Every organization deals with data that keeps increasing on a day-to-day basis. This data is highly significant because it is the key to understanding what is going on in your business. The quality of data directly affects the quality of the data analysis. That is why you need to ensure clean data with the support of data cleansing services. Poor data may not pose a problem initially, but conclusions and decisions made on the basis of this data could be inaccurate or inappropriate and lead to major setbacks in the organization. This makes it vital for Chief Information Officers (CIOs) and IT managers to implement data quality rules to remove inaccurate data and ensure the best quality data based on which important decisions can be taken.
The Need for Good Data
One of the major reasons for poor data quality is human error. This is because most organizations still rely on manual data entry. This is substantiated by a recent study by Experian QAS that revealed that 65 percent of businesses consider human error to be the cause of most of their data quality issues. Any minor error can lead to problems like duplication of records, misspelled words, and inconsistency in data formats.
Data quality should be optimal to ensure that the data used in the organization is accurate, reliable and actionable. Good data leads to better functioning of organizations, and all basic operations can be performed quickly and efficiently. Data completeness and relevance can be achieved by properly managing data quality in the organization. Even if the data is of high quality there is no guarantee that it will provide valuable business insights. Why? To capture the vast amounts of data from which businesses draw insights, most data systems include user-defined fields. These fields provide input flexibility but could lead to the buildup of redundant and valueless data, creating greater difficulty for analysts.
Big Data Analytics Software Can Help
Now big data analytics software helps in analyzing and managing large-volume data. Research firm McKinsey predicts that big data has a potential annual value of 250 billion for Europe’s public sector. Mid Kent Service, a local authority partnership in the UK, used business analytics software to deal with staggering volume of data. This consisted of over 20 million car parking records, 500,000 service calls records and 65,000 council tax records. The administrative staff had previously spent time combining data from multiple sources to prepare reports for managers. This manual process was obviously time consuming and inefficient. But with the help of software, managers could see all of this information in one format.
Data Cleansing to Get Rid of Corrupt and Inaccurate Data
Data cleaning is the process of detecting and correcting inaccurate or corrupt data. Client details are often typed wrong or may be inaccurate; such data is no longer clean. Before implementing ERP (Enterprise Resource Planning) in your organization, make sure that your data is clean. Before cleaning your data, decide what type of data you have, how much data you need and how accurate the information is. Once you have decided what data you need, then ensure that all records are standardized, remove all useless data, and fix all duplicate and input missing fields. Various automated tools are available for cleaning data. All the business data should formatted and organized for better consistency and with a good ERP system all business tools and data can be connected well.
3 Steps to Take to Ensure Good Data Quality
So how can you ensure good data quality? Data quality issues can be avoided to a great extent if the data inaccuracies are corrected at the source itself.
- Quality audits: The objective is to find data sources that contain low quality data, and correcting the information at source. Low quality data sources may use only few rules to govern data input and formatting. A rules-based approach to the data quality audit can reveal the probability of inaccurate data.
- Implementing data matching: This method is slightly more challenging because unique identifiers will have to be built within the data using uncommon values in the database. By selecting several such uncommon values, a unique identifier can be created, which will help reduce duplicate data and improve data accuracy.
- Use more powerful data analytics tools: With such analytics software, you can define a master record or a data base that maintains accurate and updated data that can drive amendments to other data sources containing duplicates and other invalid data values.
Data entry, cleansing, and analytics are important processes that have to be completed in a timely manner. If this proves to be too time-consuming or tedious, these processes can be conveniently outsourced to a reliable data entry service provider. These services offer high quality data entry for a wide range of industries. Skilled and experienced workers ensure that the data is streamlined and managed well. They maintain confidentiality of your data and provide customized service according to your specific requirements at economical rates.