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Dirty Data Done Dirt Cheap

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The Daily Dire Dirty Data Disasters

In today’s data-driven world, businesses rely on data to make informed decisions, plan projects, and increase efficiency. However, dirty data negatively impacts outcomes at every turn, causing businesses to suffer wide-ranging consequences.

What Is Dirty Data?

Dirty data is inaccurate, incomplete, or inconsistent. It includes misspelt names, incorrect phone numbers, or even entire fields missing information. Dirty data can be caused by a variety of factors, including human error, system issues, and data integration problems.

Dirty Data's Impact on Business Efficiency

Dirty data can have a significant impact on business efficiency, as it slows down processes and causes major delays. Put simply: if a customer’s contact details are incorrect or improperly stored, there are delays in communication that ultimately lead to missed opportunities and business left on the table. Similarly, if data is missing or incomplete, it can be challenging to complete tasks, resulting in wasted time and resources. 

It is difficult to estimate the exact cost of dirty data to the global economy, as it can vary greatly depending on the industry and the specific nature of the data. However, studies have shown that dirty data can cost businesses eye-watering amounts of money.

For example, a recent study by Gartner estimated that the average financial impact of poor data quality on businesses is around $15 million per year. Another study by IBM found that poor data quality costs the US economy around $3.1 trillion per year.

Dirty Data's Impact on Project Planning

Project planning relies heavily on accurate data to make informed decisions. Dirty data can severely impact project planning because you can’t expect an accurate plan to come from inaccurate data. If a project manager relies on inaccurate data to plan a project timeline, deadlines will be missed and budget overruns will appear as unforeseen roadblocks come rear their ugly heads. Unforeseen hurdles are part and parcel of dirty data, and are the natural outcome of not having a complete picture of the lay of the land.

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Dirty Data's Impact on Decision Making

Data-driven decision-making necessitates clean and accurate data. If a business relies on inaccurate financial data to make decisions, then it can hardly be a surprise when those decisions come back to haunt them, resulting in financial losses or even legal trouble. Dirty data causes businesses to make the wrong decisions, resulting in lost opportunities or even legal issues. 

Dirty data can also have indirect costs, such as the cost of missed opportunities or lost business due to inaccurate or incomplete data. It can also result in legal issues, such as regulatory fines or lawsuits, which can further add to the cost. It can also eat up valuable time as teams are stuck chasing their tails as they attempt to make up the shortfall.

Overcoming Dirty Data

Identifying the root cause of your dirty data is naturally the first port of call when developing the solution. Knowing the solution is only half the battle, and developing solutions can result in a whole new set of headaches. Conducting an audit of existing data or investing in tools that can help clean and maintain data are enormous business decisions in their own right. It’s also important to establish processes for data entry and management to ensure that data remains accurate and consistent. The key to this is maintaining end-to-end visibility during implementation of these solutions, lest the solution be based on its own set of erroneous assumptions. 

Dirty data can severely impact business efficiency, project planning, and decision-making. It’s crucial for businesses to invest in tools and processes that can help maintain data accuracy and consistency to avoid the negative consequences of dirty data. By doing so, businesses can make informed decisions, plan projects effectively, and increase efficiency – ultimately leading to greater success in the long

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