Data quality is about having the right data, in the right place, for analytics and operational support.

Errors in data mean much more than bad decisions.

For every business process, data errors create risk and increase costs.

We provide sustainable and cost-effective solutions to deliver trusted data, no matter where it enters and how it flows through your enterprise.

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 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 - irrespective of when, how or why they entered the organisation.

While tactical data cleansing approaches have a role to play at a project level, we suggest that a consistent, strategic approach to ensuring fit for purpose data is needed at an enterprise level. Data standards and policies defined and governed at an enterprise-level ensure that the impact of data changes is clearly understood, even across business silos, and that data cleansing exercises are optimised for multiple purposes. 

We assist you to ensure that the data that powers your business is fit for purpose, through a combination of implementation, education and the enterprise data quality platform.

eBook

eBook

Governing Volume: Ensuring Trust and Quality in Big Data

Register

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 solutions range from:

  • the delivery of a quick data audit in support of any data-intensive project,
  • once-off data migrations and cleansing projects
  • a data quality strategy aligned to your data governance goals,
  • batch cleansing and matching projects - for example, to support a data warehouse
  • application data management with native plugins for popular CRM and ERP systems such as Salesforce®, SugarCRM®, NetSuite®, Microsoft Dynamics® and SAP® to ensure trusted, reliable and consistent data.
  • data validation and enrichment at point of capture - address validation, geocoding, telephone and email validation.

We help you to move beyond a limited, application-centric view of data quality to ensuring consistent application of data governance policies and standards across all applications in your architecture.

CIMP in Data Quality

CIMP in Data Quality

Data Quality courses and certification

Get started

15 years data quality experience

Our consultants have delivered 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 integrity 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 cleansing processes and services
Case Study

Case Study

Absa Capital Client Static

Download

Master Data Management

Master Data Management

Create consistent, quality master data for reuse across systems and processes

Integration

Whitepaper

Whitepaper

Data Quality gets smart

Register free

Precisely Trillium

Precisely Trillium

Enterprise Data Quality Platform

Explore

Precisely Spectrum Quality

Precisely Spectrum Quality

Machine-learning Data Quality

Explore