Why You Need Data Governance for Analytics

How can you ensure data quality and security across your data analytics pipeline? With data governance – the exercising of authority and control over your data assets. It includes tracking, maintaining and protecting data at every stage of the lifecycle.

Bad, incomplete or incorrect data is more than annoying – it undermines everything else a business is trying to accomplish. You could have the best information systems, but they won’t give you the right results when the data is wrong. If you can’t trust the information, nothing else matters.

“Without good data, you’ve likely been making decisions with an incomplete picture of where your organization stands, not getting the results you expect or developing strategies that lead to losses,” says Jacqui Visch, Chief Digital Information Officer, PwC Australia. “This is data debt. It wastes time, costs money, undermines decisions, causes platform stability issues and can render cutting-edge technology moot.”[1]

Regulations such as GDPR and HIPAA are also pushing requirements for better data management, privacy, security and controls. Running counter to regulatory mandates can result in crippling fines and business costs. The more you share APIs and data, the more you need to protect against these failures.

Data governance to the rescue

The best way to turn your data trust issues around is to implement a data governance framework. It encompasses processes, policies and tools to manage the security, quality, usability and availability of data throughout its lifecycle.

Automation plays a big role too, says Oskar Grondahl, Senior Director of Product Management at Qlik.

“Data governance processes need to be automated. If you’re relying on people to perform manual processes in order to achieve a properly governed data landscape, you will never have 100% coverage. If you manage to achieve 90% effective governance, you will still have that 10% uncertainty looming over all your decisions.”

Modern data delivery tools can automate most aspects of a governance initiative. With automation, you can embed rules and policies to manage data discovery and inject quality improvements early and often. Encryption and authentication also prevent users from seeing what they shouldn’t.

In addition, a governed data catalog can document every data asset and control who is allowed to take action on which information. The catalog uses metadata to build data lineage views, while validation and profiling measures define the content quality. Data schemas are used to manage, secure and control access for different users.

According to Joe DosSantos, Chief Data Officer at Qlik: “By profiling, cataloging and managing access to data, you can ensure that the right data is accessible to the right person at the right time. With the correct measures in place to do this, it is possible to create an effective data pipeline that provides everyone with access to well-structured data sets and accurate insights.”

Reliable analytics pipelines

Qlik Data Integration automates the entire data analytics pipeline, from raw data ingestion to publishing analytics-ready datasets. The platform delivers clean data using features like deduplication, standardization, filtering and validation. It includes a governed data catalog too, which allows users to access trusted content for their analysis and data exploration.

By furnishing technology that can enable a data governance framework, Qlik may be just the partner you need to raise the level of trust in your data and analytics.

To learn more about data governance strategies and best practices, download the white paper here.



[1] https://www.pwc.com.au/digitalpulse/data-debt-transformation.html

Bad, incomplete or incorrect data is more than annoying – it undermines everything else a business is trying to accomplish.

Keep up with the latest insights to drive the most value from your data.

Get ready to transform your entire business with data.

Follow Qlik