With the explosion and higher dependability on data assets, it is essential for businesses to prioritize data hygiene practices more than ever.
Accurate data is the foundation for any company. Data quality and integrity are vital to any data-driven organization for boosting overall productivity, enhancing marketing efforts, getting actionable insight, improving AI/ML model accuracy, or making informed business decisions. Monitoring data quality and investing in data quality management practices is a sure-shot way to ensure long-term business success.
Low-quality data- Skewed results
Poor data is inevitable for companies dealing with voluminous data every day. Dirty data may creep into and persist in company databases due to several reasons. If not addressed on time, the data quality degrades regularly and impacts all business decisions and marketing efforts.
Common ways through which dirty data seeps into company databases:
1. Data Entry Errors-
When performing manual data entry, errors are inevitable. These errors can occur due to typos, misinterpretation of data, or even unintentional mistakes by data entry personnel. These mistakes in the data entry process lead to inaccurate, incomplete, or duplicate entries, thereby hampering the overall data quality and integrity.
2. Lack of data standards-
Companies that don’t have well-defined data structures, formats, and taxonomy can have the most inconsistencies in their datasets. As different team members within the organization follow different guidelines for data input, your dataset is bound to have discrepancies. Moreover, it becomes challenging to ensure data consistency and accuracy across the different datasets.
3. Diversified data sources-
Depending on multiple use cases, businesses gather data from several sources. Simply collecting, merging, and integrating this data into the company systems, without data cleansing or validation can lead to duplicate entries, inconsistency, and unstandardized data.
4. Voluminous & complex data-
Businesses often deal with massive, multifaceted data on a daily basis. Managing such large-volumes of data can be highly challenging for your in-house team. Moreover, if the data is complex it becomes even more challenging to transform and process this data, leading to errors and data mishandling.
5. Poor system integration-
Different teams within the organization may use different software or databases to store, m3anage, and retrieve data. When merging data, inadequate integration between these systems/databases can lead to discrepancies and errors in data.
6. Lack of quality checks-
Regular data audits are critical to ensuring data relevance and accuracy. Failure to implement data hygiene checks during the data collection or transformation process can result in missing values, outliers, or other inconsistencies in datasets over time.
To deal with these data quality issues, companies require robust data cleansing practices and skilled resources to transform, process, and manage this data for meaningful insights.
Six Elements of Quality Data
How does data cleansing help businesses tackle these data quality issues?
Data cleansing techniques ensure error-free, complete, standardized, and reliable information that can be trusted to make informed business decisions and draw insights. By identifying gaps, inconsistencies, and errors in the data, data cleansing ensures that your data assets are up-to-date using the following techniques-
● Data Scrubbing
Data scrubbing techniques involve identifying errors and discrepancies in large datasets and taking corrective measures to rectify them to improve overall data accuracy, consistency, and reliability. This provides businesses with the foundational element of high-quality data upon which to base business insights, reporting, and strategic decisions.
● Data Enrichment & Appending
Data enrichment refers to the process of identifying missing information in the databases and adding information from third-party/additional data sources to fill those gaps. It may involve filling in contact numbers, email IDs, etc. of your customers or may also involve adding additional information from the web (as per your business use case) for complete & up-to-date databases.
● Data Standardization
As a part of data cleansing, standardizing data refers to the process of transforming the data into a uniform or common format. The process makes it easy for your company to access, analyze, and retrieve data. Furthermore, it enables your business to simplify reports and visualization, improve data integration, ensure regulatory compliance, and improve the overall data quality.
● Data Verification & Validation
Data validation and verification techniques help ensure the accuracy, completeness, timeliness, and reliability of data sets. Since manually entered information may contain errors or become outdated over time, these cleansing techniques flag discrepancies within data and facilitate updating or removing faulty records. This process of continuous inspection and correction helps safeguard high-quality data that businesses can confidently utilize for daily operations and strategic initiatives.
Top 5 Reasons Why Data Cleansing Must Be A Part Of Your Business Strategy
1. Improves Data Analysis
Dirty or inaccurate data can lead to inaccurate analysis and lead to incorrect conclusions. With clean, accurate, and reliable data sets in place, you can trust the insights and trends you gain by analyzing your data, thereby helping you formulate precise business strategies. When the stakes are high, poor analysis can also lead to hefty revenue losses. Hence, investing in data management practices or is critical to ensure correct analysis and save your business from costly mistakes.
2. Boosts Marketing Efforts
The precision of customer data holds paramount importance for marketing success. Whether you are sending personalized messages, executing email marketing campaigns, or performing any form of outreach activity, the effectiveness of these marketing initiatives depends on the fidelity of the customer data. The presence of duplications, outdated email addresses, inaccurate contact numbers, and related discrepancies can lead to a waste of marketing efforts, time, and ROI.
3. Improved Customer Relations
Inaccurate customer data and the presence of duplicate entries in databases can result in miscommunications and poor customer experiences (imagine a customer getting the same message multiple times). By incorporating data cleansing strategies, you can maintain up-to-date customer profiles, ensuring that your marketing messages are accurately targeted. This not only enhances customer relations but also builds loyalty and encourages repeat business.
4. Ensures Sound Business Decisions
In today’s competitive market, data-driven decision-making is the key to business success. However, important and strategic decisions require reliable data. Making decisions based on inaccurate, irrelevant, or incomplete data can be detrimental to your business. Data cleansing practices ensure information integrity across systems and allow executives and managers to make sound decisions for planning and growth initiatives.
5. Improves Team Productivity
Disorganized databases can significantly hamper your in-house team’s productivity. Retrieving information from these databases becomes time-consuming. Additionally, it reduces manual work by automating error corrections, streamlines business processes to ensure smoother operations, enables faster decision-making, and enhances collaboration within the team by working with reliable data.
B2B Data Cleansing: A Shield To Faulty And Corrupt Data
Now that you know the importance of data cleansing as a business function, regularly cleansing data is the ultimate solution for resolving issues within your data pipelines. If managing large volumes of complex data has been a constant challenge for your in-house team, outsourcing data cleansing services to a reputed service provider can be of great help! With their services at your disposal, you can get skilled data cleansing resources by your side and also save a great deal of time and money.
Having followed these suggestions and building a robust data management strategy, you can surely mitigate risks such as financial losses, regulatory non-compliance, and reputational damage, while also harnessing the full potential of your data to drive innovation, make informed decisions, and gain a competitive edge in your industry.