It’s frustrating. Your business intelligence strategy is fraught with data quality issues, and it’s tough keeping up with fast-changing data norms. Siloed repositories are the bane of your existence, unleashing the twin woes of dwindling profits and lost opportunities. Will a governance strategy be enough, or do you need data lifecycle management?

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This article discusses what a DLM strategy entails and best practices to get the most out of it.

Read through or jump to the desired section.

• What is DLM?
  • DLM vs. ILM
• Goals
• Components of a DLM Strategy
  • People
  • Processes
  • Technology
  • KPIs
  • Governance
  • 3rd Party Vendors
• Stages and Best Practices
• Tools
• What The Future Looks Like

What is Data Lifecycle Management?

It’s a set of policies, procedures and techniques to manage the complete data journey from ingestion through storage, transformation and analysis to its archival and deletion. Committing to a DLM strategy is a start toward making full use of your data, ensuring you waste none of it.

A DLM strategy goes beyond curation to manage data throughout its useful life and after.

Signs you need a data lifecycle strategy include the following:

• You are overwhelmed with big data’s sheer volume and variety, and information retrieval is painful. You lack the processes to learn which data is useful and how much lies unused in silos.
• You’re losing clients and suspect it could be due to infrequent data quality issues.
• You’re behind on regulatory compliance.
• You’re worried about data breaches, especially within the company. You want to enforce authorization-based access but don’t know how.

A DLM plan addresses these and more data-related issues, so you can focus on other tasks that demand attention.

People use information lifecycle management, or ILM, interchangeably with DLM. But the two are different. How?

DLM vs. ILM

Simply put, it’s the same as the difference between data and information. All data might not be useful, and useful data is information. Check out their differences below.

Different as they are, ILM and DLM complement each other. Without a robust data lifecycle management plan, you can’t have an effective ILM strategy.

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Goals

Industry experts predict the data management market to grow to $122.9 billion by 2025.

By implementing a DLM strategy, you should seek to achieve the following objectives:

• Security: Establishing client trust and credibility is a primary requirement. Enforcing security protocols keeps your assets secure from unauthorized access and malware.
• Availability: Effective data management protocols make information available to the right people at the right time. Useful metrics should be readily available, with unused assets stored separately to avoid resource overload.
• Data Integrity: An efficient DLM strategy should give your users accurate, unique and up-to-date information. Without it, they might use different versions of the same information, disrupting delivery pipelines.

How much should you expect to spend on a DLM strategy? It’ll depend on where you are in terms of data management today. What is your starting point? Do you plan to build on your existing strategy, or is it a complete overhaul? How have you managed information till now? Are roles clearly defined, and do you have the expertise?

A DLM strategy is as much about the work culture as the people. Get executive buy-in and start small, showcasing every achievement to prove it works.

Let’s look at what makes a data lifecycle management strategy.

Key Components

A plan is only as good as the people, processes and technology involved. Every data lifecycle stage should incorporate the following critical elements to give you bang for your buck.

People

Data and people are your most essential commodities. Before implementing a DLM plan, it’s necessary to understand how to make the two work together. Enforcing regulatory compliance and security requires all teams to be on the same page. Your employees should respect and comply with the access restrictions you implement.

Processes

Lay down the governance rules, activities, tools and techniques involved with each stage of the data lifecycle management process. Identifying the roles and responsibilities for each phase assigns ownership and accountability.

The Technology Stack

Your software is your trusted ally in data management, automating repetitive tasks and facilitating faster analysis. Cloud compatibility and an open architecture let you extend your software to integrate with other applications, including databases. Scalability adds computing and storage to manage greater workloads and scales down the system when idle, lowering operating costs.

KPIs

To get where you want to go, you need to know how far you’ve come. Identifying key metrics helps align your efforts with your goals. Notably, it gives you a fair idea of the technology, processes and people you need.

Governance

A crucial part of your DLM strategy is risk management. Data breaches and information loss through unauthorized access are common and a primary reason enterprises opt for a data management policy. Maybe this is you.

Here, you can answer the question posed at the beginning of this article – do you need data governance or data lifecycle management? A DLM plan specifies the processes that manage your data, and governance is one.

Complying with GDPR, CCPA and HIPAA regulations can secure financial information, trade secrets and personally identifiable information. Including regulatory compliance in the DLM strategy establishes the foundation of a data-driven culture in your business.

3rd-Party Vendors

Third-party vendors complement your tech stack with easily installable, cost-effective plugins. Some are free and offer basic functionality, while additional features will likely cost you extra. Weigh their long-term cost against your expected returns to assess if the investment is worth it.

How do you implement a data lifecycle management strategy?

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Stages and Best Practices

The nuances of a DLM strategy will vary across business sizes and domains. Additionally, existing technology and disparate data types demand a specific approach. With various automated processes already in place, starting with a DLM plan might seem like getting on a moving bus.

Where do you begin?

Start by outlining the data journey stages and identifying aspects that require defined policies. For instance, when you collect data, how do you ensure sensitive information stays secure, used only when necessary?

Let’s look at the different data lifecycle stages and the steps that need fine-tuning. An effective DLM plan should factor in data curation as well as consumption. Appointing a data steward who monitors data quality, usage and cleaning consistently can help establish a culture of compliance.

1. Data Capture

You can refer to this phase as data creation, ingestion or collection, depending on how you get data into your databases. With assets coming in from PDF and Word files, images, videos, websites and the cloud, how do you ensure your users’ information is safe?

Best Practice: Establish rules to gather data in a standardized format so it’s easier to analyze. Based on its sensitivity and value, set up automated data tagging as information comes in. For instance, you could tag datasets as personal, sensitive, business, etc.

2. Data Storage and Management

Data tags support efficient storage as the system can now separate business data from sensitive information. The cloud offers hot and cold storage options depending on the importance of data and its use.

Another option is warm storage, which is space for data you don’t need right now but might soon. In simpler terms, it’s data that’s not entirely useless, so you keep it close by. Additionally, integrating data from various sources and enriching it makes it ready for use.

Best Practice: Start data archiving early instead of waiting till it’s outlived its use. Create a comprehensive data management strategy and share it with everyone to push for a data-driven culture.

Archives aren’t always dormant data stores. Often, they are active repositories of useful information that take the burden off of primary storage. Efficient data archival is a cornerstone of data management.

Archives give you quick access to desired information, which is essential especially if you struggle with vast data volumes. Additionally, backing up your data is a failsafe you cannot miss.

3. Data Usage

Many analytics and business intelligence tools provide self-service capabilities, and practically everyone can create visualizations and generate reports.

When your employees access data for decision support, governance measures must be in place to ensure data integrity.

Best Practice: Column and row-level access restrictions ensure you only see the relevant insight. Project admins should have the right to assign group and individual permissions and configure authentication protocols.

4. Data Publication

It’s the insight-sharing stage, either through shareable dashboard links or reports via email. Embedding reports in business applications and collaborative capabilities make data visualization and analytics ubiquitous.

Best Practice: Configuring internal and external data sharing and publishing protocols is essential. For instance, which users should have view access when sharing reports within and outside your company? Who will have read/write access?

5. Data Cleaning

You likely use only about 20% of your data actively. What about the rest? What happens to unused data depends on its sensitivity and future value to your business. Define data cleaning protocols in consultation with all stakeholders. Information you don’t need now but might later is better managed by defining archival and tagging policies at the onset.

Best Practice: Build a data purging strategy and enforce compliance through consistent practice, over-communication and encouragement.

Additionally, knowledge of the latest industry standards, state and federal regulations and data governance policies is a must as the rules change often.

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DLM Tools

There are a host of software solutions that cater to data management. The specific tools and technologies you’ll need will depend on your business domain. For instance, will an on-premise tool address your requirements, or do you need cloud-based software?

Augmented analytics capabilities are in demand as you need little or no technical skills to work with them. Do you need AI-ML, or are your needs more straightforward?

Here are some software categories that address enterprises’ data management needs. Maybe some of these will fit your requirements.

• Product lifecycle management tools like Teamcenter, SAP PLM, etc.
• Data quality management tools.
• Database management software.
• Big data storage solutions like Cloudera, Google Cloud Platform, etc.
• Project management systems like Trello, ClickUp, etc.
• Release management platforms
• Change management solutions

No one tool may be sufficient, so you need to figure out a suitable combination. The tools you select must be extensible to integrate with other applications.

Still confused? You can reach out to the experts to guide you through the software selection process.

Choosing the right software solution may seem daunting, but it doesn’t have to be. We have some great resources to help you get started. Get our ready-to-use requirements template to list your needs. Shortlist and compare vendors against those requirements with a personalized scorecard and our in-depth software comparison report.

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What the Future Looks Like

Large enterprises’ expensive storage, legacy infrastructure and disparate silos justify investing in a DLM strategy. What about small and medium-sized businesses? As an SMB, you don’t have the technical debt of industry behemoths, but a top-down approach to proper data management can spawn a data-driven culture that pays dividends in the long run.

What steps have you taken for data lifecycle management in your organization? Let us know in the comment section!