What is Data Management, its Types, and Strategies

Data is critical for efficient business functions. But, companies must find the relevance of data in the noise created by technologies and systems today.  

Data alone is useless. Businesses need effective governance, strategy, and data management to use all forms of data across all use cases effectively. Use cases include employee networks, supply chains, and customer and partner ecosystems.

The process and practice of collecting, organizing, and accessing data is Data management. It helps increase efficiency, productivity, and insights for good decision-making. Data plays a pivotal role in all businesses today.

So, a data management system and a solid data management strategy are necessary. The size and industry of the company do not matter.

The data management process has many tasks and procedures. They include:

• Undertaking the collection, processing, validation, and storage of data
• Integrating various data types from diverse sources
• Ensuring a high level of data availability
• Implementing measures for disaster recovery
• Overseeing the usage and accessibility of data
• Implementing robust measures to secure data

Every analytics solution, application, and algorithm used in businesses needs access to data. Data management ensures that data is available, secure, and accurate. But, the benefits of data management are much more.

If appropriately managed, big data helps empower companies with the right tools. It assists businesses with accurate predictions and deep insights.

Big data also help companies understand what customers want. It enables companies to deliver exceptional customer experiences. But, it depends on the learning that data provides.

Data-driven business models are possible with extensive data analysis. These models can include service offerings based on sensor data and real-time Internet of Things (IoT). But, these models will only be obvious and evident with the ability to interpret and analyze big data.

Data-driven organizations have a significant edge in the competitive landscape. Sophisticated tools make companies handle an unprecedented volume of data. Moreover, they can harness various forms of data in real-time. These include IoT device data, video and audio files, and internet clickstream data.

These data can be structured or unstructured. This ability brings opportunities to transform data into assets and generate revenue.

Types of data management

Data management experts concentrate on specific specialties. A blend of all types of data management processes makes a stable organization. But efficient data management needs adequate research. Only then can data managers develop suitable solutions for any data environment.

Let us look into the different kinds of data management.

1. Master Data Management

All the data is encapsulated into one single version in Master Data Management (MDM). It ensures that the company is working on that exact version. It also ensures that the data is reliable and up to date and divided into diverse systems for usability.

2. Data Stewardship

A data steward’s responsibility entails overseeing the implementation of data policies. They also ensure strict adherence to rules. Their duty involves monitoring data collection processes. They also have to provide data movement that aligns with established policies.

3. Data Quality Management

Data quality management watches over the quality of data. The professionals scrutinize the entire set of collected data. They look for any potential duplications or inaccuracies. Their role encompasses managing the data management system with the utmost skill.

4. Data security

Security is important in all kinds of work, as also in Data Management. Data security is important at every level of Data Management. They have the task of:

• Preventing unauthorized access,
• Encryption management,
• Guarding against accidental movement or deletion,
• And other security-related activities.

5. Data governance

Data governance brings in stronger laws for data management. They make policies for data flow, intake, and security. They also oversee all the other data management work types like:

• Quality management professionals,
• Data stewards,
• Security teams,
• And other people are required for data management.

6. Big data management

Big data management is the process of collecting and analyzing all digital data. It is to improve operations in a company. They collect and analyze the raw data to process it for use in all aspects of business. It helps to improve the operations of the business.

7. Data warehousing

The collected data requires its own space for storage. Data warehousing gives a cloud-based or physical infrastructure for sorting and storing data.

Effective Data Management Strategies

If companies face data management challenges, developing a data management strategy is good. It will take time to complete a strategy that works well for businesses. But companies can start with the below steps:

• Identify business objectives

Companies create billions of data points every day. Organizations should inform the business objectives of the data management team. If not, companies will lose time collecting and analyzing the wrong data types.

Focus on at least five most critical use cases of the company data. And companies can start to build data management strategies from there. Companies should have objectives at the top of their mind. So, these priorities will help determine tools, processes, governance, and more.

• Create strong data processes

Organizations must have a clear understanding of how they intend to use data. Considering the procedures for gathering, organizing, and maintaining data is crucial. The initial step is identifying the individuals responsible for overseeing the data management.

• Find the right technology

Identifying the appropriate tools, platforms, or technology solutions is crucial. This constructs an effective data management strategy. It is advisable to consider the hardware and software necessary to establish a robust data infrastructure.

Data Management tools ease data management within an organization’s existing analytics environment. It simplifies the process and ensures that individuals can access the required information.

Data Management technology guarantees that reliable and up-to-date data is used to drive business decisions. This process happens from data preparation to cataloging, search, and governance.

• Establish data governance

The expansion in data use and data infrastructure development led to significant advantages. It also carries a considerable level of responsibility. It is crucial not to cut corners when establishing data governance. Instead, companies can invest the time to plan and communicate policies for data usage.

Data governance ensures the use of data throughout the entire organization. So, policies and procedures should be understandable and acknowledged by all. Undertaking this endeavor is a stride toward cultivating a data culture within the organization.

• Train and execute

Lack of data expertise among business owners is the main obstacle to effective data use. The team needs to have data analysis skills to ensure successful data management.

Non-IT teams too need this skill. It also can involve leadership support. This encourages support for data-driven initiatives.

Every individual must understand the company’s data management strategy. And all should be proficient in executing their respective responsibilities.

Also Read: Best Practices for Successful Data Management

Summing Up

Data management involves gathering, safeguarding, storing, and employing data. Organizations use varied data sources. This necessitates thorough analysis and integration of this data to derive business intelligence for effective strategic planning.

Data management also involves many policies and regulations. They aim to enhance data usability within the legal frameworks of regulatory constraints.

Data is a valuable asset in any organization. Big investments are budgeted for data storage and management. Efficient data management systems optimize business intelligence and data analytics process efficiency. Smart data management delivers:

• Augmentations in revenue and profits,
• Mitigation of data inconsistency
• And adherence to regulatory compliance requirements.

Extensive data analysis enables the development of data-driven business models. It consists of inputs from sensor data and real-time Internet of Things (IoT) data.

Nonetheless, realizing the true potential of these models can be difficult. It is possible only by interpreting and analyzing vast amounts of big data. With sophisticated tools, companies can make this possible.

They can use data management tools to analyze these diverse data types in real-time. So, it’s always better for companies to depend on these tools to provide data-based decision-making insights.