What's the best way to pick the most important data sources for your organization?

1 Data Lifecycle Management

Data lifecycle management (DLM) is a process that covers the creation, storage, usage, and disposal of data. It helps you ensure that your data is accurate, relevant, secure, and compliant with regulations. DLM consists of six stages: data identification, data acquisition, data quality, data analysis, data dissemination, and data disposal. Each stage has its own challenges and opportunities for optimizing your data sources.

2 Data Identification

Data identification is the first stage of DLM, where you define your data needs, objectives, and sources. You should start by asking yourself some key questions, such as: What problem are you trying to solve? What data do you need to answer your questions? What data sources are available and accessible? How reliable and trustworthy are they? How often do they update? How do they align with your business goals and strategy? By answering these questions, you can narrow down your data sources to the most relevant and important ones for your organization.

3 Data Acquisition

Data acquisition is the second stage of DLM, where you collect and store your data from your chosen sources. You should consider the best methods and tools for acquiring your data, such as: How will you extract, transform, and load (ETL) your data from different sources? What format and structure will you use to store your data? What platform and infrastructure will you use to host your data? How will you ensure the security and privacy of your data? How will you manage the costs and risks of data acquisition? By considering these factors, you can optimize your data sources for efficiency and quality.

4 Data Quality

Data quality is the third stage of DLM, where you assess and improve the accuracy, completeness, consistency, and timeliness of your data. You should implement some measures and standards for ensuring your data quality, such as: How will you validate, verify, and clean your data from errors and duplicates? How will you monitor and measure your data quality over time? How will you handle missing, incomplete, or outdated data? How will you document and communicate your data quality issues and solutions? By implementing these measures, you can enhance your data sources for reliability and usability.

5 Data Analysis

Data analysis is the fourth stage of DLM, where you process and interpret your data to generate insights and value. You should apply some techniques and tools for analyzing your data, such as: How will you query, manipulate, and visualize your data? What models and algorithms will you use to explore, explain, or predict your data? What metrics and indicators will you use to evaluate your data performance and outcomes? How will you test and validate your data analysis results? By applying these techniques, you can leverage your data sources for decision making and action.

6 Data Dissemination

Data dissemination is the fifth stage of DLM, where you share and communicate your data and insights with your stakeholders and audiences. You should design some formats and channels for disseminating your data, such as: How will you present and report your data findings and recommendations? What level of detail and granularity will you provide? What tone and style will you use to convey your data story? What medium and platform will you use to deliver your data message? How will you solicit and incorporate feedback and suggestions? By designing these formats, you can maximize your data sources for impact and engagement.

7 Data Disposal

Data disposal is the sixth and final stage of DLM, where you delete or archive your data when it is no longer needed or useful. You should follow some policies and procedures for disposing your data, such as: How will you identify and classify your data for disposal? How will you ensure the proper and secure deletion or archiving of your data? How will you comply with the legal and ethical requirements for data retention and destruction? How will you review and update your data disposal practices? By following these policies, you can optimize your data sources for sustainability and compliance.

8 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?