[Editor’s Note: This is one in a three part series featuring Analytics Automation and its role in digital transformation for Supply Chain.]
With hundreds of thousands of passionate users spanning many thousands of organizations, Alteryx benefits from a user community unlike any other.
Equally impressive are the gains made by these businesses and public institutions using Analytics Automation to improve all manner of decisions. While Salesforce pioneered the advantages of low and no code CRM, Alteryx has essentially done the same for analytics.
Like anything, however, some do better than others and it’s that incremental lift that makes the difference between the best in class and everyone else.
Every organization is unique, but those considered best in class applying Alteryx to Supply Chain challenges share several important traits.
For any executive, especially those in the Chief Data or Analytics Officer’s seat, the characteristics of these Analytics Automation leaders should be of high interest. The simple fact is, that almost every organization can make incremental improvements to decisions happening right now that lead to significantly better outcomes. It’s just a question of prioritization and a cost-benefit exercise. Whether you are a current or potential Alteryx customer, here are three best practices of leading Alteryx supply chain customers.
Leaders organize centrally but execute locally
There are many ways to organize people tasked with analytical work, but one of the most popular is a centralized approach, or a Center of Excellence (COE). In this model, experts huddle together to work on use cases relative to the organization’s priorities. They necessarily must partner with the business to understand requirements and support the rollout and scale of successful projects.
Data Science, or those working on more custom projects using combinations of coding, open source, and commercial software, has traditionally organized centrally due to a scarcity of expertise. This resulted in fewer but bigger bang projects.
Over time the role of “Analytics Translator” emerged as an overlay between data science and the business to filter use cases into the funnel while also helping the organization adopt and scale successful use cases so that business value depended on fewer and often risker moonshot projects.
Analytics Automation is a different animal. Workers of any skill level have the potential to execute a use case using an application-centric approach (low-no code). Up to and including tasks more commonly associated with data science, such as machine learning model development, testing, and validation.
If you are an Alteryx Designer user, this scenario may read familiar. You very likely adopted Alteryx with an eye on solving a problem, be it a complex or error-prone spreadsheet analysis or addressing the data preparation steps behind an important insight or predictive model.
You essentially adopted Alteryx in a manner opposite the COE model, by addressing a specific problem or opportunity that you identified yourself, or on behalf of some of your direct reports.
Some customers that started this way eventually landed on a blend of COE and self-discovery approaches. You might call it a “Center of Analytics Automation” or CAA, which involves a small group of power users and leaders tasked with enabling workers across the organization to achieve business outcomes with Analytics Automation.
In this model, users populate the business to work independently to address data and analysis challenges. Finance, Supply Chain, Sales, Marketing, Service, e-Commerce, Human Resources, and beyond. Stated another way, democratizing analytics.
The CAA team owns the training and enablement of users to this end, while occasionally being tasked with projects their expertise demands — ones of greater complexity than is typical, or that filter down from the c-suite of a strategic nature not suitable for the data science team.
While experts themselves, members of a CAA team value third-party perspectives and employ services partners with Analytics Automation practices. Consulting companies have experience beyond the confines of a single organization or industry. These relationships offer paths to training, best practices, and new ideas that can be leveraged across the entire user community.
Ultimately, the CAA team helps the organization appreciate the value by aggregating and communicating results — especially to executives who need to see the contribution of Analytics Automation to data-driven transformation. Pragmatically, this supports continued investment by the organization and makes Analytics Automation a worthwhile career pursuit.
Customers with hundreds or thousands of Alteryx user licenses will often organize this way. Not all started in this fashion. Many began adopting Alteryx from a grassroots level to address a use case personally important, then over time as their expertise grew, discovered other applications for Analytics Automation in their functional area (often Finance or Supply Chain).
Yet the best find their way to a COE-style model eventually, having learned that the best path to scaling value is to enable the entire organization to benefit from Analytics Automation. It goes without saying that these customers wish to have started at this point since it would have accelerated their success. Which leads to the next best practice.
Check out this report by IDC on Enabling Growth and Resilience in Supply Chain.
For more use cases, get the Use Case Discovery Guide.