It’s common knowledge that a data governance tool can help translate your data governance framework into practical steps and automate them.
But the tool should have other mission-critical functionalities to propel the adoption of data governance mindset and behaviours across your organisation. These include cohesion with your existing system landscape and business processes as well as being a one-stop platform for all your data governance and data quality needs.
Deploying a data governance solution without considering the crucial functionalities can manifest in epic failures. The tool ends up being a white elephant because no one uses it—and you still have to bear its cost!
What are the major drawbacks of a data governance tool that cause people to hate it and do whatever it takes to pull the plug? Let’s have a look.
Lack of integration and interoperability
Modern companies rely on different applications like ERP for financial and transactional processing, SCM for supply chain management, and CRM for customer relations activities. Even (and especially) in this situation, you’ll have to ensure that the data residing in all the systems undergoes proper governance and quality checks. It’s crucial for your data governance tool to seamlessly integrate with the existing landscape to allow the setup of standardised business rules and validations via a central platform.
Without this interoperability, it’ll be hard for you to govern your data at scale. Performing validations and monitoring data health in each individual system can become tedious, keeping your people away from more strategic undertakings and thus, demotivating them.
Unavailability of crucial data-related tools
You may think that it’s sufficient for a data governance solution to support all the standard master data models. But it goes beyond that.
Data governance encompasses activities like data issue tracking and resolution, data quality surveillance and testing, data exchange, and data lineage tracking.
All this gives robustness to your data governance programme. For example, if you don’t have data quality testing and surveillance functionality, how can you know for sure that your data meets the quality requirements? It defeats the purpose of embedding data governance in the first place!
You’ll have to procure new tools to execute these activities. This could add another layer of complexity as now you’re forced to integrate and deploy them with your data governance tool and other existing applications. No one will look forward to that!
This also includes functionalities that facilitate mass uploads and processing of data because let’s face it—your data will grow exponentially over time.
Limited data modelling capabilities to address various master data areas
Vendor, customer, and product masters are more structured and hence, easier to manage. Most off-the-shelf data governance solutions come pre-packaged with these ready-to-use data models.
At the other end of the spectrum, we have complex master data areas like assets and spares. They’re harder to govern due to inherent issues like the structuring and categorising of material descriptions that vary according to business needs. But you still need to manage, govern, and catalogue them.
It’s a huge endeavour to build the data models, define the rules for descriptions and attributes’ populations, and fill all the master data fields. If the chosen data governance solution doesn’t have extensive data modelling and automation capabilities, people would start rethinking its relevance to the business.
No flexibility to support different operating models and system landscapes
Instead of having a traditional organisational model with clear segregation of data governance duties, companies might opt for business process outsourcing (BPO) and outsource data governance tasks to an external party.
A data governance tool should be flexible enough to cater to these different operating models. If BPO is chosen, then there should be no hassle in setting up the access and rights for third parties, at the same time, guaranteeing security.
Organisations that run on multiple cloud data platforms could find that role-based access control may not be suitable and scalable. Attribute-based access control is more favourable as it dynamically grants data access based on specific user and data attributes.
It’d be hard to navigate these complexities if your data governance tool isn’t equipped for this.
No means to tie back to KPI measurements
At the end of the day, you’d want to measure the success of your data governance programme. It’d be hard to do this if there’s no means to monitor data health and remediation status within your data governance platform. This serves as input to your KPIs.
To illustrate an example, you implement governance and cataloguing on your spares data to reduce free-text spending as one of your KPIs. No doubt, you’ll want to track the status of spares that have been catalogued. Once it has achieved its target, you can attribute the subsequent reduction of free-text POs to this.
Without monitoring and analytics capabilities, you don’t have visibility of your data status and how it impacts your KPIs. As the top management sees nothing tangible, they might pull the plug on your data governance programme.
Use MDO for your data governance needs
OK—Now you know the tell-tale signs to watch out for in a data governance solution. The next question is which data governance tools to choose from.
You can consider Master Data Online (MDO). MDO is your ideal data management and governance platform. MDO doesn’t just automate and simplify your master data processes—it helps you implement data governance and execute the embedment activities with minimal human intervention.
It adopts a comprehensive governance framework that encompasses all data scenarios. Active governance ensures your data is cleansed, deduplicated, and enriched before entering your system. While passive governance supports ongoing validation and remediation of data within your system.
MDO helps you apply rules-based governance policies, enhanced with approval workflows and audit trails. This drives data ownership, builds trust in data, and increases its usage across your organisation—essentially the goals of data governance.
Look no further than MDO to solidify your data governance journey.
Author: Shigim Yusof