Why Data Quality and Analytics Should Always Go Hand In Hand

Analytics Adoption

Providing analysis for everyone means companies need to take the necessary steps toward full analytics adoption to become modern, agile, data-driven organizations. Why haven’t some organizations reached this point? It’s probably a mix of many factors: technology hurdles, organizational challenges, culture, human interaction, etc.

Another factor is a lack of focus on data quality. During the evolution of analytics 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 ease of access for business users is 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 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. And, by data, I mean more than just the raw values. One needs to include calculations and analytic expressions. There are several things to be gained by doing this, and, based on my experience, I have categorized the benefits into three main areas:

TRUST

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 — starting with the data source, through all transformations, in calculations, within visualizations, and finally any triggered actions.

TIME

Agile, fast-moving distribution and access to data are key in the business climate of today. An analytics 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 dampen the adoption of analytics in the organization and increase the cost.

TRANSFORMATION

A hot topic today is the critical importance of digital transformation. To survive, you must quickly adapt to a changing market and new customer behaviors. 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 are 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?

• Focus on governance for the whole data value chain – from source to visualizations and actions. 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-encompassing.
• 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 will be able to steer the behavior. A successful business is agile and fast-moving, and so must be the analytics environment and strategy as well.
• Start small, get big results!

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