What Is a Data Management Strategy and How to Create One

In today’s data-driven world, data isn’t just a byproduct of your business operations—it’s an asset; data management can improve decision-making for businesses by 35%.

However, a large majority of companies estimate that most of their data is unstructured, around 50-90%, meaning there is no organizational structure to the data, effectively creating chaos.

To prevent the chaos that large amounts of unstructured data bring, building an effective data management strategy and architecture for your business is essential.

What Is a Data Management Strategy?

A data management strategy is a framework designed by an organization to systematically handle data assets. Data management strategies are the foundation for policies, procedures, and technologies that collect, store, manage, and utilize data.

Benefits of a Robust Data Management Strategy

Here are some of the benefits offered by a data management strategy:

• Improved decision-making through accurate and accessible data.
• Enhanced data security through strong data governance policies that protect sensitive information.
• Increased efficiency with advanced technologies that streamline data processes.
• Ensures adherence to data privacy and security regulations.
• Drives business growth through effective data leveraging.

Data Management Strategy Components

There are many components to a successful data management strategy. Here are a few:

Data Storage

Databases

Databases hold data and facilitate its retrieval and management. There are relational databases (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., MongoDB, Cassandra)​​. Relational databases rely on structured data and complex querying. NoSQL databases are used more for large-scale, unstructured data.

Data Warehouses

Data warehouses store integrated data from multiple sources. The data stored in data warehouses is optimized for query and analysis. Data warehouses are primarily used for business intelligence and reporting. This helps organizations perform data analysis on a larger scale and get helpful business insights from historical and real-time data.

Data Lakes

Data lakes are another type of data storage system that collects large volumes of raw data in its native format until needed. This is useful for storing diverse data types, including structured, semi-structured, and unstructured data. Data lakes are best used for advanced analytics and big data processing​.

Data Integration

Data integration combines data from different sources to create a unified, holistic view of data in a data management strategy. Here are some methods of data integration commonly used in data management strategies:

Extract, Transform, Load (ETL)

The ETL data integration method extracts data from various sources, standardizes it, and loads it into a target repository like a data warehouse. ETL is commonly used when data needs to be cleaned and formatted before loading. This is best for small to medium datasets that require complex transformations​.

Extract, Load, Transform (ELT)

Unlike ETL, ELT loads raw data directly into the target system, such as a data lake, and then transforms it as needed. This method is more effective for large datasets and real-time processing.

Federated Data Integration

Federated Data Integration connects different data sources without moving the data to a centralized repository. Data can be queried and retrieved in real time by users across various systems. This method is used best with an enterprise data management strategy with complex data ecosystems.

Middleware-Based Integration

Middleware facilitates communication and data exchange within a data management strategy by acting as a bridge between systems. Middleware supports integration capabilities like message queuing and data transformation. This method is mainly used in industries that require communication between legacy systems and modern applications, such as financial services and e-commerce​.

Data Accessibility

Data accessibility makes relevant data available in a format that is easy to understand and use. Here are some of the ways that data catalogs, portals, and data governance make data more accessible:

Data Catalogs

Data catalogs provide detailed metadata, including the data’s source, usage, and relationships with other data. This helps users understand the context and relevance of the data and discover data sets relevant to their needs. By indexing data and making it searchable, data catalogs enhance data visibility and accessibility​.

Data Portals

Data portals let users access a wide variety of data sets from different sources. Many data portals offer user-friendly interfaces that allow non-technical users to interact with data through data visualizations and dashboards.

Facilitating Data Governance

Data catalogs and portals ensure data usage complies with regulatory requirements and organizational policies. These tools can also promote data literacy by providing training resources and documentation that help users understand and effectively utilize the data available to them​.

What Is a Master Data Management Strategy?

A master data management (MDM) strategy is a holistic record that unifies the accuracy, consistency, and reliability of data across an entire organization. Master data management strategies provide a singular source of truth for organizations so they can make better decisions, enhance operational efficiency, and improve compliance.

Common Challenges Implementing an MDM Strategy

1. Data Silos

When data is isolated within different departments or systems, it creates a data silo, leading to inconsistencies and redundancy in data.

2. Data Quality

Maintaining high data quality is critical for MDM success. Organizations often struggle with inconsistent, incomplete, and erroneous data. To mitigate this, organizations need data cleansing to identify and rectify errors, inconsistencies, and duplicates.

3. Data Governance

Managing data assets to ensure compliance, security, and usability can be challenging without well-thought-out data governance. A solution: establish a centralized data governance framework that includes policies, procedures, and roles defining how data is managed, accessed, and used within the organization.

How to Build a Data Management Strategy: Step-By-Step

Here’s how to build a data management strategy for your business.

Define Your Data Goals and Objectives

The first step in creating a data management strategy is to define the organization’s data goals and objectives, which begins with:

• Identifying the types of data the organization needs to collect, access, or report on. It helps to begin with needs expressed by your end users and then work your way back to the underlying data sources.
• Determining the sources of data and the data transformation required to make it reliable and usable by downstream systems and reporting solutions. Raw data often requires pre-processing to be usable by end users.
• Understanding how the data will be used. It’s hard to determine what data pre-processing will be needed without clearly understanding all of the end users and use cases the data source will support.
• Starting with clear goals and objectives helps to align the data management strategy with the organization’s overall business strategy and ensure that data initiatives support business priorities.

Establish Data Governance Policies

Establishing data governance policies is a crucial step in creating a robust data management strategy. To establish data governance policies, you have to set rules that ensure data quality, security, and privacy. When you’re setting up these policies, here’s what you should do:

• Implement data validation processes, regular audits, and data cleansing practices to keep accuracy and consistency.
• Protect sensitive information with encryption, access controls, and regular security assessments.
• Adhere to data privacy regulations and outline transparent data collection, storage, and sharing practices.

Key components of data governance include:

• Appointing data stewards, custodians, and owners who are accountable for the security of the data.
• Keeping up with data quality, resolving issues, and maintaining documentation.
• Implementing data quality controls through the use of data profiling, lineage tracking, and quality dashboards to monitor and improve data quality.
• Developing policies, procedures, and guidelines for data management, classification, retention, and disposal.
• Staying updated with regulatory requirements and conducting regular compliance audits and assessments.

Select Appropriate Data Management Solutions

Another critical component of a data management strategy is the selection of appropriate data management solutions. This includes:

• Choosing databases
• Selecting data warehouses
• Opting for data integration platforms
• Picking data analytics tools

The right technologies enable efficient data storage, processing, and analysis, allowing organizations to derive valuable insights from their data.

Develop a Data Management Strategy Roadmap

Developing a data management strategy roadmap is essential for implementing a successful data management strategy. The data management strategy roadmap should outline the implementation plan and include the following key components:

• Timelines: Define clear timelines for each phase of the data management strategy.
• Milestones: Set specific milestones to track progress and ensure timely completion of tasks.
• Resource Requirements: Identify the resources needed, including personnel, technology, and budget, for each phase.

Additionally, the data management strategy roadmap should include plans for:

• Monitoring and Evaluation: Establish processes for regularly monitoring and evaluating the effectiveness of the data management strategy.
• Continuous Improvement: Implement regular reviews and updates to see if the strategy evolves and improves over time.

Develop Your Data Management Strategy

Do you need assistance building a data management strategy that fits with your organization’s business operations? It’s time to partner with SOLTECH.

SOLTECH provides expert data management strategy and architecture services that can transform your data into strategic assets. From data assessments and audits to implementing a cloud data strategy, SOLTECH will help you every step of the way so you can better utilize your data and enhance your organization’s operations. Contact SOLTECH today to learn more.

Thayer Tate

Thayer is the Chief Technology Officer at SOLTECH, bringing over 20 years of experience in technology and consulting to his role. Throughout his career, Thayer has focused on successfully implementing and delivering projects of all sizes. He began his journey in the technology industry with renowned consulting firms like PricewaterhouseCoopers and IBM, where he gained valuable insights into handling complex challenges faced by large enterprises and developed detailed implementation methodologies.

Thayer’s expertise expanded as he obtained his Project Management Professional (PMP) certification and joined SOLTECH, an Atlanta-based technology firm specializing in custom software development, Technology Consulting and IT staffing. During his tenure at SOLTECH, Thayer honed his skills by managing the design and development of numerous projects, eventually assuming executive responsibility for leading the technical direction of SOLTECH’s software solutions.

As a thought leader and industry expert, Thayer writes articles on technology strategy and planning, software development, project implementation, and technology integration. Thayer’s aim is to empower readers with practical insights and actionable advice based on his extensive experience.