Spring Clean Your Data for Accurate Insights
“A place for everything and everything in its place.”
In today’s world where digital data is part and parcel of every application, a process like spring cleaning is just as important for data as it is for our homes and offices. Organizing and getting rid of unnecessary data is an essential practice that every organization should follow. Also, we may find some valuable things during our cleaning process. You might uncover some great insights from hidden or old data through this spring clean your data. In this blog, we will discuss ways to keep data in your databases and dashboards clean and the reasons to do so.
Why cleaning matters
How does your attic look? Full of clutter and untouched for a long time? It’s the same for data that sits on physical devices or somewhere in the cloud, in folders or databases, untouched for years. Here are some reasons to clean your data regularly:
· Cleaning data is crucial: Data that is left untouched in physical or digital storage can result in unwanted costs and legal liabilities.
· Compliance with data regulations: Regulations such as GDPR apply to data currently stored as well as data that will be stored, so it is important to get rid of any data that should not be retained.
· Cost reduction: Unwanted data can lead to additional costs for backup and replication, making it essential to regularly clean and organize data.
Dirty data eats your time and energy
Ineffective data management can have significant negative consequences for businesses. This is especially true for sales and marketing departments. Keeping outdated information about opportunities is ineffective, as people may have different emails or jobs. As a result, a marketing team’s efforts and time will be drained by using that information to try to find a person who might not be there to target. Leaving such data in your system exhausts marketing and sales campaigns.
Dirty data can affect a business in several ways, including:
· Inaccurate decision-making: Dirty data can result in inaccurate analysis, which can cause poor decision-making.
· Decreased customer satisfaction: Incorrect data can lead to mistakes in customer communications that lead to decreased customer satisfaction and loyalty.
· Reduced efficiency: Dirty data can cause errors that can slow down business operations and reduce productivity.
· Loss of revenue: Dirty data can result in missed opportunities or incorrect financial reporting, leading to a loss of revenue.
Debate the best solution
While cleaning out your home, someone will come across an object and say to another, “We haven’t used this for a long time. I don’t know why we bought it in the first place. Can we trash it?” The response from the other? “No!” This scenario comes up in companies, too, while cleaning up old data stores. Before deleting a database or table, or a file, a conversation needs to be held by a lead and their engineers. The lead needs to interrogate why something is implemented if it hasn’t been used for a long time, or why it was not created or maintained properly, and whether it’s still needed. Either the team changes to an improved version of the database or file, or they clean up the stored one.
Best practices for maintaining high-quality data
Here are some potential solutions to maintaining the high-quality data:
· Regular data audits: Conducting regular data audits can help find and fix problems with data before they grow into bigger problems.
· Data governance: Creating a detailed strategy for data governance can help ensure that data is correctly managed, validated, and maintained.
· Implement data cleaning techniques: Use data cleaning techniques to find and fix errors in existing data.
· Standardize data entry: Implementing clear standards for data entry can help reduce the occurrence of errors and inconsistencies.
7 tips to avoid data clutter
1. Descriptions of data sets should be clear so that even a layman can understand them.
2. Verify email addresses regularly, not just once at signup. For example, tracking marketing email engagement will help you identify and purge frozen leads and spend more time on hot leads.
3. Set a retention period for your data from the beginning and track it with reminders.
4. Reduce having nullable columns as much as possible in your database schema. This will make sure that a proper table is prepared at the initial stage.
5. Make sure naming conventions and best practices like database normalization in storage are strictly followed by each data engineer.
6. If you’re working with either a mobile phone or a database, the following will help you find some extra space:
a. Find duplicate copies and remove whichever copy is not required.
b. Try cleaning out large-sized items (files/tables) you feel are not important anymore.
7. If you see that the schema changes frequently, investigate choosing document databases like MongoDB or Azure Cosmos DB, but do it with clear documentation about each field.
Finally, repeat this mantra periodically:
“A place for everything and everything in its place.”
Spring-clean your dashboards
Dashboards, reports, and extracted data sources in dashboards and reporting tools should be cleaned out because:
a. Over time, some dashboards and reports may get archived — i.e. not used at all. Cleaning up or organizing such dashboards and reports will tell you whether you might be missing one or more dashboards that could be helping you make certain decisions with better insight.
b. Some dashboards with sensitive information might have been left lying around after their purpose was served.
c. Extracted data sources, if scheduled to automatically refresh and left unnoticed, will use an application’s resource calls and storage. So, it’s good to stop unwanted refreshes or delete the data sources once their purpose is fulfilled.
Tools for data cleaning
There are many data cleansing tools that can automate the process, but there is some benefit to manually cleaning data. Also, it’s good to have some benchmarks to ensure data cleanliness. Cleaning data isn’t a one-time process. Put it on your schedule to do it at regular intervals. Business Intelligence (BI) tools are helpful for data cleaning in the following ways:
1. Data transformation: BI tools can transform raw data in a variety of ways, such as eliminate duplicates, convert data types, and standardize data formats.
2. Data integration: BI tools can integrate data from different sources, which may have different data formats or data quality issues. Integration can help identify data inaccuracies and facilitate data cleaning.
3. Automated data cleansing: Some BI tools offer built-in features for automated data cleansing. This can involve processes such as removing outliers, filling in missing values, and correcting data formatting errors.
4. Visualization: BI tools can visualize data which can help identify quality issues, such as missing data, outliers, and data inaccuracies. Visualization can also help identify data patterns and trends that may require further inspection.
Bold BI is business intelligence dashboard software by Syncfusion that empowers you to transform your data into actionable insights. Try Bold BI by signing up for a free 15-day trial. You can contact us by submitting questions through the Bold BI website, or if you already have an account, you can log in to submit your support questions.
We also recommend that you read our other blog posts to find the best ways to get started with data cleaning and gain actionable insights from your highest quality information:
· 6 Red Flags Your Analytical Dashboard Needs a Refresh
· Data Catalog: What Is It and Why Is It Important?
· Optimize Your Business Operations Using Data Intelligence
· Data Preparation, Data Transformation, and Identifying Business Trends
· Data Analysis: Identify Trends and Boost Organizational Growth
Originally published at https://www.boldbi.com on March 2, 2020.
