Leaders Manage Analytics Automation Together With Their Business Intelligence and Data Science Teams

Technology   |   Gib Bassett   |   May 10, 2022

[Editor’s Note: This is two in a three part series featuring Analytics Automation and its role in digital transformation for Supply Chain.]


A good reason to start the journey via an Analytics Automation Center of Excellence is to ensure alignment with other investments your organization makes in analytics — specifically, Business Intelligence (BI) tools that support reporting, dashboards, and KPI measurement, and the previously mentioned data science team.


When all these resources are aligned and managed as a unit to serve data-driven outcomes, the benefits accrue quickly.


For example, while Analytics Automation speeds any element of an analytics use case, it’s not usually the standard-bearer for communicating results. This is typically the purview of the BI team, which specializes in the art and science of visual data analysis and interpreting insight at scale. The Tableau community is a good example.


For the Analytics Automation team, BI is complementary. Not only must results and outcomes from use cases be communicated to their stakeholders, but the team itself must maintain oversight of its use case portfolio so as to communicate business value to the c-suite. The availability of Auto Insights, formerly Hyper Anna, supports a use case like this.


Placing Analytics Automation expertise alongside data science has its advantages as well.


First, there are the use case requests that filter into data science, that would be served more ideally through a low-no code approach. This is now easier than ever with the availability of Designer Cloud and Alteryx Machine Learning (also Cloud-based). Unless the data science team understands the possibilities presented by Analytics Automation, they cannot discern cases when their time is better spent on other projects.


When Analytics Translators permeate the organization on behalf of the data science team, they can act as brokers to align the right solution with the right use cases.


Secondly, understanding the possibilities of Analytics Automation highlights to data science the potential to speed their own custom-coded work. Data-related tasks still consume far too much time in the data science process and low-no code tools can speed these steps for workers of varying skills and educational backgrounds. Another new cloud service now part of the Alteryx Analytics Cloud, Trifacta, can help to this end by speeding and simplifying the migration of a data warehouse to the cloud.


The same goes for machine learning and predictive modeling work, which benefits from faster prototyping in the early stages of projects when hypotheses are being formed and after when results are validated, and tuning may be necessary.


Read the first blog in this three-part series: The Adoption and Business Value Practices of the Best Alteryx Supply Chain Customers.