The Causes of Dirty Data and How to Fight Them

 

Dirty data is described as any information that is inaccurate, unfinished and has little to no consistency. According to multiple experts, over a quarter of the world's data is considered to be dirty. This incorrect data contributes to great losses experienced by small businesses across the globe.

 

The average business in the US would experience a loss of 15% to 20% of their income. The entire US economy would lose upwards of $3 trillion per year. These numbers show that dirty data has a great impact on the business world.

 

The topic of dirty data is one that is difficult to truly understand, mainly because of the mismanagement of the numbers. For this reason, it is a wise decision not only to understand dirty data but also its many origins and possible solutions.

 

Contents:

The Origins of Dirty Data

1. Human error
2. Insufficient data strategy
3. Failing interdepartmental communication

Dirty Data’s Impact

Dirty Data and Banking

How to Deal with Dirty Data

Final Thoughts

 

The Origins of Dirty Data

The causes of dirty data are usually cited as the following:

• Human error
• Insufficient data strategy
• Failing interdepartmental communication

 

Let’s check every point in detail. This classification will help to understand how to provide quality data, enabling cost and time savings for procurement and teams. Besides this, it can help you make better business decisions.

 

1. Human error

It is common for separate departments to enter related data into their specific silos. Unfortunately, any good data strategy will not salvage the system. Everything from downstream data warehouses to marts and lakes will be negatively affected.

 

The main cause of this is the aspect of human error. If a record is duplicated, it may contain non-canonical data which includes the misspellings of addresses and names.

 

A data silo that contains incorrect information will inevitably show dates, account numbers and other information in different formats. This is a problem because it is nearly impossible to automatically reconcile.

 

2. Insufficient data strategy

Dirty data has been shown to be notoriously difficult to detect. In fact, it can remain undetected for years. In this time, the data could have been detected and hopefully dealt with.

 

Unfortunately for over 50% of businesses often discover the glitch when it’s too late. Prospects and even customers will find the glitch and report it. This can cause a company that is ill-equipped to hasten its response.

 

3. Failing interdepartmental communication

The typical company will try to find incorrect data manually because the information is often decentralized. This is an issue because like the data, each department is responsible for its own errors and data inaccuracies.

 

It is possible for some dirty data to be caught, but it exasperates the internal inconsistencies between department silos. A fix in one place will not solve an issue and as a result, more data problems could be brought forward.

 

Dirty Data’s Impact

The problem of dirty data usually impacts businesses when they least expect it. An example of this can be found by looking at the way dirty data negatively affects production in many of its key stages. Personnel from data scientists and knowledge workers focus 50%-60% of their time trying to deal with this instead of the main areas of production.

 

Users will spend too much of their time verifying data because of the lack of credibility that comes with dirty data. This is a manual process so the increasing inconsistencies and inaccuracies will inhibit production.

 

Revenue loss is not the only aspect of business that is affected by dirty data. Dirty data can also affect the business decisions of business executives. This is where the issue will form its base and persist.

 

Dirty Data and Banking

The global revenues for companies across the world are around the $2 trillion mark. Due to dirty data, at least 15% of that has been lost in the banking industry. Dirty data can also bring risks that are specific to banks.

 

Organizations possessing silos that contain inaccurate information will result in transactional issues which may be fraudulent. These fraudulent and fake accounts should be found early, otherwise, the bank’s reputation may be in jeopardy.

 

Dirty data has also caused executives to distrust their current information, thus resulting in poor decision-making. With the constant evolution of regulations, especially in Europe, a burden for data management has emerged, pressuring compliance teams.

 

How to Deal with Dirty Data

It is difficult to clean dirty data of its invalid entries and duplicate data without erasing all the data. On top of this, all other existing data needs to have its consistency improved. If this is done, the data then has to be maintained and all new information has to be checked to ensure that trust is held. The following practices can help with cleaning dirty data as well as governance:

1. Data across different siloes can be correlated and metadata is harnessed for provenance and lineage by harmonizing;
2. It is also possible to form a single platform by leveraging core smart mastering capabilities, thus merging entities;
3. Applying semantics ensures consistency by capturing the relationships between data;
4. Integrating all data sources will create a 360-degree view of them;
5. Usage of natural language searching, machine learning and data modelling to find dirty data.

 

Final Thoughts

The presence of dirty data hurts many businesses globally. Fortunately, we live in modern times where technology has helped mitigate the full impact of dirty data. You can find plenty of tools and apps that can help find dirty data. Despite this, the future sees that this issue will persist due to the fact that solutions usually affect particular departments. This can lead to further data inaccuracies and losses in production. To overcome the problem, you can work with experts in this field and get clean, well-structured data that doesn’t just appear in an organization’s lap. Quality data is about patience, good care, planning and attention to detail.

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