Any small business has a means of collecting information from its clients. This information helps decision-makers identify trends and make forecasts. To make these reports credible, there is a need for efficient data management approaches. Therefore, small businesses must invest in quality data storage management processes and tools to make the most of their data and to save time and money. Here is a look at the benefits of data management and the best practices for cleaning your database.
Importance of Data Management
It is crucial for business data to be clean and organized for top management to spot trends, marketing opportunities, and workflow solutions. You will waste time and money when customer data is disorganized or inaccurate. Some of the key benefits of data management include the following.
1. Low Costs and High Productivity
A study by IBM established that poor data management costs the U.S. economy $3.1 trillion. This is caused by a decline in employee productivity and low costs for data maintenance. Up to 50% of an employee’s time is spent searching for information, correcting mistakes, and vetting unreliable data sources. Employees spend additional time correcting the mistakes they find. Failing to notify data managers about the causes of these errors leads to redundancies and lower productivity.
2. Improved Data for Sales
Sales teams and managers rely on data to forecast marketing trends and sales leads. However, when you have poor-quality data, your sales analysis is flawed. As a result, your business uses unreliable variables to target potential customers.
According to a survey by SnapLogic, 77% of decision-makers and data managers regarded their business’s data as untrustworthy. In the same study, 82% confessed to altering their project because of poor data quality. With a clean database, you are guaranteed accurate records which guide your marketing and sales approaches to reach potential customers more efficiently.
3. Improved Customer Services
Quality services motivate customers to make repeat purchases. However, dissatisfied customers with your business’s services will look elsewhere for their products. The results of bad data management include sending orders to the wrong places and asking customers to fill in their personal details more than once.
Incorrect or irrelevant communication is a turn-off for many customers and will ultimately hurt your business prospects. Good data management practices ensure your marketing strategies achieve customer satisfaction and save your business significant time and money.
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Ways of Keeping Your Database Clean
A disorganized and difficult-to-understand database can be overwhelming for any business. This is why it is essential to ensure your database is organized, clear, and easy to use. Here are some ways of cleaning your database.
1. Ensure Departments are Unanimous
The low productivity in any business arises when company departments have different interpretations of data uses and goals. This is why all employees should receive adequate training on business data applications. To improve data management, you should ensure all team members understand how to use data sets. This leads to better classification and organization of data, hence a clean database.
When both data managers and the sales team are unanimous on data uses, this improves decision-making processes. This is because standardized data is shared between departments leading to accurate reports and analysis.
2. Remove Irrelevant Data
It is crucial to develop a system for removing irrelevant data. Unnecessary information complicates data analysis. It also makes it more time-consuming to understand data and compile an accurate report. Therefore, you should create a system for differentiating relevant and irrelevant information before cleaning data. For example, if you want to analyze the age range of customers, there is no need to include their email addresses.
Other elements you need to remove to simplify your data include:
- URLs
- Tracking codes
- HTML tags
- Blank space between text
- Personal Identifiable (PII) data
3. Convert Data and Clear Formatting
When cleaning your data, don’t forget to convert data types. In many cases, numbers are imputed as text, but they need to be written as numerals for easier processing.
If numbers appear as text, they are classified as a string, which means you cannot perform equations in your analysis. The same applies to dates that are written as text. You should convert all this data to numerals. For example, for an entry that appears as October 15th, 2023, you need to change that to appear as 10/15/2023.
Another important part of the cleaning process is to clear formatting. Many machines cannot process heavily formatted text. There are likely different document formats if your data is derived from different sources. Therefore, remove all the formatting applied to your documents. This isn’t a difficult task. For example, Google sheets and excel have a standardization function that clears formatting.
4. Language Translation
Cleaning data involves ensuring all the information is in one uniform language. The Natural Language Processing (NLP) models used in software for analyzing data are monolingual. This means they cannot process multiple languages. Therefore, translate all your information into one language. Manual translation can be laborious, so you should consider CRM software.
5. Implement Processes for New Data Creation
Reviewing all your business data to fix errors is time-consuming and a waste of financial resources. Identifying duplicate content is also overwhelming. Instead, focus on the reason why the data is erroneous. In most cases, collecting irrelevant data and poor classification of information results in numerous data errors and irregularities.
To improve data analysis, you should implement standard processes for collecting new data that your staff should familiarize themselves with. For example, data forms should use consistent formatting and measurement.
When databases are consistent, it is easier to analyze information. Setting new data rules helps prevent unnecessary information capture. Studies show that up to 60% of dirty data is caused by human error. Transparent constraints will help minimize human mistakes.
6. Improve Storage Management
Keeping your database clean involves improving your storage management practices. Storage management entails all processes and technologies for managing your data storage system. The ideal storage management system is cost-effective, efficient, and secure.
Some storage management processes you should consider include data compression, replication, migration, and disaster recovery. Data compression helps create drive space, minimize memory gaps, and reduce retrieval times.
Data migration enables you to move data between different storage locations. On the other hand, data replication is storing the same data in multiple locations for resilience, redundancy, and reliability. Lastly, disaster recovery helps you protect your data in case of a security breach or any related disaster. You should implement tools, policies, and procedures like cyber resilience to restore business operations after a disaster.
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7. Keep Updating Data
Part of cleaning your database should involve updating your data. This eliminates irrelevant data and ensures all the available information reflects the current situation of your business. The easiest way to keep your data up to date is to use an automated data refresh system.
Data optimization and updates are necessary when a customer changes their address or moves to another location, changes their status through marriage or divorce, or changes their phone numbers and email addresses. Additionally, a customer may change their job titles and positions. Failing to update information means you won’t be able to reach your customers. This will also lead to inaccurate data analysis.
8. Use CRM Software and Automation
Another efficient way to clean data is by using content management software (CMS). This software will help classify and organize your data in a searchable system. It also automates the process of identifying errors, removing duplicates, weeding out irrelevant data, and converting data.
Another more cost-effective and efficient solution is customer relations management (CRM) software. CRM tracks customer data and consists of critical business tools in areas like CRM workflows, sales funnels, and lead management. CRM software minimizes the time it takes to identify relevant data and analyze it for improved business decisions.
CRMs also provide artificial intelligence (AI) components to reduce the likelihood of human error. The following are some of the popular automated tools you can expect in most CRM systems:
- Robotic Process Automation (RPA): This tool is helpful for repetitive and routine tasks. RPA involves simulating human interaction at high speeds.
- Machine Learning: This tool identifies patterns and trends through a comprehensive analysis of customer data.
- Smart Workflow: This tool monitors and manages tasks conducted by humans and machines. As a result, smart workflow enables you to spot any inefficiencies by either your staff or the automated systems.
- Natural Language Processing: This tool breaks down complex data into easy-to-understand bits for clear reports.
- Cognitive Agents: These are virtual agents that analyze all accumulated data to offer quality customer services.
Wrapping It Up About Keeping Your Small Business Data Clean
Data management is a fundamental process for any business. Databases are central to analyzing the feasibility and profitability of a business. Business owners can identify their strengths and weaknesses by reviewing information in databases.
Disorganized and poorly classified information is difficult to decipher. Furthermore, it results in errors and inaccurate reports. Over time, this costs you in terms of losing customers and low sales. An essential step in ensuring data reflects the status of your business operation is data cleansing.
Many businesses have moved from the manual system of cleaning data to automated systems. These systems ensure there are no data errors and irrelevant data isn’t collected in your databases. Additionally, CRM systems are easy to use and help you gather necessary insights quickly.
Consequently, the top management staff can improve on poor performance and measure the business’s productivity. Ultimately, a clean database equals accurate reports and informed decision-making processes. Invest heavily in cleaning your data and enjoy increased profits and high customer retention rates to increase your edge over your competition.
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