Enterprises have begun to understand the importance of data quality and the value of data analytics and decisions that are driven by data. We are here to discuss the five reasons data quality initiatives fail and the ways in which they could be avoided. Stay with us to the end, as uArrow brings to light the best ways for your enterprise to utilize data.
What are the five reasons the data quality initiative failed?
Understanding the importance of data quality and implementing a strategy is vital. But, implementing the best methods to ensure DQ data quality is key. Here, we shed light on the reasons why data quality initiatives have failed.
Unclear objectives
The first step one should take while determining the need for data quality is to define its outcomes. Vague ideas and loose statements will not yield changes to data quality. Instead, issues should be well defined to be able to resolve them. Define the top priorities that your business wishes to achieve with data and distinguish between the results and gains that are to be attained using data.
Incompatibility
It is important that every member is on board, but this will be a progressive task. It is necessary that a daily minimum sacrifice be made to attain the level of data quality you wish to collectively achieve. Get everyone on board through effective communication and explanation. The combination of the right investment of money and time will avoid any failures when it comes to data quality.
Hostility towards data
Irrespective of opinions and preferences, data must attain a certain quality level in order for it to be used to make decisions based on facts. With the improvement of data quality, the elements of chance and hunches will be removed from the equation. Decisions will now be purely based upon concrete facts and data with human intelligence.
Misplaced cornerstones
The first steps of attaining data quality can be a task, but with the focus on the end results, the burden should be lightened. Planning with goals, timelines, and executive actions should be in place to make sure that a smooth transformation of the process takes place. Set milestones and cross them off one by one.
Wrong tools
It is vital that the tools that are used in the process to gauge data quality play the most significant role. This is where uArrow can step in. uArrow stands out as the best Data Quality solution to provide a tool that runs a series of attribute checks, integrity checks, and functional checks. With the right tools, accurate measures can be taken to attain the desired levels of data quality.
Conclusion
With these precautionary measures, data quality initiatives can refrain from failing. Understanding the reasons behind failed data quality initiatives and avoiding them will result in progress towards attaining data quality.
Contact uArrow for more information to gain access to our data quality tools that will allow your business to align its goals and attain success.