The governance of data and technology by IT gets a bad rap. Many see it as a necessary evil that prevents moving faster and more flexibly as conditions change.
When applied to analytics, however, there are a number of benefits that make governance more of a contributor than a hindrance to success.
The number one reason is to free the team to think big. As use cases grow in complexity and involve more systems and data sources both internal and external, the greater the chance that data, IT, and security leaders become aware of this work. If these projects involve data from customers or partners, or integration with customer-facing systems, these leaders will understandably put a stop to things if they have no idea what is happening.
You don’t want to put blinders on people tasked with developing insights and actions intended to improve business performance.
Ungoverned or “shadow IT” is the bane of CIOs responsible for aligning technology strategy with the goals of the organization. What they cannot see, they are still accountable for. Thus, it is essential that IT leaders understand the role and value of Analytics Automation. Much like they typically already do for BI and data science investments.
With an awareness like this, comes support. CIOs know the value of data-driven practices but many struggle to get a handle on scaling the value of analytics given a huge diversity of investments in people, process, and technology. Legacy, on-premise, and Cloud need to be reconciled and optimized.
To appreciate the importance of governance, consider too that as Analytics Automation projects increase in complexity, the potential for chaos arises if one or few users know the provenance of such use cases. Should a user leave the company or be on vacation and something goes awry, it is IT leadership that will be taking the brunt of questions from business stakeholders about remediation.
In many ways, to achieve the most value from Analytics Automation, it’s absolutely essential to align with leaders in data, IT, and security governance.
Final point: Leaders recognize the connection between Analytics Automation and Digital Transformation Success.
Organizations both large and small will ultimately employ dozens of technologies, tools, platforms, and human resources to power data-driven work. Those that do it best, align these investments with the priorities of the business such that each plays a clear role in supporting the path to digital transformation success.
When it comes to Analytics Automation, right now is the time to start thinking this way. The very definition of “Analytics Automation” is expanding to encompass cloud data engineering tasks (by way of Trifacta), and elements of traditional Business Intelligence (by way of Auto Insights). Making sure that the use cases supported by these new Cloud services complement and add value to other data and analytics investments is a good way to mirror the best practices of Alteryx Supply Chain customers who are achieving the greatest value.
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