Tamr’s Five Essential Data Cleaning Techniques

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Key Takeaways

⇨ Data management is an essential part of digital transformation and innovation for SAP users as they move from ECC to SAP S/4HANA.

⇨ Organizations often have to contend with missing, duplicated, and incorrect data points.

⇨ Companies can leverage AI and ML solutions to find outliers, correct inaccurate and inconsistent data, and reduce the time data scientists spend on cleansing data.

Data management is an essential part of digital transformation and innovation for SAP users as they move from ECC to SAP S/4HANA. In the 2023 SAPinsider Data Management Strategies research report, nearly half of all respondents (48%) said the increasing demand to provide real-time data to users was a driver of their data management strategy – making it the top overall driver.

Without cleansed, validated, and harmonized data, high-performance and more intuitive dashboards and reports lose their meaning. Organizations must consistently clean their data by identifying and correcting errors and deleting inconsistencies and inaccuracies. Ensuring that data is scrubbed and refined is an essential step.

Organizations must take a consistent, methodical approach to cleansing data to ensure that nothing slips through the cracks. The data quality experts at Tamr have shared a list of five data cleaning techniques. After performing a data quality analysis, companies can rely on these techniques to ensure their data is free from errors, inconsistencies, or other issues.

Data Cleaning Techniques

  1. Standardize formats: Organizations should ensure that all data of the same type is formatted the same way. Tamr highlights dates as one example of a commonly misaligned type of data. “In some systems, the year may be four digits, while in others it may be two digits. Some systems may capture month first, while others start with day. Even though these differences seem insignificant, when each system tracks data differently, it’s challenging to integrate the data and create a master view. Standardizing formats across systems and data sets using data products eliminates inconsistencies and enables more accurate data analysis.”
  2. Fill in missing values: Though it may seem obvious many crucial data sets have missing or null values. Data teams should fill them in with accurate information. Often, when filling out datasheets, workers will leave certain fields blank if they do not have the necessary information readily available. These fields should all be filled out to the best of the ability of those responsible for the datasets.
  3. Eliminate duplicates: Data teams should carefully comb through datasets and remove any duplicated data points. Excess data points are not just cumbersome, they can throw off analysis and calculations. This can be one of the trickier data cleaning tasks, as it can be more difficult to identify duplicated data than a blank space. Identifying and eliminating duplicate records often requires data products that standardize data, match records, and perform comparisons.
  4. Correct typos and inconsistencies: Manual data entry can lead to typos and other errors and inconsistencies. Organizations should take care to review datasets and find any issues, as they can obscure insights and lead to faulty decision-making. Typos can be another challenging issues to overcome, as it may not be readily apparent which data points might be one or two digits off. Data products that can analyze entire datasets and spot outliers are a common solution to address these concerns.
  5. Filter and remove outliers: Finding outlying data points that deviate significantly from the rest of a dataset can be a straightforward task. However, once outliers are identified, companies must decide whether those outliers should be removed, transformed, or analyzed separately. Outliers do skew datasets, but organizations need to decide how much skew is acceptable.

Improving data

With all that can go wrong in a given dataset, companies spend a significant amount of time and effort cleansing data. It is estimated that data scientists spend more than 80% of their time cleaning data. Many organizations are beginning to rely on data products, such as those offered by Tamr, which use AI to help organizations access, connect, organize, and enrich data.

Data products like data enrichment can address some of the most common data issues, filling in blank fields or adding in new columns to standardize datasets. Tamr data products also infuse machine learning into data enrichment tools, adding referential matching to identify matches and relationships that human eyes can’t spot without external data, helping organizations to gain the best, most complete version of their data.

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