Data Management 101

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Data has the potential to provide useful and necessary information when making important business decisions. To ensure management and staff have the right data for decision-making, companies must invest in Data Management solutions that improve visibility, reliability, security, and scalability.

In today’s digital economy, organizations are collecting and accessing massive amounts of data. Storing accurate, high-quality data that can be accessed and evaluated is a necessary part of Data Management.

The sheer volume of incoming data makes it difficult to manage. Effectively managing data has become extremely important for modern organizations. Additionally, businesses are subject to a growing number of compliance regulations.

Accidentally destroying or losing data can have disastrous effects, such as the loss of customers, and business deals falling apart. Time and resources spent on collecting data and business intelligence become wasted efforts. Data Management helps prevent this from happening.

What Is Data Management?

Data Management describes the process of gathering, storing, and using data efficiently. Data Management uses policies and procedures that allow an organization to control their business data.

As organizations increasingly rely on the internet and digital technologies, a robust Data Management strategy has become more important than ever. Managing an organization’s data includes a broad range of policies, procedures, practices, and tasks.

Data Management covers such factors as:

  • Archiving and destroying data per compliance requirements
  • Automated services
  • Data accessibility
  • Creating and updating data
  • Disaster recovery
  • Multiple storage locations
  • Coordination of apps, algorithms, and analytics
  • Data privacy and data security

What Is a Data Management Program?

A Data Management program is based on a data strategy and data architecture, and often includes Data Governance. The strategy deals with how the data program is planned and executed. Strategic planning includes ensuring the data is managed and analyzed in support of an organization’s goals. Strategies may involve automating certain services, cleaning data for research purposes, and educating the staff, with an emphasis on providing a uniform Data Management vocabulary.

The Data Strategy should also include the costs in terms of time, people, and financial limitations. Wes Flores, cofounder of Simatree, a business advisory company, wrote:

“A Data Management strategy is the foundation of any Data Management program. The strategy provides both the framework and the architecture that will last throughout the life of your program.”

Data Architecture deals with how the business’s data and metadata are organized. A model is created, describing how metadata and data can be used to monitor and manage inventory. The model shows how policies, rules, and standards are to be used. It also describes how data is gathered, stored, arranged, and integrated.

Data integration, which requires the interactions of two or more systems, is a part of the architecture. Data Architecture includes how to handle data in storage and data in motion.

Data Governance is typically an important part of a Data Management program. At its most basic, Data Governance organizes policies and procedures for data, while Data Management carries out those policies and procedures. It is a central component of successful Data Management.

Data Governance has become increasingly important in response to personal privacy laws, such as Europe’s General Data Protection Regulations (GDPR), the California Consumer Privacy Act (CCPA), Brazil’s Lei Geral de Proteção de Dados (LGPD), and South Africa’s Protection of personal information (POPI)

What Are Data Management Skills?

The skills used for Data Management require an understanding of how the data will be used. These skills also involve analyzing data, a good grasp of database design concepts, and an understanding of data integrity.

Some useful Data Management skills that help managing and using data are:

  • Data Integrity: This requires understanding definitions, program guidelines, and sources of data. It also involves reviewing the data, and working with staff to ensure that data is accurate.
  • Database Design and Planning: Understanding the database design concepts, and the benefits and limitations of various types of databases.
  • Data Analytics: Requires understanding how to use data intelligently for improving the various programs, including a familiarity with the data and a knowledge of the organization’s goals, and the desire to explore new approaches to using data.
  • Navigating the Software: The ability to find records, sort and review them, and write queries and reports; should understand how to access and modify the built-in forms within the database.
  • Managing Accounts: Knowing how to keep track of online accounts and assisting staff in keeping track of their accounts, passwords, and usernames.

What Is a Data Management Tool?

Data Management tools (software) combine and organize data from a variety of data sources. They extract, cleanse, transform, and integrate the data without damaging its integrity. The responsibility of managing data with software doesn’t fall on one or two employees, but on the entire staff.

Use of software has become essential for maximizing the use of data for business purposes. It helps in minimizing human error and provides a secure method of accessing and using data. Some of the basic features help to identify and correct errors, make improvements, and support data analytics.

Data Management software typically provide the following features, or tools:

  • Data Security: Features such as tokenization, access control, and encryption are used for data security. The software promotes secure access to business data and defends it from breaches.
  • Data Quality: Data Management software can help to identify gaps and fill in missing information. It also decreases redundancy that could impact the accuracy of analytics using statistics.
  • Data Risk Management: Allows a business to control the processes needed for gathering, integrating, and storing data, with the goal of minimizing data risks.
  • Data Control: Gives control over the data, providing quick identification of data that has been altered, and creates alerts that promptly notify the appropriate managers about any changes.
  • Workflow Automation: Repetitive tasks are automated, speeding up the workflow and reducing errors.

What Are Data Management Challenges?

Companies are becoming more and more reliant on data in their day-to-day business operations and in making intelligent decisions. The vast amounts of data created daily have become a challenge to manage. After being received, data may be shared with different geolocations and by using multiple business applications.

Data Management challenges can impact an organization in a variety of ways. Examples of these challenges are:

  • Multiple Data Storage Systems: This is one of the most significant challenges that businesses face. Large organizations with several locations may thoughtlessly develop multiple business solutions, each having its own unique data repository. This kind of data storage system creates a significant barrier that must be addressed, as it may develop into a form of data silos. Generally, this can be resolved by establishing uniformity to maximize efficiency.
  • Unstructured Data: Studies have shown that over 80% of the data gathered is unstructured, which is a problem. Most organizations are converting their research data into a uniform format to make the research more streamlined and efficient. Data Management software should include tools that transform data into a uniform format. 
  • Data Quality: One of the pressing issues many businesses face is poor Data Quality.

It’s necessary to get rid of unneeded data while still retaining accurate, high-quality data that businesses require to function efficiently.

  • A Lack of Established Processes: If data is gathered from a variety of sources, mistakes and inconsistencies within the data become unavoidable. Poor Data Management processes can contribute to the collection of inaccurate data.
  • Data Integration: The goal of having quality data available for data analytics and processing improves business intelligence and decision-making. Good Data Management software can integrate data through a variety of methods.
  • Data Analytics: Even high-quality data has little meaning in its raw form. The use of accurate, or high-quality, data is necessary for making accurate predictions. Good Data Management supports accurate data and good decision-making.
  • Data Security: While the Data Management software may come with its own security features, it is not uncommon for a business to use additional security software and services. The additional security is monitored and controlled by the Data Management software. This includes the use of encryption and passwords.
  • Data Automation: The solution for reducing errors and having tasks performed more efficiently than humans are capable of.

The Potential of Data

Data has the potential to provide useful information when making important business decisions.

However, organizations should not treat all data as though it were equal. Some data is simply useless for business purposes, while other data may be inaccurate. Selecting the right people and tools to ensure the good monitoring and management of data makes a business more efficient and more profitable.

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