How to Improve the Quality and Consistency of Your Data

Most modern, growth-oriented enterprises of the 21^st century have utmost faith in the power of data. Insights deduced from consistent, high-quality data have proven to help businesses thrive and gain a significant competitive advantage in the market. 

However, inferring these insights is not as easy as it sounds. Enterprise data comes from a wide variety of sources and is recorded differently in most cases. Thus, consolidating data into a single database is a daunting challenge and one that needs to be tackled strategically. 

How do you convert unstructured, incomplete, and inaccurate data collected from disparate sources and convert it into consistent, high-quality information that can be analyzed? Additionally, how do you automate the conversion process, so the incoming records are in accordance with the data quality standards that you have set?

This blog will go over how data integration can help your business collect data, and improve its overall quality and consistency. Furthermore, we will look at how consistent, high-quality data can be leveraged to boost your company’s sales, productivity, and efficiency. 

But first, we need to understand what data quality and consistency means and why they are so important.

Data Quality

Data quality refers to the extent to which the data reflects the information it is collected to measure. For example, auxiliary tools are very effective for tasks like mysql optimize table., the data about a person’s physical features will be regarded as high-quality data if it has no inconsistencies, doesn’t include extra spaces, etc. 

Different quality checks can be implemented at different stages of the data lifecycle to ensure that enterprise data is of high quality. Although there is no arbitrary way to determine data quality, several factors can help data analysts make judgments about this metric. These include:

1. Accuracy: The degree to which the information represents the event or object it seeks to describe.
2. Completeness: This refers to notions of comprehensiveness and whether all aspects of the study are recorded. 
3. Consistency: Relates to whether the facts recorded in one place are logically backed by other data.
4. Timeliness: If your data is readily available when needed, it can be regarded as timely data.
5. Validity: This is related to the structure of datasets and conformity in data streams.
6. Uniqueness: Data that has only one instance of each record is unique. Duplicate data can contaminate a business’s datasets and lead to inaccurate insights.

High-quality data lays down the groundwork for actionable insights and helps develop business wisdom over time. Data analysts and managers need clean, concise, and structured data to back up their business decisions with supporting evidence.

Data Consistency

Although data consistency is only one dimension of overall data quality, the measured variables must remain the same throughout data collection. Discrepancies in datasets can lead to inadequate insights and misinformed business decisions for professionals. 

Enterprises often use data integration tools when aggregating data from multiple sources and recording it into a unified database to avoid errors and identify potential issues with data consistency. These tools have built-in features that analyze the data and point out any outliers and inconsistencies. 

Improving Data Quality – An Actionable Plan

The essence of improving data quality is dependent on understanding what the data will be used for. The standards and requirements for high-quality, consistent data vary across organizations because of the differences in key performance indicators (KPIs) for each business. However, every enterprise can take specific measures and steps to ensure that their datasets can be consolidated into a unified database from which actionable insights can be derived, especially if they have plans to use more ambitious analyst services like data valuation. Some of these are outlined below:

Guidelines for Internal Departments

A significant chunk of enterprise data is produced internally in companies; therefore, department managers must be given clear instructions about the format and structure of data. 

Examples of business data that is produced internally might include supply chain and marketing data, financial information, expense reports, and human resource records. Employees need to make sure that all the necessary fields are filled and the data formats are consistent across the company to easily unify them in a single database. 

Planning Data Pipeline Structure

One of the most common mistakes companies make when implementing a data management strategy is extracting insights without proper planning. Planning an effective data pipeline of data structure can improve the quality of your data by a significant margin. This can potentially save hours spent on bug-fixing, record inspection, and error correction while acting as a mechanism to ensure that data is consistent, timely and complete.

Normalize Data

The web is the most extensive repository for data around the world. Although there are some established guidelines for data recording and transmission across industries, companies often differ in formatting preferences. 

For example, companies from the US often record currency values in USD while European countries use their native currencies or Euro. Similarly, date formats vary around the world and consolidating differently formatted datasets can become confusing. Therefore, it is a good practice to convert these fields into your company’s preferred format at the first stage of the data lifecycle before it is mapped onto other datasets. 

Emphasis on Metadata

Metadata refers to the information that describes the data, explains formatting preferences across datasets, and provides definitions for vague terms within them. Data quality and consistency can be increased by embedding descriptive metadata into the datasets, allowing users to make sense of it easily. Wherever possible, data professionals should seek to give detailed descriptions to enhance transparency, provide contextual information, and declare formatting rules. 

Automate Repetitive Tasks

Repetitive data validation tasks must be automated using data integration tools to boost efficiency and avoid errors. These tools automatically detect discrepancies and issues with incoming data and can be set to fix these problems automatically. Therefore, the firm only stores clean, consistent data in its data warehouse. 

For example, if a person accidentally enters string data in the data field for an integer, the software will detect an error and suggest a correction. Thus, automating data processes saves time and makes up for human fallibilities and maximizes productivity within the enterprise.  

Manually Correcting Common Errors

Errors are an inevitable aspect of the data lifecycle process. While most errors can be taken care of through data validation checks performed by your enterprise’s native data management suite, some might persist. 

A manual data quality assurance mechanism must be set up to fix any obvious issues that the software has overlooked promptly. This saves valuable time and effort required to fix the same problems at later stages and improves the relevance and consistency of data. 

Conclusion

Improved data quality will immediately benefit your organization in several ways; it can lead to higher turnovers, better decision-making, and higher productivity across departments. Data needs to be tested against the six dimensions of data quality to understand how adequate it is for use. 

While most data integration tools can handle common errors, manually skimming the data for any obvious errors and fixing them can add a layer of quality to your data. Big data is quickly becoming synonymous with business insights, and hence, enterprises need to adapt quickly by taking the necessary steps to improve data quality.

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