Dealing in a business means dealing with data. The entire business world is dependent on any form of data. Data is present in every form, including sales figures or stock trends, implicating that a good part of the business involves dealing with data. This is more important because data is frequently used in the digital world, and the maximum work is performed online.
As and when a business develops, the data saturation level also grows. This means that there is much more data collection than ever before, implying that one cannot ignore any business issue. However, it is important to note that not all data is valuable. Saturating unwanted or the least important data is one of the significant issues a company faces.
What is bad data?
Bad data can be defined as an unstructured form of data that includes quality issues such as inaccurate, inconsistent, incomplete, and duplicated information. But here, inaccuracy does not mean that the data is false—true data can also be bad data.
Bad data is even considered as an inherent characteristic of data that is collected in its raw form. For instance, social media data is unstructured data that needs to be processed before being used for analyzing or business intelligence.
Missing crucial elements, data that is irrelevant to the objectives for which to be utilized, duplicated data, improperly produced data, and so on are all examples of bad data. Businesses’ use of faulty data can substantially impact their success, and it can be disastrous in certain situations.
Organizations need to remain alert about their data collection and management practices. These can be just as important as the actual product or service marketed to the public.
Why is bad data bad for any organization?
Spending time fixing minor problems like spellings and typos can harm businesses in a wide variety of ways. The following areas show how it can affect businesses –
Creating flawed insights –
One of the leading causes of flawed insights is duplicated data. For example, a company may assume that there are 100 active users, as data duplication takes place over time in multiple data sources; there might be chances that the company has 63 active users wherein the remaining 37 are duplicates. Imagining this data at an exponentially higher level with millions of rows of data is likely to draw an inaccurate conclusion from the data.
Complicates migration of projects –
When an organization tries to move its data from one platform to another, there might be a case where the new platform has a different set of data governance and standardization rules.
The new system may have a different data storing method. In such situations, it becomes difficult to move and map data accurately. So before migrating data, the company should first give it a thorough check to remove any inconsistency.
Directly impacts organizations’ efficiency –
Data is the core operating system of any organization. Quality of data directly impacts organizational efficiency. A company’s processes, people, and goals are all affected when the data is inappropriate.
Consider an example of a marketing team from an organization where the team makes a costly mistake after sending emails to the wrong target audience. The situation would have been under control if the team had access to clean and accurate data.
Data acts as a core part of any organization – at times when the quality cannot be trusted, and resulting actions are incorrect, it may lead to loss of the organization.
Obstructs digital transformation –
Poor data quality has severe impacts on processes, the environment, and people. It eventually affects digital transformation goals. At times, there might be a situation where companies might face obstruction that needs to halt a project to fix a data quality problem. Handling such cases consumes a lot of time and effort, resulting in transformation delay and the slowdown of company processes.
Along with these major problems, poor data quality has become a significant reason behind a dozen other minor issues. Business leaders usually ignore these issues until they become a major bottleneck for companies to deal with.
Continued growth and business expansion lead to changes in data collection needs, and the sort of data relevant to them evolves with time.
Bad data will always be there and will keep growing with time. It is important for organizations to architect systems that effectively handle data errors. Building up proper structure will help eliminate unexpected downtime, prevent data loss and avoid operational delays.
For an organization to remain data-driven and prepare for the information era, it is essential to implement a data quality framework fast. This will help to overcome the consequences of the bad data.
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