In today’s data-driven world, effective data management is vital for any business and organization that wants to thrive. Statistics show that companies that make data-driven decisions are 58% more likely to hit and surpass their revenue targets compared to those that don’t.
Even when you have all the data you need, it’s impossible to unlock the full benefits of this data without effective practices for managing it. In other words, you need to properly organize, store, protect, and maintain your data to ensure its accuracy, completeness, and reliability.
Poor data management practices can affect your bottom line negatively by causing increased costs, decreased productivity, and — in extreme cases — damaged brand reputation.
Let’s explore five signs that indicate your organization may need to improve its data management practices, as well as what you can do if you notice any of these signs.
1. Data inconsistency
Data inconsistency means that the different data sets in your organization don’t match, which can lead to confusion and errors in decision-making processes. Here are some ways to tell if you have inconsistent data:
- Data reported by different teams or departments don’t match
- Reports generated from different data sources produce different results
- Customers receive conflicting information from different departments
- Employees spend too much time on data reconciliation.
Data inconsistency can be caused by data entry errors, poor data validation processes, or a lack of standardization across different departments.
Inconsistent data can have severe consequences for your organization, such as lost revenue, decreased productivity, and reputational damage. For example, if your sales data doesn’t match inventory data, this could lead to inventory shortages, lost sales opportunities, and dissatisfied customers.
The best way to address data inconsistency is to adopt standardized data formats, implement data validation processes, and establish clear data governance policies. Additionally, you can have regular data audits to identify and resolve data inconsistencies before they lead to significant problems.
2. Data inaccuracy
Inaccuracy means that your data contains errors or mistakes and therefore does not reflect the actual values or information it represents. Some common causes of inaccurate data include human error during data entry, faulty data collection methods, or outdated data sources.
Making decisions based on inaccurate data can lead to incorrect decisions, wasted time and resources, and loss of reputation for the organization. For example, if a hospital’s patient data is inaccurate, it could lead to incorrect diagnoses, improper treatment plans, and potential harm to patients.
If your organization has inaccurate data, the best solution is to invest in data quality control processes such as data validation, cleansing, and normalization. With these processes in place, you’re more likely to catch and correct data errors on time. You can also prevent data inaccuracy by improving your data collection methods and using up-to-date data sources.
3. Duplicated data
This refers to situations where you have identical or nearly identical copies of the same data. The most common causes of duplicate data are human errors, system glitches, and inefficient data management practices.
Duplicated data can have a significant impact on an organization’s operations, including miscommunication, wasted resources, and inaccurate reporting. For instance, if your organization has duplicated supplier data, it can easily result in incorrect payments and billing discrepancies, leading to financial losses.
If you suspect that you have duplicated data, you can take any of the following actions to confirm it.
- Manual inspection — involves manually comparing data to see whether identical information already exists
- Automated data matching — involves using data matching algorithms to compare data across different databases and identify duplicates
- Data profiling — involves using tools to scan data repositories and flag duplicates.
If you spot any duplicated data, you’ll need to merge it or remove the duplicates to ensure accurate and consistent data.
A good way to prevent data duplication is to implement data governance policies that standardize data entry and management practices across different systems and departments. Additionally, you can establish processes to help identify and merge duplicate data entries, such as data matching algorithms and manual data reviews.
4. Data security issues
Data security issues are vulnerabilities that threaten the confidentiality, integrity, and availability of your organization’s data. These vulnerabilities are particularly common in organizations that embrace data democratization without implementing proper mechanisms for safeguarding data.
Some common indicators of data security issues include:
- Unauthorized access to sensitive data or login attempts from unknown users or devices
- Suspicious activity on systems or applications, such as unexpected changes to user accounts or data
- Slow or unresponsive systems, which could be a sign of a malware infection
- Unexpected data loss or data corruption could indicate a security breach or a failure in data backup and recovery processes.
Common culprits behind security issues include cyber-attacks, human error, and system failures. Organizations that do not effectively manage their data are at risk of security breaches that could lead to data loss, financial losses, and PR crises.
For example, if a cyber-attack causes the breach of confidential consumer information stored without proper encryption or access controls, you could face legal and financial liabilities.
Data security is a serious issue. Therefore, it’s important to implement robust security protocols and standards to safeguard your data, such as access control, encryption, and network monitoring. You can also do regular security audits and vulnerability assessments to help identify potential security weaknesses, and enable proactive measures to address them.
Another important must-have is a comprehensive data backup and recovery plan to ensure you can easily recover your data in case of a disaster or system failure.
5. Inefficient data management processes
This means you’re using outdated or ineffective methods for organizing, storing, and processing data. Inefficient data management processes are the main cause of all the other problems discussed above.
Here are some tell-tale signs of inefficient data management processes:
- Difficulty finding and accessing required data
- Inconsistent, inaccurate, and redundant data
- Time–consuming manual data entry
- Low data applicability for decision-making or driving business outcomes.
Inefficient data management processes have a significant impact on your organization. They often lead to delayed decision-making, decreased productivity, and increased costs due to manual data entry or rework. They also affect customer satisfaction since inaccurate or incomplete data leads to poor customer experiences.
The best way to address inefficient data management processes is to implement modern data management systems and tools, such as cloud-based data storage solutions, data analytics software, and data visualization tools.
You can also establish clear data governance policies and procedures, such as data quality standards, data access controls, and data retention policies, to ensure that data is managed efficiently and effectively. Training your employees on data management best practices can also give them the necessary skills and knowledge to manage data effectively.
The consequences of poor data management can be severe. If you want your organization to remain competitive, you have to ensure that you’re effectively managing your data and using it to make better business decisions.
By recognizing the signs indicating a need for better data management, you can take steps to improve your practices and avoid the negative consequences of poor data management. Data inconsistency, inaccurate data, duplicate data, security issues, and inefficient data management processes are all signs that your organization may need to re-evaluate its data management practices.
Fortunately, many solutions are available to address these issues and improve your organization’s data management practices. These include investing in modern data management tools, providing employee training, and implementing better data governance policies.
Ben is an experienced tech leader and book author with a background in endpoint security, analytics, and application & data security. Ben filled roles such as the CTO of Cynet, and Director of Threat Research at Imperva. Ben is the Chief Scientist for Satori, the DataSecOps platform, as well as VP of Marketing.