What is a Data Management Strategy?

What Is a Data Management Strategy?

Organizations lose millions of dollars annually to poor-quality data and ineffective data governance. Most business leaders make data-driven decisions and generate strategies to ensure data validity and reliability. However, if their data is bad, their data-driven decisions will lead to problematic outcomes. To avoid this, organizations need a comprehensive data management strategy.

By following effective data management techniques, your business can maximize the data you collect to make your projects and initiatives more successful. This article discusses what data management strategy is and details the components of a data lifecycle. It then explores various data management strategy templates and frameworks. Finally, it provides steps to create an effective data management approach.

An Overview of Data Management Strategy

A data management strategy is an overarching framework that guides how your business intends to collect, keep, and use your data to make decisions. The strategy must be crafted according to established data management principles. Some of these data quality management practices include setting roles so individuals are accountable for each piece of data, maintaining data quality, analyzing metadata, and comprehending the complete data lifecycle.

Data Lifecycle: How to Collect and Manage Your Data

With the advent of big data, data management and analysis have become increasingly complicated. Many enterprises now collect hundreds of data points from thousands of sources every hour, resulting in highly complex data lifecycles dealing with massive amounts of data. Many modern businesses have opted to use data pipelines to manage the data lifecycle more effectively.

A data pipeline is the path your data follows from its generation or entry into your business to the point of storage or destruction. Data pipelines automate many of the manual steps in the data engineering lifecycle. Typically, data in a pipeline moves through the following stages:

• Data collection: This involves gathering raw data from the source and ingesting it into the data pipeline. These sources include data lakes, data warehouses, .csv files, etc. Acceldata allows you to add multiple data sources to your pipeline and measures this data for quality to discover issues at the source.
• Data storage: This involves ingesting data into a secure storage. It may include an on-premise or cloud-based data lake, data warehouse, etc.
• Data preparation and analysis: It includes cleaning data to make analysis easier. What follows is exploring, interpreting, and extracting insights.
• Data sharing: It involves transforming data into a format that makes it accessible to applications and sharable by users.
• Data archiving and disposal: This includes storing infrequently accessed data in cold storage. This data may be useful for regulatory compliance and future reference. Destroy the data that you no longer need.

How to Streamline Your Lifecycle/ Data Pipeline

You can implement data observability to improve the efficiency of your data pipeline. Not only data pipeline but data observability also enhances your entire data management strategy. It gives you visibility into your overall data landscape and dependencies, such as data infrastructure, applications, and pipelines.

Data observability solutions such as Acceldata Torch can help you achieve data observability. Acceldata Torch helps you better understand how your data is used and altered as it moves through your organization. It provides a unified view of your entire data pipeline, allowing you to see your data lifecycle.

Another practice for improving your data pipeline performance is leveraging data management software. A data management solution takes data from multiple sources and stores it in a single location. Then, it provides you with various options for analyzing that data. Generally, data management software is interchangeable with database management software, and both serve similar functions. However, some data management software can provide far more features and functionality, helping your company manage your data more efficiently.

Data Quality

Maintaining data quality is a challenge. As you build your own framework and goals to achieve higher data quality, case studies can be helpful. This can give you a data management strategy example for improving data quality within the organization. Do you know what data quality is? It’s a measure of how well that data describes the real world. There are six main pillars of data quality:

1. Accuracy
2. Completeness
3. Consistency
4. Freshness
5. Validity
6. Uniqueness

If all of these criteria are met, then the data can be trusted. However, in order to maintain each of these points, you need to establish checkpoints throughout the data pipeline. This is the best way to monitor for any issues that could cause downtime or negatively impact data quality

Data Strategy Framework

Your data strategy framework provides the foundation for how your business handles data. Looking at a data strategy example or data strategy framework PDF can be a great way to learn more about how to build one. In January, McKinsey published an article describing the data-driven enterprise of 2025.

In this article, McKinsey lays out a data strategy implementation plan. It shows how important data already is and how much more important it will be in the future. If your organization is not already working towards getting the most out of your data, you will be left behind by your competitors. The McKinsey piece provides a general data strategy framework template, describing the tools and the roles required to build a successful data strategy. Reading through a data strategy PDF use case can helpful you learn more about how to put together a data strategy framework.

Data Management Strategy Framework

Without a data management strategy roadmap, your organization is flying blind. This is an untenable position in today’s highly competitive digital economy and will inevitably lead to poor data quality, increased outages, and data silos. Data silos can be particularly problematic for large enterprises as they make it difficult for users outside of the silo to access and use the data. This means much of your data is not being used to its full potential.

By looking at examples of data management goals and objectives, you’ll see that metadata analysis is one of the core principles of good data management. Metadata has been called the data about data. In many organizations, metadata is entered manually and then never changed. This can create issues as the metadata does not change when the data is transformed.

Acceldata Torch provides effective data discovery capabilities. At the heart of data, discovery is the concept of regular, automated updating of the metadata to ensure that it is always as accurate as possible. By maintaining the quality of your metadata, you can ensure that your analysis is more effective and meets the requirements found in most data management roadmap templates. All of the features and functionalities provided by Acceldata make it easier to build and adhere to a consistent data roadmap.

Data Strategy Template

A data strategy template provides a methodology that data engineering teams can apply to your company’s unique situation. Data strategy example PDF files available online lay out a variety of different workflows and processes.

Below are steps for defining a good data management strategy:

• Let your business objectives shape your data management approach. This will help you discover the data required to achieve goals and the insights needed to meet the objectives.
• Create a strong data process with effective data collection, preparation, storage, analysis, and distribution mechanisms.
• Implement the right data management solutions and adopt hardware resources to create a strong data infrastructure.
• Create data governance policies that address data quality, security, transparency, and privacy issues. This will promote the right culture for proper data handling and use.
• Use organizational change management (OCM) initiative to train and prepare employees and stakeholders for the changes in your data management strategy.

At the heart of your strategy is a tool that lets you get true visibility into your data lifecycle. Ideally, this platform automates many of the tasks required to reach the goals outlined in your data management strategy. Acceldata is the ideal solution for this purpose. With Acceldata, you can optimize the performance of your data, as well as increase reliability, and lower your costs.

Request a demo or sign up for a free trial to see how Acceldata can help meet your data management strategy needs.