Applying Data Governance to Agile Projects

Is that really possible?

Applying Data Governance to Agile Projects

Data governance can be applied to agile projects. Although it is commonly believed that agile development norms and Data Governance norms are not aligned with one another. Nevertheless, we can still find common ground between governance and agile projects. There are a few best practices around applying governance to agile projects which makes it possible.

Data requires enforcement of authority and formalization of the authority to be in its most stable and useful version.

In terms of data governance, the main concern of senior leadership of a company is different for each type of data:

  • For “understood data”, the main concern is to govern the data definitions.
  • For “good quality data”, the main concern is to govern data protection.
  • For “well-protected data”, the concern in to govern data usage.

In terms of agile development, the main concern of senior leadership is to get speedy, incremental, and high-quality delivery. How does upper management decide how agile processes should be controlled and implemented sustainably and successfully?

Non-Invasive Data Governance

First, let’s have a look at non-invasive data governance which aims to acquire formalization. It is the practice of applying formal accountability and behavior through non-invasive roles and responsibilities. Governance works as the foundation on which all enterprise data is based. In the process of creating new processes or enhancing previous processes, it is important to have definitions and usage of data planned out. This assures the data compliance in terms of security, privacy, protection, and quality.

What means "Agile"?

Now moving on to understanding agile, agile is the methodology to do development with a good design where the main focus is to get an all-rounded result. This focuses on people, their interaction, speed, effectiveness, software projects, collaboration to devise requirements and evolvement of the project towards the finer final product.

Do they harmonize?

At the core, both are looking for synergy, user participation, efficient data gathering leading to reliable results with an improvement in each iteration/step. We do not see a reason as to why they can’t be friends? Be it the simple waterfall model, multi-functional spiral model, or scrum-based, data governance can be given importance at the start of each. This would lead to data being utilized best and would become useful for Business Intelligence, Business Analytics, Data Analytics, and Data Modeling teams of the organization.

On an enterprise level, the senior management cannot neglect data governance, as data must be managed as a valuable asset and it must be protected. It also drives analytics and critical decision-making processes. On top of everything, nowadays it is a market requirement due to the competitive nature of the IT industry. The topic of data quality management and data governance has never been so important! It sits at the heart of any business and should be carried out thoroughly and seriously

But we are not living in an ideal world, most commonly, the task of data governance intimidates many small to medium organizations. This is one of the biggest reasons why some companies stop expanding after a certain point. Many companies do not understand the approaches available to put data governance in place and that’s where BiG EVAL comes in for you.

Automation is king!

With the help of the right consultation, the leading tool and the right partnerships, companies can grow extensivelyTo get the best results and the most value out of enterprise data, you need to ensure the quality of your data (= data quality management) as well as the quality of the data processing system components. Automating the latter (= data test automation) is extremely important within an agile data oriented project to support short release cycles and to be on time and within budget.


There are still many interesting articles here:

Disastrous consequences due to poor data quality

Actions to take for successful Data Governance Implementation

Dolomites

Humans vs. Bot. What to use in data oriented Testing?

BiG EVAL News Highlights 2.7.0.