5 Simple Steps for Effective Data Cleansing

Companies across the world will invest over $275 billion per year on data and analytics by the end of 2022, according to IDC Research.

Today's businesses are investing billions of dollars in big data and analytics solutions, as well as considerably more in the infrastructure required to support them. Companies across the world will invest over $275 billion per year on data and analytics by the end of 2022, according to IDC Research. For leaders seeking to innovate in a fast-changing and rapidly digitizing business climate, digital transformation – and the ways it can enable data-driven decision-making across the business – remains top-of-mind.

 

These initiatives, however, will fail if they do not have access to clean, high-quality data. According to IBM researchers, poor data quality costs businesses in the United States $3.1 trillion per year. The reality is no matter how much an organization spends on data systems, they’ll still produce garbage if you put garbage into them. Improving data quality, without a doubt, presents a huge opportunity for cost savings and improved business intelligence.

 

What is data cleansing?

Data cleansing is a vital stage in preparing data for analysis. In general, it entails locating and replacing incomplete, inaccurate, or irrelevant records in a data set, as well as modifying or deleting those records. If data cleansing is effective, all data sets should be consistent across the enterprise, and all should be error-free. Data is the fuel for today's business decision-making, so ensuring its quality aids the company in making better strategic decisions. Data quality also cuts down on wasted effort (for example, the sales team won't waste time cold-calling prospects at the wrong phone number) and streamlines business processes, improving overall operational efficiency.

 

The researchers identified several criteria that should be met in order to classify the data as high quality. These include:

Validity: Does the data conform to pre-specified business rules or constraints? These can include data ranges, maximum or minimum values, or limits such as ‘this field cannot be empty.’

Accuracy: How well does the data represent the truth? How closely does it match what’s been measured or recorded in the real world?

Completeness: Is the data set thorough and comprehensive?

Consistency: Are measures equivalent in multiple data sets across the enterprise?

Uniformity: Are the same units of measure used in all systems?

Timeliness: Is the data recent enough to retain value and relevance?

 

5 Steps to better-quality data

Manually cleaning up a single small data set is not a tedious task. However, ensuring that the company has the correct governance processes and business rules to eliminate most errors in most records usually requires concerted efforts and approval from leaders, especially as the company collects more and more data. To find the root cause of system failures, you need to have a semantic understanding of the business and its data modeling and analysis requirements. With this in mind, here are some general steps that data teams and business stakeholders can follow to improve the quality of data in their organization

 

No. 1: Correct data errors at the source, or as early as possible.

The sooner errors are fixed in the data collection process, the less frequently they are copied and the less trouble they cause in the long run. Sometimes corrections are easy: for example, redesigning Web data entry forms can greatly reduce the number of errors customers make when filling in. Sometimes it may be difficult to identify the source of the error, but it is always worth the time and engineering effort.

 

No. 2: Do the simplest things first.

Certain data cleaning tasks require much less work than others. These are always the best candidates for automation. Removal of extra spaces, empty cells, incorrect formatting, and duplicate values ​​is relatively simple and should be resolved at the earliest stage of the data cleaning process.

No. 3: Measure data accuracy and monitor errors.

Although the accuracy of the data can be verified through continuous research, it is often beneficial to invest in data quality monitoring tools that can handle enterprise-level data sets and alert your team to errors or issues that require further attention. real time. Cloud-based solutions that do not require any special hardware or management work can be provided on a cost-effective subscription basis.

 

No. 4: Have a steward who takes ownership of the challenge within the enterprise.

In larger companies, it is important to appoint a person who can support the importance of data quality within the organization. This person can contact external experts, suppliers, board of directors, and C-suite to promote the business value of clean data to stakeholders.

 

No. 5: Leverage pre-built tools, including semantic modeling and machine learning.

Although large data sets are generally considered valuable because they can be used to train machine learning (ML) and artificial intelligence (AI) algorithms, ML-based automation solutions also have powerful features for data cleaning applications. Algorithms can use clustering to find duplicate values, identify outliers to flag possible errors, and automatically delete records that conflict with other records elsewhere in the company. 

 Although data cleaning requires your team to spend time and effort, the benefits that high-quality data can bring to the business are well worth it.

 

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Aakash Karki

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