Data product mindset powers a new critical expense management dashboard
A U.S. manufacturer gains trusted insights and a sustainable foundation for future data products
A U.S. paper and building products manufacturer relied on a high-visibility analytics tool to manage maintenance and operations expenses, but accuracy issues and fragmented views and reporting limited its usefulness.
Working with NTT DATA’s Data & Artificial Intelligence team, the company established a single source of truth not only for the re-invented version of its expense dashboard, but also for future analytics applications. The team used Amazon Redshift to build a data layer and robust data model that integrates data from four accounting/ERP systems — and then leveraged visualization tools including to PowerBI and Tableau to create user-friendly dashboards and reports that support informed decision-making. Unified data platform rapidly increased the company’s data self-service capabilities along with user’s trust in and adoption of the new analytics tools.
Besoin de l’entreprise
As one of the world’s largest manufacturers of paper and building products, this U.S. manufacturer relied on a dashboard system to manage approximately $400 million in maintenance and miscellaneous operations expenses across several multibillion-dollar production facilities.
When the company needed to update and expand its maintenance dashboard, it became clear that its data environment and processes didn’t support a next-level iteration of this analytics dashboard.
Challenges included:
Accuracy issues. Existing reporting was connected to multiple fragmented data sources that were missing key information. Insights were often contradictory and the process to resolve discrepancies was inefficient, driven largely by ad-hoc conversations. The business needed all actuals to tie to its future central FP&A system (Infor d/EPM).
Fragmented views and reporting. Because different facilities had different ERP systems, teams were using multiple reports and dashboards to look across production facilities. And business users complained about the "clunky" user interface. Unless you were highly trained on the dashboards, getting an accurate and holistic view was a time-consuming and arduous process — and not always a successful one.
Major transition on the horizon: In the coming months and years, a number of facilities would be phasing out current systems and adopting Infor M3 for accounting and ERP. During this transition the company would need a solution that could pull together a complete view of financial transactions from across the different segments. Doing so would put everyone on the same page, speaking the same language.
Résultats
- Gains an easy-to-access single source of insights for better decision making
- Sees rapid adoption by users that rely on financial transaction data
- Adopts a robust model that is adaptable, data-rich, and trusted
The NTT DATA team enabled the client to achieve trusted insights in less time, rapid user adoption and a sustainable foundation for future applications.
Solution
To create a more powerful and reliable version of this critical dashboard, the client would need to resolve the underlying disconnects in its data environment. It also demanded a multidisciplinary team of business analysts, program managers, visualization specialists and data engineers using an agile approach that focused on delivering value in an iterative manner.
As a first step, NTT DATA worked with the manufacturer to gather requirements, ask users how they use the dashboards and develop a backlog of user stories to inform the creation of the new analytics products. From there, visualization specialists from NTT DATA used the information to build wireframes that showed the business what was possible.
With client approval in hand, data engineers got to work. Using Amazon Redshift, they built a unified and scalable data layer and robust data model that integrates data from four accounting/ERP systems. From this single source of truth, the business can easily develop future analytics products (beyond just maintenance) and greater self-service capabilities.
- The model had 13 different dimensions focusing on the key activities — such as vendor service agreements, purchase orders and contracts — in the maintenance lifecycle at each facility.
- The model contained four facts to ensure an accurate depiction of financial spend, including fact tables for forecasting and commitment values.
- The model utilized a modern, flexible cloud platform (Redshift) with the latest technology (Glue and S3) to source, transform and further refine data for increased quality.
The NTT DATA team also assisted in the roll-out campaign for the new analytics product to help end users better understand and trust the data layer behind it and drive adoption. The team showed the manufacturer how to navigate the new dashboard, how it would improve visibility and accuracy of insights, and how the company could save time and effort for the business — and its employees — by using it. The teams implemented a change management system to evaluate user adoption and track dashboard usage to targets.
By looking beneath the surface-level dashboard enhancements that were initially on the table and applying a product mindset to the delivery of business insights, the teams achieved:
Trusted insights in less time. The new dashboard product replaces the manually intensive legacy reports of old, providing an accurate and easy-to-access single source of insights for maintenance expense reporting and decision making. Fragmented reporting and data discrepancies are now a thing of the past.
Rapid adoption. In just the first two weeks, nearly 100 users flocked to the revamped maintenance dashboard — from executives seeking cross-facility insights for decision-making purposes to facility managers and FP&A professionals requiring more granular details on spend in their areas of responsibility.
Sustainable foundation for future applications. The maintenance data product is built and primed for analytics development. If new dashboards are requested — examining contracts, material requests, new commitments vs. maintenance spend, etc. — the foundation can be quickly and efficiently expanded because it’s grounded in an adaptable, data-rich, and trusted data model.
About the case study
A U.S. paper and building products manufacturer developed a single source of data truth, enabling the business to gain more decision-making power from an essential dashboard, develop future analytics products with ease and expand user self-service capabilities.