Although a single use case often justifies an investment in Alteryx, it’s customers that apply Alteryx to many use cases that realize the greatest value. Getting to that point is not always obvious and so learning how others achieve this level of maturity often paves the way for those just considering the benefits of Analytics Automation.
It’s also true that data-driven excellence is a journey that most organizations have only just begun. Not only is the ideal state a moving target as the competition continues to improve, but the work of business simply cannot stop while a harmonious approach to data and analytics is implemented.
Particularly within supply chain functions like demand and supply planning, as well as procurement, improvement must be pursued in parallel to any enterprise-wide transformational initiatives. The realities of today’s business climate dictate that supply chain decisions reflect the most current demand and supply conditions – which is no guarantee given ongoing pandemic-driven disruption, changing consumer behaviors, economic uncertainty, and the typical timelines associated with technology projects.
A good example of this is Amway, a customer using Alteryx in many areas of its business but especially within the global supply chain. Here, Director of Global Demand Planning and Supply Chain Analytics, Andrew Sawhill, oversees the application of Alteryx to support a variety of decisions that serve a diverse audience of supply chain stakeholders.
The approach applied at Amway sets a good example for others considering Analytics Automation in any setting – supply chain or otherwise – and Andrew has been kind enough to share his experiences with us in this Q&A.
Thank you for taking the time to answer a few questions about your experience developing the supply chain analytics muscle at Amway. I previously talked with Casey Koopmans about his Alteryx supply chain journey at Amway, but the Supply Chain Analytics organization started working with Alteryx earlier. Can you describe how Amway discovered Alteryx and identified initial use cases?
Thank you for the invitation, Gib – it is my pleasure to speak about our analytical journey with Alteryx. Amway came across Alteryx about six to seven years ago, in an effort to automate and streamline the creation of standard KPIs and reports that were being done manually at the time. Like most things new, Alteryx was introduced through one or two individuals trying it out to solve for these use cases.
Analysts were spending hours, days, and even weeks collecting reports, extracting flat file downloads from a legacy BI report writer, and stitching them together into a common spreadsheet file, where the data cleansing and preparation were done through manual effort and formulas. The earliest successes were simply replacing these arduous workflows with Alteryx to speed up the data harmonization steps; thus, freeing time that was spent on getting data and allowing more time for analyzing data at new and deeper levels.
Access to good data is the heart of any analytics project. How did you go about ensuring that initial use cases were supported with the right data?
This was a big challenge early on. As I mentioned earlier, our initial use cases focused on replacing manual data preparation and aggregation steps with Alteryx workflows. In the first year of deployment, we used our legacy BI report writer to extract basic data and connected Alteryx to those files for the transformation and loading steps – clearly not the most effective method to harness the power of Alteryx. From there, we partnered with our technology organization, connecting directly into the data warehouses, and improving the data extraction process. This still left the ELT (Extract-Load-Transform) process rather clunky, as we were moving larger amounts of data faster than ever and needed a more robust way to store that information.
We then made the decision to invest in a data lake, that our team managed, to improve the up-front extraction and collection of data. We felt strongly that this was necessary to provide the autonomy and agility required of us to drive innovations through data and support the business. We also partnered with other analysts around Amway to standardize jobs and data sources to reduce the amount of duplication amongst the analytical community.
This has been a multi-year journey as Amway, like many companies, has a portfolio of systems across the globe. However, this effort has allowed us to move from a data paradigm that was manual, latent, and only of basic visibility to one that is automated, refreshed daily, and offers visibility at the lowest levels of granularity. Having this depth and quality of data has been a game-changer in how quickly we are able to observe and diagnose issues, leverage more advanced capabilities (e.g., operationally embedded multi-echelon safety stock modeling), and explore new avenues of innovation that were not previously attainable.
How was success determined early on, who needed to be persuaded, and what lessons were carried forward as you rolled out Alteryx globally?
For the supply chain at Amway, the need to improve the breadth and pace of innovation in our analytics was well recognized. We also knew that the depth to which our base KPIs described the business was no longer sufficient. It was in these areas that we focused our success in the application of Alteryx. Like most disruptor innovations, things started small, focusing on the areas ripe for improvement with an intent to extract value early – whether that be efficiencies in work or introducing a new analytical capability.
To prove out the ROI of this effort we tracked business cases for each user, with a goal to understand the impact of the solutions, the hours we were eliminating, and the frequency of data refreshes. In the supply chain, we moved from spending hundreds of hours reporting out on monthly KPIs to data process automation and near real-time information for our customers to make decisions. Once we hit over 1000 hours saved, we stopped tracking and partnered with our technology group in signing the first enterprise license agreement with Alteryx.
From there, momentum was built mostly through a grass-roots approach within the different areas of analytics across Amway. We also partnered with Alteryx in holding company-wide training, showcases, and competitions that really helped a broader user base see what was possible. Today, we have Alteryx Designer licenses being used widely across the company and in many different functions, enhancing the speed and pace of Amway’s ability to translate information into insights. In order to support these functions, our Server environment processes over 1,500 unique workflows across a 90-day horizon.
One of the key lessons we carry forward with us today is the notion of data democratization. In today’s world of continual disruption, any enterprise will need to continually pivot on the tactics it uses to execute its strategy. To carry this out in a sustainable way, consistent, connected, and actionable data is needed. From the very onset of our journey, we have subscribed to this concept; we insist on sharing data openly across functions and focus on developing common sources of truth that all may access and use to make decisions.
Benefits we often talk about are the re-use of Alteryx project assets (workflows) to speed adoption and value, and also the oversight and governance possible when a customer uses Alteryx Server alongside Designer users. Can you explain the way you architected Alteryx in the supply chain at Amway to facilitate decision consistency and recruit others to leverage Alteryx?
This is an ongoing ‘next step’ evolution in our journey in the use of Alteryx. Even in leveraging our data lake, there were still more things we could do to simplify and speed up the end-to-end process – focusing in on the data curation piece first and foremost. Early on, we were mostly extracting copies of all the data tables from our different point systems and housing them in our data lake, thus requiring analysts to understand all those elements and curate them separately for their individual needs. The result was ‘dashboard proliferation’, with solutions for a given domain attacking the transform/load steps in slightly different ways. I think this is a fairly common evolution, and perhaps a bit necessary, as we were still growing in our understanding of use cases for enhanced analytics and the evolution of our metrics with new capabilities.
In recent years, we have focused on establishing curated layers of data that offers a more simplistic view of each data domain. This will speed up onboarding and training for new analysts and standardize those up-front transformation steps so that different solutions in a given domain are working from a common, foundational data layer. We are also able to leverage these digital assets outside the supply chain analytical community. For example, if our finance department wishes to connect to our inventory or procurement spend data, we offer these curated layers as the connection point, rather than the raw data upstream in the process. Again, this speeds up deployment of solutions, consistency of the analytics, and improves the pace of value-capture across the enterprise.
Amway is among our most experienced customers, but it’s been a journey to get to your current state. With so much focus today on supply chain improvement, what can you recommend that customers new to Alteryx do to accelerate their time to value?
To get started, focus on use cases that have the highest index of manual effort, lowest quality of output, and greatest latency in producing actionable intelligence (or some combination therein). Focus on delivering value as quickly as possible and building out your confidence in the use of Alteryx, even if imperfect or on a smaller scale. This is a journey and not a destination; you will never be done iterating, improving, and expanding your capabilities. As users and use cases expand, focus on skill set maintenance; partner with Alteryx in training and consultation, and utilize third parties to help you think through the more difficult questions to tackle.
Beyond this, you will need to partner closely with your technology organization to ensure there is good alignment across the board in data management strategies that enable both the autonomy and agility you desire as well as the standardization and governance needed to ensure a robust, secure, and cost-effective ecosystem. Tools like Alteryx blur the lines between what we used to think of as the traditional division of labor between technology and the business. Questions of governance, scalability, security, cost, decision rights, workstream ownership, and handoffs need to be reconciled with interests in data democratization, analytical autonomy, skill set needs/gaps, use case application, change management, and workload prioritization.
This is something we are currently working on at Amway, as we continue to modernize our data infrastructure and determine the best strategy to manage the end-to-end analytical process in a more robust ecosystem and support the wide range of use cases we have across our community. This is a transformative evolution, and we are partnering with our Technology organization closer than ever before, focusing on the simplification, standardization, and governance of the core data sets across the organization.
These are not always easy circles to square, but imperative to ensure the long-term stability and scalability of the data and analytical capabilities in your company.
Check out Amway's Success Story here.
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