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Data culture trends and challenges in North…

This is part II of a 3-part blog that discusses the data culture trends and challenges in the North American market, comprising the US and Canada. Part I gives an introduction to the state of data culture adoption in North America today as compared to 15 years ago. Advanced analytics has served as a catalyst for the growing adoption of data management and governance solutions.

Common data and process challenges

While advanced analytics may have an influence on how modern companies perceive and treat data, some common challenges have made the need for data governance and data quality even more pressing. This is especially true for companies looking to get their house in order by tackling immediate issues surrounding their processes and operations.

Rampant free-text spend

For asset-intensive companies, free-text spending has always been the bane of their existence. It refers to the purchasing of materials that bypasses catalogs and supplier contracts, a.k.a. rogue purchasing. All data associated with the purchase is entered at the point of need by the requisitioner, often in urgent situations like an unplanned breakdown where a high-criticality part has to be urgently purchased.

Materials acquired from free-text POs are either unrecorded or populated with inaccurate data in the system. You have zero visibility of these materials once they’re received. It’ll be harder to manage and govern materials data, let alone standardize and catalog them.

Important details like lead time and quantities aren’t maintained, so maintenance planning becomes impossible. And as free-text POs are not part of contracts, returns aren’t possible if the materials turn out to be defective.

Despite these drawbacks, one of our customers reported having 50% of their materials procured under free-text spend!

This is largely due to process loopholes that allow free-text POs (in the absence of a more elegant solution) as well as poor-quality data stemming from no robust rules in materials standardization and cataloging.

Supplier collaboration

Companies may have thousands of suppliers to manage. Hence, it’s counter-intuitive to expect companies to populate data for raw materials or products when it makes more sense for the respective suppliers to do it for them. The same goes for details belonging to the suppliers, e.g., bank account number, registration number, and address.

In the absence of collaboration with suppliers for data population, it can be a time-consuming and error-prone process. Without data governance, there’s no way to ensure the integrity and accuracy of entered data.

This is where self-service onboarding helps where suppliers can enter their own data, effectively shifting ownership to them. In the same breath, they should be given the authorization to fill in information for the materials/goods they’re supplying.

This process can be enhanced with validation mechanisms like rules and authorized approvals to ensure data accuracy. Companies in highly-regulated industries like pharmaceuticals would regard this as a non-negotiable as there would be grievous implications for data misrepresentation.

Scaling up operations to match growth

While it’s generally agreed that companies, either big or small, can benefit from implementing data governance, the urgency is greater as the company grows.

Yes, a growing company is always good news. But the growth needs to be sustained to ensure a continuous upward trajectory. For instance, they’ll have to scale up their operations by way of differentiating their processes, increasing raw materials, finding the right suppliers, and reaching out to more prospects.

Without control and governance around core master data areas like finished goods, materials, customers, and suppliers, it’s hard to gain a complete picture of essential operations like supply chain planning, inventory management, and order fulfillment. Incomplete and inaccurate datasets make it even harder to derive useful insights, leading to ill-informed decisions that can cost millions to the company.

Think of data governance as a safety net where your core data is checked and validated first before being used in your processes. Without the safety net, things will fall through the cracks, unchecked and unvalidated, causing chaos in your operations that are just about to grow.

Now that we have gone through the common challenges that necessitate the implementation of data governance, check out part III where we delve into adoption modes by data area and deployment methods among North American companies to conclude this 3-part blog series!

Author

Richard Anderson,

Prospecta’s Executive Vice President Sales – Americas