the business in a suitable format and in a timely manner; and data quality is fundamental to realising the value of that information".
They conclude that a data quality platform that can be directly integrated within in a wide variety of contexts - batch and real-time, at the point of data capture and the point of data extraction, in data migrations and in on-going database maintenance operations - is likely to be quicker to deploy and cheaper to operate than data quality components that are wrapped within a specific environments.
The first step toward success is taken when you refuse to be a captive of the environment in which you first find yourself.
Achieving peak-condition data requires a data quality solution that allows you to define and automate the consistent enforcement of your corporate data standards across business-critical applications, systems, and platforms. We have enhanced the Trillium Software System to provide rapid South African address data quality implementations
Our "out of the box" business rules for South African data have been deployed against hundreds of million of South African name and address records. Your data quality and data governance team can leverage these rules to ensure a best practise deployment of data cleansing and matching without having to re-invent the wheel - saving thousands of development hours and ensuring rapid time to value for your dat aquality and master data management projects.