Data Quality

Data Quality Dimensions - Consistency, timelyness, accuracy, completeness, and more

Data quality is the process that ensures validity and should be the standard for the company.

Basically, this means that the quality of data is only defined like this by the company’s context of quality. If the data process is fluid and consistent with the correct inputs, then the outputs of your data analysis system will yield the information you desire.

Common Problems

Here are a few problems companies have when ensuring the quality of their data:

  • Trust issues with BI applications
  • Incorrect data from data sources
  • Data copies generated from the lack of guidelines within the company’s data policies
  • No uniform company standards for ensuring data quality
  • Use of platforms with no API’s for integration
  • Data isn’t defined in terms of business processes

Prevent These Issues

In order to prevent these common issues, let’s look further into the process of ensuring how data can meet company standards. It’s important to first understand the flow of data within your business. List this out in a visual representation so you can understand and even design the way you want the information to flow through the company. The ultimate goal of visualizing data flow in business intelligence is to consolidate all the data into one dashboard; this requires the data to be consistent across all systems your company is utilizing. This could include CRM, website monitoring reports, and sales data.

The organization as a whole should recognize data as a key production factor and take on roles that ensure the information is correct by implementing a data owner, one that defines requirements and ensure the quality of data within the organization. The rest is taken care of by full-service analytic businesses like us at BluWave. As long as there is a sequential process where data is inputted, monitored for quality assurance and stored we then become:

  • The Data steward, one that coordinates data delivery and operational clarification (checking for duplicate values)
  • The data manager, the person who works with the data owner to meet requirements and manage the technological infrastructure of the data flow process
  • The data supplier, the entity that provides refined, understandable, 24/7 access to data users in all departments.

Ensure Quality

Ensuring data quality can be represented as a continuous process that needs to be consistent in the following phases:

  • Defined data quality goals and metrics
  • Analyzing your Data
  • Cleaning your Data
  • Enriching your data
  • Continuous checking and monitoring of data with ongoing protection.

At BluWave we help companies automate this process by first educating them on the importance of data quality, defining the steps, and then implementing a system that will output the valuable information they can utilize to see sustainable growth. Give us a look to see what we can do for you today!

16 Comments

  1. henry on October 8, 2019 at 5:49 am

    commendable work …….clear elaboration for quick understanding



    • bluwave.grant on October 8, 2019 at 7:41 am

      Thank you Henry, we do our best to help others learn with us.



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