Data is the most vital aspect of the digital transformation of business enterprises. Companies require data to achieve transformation using high-level technologies such as Artificial Intelligence (AI) and Machine Learning (ML). Business enterprises have massive sets of data collected and processed over the years, and if the data strategy is managed correctly, data can drive superior business results.
However, data can be complicated and fragile, and not all the data in a company’s dataset is useful. Many times, companies fail to manage and use their data properly. Let’s uncover the effects of poor data quality.
Many companies invest millions in technologies, cloud-based systems, software solutions, and hardware infrastructure. However, both good and bad data can be collected by these solutions. Unfortunately, most companies are unaware of the severe impact of bad data on their projects.
Over the years, both structured and unstructured data have been collected by companies in raw form, which means both types of data are mixed together. Companies must separate structured or good data from unstructured or bad data to achieve digital transformation.
Unstructured data is incomplete, invalid, inaccurate, inconsistent, redundant, and has quality issues. Here are some examples of bad data:
The primary cause is human error during data entry, poor data collection methods, deliberately using incorrect data, etc. When this data is migrated to business intelligence platforms, it can cause failure.
Bad data is incorrect and flawed, which means all the information stored is incorrect and redundant. Adding bad data will provide inaccurate results.
For example, a company has 60 leads for a particular service, but due to duplicate data, the company can see 70 potential leads, where 10 leads are duplicated. This affects the entire calculation and analysis made by the company, and on a larger scale, such mistakes can affect financial forecasts and set the company up for failure.
Companies migrate from one platform to another to upgrade their business processes. There is a significant chance the new platform has different data governance protocols and data storage requirements which will require a lot of time to move and map out the data. It is also essential to clean up data and eliminate inconsistencies before beginning the migration process.
For example, a new platform only supports PDF formats for data; but your company is inconsistent with the data formats you store. In this case, it will be difficult for your team to move data in the same format, and a simple task will become a time-intensive process, causing a delay in data migration.
Organizations today are built around data. Inadequate data has a direct impact on organizational efficiency. When data is inaccurate, it affects your company’s processes, people, and goals.
For example, a marketing team may make a costly error by sending emails to the incorrect target audience, which they could have avoided if they had access to clean data.
Teams can easily clean their data across data sets using automated solutions. Data cleansing involves removing typos, spelling errors, character issues, punctuation, and minor details that human data operators frequently overlook.
One root cause of duplicate data is bad data. Companies have multiple systems and run various applications, so data redundancy is unavoidable. Let’s say, for example, three departments of a company, sales, marketing, and customer service, are storing customer data in three separate applications or systems, causing duplicate data. This situation will make it difficult to get a clear overview of data and will provide inaccurate data insights.
Data deduplication software can be beneficial in such situations. It checks data across data sets to identify duplication and helps eliminate redundant data.
When you use a commercial tool, you can create improved data governance rules for the entire organization. Once you’ve identified the most common issues plaguing your data and their solutions, you’ll want to ensure they don’t happen again. This can be accomplished by developing a data governance strategy based on insights provided by the tool.
One vital aspect of reducing the chances of duplicate data is put a duplicate data framework in place. It ensures the data is clean and ready to use in real-time. The framework can be implemented when a data specialist has a solution that allows the application of quality benchmarks at different stages of the data cleansing process.