Enabling data-driven business with data governance
How to implement business automation that utilizes reliable data? How to create a competitive business advantage with data-driven business? In this post within the blog series on managing and utilizing data, Enfo’s Business Development Manager Jarkko Jormanainen talks about data governance.
How should you use data governance to get the most value out of your data? Enfo has chosen the Data Management Association’s (DAMA) model, a best-practice framework used commonly throughout the market.
Over the course of this blog series, I have looked into how vertical systems and vertical data affects the Customer experience and how the business data created within the business process is often not utilized for business benefits. When your company needs to utilize data across business processes or businesses, or even with business partners, data governance provides the company with tools to organize the data management and usage.
In this blog article, I give some pointers that data governance could potentially be very useful when trying to implement a data-driven business. So, how can it actually be done?
Would you rely on an automation that uses unreliable or outdated data?
The common use of the DAMA model
The DAMA model provides a framework to implement governance methods for all data processing within the company. It focuses on strategies, policies, roles and responsibilities. It creates the framework to which the different operations can build on.
Usually, data governance tends to be applied only with the current system in scope – I talk about this issue in more details in my blog article Become data-driven by improving your data utilization – which results in data being managed in vertical process phase rather than on a corporate level.
This is a quite natural and human step when you are developing a system to meet business process requirements. You need to have focus.
The Enfo approach
At Enfo, our approach is to use data governance throughout the data lifecycle from a business process point of view.
The benefits of this approach to our customer are:
- Reduced cost by focusing on the data that is valuable for the business instead of all data
- Faster time to market by applying agile and phased methods
- Provide new business opportunities by identifying valuable business data
The lifecycle approach means that:
- Data is created
- Data is presented in various formats
- Data is refined, altered
* Sometimes a new instance of the data is created
- Data is stored and archived
* in system A, system B…
* copies in email
* copies in paper format (where?)
- Data is shared to other systems and parties
* Different sharing methods apply
- Data is disposed
* Are all instances of data removed?
* Where are all these instances?
Reliable data creates real business value
Not every aspect or instance of the data is reasonable or feasible to manage. But for key business data, when utilized by the business process, the data lifecycle approach will enable:
- better data utilization throughout the process,
- improving process performance by recognizing data usage or handling bottlenecks,
- cost savings due improved performance, as well as
- better customer experience by improving service quality and providing more intimate customer experience.
When the data is governed throughout the lifecycle, you know the reliability of your data assets. The advanced data utilizing technologies create real business value only when the lifecycle behind the data is known – if we cannot rely on the data to be consistent, the advanced technologies cannot deliver precise results. Would you rely on an automation that uses unreliable or outdated data?
In case a business wants to use advanced data utilization technologies such as Artificial Intelligence (AI), Machine Learning (ML) or Robotic Process Automation (RPA), or simply more sophisticated business planning and reporting features, data reliability becomes important. Data reliability consists e.g. of following details:
- Where the correct version of data is located
- When data is updated and what process updates it
- What methods are used to transfer or transform the data before it is used
Data governance provides you with the foundation to build your data management operations.
Would you need a trusted advisor providing your business architects with the thorough knowledge about what data your business produces, utilizes and shares or how to enable your new business visions to utilize your data for competitive advantage?
Jarkko Jormanainen works as a business development manager at Enfo