Governance Considerations for Democratizing Your Organization’s Data in 2021

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^[1]
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^[2].

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^[3] helps you focus on your
data instead of worrying about deployments and monitoring. Data
security is an important part of data access policies^[4], 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^[5], anywhere, so it
naturally allows account admins to customize user roles^[6]
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^[7]
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.