How Data Digitalization Makes For A Self-Funding Journey

In this age of rapid digitalization, automotive and industrial manufacturing companies are focusing on cleansing and unlocking the value in their master data.

However, master data management programs often fall short of expectations because the manufacturer tries to solve for everything or fix the entire company’s data infrastructure in a single, broad initiative.

Managing master data (which includes item master, vendor master, and pricing master) is more effective when the data management needs are identified based on business processes the company wants to improve or digitalize. Enterprises should not tackle all master data at once; they should prioritize core initiatives by starting with the basic functions: budgeting, planning, performance management, pricing analytics and risk management.

Importantly, this sprint-by-sprint or pilot-by-pilot project-based approach enables the company to make data digitalization a self-funding journey through the savings delivered over time.

Manufacturers can get the best out of master data management by focusing on these business process improvements:

1. Defining the data strategy

Starts by identifying and prioritizing the core initiative and involving cross-functional teams, such as IT and the end-user communities, to strategize on data architecture and enterprise-wide data planning. While the focus in on the core initiative, it is also important to have a data strategy that is scalable across functions, locations and organizations and is flexible and reusable.

The data strategy should factor in the company’s existing systems and tools. What will happen to the legacy systems? Can they be leveraged, or should new tools be added?

The strategy should also consider data quality and data security because those aspects will determine who owns the data before it goes into the data lake where it will be stored in its raw format.

Decide who will manage the data quality and how will the data be secured. It is also important to define who will have access to the data and at what levels. Companies must find a balance between what to share and what not to share.

Lastly, governance strategy ties everything together: from data retention policy to uploading policy.

2. Data conversion design

This step involves assessing the existing tools, architecture, and data landscape (keeping in mind design, quality, security and governance) as part of the data design. Since not all the data will be in the same format, it must be converted into a common denominator, followed by a “source-to-target mapping” to understand the data source and make it “a single source of truth”. The data will also have to be reconciled at a certain frequency (e.g., every three months or six months) and validated. A proper reconciliation requires a robust change process. Once the business or core team sign off on the data design, the implementation road map can be developed.

3. Implementation roadmap

The implementation process is based on sprints or a set of pilots. It starts with one area and then moves to the next. A 360-degree closed loop is created with steps such as testing, feedback, improvement and implementation. As part of the process, there is analysis for potential sprints and pilots for business priorities. Cost savings insights are used to build a multi-stage roadmap, which is also self-funding.

Once the pilot is ready, the user community should be made aware of the change and encouraged to start using it. For example, when a sprint is driving analytics, the manufacturer can utilize past or existing data and build an analytics model. Leveraging this big data, the company can manage to save an incremental percentage on cost savings.

How does data digitalization enable cost management?

Data digitalization can help drive costs improvements through steering product development and optimizing product specifications.

An automotive manufacturer, for example, is likely to have 30 to 40 years’ worth of data which can be utilized by the product development teams. By understanding the relationship between components and cost, they can focus on developing new products or modifying products in ways that will likely have the largest impact on costs and, over the long term, drive savings.

Also, automotive companies today face a lot of “black box pricing” from suppliers.

For example, often suppliers do not provide cost breakdowns for an electronic control unit. But with insights from five to 10 years’ of well-structured data, a buyer may be able to determine which parameters drive ECU pricing (based on SKU pricing over time) and which specification changes should reduce the cost and by how much. In cases where even the suppliers may not know of cost reduction opportunities, the buyer can educate them.

Conclusion

If companies digitalize data management by having updates and quality data, they can improve compliance and innovation while self-funding the process through an increase in savings-producing insights.