As an example, in the pharmaceutical industry, as in most industries- most companies are now eager to learn more – who is buying their product, for how long is someone using it, etc. This information alone increases the amount of data, as hundreds of thousands of products are being sold in hundreds of thousands retail shops, or used in hospitals and doctors’ offices across the globe.

For the companies to be able to have visibility and make accurate business decisions, they need to bring all this data together which in most cases means an army of developers writing lines of code to achieve this. With developers writing, changing and updating code, and collecting data, the risk of human error is lurking and a million things can go wrong.

In the case of medicines, we have the simple case, when a doctor writes a prescription and then patient gets it from the pharmacy. There could be also the case when a doctor writes a prescription, the pharmacy prepares it but the patient doesn’t pick it up. In addition, a pharmacist could enter the wrong code or a developer could change something in the data that has an overall downstream effect. Errors in data can cost your company millions, resulting in missed revenue opportunities and exposing your company to unnecessary risk.

For companies to have confidence in their analytics, they need to be able to standardize, measure and monitor data quality and data governance, building a data pipeline that creates and sustains good data quality from the beginning - from the early stage of extraction to the final loading of your data into readable databases.

Improve Data Quality to Accelerate Data-Driven Digital Transformation

Similarly, to healthcare, banks and financial services organisations must identify and protect sensitive data, automate reporting processes, and monitor and remediate regulatory compliance. Data quality means trusted data and that is a key driver for digital transformation and smooth cloud adoption.

Validata ConnectIQ is designed to ensure that teams, across lines of business or IT, can easily deploy data quality for all workloads whether in on-premises, public or hybrid cloud environments. This essentially involves:

• Data profiling: the data is looked at in terms of quality, volume format etc.
• Data cleansing and matching: related entries are merged while duplicates are removed.
• Data enrichment: the usefulness of your data is increased by adding other relevant information.
• Data normalization and validation: the integrity of information is checked, and validation errors are managed.

ConnectIQ focuses on understanding data from a business context through AI and ML-powered automated processes like semantic discovery and classification, enforcing data integrity and data governance.