As we move further into the age of information, database management becomes more important than ever. This style of management involves a series of practices that aim to maintain the quality of an organization's data. At most organizations, a data management team or data steward is responsible for managing said data. Of course, errors are inevitable, but they can lead to serious consequences.
Fortunately, it's possible to fix many data management issues by adopting a proactive approach. Focusing on prevention and continual process improvement can help you clean up your data and push your business to the next level.
Let's take a look at the potential impacts of a poorly managed database and what you can do to improve your practices.
Table of Contents
- How Bad Data Impacts Your Company
- How to Prevent Data Quality Issues
- How to Improve Data Quality
- The Benefits of Improved Data Quality
How Bad Data Impacts Your Company
Improperly managing data can harm every level of an organization, regardless of its size. Some of the most common consequences of poor data management include:
- Financial losses: A study by Gartner estimates that poor data management costs businesses an average of $15 million in losses. These losses primarily come from the time it takes to correct the error, such as deleting the bad data.
- Security breaches: If you're not careful, hackers can steal or encrypt your data during ransomware attacks. In the first half of 2021, the Internet Crime Complaint Center (IC3) received over 2,000 ransomware complaints, marking a 20% increase in reported losses compared to the first half of 2020.
- Legal complications: Failure to follow the latest data management practices could land you in trouble with the Federal Trade Commission (FTC). FTC civil penalties can be costly, so it's best to avoid having to pay them altogether.
- Damaged reputation: If hackers manage to breach your defenses and steal customer data, spam mail is the least of your concerns — you'll likely lose both the clients directly affected by the attack and potential clients who may see doing business with you as a risk.
While data management issues can be difficult to solve when they arise, they're mostly preventable.
How to Prevent Data Quality Issues
The best way to prevent data quality issues is to change your company's mindset from reactive to proactive. By anticipating the problems your organization may face, you'll be able to keep issues to a minimum and protect your data.
1. Hire a Data Steward
A data steward is a member of your organization who engages directly with data to effectively manage it. These employees are responsible for duties like:
- Organizing metadata collections
- Maintaining high data quality
- Documenting important policies and standards
- Creating rules concerning data management
- Working with data governance committees to improve processes
If your organization doesn't yet have a designated person to fulfill data management responsibilities, you should think about hiring a data steward or assigning someone in the organization to take on that role.
2. Fix the Source
An incorrect or incomplete source material will result in corrupted data. There are several ways you can fix that data:
- Resolve issues in ETL: Before reaching your database, data goes through the process of extraction, transformation and loading (ETL). If you can sift through bad data in the ETL phase, you can prevent it from becoming a more significant issue later.
- Adopt automation: Creating an automated system that cleans up data before it touches your database can proactively solve data issues and free up time for data stewards. While you'll need to check back regularly to ensure your program is doing its job correctly, you may be able to automate some parts of it to ease the process.
- Manually sort data: If you have the staff and time available, you could assign the task to your data stewards or other team members involved in data management. This option is cheaper than automation yet more time-consuming.
3. Invest in New Technology
While a data breach may seem like a one-time event, it's often a symptom of a larger problem. Take a step back from the incident at hand to assess how your organization usually handles its data. Look out for patterns in your processes.
Because your data stack is a crucial part of your company's infrastructure, you should treat it like so. Evaluate your current data technology. Is it able to keep up with your company's current demands? If not, it's time for an update. Replacing your old legacy tech with new data architecture can both improve the quality of your data and save you money in the long run.
How to Improve Data Quality
Having high-quality data is the backbone of any organization. Factors like accuracy, completeness and consistency determine the quality of a data set. Additionally, you need to make sure the data you collect is relevant to your organization's needs — you could have the highest quality data in the world and still not be able to use it because it doesn't make sense for your situation.
Here are some tried-and-true methods your business can use to clean up your data:
1. Use Data Governance
The term "data governance" refers to processes involved with maintaining good data quality. It consists of the following practices:
- Data management: Managing the full data lifecycle for an organization — includes other operations like data security, database operations, metadata management and data warehousing
- Master data management: Reconciling all data within an organization into one unified version
- Data stewardship: Ensuring data is high-quality and accessible to appropriate systems
The result is better, stronger data that can help improve processes and assist in the digital transformation of your business. A data governance strategy can help you combat the inevitable human error of data entry and streamline the data cleansing process.
2. Master Data Management
Using data governance strategies, you can create a master set of data for your organization. Your organization will refer to this set from now on.
The important thing now is to maintain that master set. You'll want to protect this one most heavily since it's your most accurate data, and prevent more bad data from entering the set. That's why master data management is a crucial part of data governance.
3. Filter Unnecessary Data
Examine the customer-generated data you normally collect. The chances are high that you've compiled a significant amount of data your organization can't use.
Solve that problem by cleaning up your forms — make all relevant fields mandatory if they're still optional and remove any fields that contain data you don't normally use. By streamlining your data generation strategy, you can prevent bad data from cluttering up your storage space.
The Benefits of Improved Data Quality
When you improve your data quality, you'll be able to take on operations with increased efficiency and stability.
- More informed decision-making: Compiling complete, correct data can help you make the most beneficial decisions for your company.
- More effective marketing: If you know more about your customers, your marketing team is better equipped to serve them.
- Stronger customer relationships: Whether your business is B2C or B2B, your data will reflect your customer base, so you'll be able to more accurately market to them.
- Higher efficiency: Maintaining proper data management ensures your team will be spending less time searching for information and more time working.
- More accessible data: Every piece of data will be easier to find and easier to read, ensuring your organization can find what they need with ease.
Take the first step today to push your organization to the next level.
Learn More About Database Management With Technology Partners
Do you need help getting started? Technology Partners' database performance solutions can create and implement a manageable system for your organization. Better data management means your company will enjoy increased efficiency, security, revenue and competitiveness.
Contact us today to learn how we can help you maximize your organization's technology.
