Industry Viewpoints

In Data Privacy We Trust

By Adam Mayer

Headshot for blog author Adam Mayer

Adam Mayer

3 min read

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January 28th is Data Privacy Day, an international effort to empower individuals and encourage businesses to respect privacy, safeguard personal data and enable trust.

In fact, it so important that the National Cybersecurity Alliance has a focused program for a full week.

Data privacy is not just a legal concern, it concerns everyone. With data privacy regulations such as GDPR, CCPA, and LGPD spanning many regions across the globe now, it has become more important than ever for organizations to ensure they are responsible in how they use customer data or risk serious impact to brand, trust and financial repercussions.

Data privacy should be front of mind for CIOs, CDOs and every individual who touches data within the organization. While data literacy programs and data governance controls are important, there’s a growing need to ensure the highest veracity with your data and have a deep understanding of the lineage to build solid data foundations. Ultimately, a holistic approach should be adopted to ensure that organizations can harness real-time data insights without data privacy issues arising.

I like to use Data Privacy Day as a timely reminder to look beyond the usual access controls and think about other use cases in the world of data and analytics that help support compliance.

When moving data between environments such as Testing and Production and vice versa, particularly with mission critical systems like SAP, ensuring personal and sensitive data (PII) is easily identifiable will help reduce the risk of regulatory non-compliance. It’s important to avoid any PII data being moved outside of Production environments or make it anonymized it in some way if it must. Qlik Gold Client is a big boon to any SAP customers for their test data management, as well as helping to enforce the right to erasure.

If you are considering modernizing your data warehouse for better analytics and embracing AI projects, when moving data from a wide variety of source systems to cloud targets and data platforms such as Snowflake, having good data transformation pipelines that are automated and provide good visibility across the linage will be key to building trust in your data.

Ensuring Data Quality and Governance are underpinning the whole process throughout will improve the accuracy by eliminating dirty data, further increasing trust. I’d recommend following the first principles of the DAMA framework for Governance, data security and more.

The Qlik Talend Data Integration portfolio can help automate data transformation pipelines with governance and quality all taking a no code, low code or code as you want approach; Talend Data Catalog offers rich metadata management in depth as well as PII detection and sensitivity labelling capabilities, Talend Data Inventory helps you keep track of all your data health including PII, via a trust score, Talend Data Fabric offers comprehensive ETL data management and Qlik Cloud Data Integration can help automate data warehouse and data lakes with ELT push down SQL.

Good data management is the bedrock of a good data strategy, even more so if you are considering any GenAI based projects. Building out your training data with Large (or Small) Language Models (LLMs) with good quality data is a must to avoid inconsistent outputs and hallucinations – because AI is only as good as its data.

Now you have your data in trusted order, analytics programs, such as Qlik Analytics, can help IT teams visualize who has access to what information and if that remains relevant to their role. For instance, this could be through bringing together disparate data sets on user access controls and HR lists of leavers, starters and changers to ensure that there are no anomalies where people retain access to information that is no longer appropriate to their role.

Analytics can also help promote good organizational data hygiene, by reporting on any personal or customer data that is being held that may no longer be needed, so it can be marked for deletion in line with data retention and other policies.

Trust in your data comes with better governance, origin, and lineage which in turn will generate more trustworthy and actionable insights down the line.

Learn about Qlik’s commitment to Trust & Privacy

Find out more about Qlik’s Data Integration and Quality Solutions Data Integration & Data Quality Solutions | Qlik

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