5-Ways BI Software Improves Your Company’s Data Quality

We live in an information-overloaded society at the moment. Useful information, or data, can be found in everything from cloud-based software applications to your DNA. Organizations in various fields can use data as their most powerful resource to discover hidden connections and meanings in seemingly unrelated phenomena. 

 

According to Gartner, growth in data worldwide in 2011 was approximately 59%. In particular, corporations are heavy users. Information can forecast consumer needs, create superior products, and make intelligent business choices, and BI software can make it easy for you. With Grow pricing packages to add data value, everything is possible. 

 

Poor data quality costs companies an estimated $15 million per year, as reported by Gartner. Because of this, DQM (Data Quality Management) should be one of your company’s top concerns. However, remember that even a small error in data can have repercussions throughout the company. 

 

Here’s where choosing the right KPI dashboard to ensure consistency in the quality of your data comes to play, and with Grow pricing modules, one can not think of going elsewhere. 

 

What steps improve your company’s data quality using BI software? 

 

Now that you know why Data Quality Management is essential, we can discuss how to get it set up at your company with the relevant KPI dashboard. 

 

  1. Establish a system of command and control. 

 

People are essential to the execution and oversight of any business process. Form a team focused solely on Data Quality Management and delegate tasks to members according to their areas of expertise. The reasonable Grow com pricing modules can help you establish a chain of command in the manner you want. 

 

Some essential members of your DQM crew are: 

– Data Consumer

– Data Analyst

– Chief Data Officer

– DQM Program Manager

– Data Custodian

 

  1. Specify the quality of the data. 

 

Grow pricing plans make it possible to add value to any dataset. You can’t tell if your data is up to par unless you establish some criteria on your BI software. Let’s consider the following seven factors: 

 

– Do the numbers add up? 

– Whether or not it agrees with the values in the provided dataset is what we mean by “validity.” 

– How much of the required information has been gathered? 

– Is there a standard way to collect and store data across different datasets? 

– Is the information on the KPI dashboard timely, meaning does it apply to the current situation? 

– Differential: How fine-grained or specific is the data you collect? 

– Easily Obtainable: How quickly and painlessly can you get your hands on this information? 

 

Each department using BI software can tailor its definition of data quality to meet its specific requirements. 

 

  1. Get started with data profiling 

 

After those boundaries are established, Data Quality Management can begin. Data profiling is primarily used in an audit process that looks into and fixes data quality issues. If you read Grow’s reviews and Grow pricing, such problems may involve repetition or a general lack of reliability, precision, or thoroughness. 

You can automate data profiling with various open-source and paid tools. Some of the paid options are designed for use in large enterprises. 

 

  1. Track data 

 

DQM reporting monitors, reports, and records data and data-dependent business exceptions. BI software can record exceptions and identifies data exceptions. 

 

Your data specialist team powered by the KPI dashboard can now aggregate these exceptions to generate patterns that explain why data gathering and processing stray from norms. After identifying the issue, you can plan data cleanup. 

 

  1. Data repair 

 

Data remediation entails changing data collecting, processing, and analysis procedures. 

 

Clean your company’s data with leading data profiling tools and access the most economical cost with Grow pricing. Next, restart processes that use insufficient data. It may involve rerunning reports, ads, or financial documents with clean data. 

 

At this stage, review and update data quality guidelines. Your essential business processes will function efficiently with revised data quality requirements and high-quality data.

 

Closing words-

 

DQM isn’t a one-time event.

 

Given today’s data volumes, manually carrying out Data Quality Management can be brutal. Grow’s BI software offers Automated Data Quality Management to refine your data and processes iteratively and continuously. To know more, visit Grow pricing packages. 

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