Data Strategy Best Practices in 2024

The most effective ways to manage and leverage your data to achieve your organization's objectives.

Author:  Data Strategy Professionals team  |  Post Date:  Mar 16, 2024  |  Last Update:  Mar 28, 2024  |  Related Posts

Data-driven decision-making has increasingly defined organizational strategy over the last decades. These best practices will help your organization use data to capitalize on comparative advantage.

Contents

• Objective
• Best Practices
• Additional Detail
• Methodology
• Limitations
• Next Steps
• Conclusion
• Related Posts

Objective

We've read many articles across a range of sources to collect this list of best practices from leading organizations and individuals working in Data Management. This post aims to identify and rank the most important Data Strategy best practices.

Best Practices

Additional Detail

Alignment of Data Strategy and Business Strategy

Key theme:  Strategy

Everything that we do as data practitioners should be in service of the business goals of our organization. There's no sense improving data quality or enhancing metadata for its own sake. Arguably, data only has value when used. Therefore, it's crucial to ensure that Data Strategy is aligned with Business Strategy.

Cultural change to support Data Management

Key theme:  People

Change management is a very important component of technological change that should be accounted for and acted upon from the very beginning of a Data Management project. Peter Aiken estimates that for every such project, there is a 1:4 ratio of technology spend to resources required for people, process, and culture changes. You may be interested in reading this article about how to foster a data-driven culture.

Elevation of Data Security as a top priority

Key theme:  Technology

Although Data Security is typically handled by a dedicated function, a Data Management practitioner should be aware of current best practices in order to incorporate them into plans, policies, and standards.

Identification and engagement of stakeholders

Key theme:  People

Leadership buy-in is a key input to the success of a Data Management initiative. Consider strategies such as newsletters and Teams/Slack posts to communicate progress. Throw a party to celebrate a big win. You may want to bring key stakeholders and data leaders together at a regular cadence in order to share best practices and seek input for crucial decisions.

Documentation of standards and processes

Key theme:  Process

Good documentation is the foundation of an effective Data Management program. Supporting the work of data teams with standards and policies helps to promote consistency, sustainability, and reliability. Check out our Document Checklist for a comprehensive list of documents that you should consider developing in order to enhance your Data Management capabilities across all functions.

Recruitment and training of team members

Key theme:  People

Improving the Data Management capabilities of your organization requires the development of a skilled workforce. As stated in the DMBOK, "the best data stewards are found, not made." Data stewards are typically identified by interacting with data producers and consumers and noticing who is excited about working with the data, who has a good attention to detail and other skills required for Data Management. Team training can play an important role in fostering a skilled and knowledgeable Data Management workforce.

Data Architecture enhancement

Key theme:  Technology

Data Architecture plays an important role in helping an organization to understand what data assets it has and how these data assets are structured. An Enterprise Data Model (EDM) provides a high-level view of data across an organization. A data flow diagram demonstrates how data is used and how it flows through the organization.

Data Management Maturity Assessment (DMMA)

Key theme:  Strategy

A DMMA is a very important input into the overall understanding of Data Management capabilities. It can help your organization evaluate the realized, probable, and potential value of data capabilities.

Data Strategy roadmap

Key theme:  Strategy

Creating a Data Strategy roadmap is a useful way to guide current and future projects so they are more closely aligned with the objectives of the business. To learn about the Data Strategy roadmap and other supporting documents, check out our Data Strategy Document Checklist.

Data Storage using data lakes

Key theme:  Technology

Data lakes are a cost-effective and scalable way to store and manage large volumes of data of different formats. Unlike traditional Data Warehousing, which requires data to be cleansed and structured beforehand, data lakes allow organizations to store data without extensive preprocessing. This characteristic of data lakes is useful for an organization that aims to ingest large amounts of data and use it for advanced analytics.

Monitor KPIs

Key theme:  Process

Using key performance indicators (KPIs) is an important step toward a successful Data Management program. A metrics scorecard can help ensure that data is fit for purpose across the organization by making sure that data is accurate, complete, and up-to-date. Continuously monitoring and regularly reporting on KPIs can help to ensure that the organization's data is being used effectively.

Integrate AI/ML

Key theme:  Technology

Machine learning has many uses that can benefit Data Management practitioners. For example, machine learning can be used to automate data processing tasks, generate a first draft of Data Governance documents, serve as a copilot to data engineers, predict the likelihood of data-related risks, provide structure to semi-structured information, suggest sensitivity classifications for datasets, identify duplicate data assets that should be deleted, summarize logs and produce actionable recommendations, perform Data Quality checks with minimal human intervention, and identify patterns in a dataset.

Methodology

We explored an initial list of 27 articles based on keyword search. Not all sources or best practices were determined to be relevant to our research.

An article was listed as a source only if it explicitly referenced a given best practice. However, many of the same best practices were explained in different ways across different articles.

A total of twelve best practices were included in the final selection. Four key themes (Strategy, People, Process, and Technology) were then identified to categorize the best practices. The themes that we used to categorize these best practices are reflective of our opinions only and may not reflect the opinions of the author.

Limitations

• This list of best practices is not exhaustive
• Sources have not been assessed for their degree of independence (i.e., the extent to which to best practices were identified by one source without referencing other sources)
• Given the pace of advancement, it is important to consider the current relevance of best practices, especially those within the Technology theme

Next Steps

In order to integrate these best practices at your organization, we recommend the following:

• Investigate how best practices interact with industry standards
• Take into account your organization's level of Data Maturity when considering which best practices to implement
• Consult data leaders to better understand the potential impact on your organization's Data Management practice
• Conduct in-depth literature reviews for each of the most promising best practices
• Evaluate best practices in light of recent technological developments

Conclusion

This report is a starting point for understanding the best practices in Data Strategy. We hope that this list will help you to make informed decisions about how to effectively implement Data Management in your organization.

For more help with Data Strategy best practices, we recommend reading this article on the Foundations of Data Strategy and checking out our Data Strategy Workbook and Document Checklist.