This is the final part 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 sets the scene to reveal the state of data culture adoption in North America today while part II homes in on common data and process challenges.
We will proceed to dissect the adoption modes by data area and deployment methods as well as the reasons driving these different decisions.
Adoption by business area and industry
While it makes sense to implement data governance on problematic process areas like procurement, inventory, and supplier management, other factors have to be weighed in too such as the complexity of master data domains and existing technology solutions that can support them. This explains the varying level of adoption by business areas in the North American market.
Vendor, customer, and product masters are more structured and easier to manage. Most off-the-shelf enterprise master data management solutions can fulfill the requirements. Coupled with the fact that they make up the bulk of day-to-day business transactions, it’s no wonder these data domains become the hotbed for data governance.
At the other end of the spectrum, we have material masters that consist of assets and spares. They’re harder to govern due to inherent issues like the structuring and categorizing of material descriptions that vary according to business needs. There has to be an intelligent approach to parse and identify industry-relevant attributes from the descriptions and populate them into the correct fields. Owing to the complexity, the master data management solutions to address it is also limited.
Of course, the benefits of having a comprehensive materials catalog as the go-to reference for people working along the supply chain far outweigh the complexity of the efforts. So, companies are still willing to allocate budget, time, and resources to collect and classify their assets and spares data as well as embed governance steps.
Based on our engagement with North American customers and prospects, we’re also seeing that the adoption of data governance is driven by industry and regulations. To illustrate an example, companies belonging to highly-regulated industries like pharmaceuticals and FMCG would have a more defined data governance footprint. This is because they’re bound by strict regulations to disclose all the substances they use, especially those to be consumed by humans.
Thus, it’s of utmost importance to enforce governance in the recording and reporting of their raw materials and finished goods data to avoid legal consequences like penalties or worse, having their operating license revoked.
Deployment modes
The advent of cloud technology in data management and governance solutions has paved the way for more scalable and flexible deployment.
We have worked with an organization that has 50 divisions and each division has its own ERP instance. So, it made sense for them to implement the phased approach by division which is more economical and less resource-intensive. This is also the most recommended way to secure continued support and funding from the risk-averse top management. To accommodate unique business requirements, more autonomy is given to the respective division in determining the nitty-gritty details of the rollout.
Having said that, a few organizations still opt for big bang deployment. This approach is preferable in digital transformation initiatives where part of the steps involves consolidating system landscape and migrating to a new digital ERP platform. The data governance program is rolled-out together with the new ERP migration. The preparation and embedment of data governance are within the purview of a global team that oversees the whole digital program. Even so, it still goes hand in hand with the roll-out approach of the program (either big bang or phased).
When it comes to pre-migration activities, most companies understand that the full suite of data preparation, cleansing, and enrichment comes with the territory. They are deemed as best practices to guarantee clean data from the get-go as the new ERP begins to power up the whole operation.
Becoming an intelligent enterprise
Our North American customers have a high level of data culture adoption. This is evident through the deep understanding of the importance of high-quality data at every touchpoint. It’s systematically manifested into various data governance activities as part of a larger digital transformation agenda or in typical day-to-day operations.
Essentially, we don’t need to spend a lot of time laying the groundwork with our North American customers. We can proceed to introduce cognitive AI capabilities for automation of data remediation and enrichment which can vastly improve and expedite existing governance processes.
By taking automation to the next level, employees don’t have to spend a lot of time manning the data checking and remediation fronts. Instead, they can just adopt a more supervisory approach and focus on projects or initiatives that directly contribute to organizational goals.
With high-quality and trustworthy data supported by a robust governance process, companies can confidently use and analyze it to innovate beyond their traditional business models, explore new market frontiers, and develop resilience against the ever-changing global economic climate. These are all desirable qualities to transform into an intelligent enterprise.
Author
Richard Anderson,
Prospecta’s Executive Vice President Sales – Americas
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