Governance in Analytics Part 3: Enterprise Analytics

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Nowadays with all the advancements in information technology, organisations collect a staggering amount of internal and external data and have access to data at a much greater level than they ever did before. Data underpins everything from regulatory compliance to day-to-day operations and is one of the most powerful assets within an organisation.

To use that data productively, manage risks, and reduce costs, effective data governance is a must. As organisations evolve, the matter of governance inevitably comes up and due to the increasing focus and importance of data and analytics, it’s becoming a necessity that helps to drive data management across the organisation.

Data governance for enterprise analytics

Governance for enterprise analytics is about taking action. Because a core element of the business case for enterprise analytics investments is about improving decisions, governance becomes a critical issue to get right. Good enterprise analytics technology is built on good data science practices and this requires an organisation to continuously monitor, act, adjust, and update.

Many organisations are establishing enterprise-wide data governance functions fueled by an inflow of analytics to support inventory, accessibility, and to create actionable intelligence from their data. This process involves engaging with the right business users and assessing current processes and business needs and many of these steps are possible only through the enhanced use of technology as an enabler.

Governance in Tableau

In Tableau, you control two things: data and content. As data governance ensures the accuracy of the data driving the users’ decisions, content governance helps business users to confidently find, share, and use relevant data sources.

Governance in Tableau is an essential step to driving usage and adoption of analytics while maintaining the security and integrity of the data.

Organisations need to design governance models that comply with their internal policies and procedures, and that should encompass both data and content management processes. An agile approach is needed to adapt to new business requirements as user adoption and engagement increase across your organisation. The purpose of data governance in the modern analytics workflow is to ensure that the right data is available to the right people in the organisation, at the time they need it.

To define your organization’s governance models, you should work through the areas of data and content governance that are outlined in the Tableau’s diagram below.

Source: help.tableau.com

How can Softelligence help you?

At Softelligence, our expertise covers the most important Business Intelligence and Analytics platforms like Tableau, Power BI, and Qlik, so we can help you make the optimal decision to invest in a data analytics solution that can offer you a proper governance process that best suits your organisation.

Tableau is the obvious choice for the professional analyst. Nevertheless, for self-service analytics, Power BI covers most of the capabilities a casual user will need to work with data.

We invite you to have a look at our side-by-side comparison of Microsoft Power BI and Tableau and to see what you need to keep in mind when you are choosing the business intelligence and analytics platform for your organisation, in one of our previous blog articles that you can find here.

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Choosing the Right Self-Service Analytics Platform for Your Organisation »

Empower Business with Analytics and Business Intelligence Platforms »

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Resources:

  • https://help.tableau.com/current/blueprint/en-us/bp_governance_in_tableau.htm
  • https://www.forbes.com/sites/forbesbusinessdevelopmentcouncil/2020/04/15/the-power-of-data-governance/#5a889a4a7872
  • https://www.bmc.com/blogs/data-governance/

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