Maximize the Value of Your Data with These Tips




Wissen Team


August 9, 2023

Data management involves collecting, storing, organizing, maintaining, protecting, and utilizing data effectively and efficiently throughout its lifecycle. As such, it invites a focus on developing policies, procedures, and standards for managing data, as well as using tools and technologies to automate and streamline data management tasks.

At the end of the day, data management aims to ensure that data is accurate, complete, consistent, timely, and secure and is available to authorized users when needed.

However, as it stands, putting data to use is not that easy. In fact, a 2021 study by Accenture outlines how only 32% of business executives were able to derive tangible value out of data at their disposal. This makes effective data management even more critical. Here are some tips for fruitful data management.

1. Identify the Value of Your Data

Data can come in many different types and formats, ranging from structured data, such as numerical or categorical data that can be easily organized and analyzed, to unstructured data, such as text, images, and videos, that require more sophisticated techniques to extract insights.

To identify the value of your data, you should consider the following factors:

  • Relevance: Is the data relevant to your business goals and objectives? Does it help you answer key questions or make better decisions?
  • Accuracy: Is the data accurate and reliable? Are there any errors or biases that could impact your analysis?
  • Timeliness: Is the data current and up-to-date? Does it reflect the most recent trends and developments in your industry?
  • Completeness: Does the data cover all the necessary dimensions and variables you need to analyze?

2. Establish Data Governance

Data governance involves managing the availability, usability, integrity, and security of the data used in an organization. To establish data governance, one needs to define roles and responsibilities, create policies and procedures, and ensure compliance with regulations.

Defining Roles and Responsibilities

To establish data governance, it is crucial to define the roles and responsibilities of the key stakeholders involved in data management. These stakeholders may include data owners, data stewards, data custodians, and data users.

3. Implement Data Security Measures

Protecting Data from Unauthorized Access

Data security measures are necessary to safeguard sensitive information from unauthorized access. Unauthorized access can lead to data breaches, loss of intellectual property, financial losses, and damage to the organization's reputation. To protect data from unauthorized access, organizations can implement the following measures:

  • Access control: This is the process of controlling who has access to the organization's data. Access control mechanisms may include passwords, biometrics, and two-factor authentication.
  • Encryption: Encryption converts plain text into cipher text, which can be read only by those with access to the decryption key. Encryption can be applied to data at rest (stored data) or in transit (data being transmitted over a network).
  • Firewall: This is a security system that controls access to a network. It can be used to block unauthorized access to the organization's data.
  • Regular software updates: Software updates often include security patches that address vulnerabilities in the software. Regular updates can help prevent unauthorized access to the organization's data.

4. Ensure Data Quality

Organizations must prioritize data quality to ensure they're making informed, strategic decisions based on reliable and accurate information. When data quality is poor, it can result in incorrect decisions, wasted resources, and missed opportunities. To ensure data quality, organizations can implement the following measures:

Implement Data Validation Procedures

Data validation is the process of checking the accuracy and completeness of data. Data validation procedures help to identify and correct errors and inconsistencies in the data. Common data validation procedures include:

  • Range validation: This involves checking that data falls within a specified range (e.g., dates falling within a specific period).
  • Format validation: Checking that data is in the correct format (e.g., email addresses in the valid format)
  • Completeness validation: Checking that all required data fields are filled in
  • Logic validation: Checking that data is consistent with other related data (e.g., an employee's job title and department are consistent)

5. Adopt a Data-Driven Culture

Encourage the Use of Data in Decision-Making

  • Providing access to data and analytics tools to all employees
  • Encouraging employees to incorporate data into their decision-making process
  • Setting data-driven goals and metrics for performance evaluation

Promote Data Literacy and Education

  • Offering training programs and resources to improve data literacy
  • Providing access to data visualization and analysis tools
  • Encouraging employees to attend data-related conferences and events

6. Leverage Technology

Utilize Data Management Software

Efficient data management through software tools can streamline and centralize processes, leading to increased productivity. Centralizing data storage ensures accuracy and completeness while enabling access and sharing across departments. This promotes collaboration and improves decision-making.

Automate Data Processes

Automating routine data collection and entry tasks can significantly improve data processing efficiency and accuracy. It reduces the risk of human error and speeds up data processing. With automation, employees can focus on more critical tasks that require human expertise, improving their overall productivity.

7. Leverage Data Visualization

Creating visual representations of data is an effective way to simplify complex information and make it easier to understand. By converting data into charts, graphs, and other visual formats, users can identify trends and patterns in the data that may not be apparent in a table format.

Additionally, visual representations enhance the overall aesthetics of data presentation, making it more appealing to business users.

Communicate Insights Effectively

Communicating insights effectively is essential in making data-driven decisions. It involves identifying key takeaways from data and creating compelling narratives to accompany data visualizations. Messaging should be tailored to specific audiences and their needs, ensuring clarity and accuracy in data communication.

8. Measure Data Management Success

Define KPIs for Data Management

To measure success in data management, identifying key performance indicators (KPIs) is critical. These KPIs should align with organizational goals and objectives and be used to establish benchmarks and targets for each metric. Examples of KPIs include data accuracy, completeness, security, accessibility, and timeliness.

By tracking these metrics, organizations can identify areas of success and improvement and pinpoint potential roadblocks and challenges to achieving the desired goals. Through continuous monitoring and evaluation, businesses can make data-driven decisions to optimize their data management processes and ensure they are on track to meet their goals.

Summing Up

Effective data management is critical for organizations to make informed, data-driven decisions, improve business outcomes, and gain a competitive edge. However, to realize this, they must:

  • Develop a data-driven culture and leverage technology to effectively manage and utilize data
  • Use data visualization to communicate insights and make data accessible to a broader audience
  • Define KPIs for data management and track progress towards goals to improve strategies and processes continuously.

Get in touch with us to learn more about how we can help you drive an enterprise-wide culture of data literacy and collaboration to realize the potential of your data.