Governance Considerations for Democratizing Your Organization’s Data in 2021

Cyber Security

With the continuing rise of IoT devices, mobile networks, and digital channels, companies face a lot of pressure to generate meaningful and actionable insights from the wealth of data they capture. Gartner Research lists data democratization as one of the top strategic technology trends to watch out for.

While empowering non-technical users to run ad-hoc reports gives enterprises the ability to get closer to business conditions, it also introduces problems of data governance and privacy compliance.

All reports are only as good as the data they’re based on, and non-technical users might not be aware of the need for data integrity and security. Even the “experts” at cybersecurity firms have been known to leak files at alarming rates.

Organizations need to implement strong data governance strategies to ensure their data is accurate, reliable and secure, while continuing to provide their employees with the resources they need to realize the full benefits of it. Here’s how they can achieve this goal.

Create Data Accessibility Policies

Traditionally, IT departments have handled all data requests and have been tasked with ensuring data integrity and privacy. With data being opened up to everyone in the organization, this structure no longer works. IT cannot be the sole owner of data anymore since this only creates workflow bottlenecks.

For example, a business user who wants to incorporate real-time data into parameterized templates will have to request IT to perform this task, despite having the ability to run reports themselves. True data democratization goes beyond giving business users the ability to run a few standard report templates. It empowers them to gather any metric they deem relevant and use it to enrich their existing reports.

Transitioning from an IT reliant workflow to a democratized workflow is best achieved by creating a BI champion team. Adding members from both business and IT functions to this team will help accelerate change. In addition to this, use tools to define and automate your ETL and data integration clearly.

ETL and data pipeline tools such as Xplenty helps you focus on your data instead of worrying about deployments and monitoring. Data security is an important part of data access policies, and Xplenty helps you customize workflow access. Give your BI team to write access to data at first and slowly integrate other roles to this function, as needed.

Note that data ownership and accessibility are different things. Giving the entire organization access to data does not mean they own it. Create teams that are tasked with owning data and have them monitor their quality throughout the organization.

Limit Visibility Through Permission Management

Data visibility policies are a key component of data governance. Your policies have to ensure that users access data that they’re capable of understanding and are skillful enough to make sense of.

Some organizations assign admin-level permissions to top management by default, but this isn’t the best policy to follow.

Data access has to be based on operational needs and relevance. Your data governance tool must have robust user permission and role module that allows you to define access and customize roles as needed.

The best self-service business intelligence platforms offer these governance settings out of the box. Sisense, for example, is made for building interactive reports and dashboards that can be accessed by anyone, anywhere, so it naturally allows account admins to customize user roles based on a variety of actions.

Using Sisense as a cloud analytics platform for business teams, you can create user groups based on data access, and this makes it easy for you to carry out user admin tasks. Data visibility extends beyond access to ad-hoc metrics. You need to extend it to dashboards and information extraction.

These functions are extremely relevant in implementing a strong data visibility strategy. Conduct regular reviews of your visibility risk and use the right tools that will help you implement your policies.

Use Master Data Management

To achieve true data democratization, you have to ensure your data is of top-notch quality. There’s no point in giving your entire organization access to irrelevant or unhygienic data. Implement a data management oversight system to ensure this doesn’t happen. Begin by gathering all data available and verify it for quality.

This doesn’t mean you should run quality checks every single time you run your ETL process. However, run periodic checks, especially on user-inputted data, to make sure your quality guidelines are being met. You should also develop clear policies on maintaining datasets for specific attributes.

Tools such as Truedat make this an easy task. The platform allows you to monitor data through every stage of its lineage for quality and other governance-related principles. You can connect your data sources using visual dashboards and monitor your data’s profile. It can be implemented as an on-premises or cloud solution with seamless integration.

One of the strengths and weaknesses of democratizing your data is that everyone will have the ability to query it (within visibility rules) and analyze it. This can lead to false conclusions and the creation of irrelevant data. Make it a point to understand and review the purpose of the data you’re trying to use.

Not all pieces of data are relevant. A little data science education and awareness go a long way towards managing your data’s quality.

Use Self-Service Analytics Routinely

To implement proper data governance, you have to install a culture of valuing data and the insights it can provide. Make self-service analytics a routine part of your organization’s decision-making process.

The more you empower your line-of-business users to use analytics and consider data from different angles, the more you educate them about the importance of data governance.

Opening up your BI platform to users throughout the organization isn’t enough. You need to back it up with proper training in the art of data analysis.

Novices to data science tend to generate a ton of false positives and end up validating their own confirmation bias. Creating collaborative teams that have technical and business users is a step in the right direction.

Better Governance for Better Insights

There are many data governance strategies for you to choose from so take the time to validate each one’s effectiveness for your organization. A good strategy gives you both a solid baseline for data management and the flexibility to implement customized solutions. In today’s data-driven business environment, a governance strategy is more important than ever.

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