How Agile Scrum increases data reliability

That data is important has now penetrated the boardroom. Executives, management teams but also customer service, production, sales, operations, etc. all use data to be able to execute their processes properly as well as to manage them. If the quality of this data is insufficient, this immediately leads to unnecessary extra work, wrong decisions and insufficient insight into the quality of business operations. Reliable data is the foundation for efficient and effective operations.

How can you ensure that your data is and stays reliable? In practice, we see that not only the IT department, but also the business has a key role in this. After all, IT has knowledge about how to manage data, whereas business has knowledge about the relevance of data. Precisely bringing IT and business together is the key to success when it comes to reliable data.

Why is reliable data so important in your organisation?

The importance of reliable data in organisations is partly reflected in three business management aspects: efficiency, laws and regulations and strategy.

Data is the foundation for all business processes. The higher the quality of this data is, the more efficient you can set up your process and the better you can rely on your management information. This then ensures less rework, higher transparency and better insight into the processes that are going well or can be optimised.

Laws and regulations requires you as an organisation to be in control of your data. The laws and regulations surrounding privacy stand out the most here, but you can think of the Financial Supervision Act (FMSA) or IFRS standards. Not having these in order can lead to sky-high fines. It is therefore necessary to know to what extent data are subject to laws and regulations, how to deal with them and their reliability.

Finally, reliable data is relevant to an organisation's strategy. Measuring the achievement or non-achievement of concrete objectives is possible only if the underlying data is in order. A late or incorrect insight can irrevocably lead to failure to achieve objectives and making the disastrous decisions.

Good data quality is how you do it!

From a theoretical point of view, a solid IT infrastructure and good data processing processes play an important role in data quality and thus reliability. However, it is also important to consider the design of data governance and stakeholder awareness regarding data quality.

Data governance is about ownership around the data. Data governance requires bringing the data owners (those ultimately responsible for the data) and data specialists together so that content and governance converge.

It is also important to make all stakeholders aware of the importance of good data quality. If people are not sufficiently aware of the impact of poor data quality on the organisation, the primary processes will be prioritised in the work.

How do you ensure that a data owner can take up her or his role more easily without taking up too much time? And do you ensure that all relevant stakeholders understand the importance of good data quality and are able to act accordingly? Practice shows that Agile Scrum is a good method to pragmatically address data quality management within your organisation.

Agile Scrum and data quality

In short, the core of Agile Scrum is to deliver added value on a product with a fixed team in fixed periods (the sprints). In this regard, data quality can be picked up as a product by a scrum team. To make the connection between data and business operations, it is important to assign responsibility for management information within this team, in addition to data quality. The team then delivers the agreed management information in consultation with the process owner of a specific business process. Based on this information, potential improvements in the data layer are then considered.

How does a Data Quality scrum team work?

In Agile Scrum, a team consists of a product owner (responsible for the product), a scrum master (responsible for the process within the team) and the development team, which includes the content experts. The product owner is responsible for coordinating with the data owner. The data owner is often the owner of a specific business process. Think, for example, of processes such as supply chain, invoicing, procurement and customer service.

In an intake meeting between the product owner and the data owner, the information needs are identified and converted into requirements (1) which are then translated into concrete management information.

The development team then sets to work on working out the requirements. Key questions in this elaboration are: which data sources do I need (2) and how can I convert this data into management information? Once this is clear, the relevant management information is then designed into a report or dashboard (3).

One source of the truth! The management information prepared is a direct reflection of the data quality of the source data. Because one team with expertise in both data management and management information works on the solution, the information displayed is transparent and as it is stored in the source systems. On this basis, in consultation with the process owners and users, it is possible to determine which interventions are necessary to increase data quality (4).

The activities that are necessary are worked out in an improvement plan, and this improvement plan leads to the implementation of various improvement actions that are carried out within the scrum team on a short cyclical basis (5). Actions may include optimising process flows, data cleansing, making input fields mandatory, the reduction of input fields et cetera. The improvement actions are then picked up by the data quality scrum team and implemented according to the Agile Scrum methodology.

The advantages of this method

The biggest advantage of Agile Scrum is that alignment with stakeholders is short-cyclical. With a two-week sprint period, results are produced, shown and tuned every fortnight. Because of this short-cycle contact and quick results, data quality is increasingly on the agenda. As a result, data-conscious action is growing at various layers in the organisation. This also reduces the agenda pressure for the process owner/data owner. The scrum team carries out the work and transfers only the improvements and the result.

Improving data quality, using the Agile Scrum method, delivers:

A data governance in which everyone is aware of his/her role;
A mechanism to structurally improve data quality;
Quick result.

When do you start with data quality in an Agile Scrum way?

The most important prerequisite for a flying start is that data governance is clear. The advice is therefore to set this up per topic and business process. And then start with one process. In this governance, it should be clear:

Who the data owner is;
About which data the data owner actually owns;
Who the data experts are;
In what way the Agile Scrum team will be positioned and how the roles of product owner and scrum master will be set up.

Another important condition is that the organisation has confidence in this approach. After all, it is not the process owner or the MT who decides what work the team picks up. This is the job of the product owner. In addition, the organisation must be ready to embrace the Agile Scrum way of working. It helps that Agile Scrum is 'hot' and there are various specialists and training courses to get your organisation ready.

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