Data Quality vs. Data Agility – A Balanced Approach!
Sometimes we are so focused on perfection that we do not see the benefit of agility. Consider the emergence of the brainstorming concept as an example. We learned quite some time ago that if used the brainstorming concept of freewheeling, non-judgmental discussion we could bounce ideas off one another and often come up with innovative ideas that would not have resulted from a discussion that was more restrictive.
When it comes to analytical quality versus analytical agility, we might see the issue in the same light. Consider the standard, restrictive concept of carefully gathering every piece of data, setting boundaries and giving requirements to IT or a data scientist and then waiting days or weeks to get a report. The need for strict analytical accuracy and absolute, binding results is often overkill for what we really need and the idea of absolute accuracy can also be misleading, because that report you are waiting for may be out of date by the time you receive the information.
If we look at the idea of data agility and delivering Augmented Analytics Tools to business users, we can encourage the use of self-serve tools with auto-suggestions and guidance to help users see the best way to visualize their data or to use a Self-Serve Data Prep tool or an Assisted Predictive Modeling tool. The information will be based on sophisticated analytics, but can be derived in minutes so users can make informed, fact-based decisions. This analytical agility will help them to see data clearly and gain insight and, while these tools may not produce 100% accuracy in the hands of a business users, there are many times throughout the work day where users need good, solid information but do NOT need strategic, analytical information that is 100% accurate.
As and when the organization needs this type of refined analysis, the original data requirement can be handed to a data scientist, and IT professional or a business analyst to produce the type of strategic analytics the organization may require.
This type of agility vs. quality balance allows the business to make data-driven decisions, encourage users to become Citizen Data Scientists and be empowered and accountable while still providing the opportunity to turn to data scientists and other professionals to further refine results.
If you want to create an environment with a culture and processes that are balanced to accommodate data agility and data quality, you can start here: Benefits of Augmented Analytics Balance
Original Post: Data Quality and Data Agility are Both Important to Success!Share