FormerGoogle CEO Eric Schmidtfamously remarked in 2010 that the amount of information created by humans every two days was equivalent to all the information produced throughout history until 2003. At that time, smartphones and social media were still in their early stages, meaning the magnitude of data generation has only intensified since.
Businesses that can effectively harvest and use this data can extract invaluable insights about their audience and market. This is where data lifecycle management (DLM) comes in—a framework that can bring purpose, structure, and security to your data. Here’s how to get started.
What is the data lifecycle?
The data lifecycle is the sequence of stages data goes through, from its creation to its eventual disposal, encompassing the entire lifespan of data within an organization or system. Data lifecycle management (DLM), in turn, is the proper administration of this lifecycle.
While each piece of data follows a linear path from creation to destruction, the data lifecycle—as the name suggests—is cyclical. Different stages may overlap or occur iteratively depending on the data and the organization’s specific requirements and context.
Data lifecycle management can:
- Streamline processes.Data lifecycle management makes data more usable across organizational functions, from marketing to HR to the C-suite. It also provides clear guidelines for processing, archiving, and deleting data, streamlining operational decisions.
- Enhance data usability.Data lifecycle management defines a clear purpose for data collection, so you only collect relevant data.
- Boost security.Businesses must safeguard the information they gather from hackers and other security breaches. You can build security into your organization's processes by clearly defining best practices for each step of the data’s lifecycle.
- Control costs.DLM helps determine when data is no longer valuable to a company by setting usability rubrics and timelines. Once the data has passed these thresholds, you can move it to less-costly storage or delete it.
5 stages of the data lifecycle
The data lifecycle process typically follows a five-step framework:
1. Data creation
Data creation is the stage where you generate data or obtain it from various sources: web analytics, apps, form data entry, surveys, third-party vendors, sensors, and so on.
Every sale, purchase, hire, communication, and interaction online can be a possible source of data, which can come in different formats, such as structured (databases), semi-structured (XML files), or unstructured (text documents). While it might be tempting to keep everything, it’s important to prioritize input based on quality (how reliable and complex is the data?) and relevance (how useful is it to our corporation?). Filtering out unusable data will help you create a more manageable dataset that is cheaper to store.
2.数据处理和存储
Once you create or acquire the raw data, it’s time to clean and transform it. This prepares it for the analysis in the next step.
Data cleaning means ensuring various pieces of data work together, are correlated, and are translated into like units. For example, in a field that collates prices, extraneous dollar signs must be removed, and currencies must be translated appropriately. Data cleaning also means removing spurious and erroneous entries that might skew the data. The result is a database of usable, verified data.
The database should then be encrypted (that is, transformed so that it is only readable to internal parties) to protect it from bad actors and ensure data confidentiality. Once encrypted, the data is stored while it awaits usage.
企业数据的存储格式s on the scale of your enterprise. Options include on-premises storage (servers held in the company’s physical site), cloud storage (making use of remote servers), and object storage (ideal for unstructured data).
Build some redundancy into your approach to storing data by keeping a physical backup on-site or a backup in the cloud.
3. Data usage
在这个阶段一的th更令人兴奋的部分e entire data lifecycle—you analyze your data to extract valuable information, discover patterns, identify trends, or make informed decisions. (Shopify Plus users can do much of this work in one spot viaShopifyQL Notebooks, a powerful data exploration and analysis tool that can be used straight from the admin.)
For example, you might turn the data into visualizations or dashboards that end users can use more readily. Machine learning and artificial intelligence can aid immensely in data analysis, the production of insights, and data sharing. In an era of ever-increasing data, top-down data access (when it is only available to a select few users) can create bottlenecks. Other teams, likemarketing intelligenceand customer service, form lines as they wait for the small team to grant access on a case-by-case basis.
Instead, spend time designing workflows that ensure appropriate levels of visibility for users across tiers and functions. Perhaps marketing needs ready access to web usage and user analytics, butcustomer serviceneeds complete visibility to returns. Consider publishing the data as support for marketing efforts or case studies.
4. Data archiving
Data that’s fulfilled its immediate purpose may still need to be retained for legal, regulatory, or historical reasons. Data archiving involves storing data in long-term archives or backups, ensuring its integrity, security, and accessibility for future reference.
Rather than immediately deleting your data, retaining archival data ensures that the data remains available for a period following active usage. Perhaps marketing determines that customer retention initiatives require longer direct access and use of data. Note that litigation may demand the data’s retrieval as well.
5. Data destruction
When you no longer need the archived data, permanently delete it to prevent unauthorized access or data breaches. The destruction of archival data also creates more storage space for active data, helping reduce storage costs.
Many industries have specific regulations governing data disposal, such as the Health Insurance Portability and Accountability Act (HIPAA) in health care, which must be followed closely to avoid legal and financial penalties. Implementing precise and well-documented data destruction procedures within an organizational framework eliminates uncertainty about proper data management. Data lifecycle management involves making these crucial decisions at an organizational level rather than ad hoc.
At this stage, you may refine the lifecycle as you glean insights from fields and input sources, influencing what data you collect and how long you store it. Hardware and storage space are filled with new data as old data moves on in the lifecycle.
Data lifecycle FAQ
What are some challenges I may face in managing the data lifecycle?
Some challenges in managing the data lifecycle include data quality assurance, data security and privacy, data integration, and data retention and deletion policies.
How can I ensure regulatory compliance throughout the data lifecycle?
To ensure regulatory compliance throughout the data lifecycle, understand applicable regulations and laws, classify data based on its sensitivity and importance, establish data retention and disposal policies, and implement security measures like encryption.
How does data lifecycle management differ from information lifecycle management?
Data lifecycle management focuses primarily on managing data from creation to disposal, including storage, processing, usage, and archival. In contrast, information lifecycle management (ILM) is a broader concept that includes data and the broader context of information within an organization.
