Why data quality and BI should always go hand in hand featured image

Why data quality and BI should always go hand in hand.

By Oskar Fahlvik
Published 10th of February 2021

I still remember my first day at work at Qlik 15 years ago, filled with energy and a feeling of being part of something really special. We were challenging the traditional BI market with, what at the time was considered, cutting-edge technology. With a fast-moving BI tool that quickly and intuitively could help businesses get the visualizations they needed. Already, the vision at that time was to “simplify analysis for everyone” – meaning this was not only a tool for IT and the superuser but something that could actually be put in the hands of everyone.

BI adoption — we still have a long way to go

Now, 15 years later, I can see that we still have not reached that vision and both vendors and customers are struggling to take the necessary steps towards full BI adoption to become a modern, agile, data-driven organization. The reasons as to why we have not come further is most probably a mix of many factors: technology hurdles, organizational challenges, culture, human interaction, etc.

Another area that I also believe is a big contributor to this is the constant lack of focus on data quality. During the evolution of BI and the race to reach customers and users, the major focus has been on building cutting-edge visualizations. The vendor that could provide the coolest and most easy-to-use visualizations has been the winner. Visualizations are of course very important and the easy access to these for the business users is a key for adoption. But over time the use of data will not evolve if the organization does not trust the data they are looking at. A cool visualization can only compensate this to a certain extent.

Start focusing on data quality

So, to reach long term adoption in your organization you must start to focus not only on the visualizations but also on the data quality. There are several things to be gained by doing this, and based on my experience, I have categorized the benefits into three main areas:


For an organization and a business user to fully embrace the importance of data, KPIs and visualizations, they must establish trust. As long as the individual user does not fully feel that they can trust the data, the adoption will fail. One key factor to achieving trust is to secure the quality of the data. Data quality must be secured — all the way from the data source, through all transformations, to the final visualization.


An agile and fast-moving distribution and access to data are key in the business climate of today. A BI environment that is not correctly governed in terms of data quality – both the data itself and the accessibility to correct data – will be time-consuming and slow-moving. This will slow down the adoption of BI in the organization and increase the cost.


A hot topic today is the critical importance of digital transformation. To survive, you must quickly adapt to a changing market and new customer behaviours. New digital business models and technologies must be integrated in order to stay ahead of competitors. A digital transformation includes a lot of critical decisions where data and the quality of data is key. In addition, as you roll out your new digital initiatives, the quality of that data must be secured as your future success depends on it.

So, what can you do?

A second look at some of the recommendations from our last blog post focusing on Data Quality is a good first step:

  • Focus on governance for the whole data value chain – from source to visualization. The perception of quality is in the hands of the business user. To achieve trust, governance is one key component but it needs to be consistent and have full coverage.
  • Add data testing into your operations process – to test and follow-up on data as changes are made is key. And changes can be done through the whole data value chain, so testing needs to be all-encapsulating.
  • Move from reactive to proactive – understand what is needed to build trust and establish a culture and governance process that can be a part of reaching this goal. It is also important to understand that business users will always find ways to get what they need. Your model must be built around this and not based on the assumption that you should be able to steer the behaviour. A successful business is agile and fast-moving and so must the BI environment and strategy be as well.
  • Start small, get big results!

Data quality is here to stay and should be a critical and integrated success factor for all existing and future BI projects.

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