Data cleaning or data scrubbing is the process of removing data that is incorrect, irrelevant, improper, duplicated or incomplete, and is an integral part of the services provided by data entry and document scanning companies. Data cleaning improves and updates information for purpose of analysis and decision making and is critical for most industries. Manufacturing is one of the important sectors where data cleaning is paramount. Corrupt or inaccurate data will lead to illegible fields, records, or files/tables, and inoperative programs and can affect the entire production process.
The manufacturing industry accumulates a vast amount of data. Big data in manufacturing consists of, but is not limited to:
- Data pertaining to production, sensors on machines, quality, maintenance, and design
- Productivity data – everything from production volume to machine power, water and air consumption, and all the different measurements needed for quality checks
- Big data generated from other software systems such as sensors, pumps, motors, compressors, or conveyers
- Data on equipment maintenance, human resources, and accounting
- Data generated from outside partners, vendors, and customers
All of this data would be meaningful only if it is collected, leveraged and analyzed to gain useful insights. After data collection, the next step is data cleaning – the process of identifying, eliminating, and/or replacing inconsistent or incorrect information from the database to ensure data quality and integrity. This can be particularly challenging in the manufacturing sector as its data tends to be unreliable and inconsistent. There are several reasons for this:
- Lack of Standard Guidelines: Manufacturing companies usually have multiple sites across different geographic locations, data entry is performed by many employees, and there are no standard guidelines.
- Fragmentation of Data: There is poor visibility on inventory data as different parties manage goods inventories, leading to bad data due to over-purchasing, inventory write-offs, stock-outs, and interruption in manufacturing operations. Moreover, massive amounts of data stored in cloud and on-premises locations exist in infrastructure system points that do not integrate, leading to data duplication. Corrupt/bad records can lead to poor inventory visibility, inventory underselling/overselling, low employee productivity etc.
- Wrong IDs: This is a data entry problem. Mistakes in IDs of employees, machines, operations, processes and products. Random errors in manual data entry can infiltrate the whole system, persist, and are difficult to correct.
- Duplicate Data: Companies often maintain duplicate data for testing and development, as back-up for disaster recovery, etc. Companies without data quality initiatives could have duplication rates of 10-30%, according to a 2019 Forbes article. Duplicate copies lead to more fragmentation, waste of resources, expense and management problems.
- Inconsistencies: Data inconsistency occurs when there are multiple tables within a database that deal with the same data but may receive it from different sources. Inconsistencies can be using different names for the same item or recording parameters with the same values as different. Redundant data can make the problem worse.
Other manufacturing data problems identified by michelbaudin.com include logical integrity violations like products with zero sales showing positive revenue and using the same ID for multiple objects, and missing records and values.
Data quality is measured in terms of completeness, validity, accuracy, consistency, and timeliness. With most organizations working towards digital transformation, accurate operational reporting or metrics is a necessity to measure performance. They need clean data to support essential decision-making processes and achieve reliable and accurate outcomes. Data quality management involves identifying the data required to generate the desired metrics and ensuring that correct and clean data is used.
Data cleaning in manufacturing can address errors, missing and incomplete information, nonalignment of schema, and inconsistencies. The process:
- Removes all redundant data from the system
- Improves decision making
- Promotes marketing activities and produces higher ROI
- Cuts storage costs
- Saves money and reduces waste
- Increases operational efficiency and productivity
- Boosts compliance with data protection standards
Manufacturing firms have complex production processes and also multifaceted relationships across the supply chain with vendors and suppliers. Data related errors and inconsistences would be reflected across these processes and relationships, and can really compromise an organization’s functionality. However, addressing errors to obtain clean, structured data can be a challenging task. Partnering with a business process outsourcing company that provides end-to-end data management solutions can ease these challenges. Outsourced solutions are provided using advanced techniques and best practices and a reliable company can help manufacturing companies achieve overall efficiency with quality data and stay competitive.