Data Lifecycle Management: A Complete Guide

High-quality data is fuel for insightful data analytics and business intelligence (BI), without which it’s difficult to build a prosperous and developing business. The right information at the right time drives your decision-making, fosters digital innovation, and gives you an advantage over competitors.

Statistics prove that BI adoption is quickly proliferating. The BI market was valued at $29.11 billion in 2023 and is expected to reach $68.72 billion by 2032, growing at a CAGR of 10.09% between 2024–2032. However, to keep up with global BI adoption trends, businesses should realize the importance of preparing their data for effective BI implementation across the entire organization, not just one or two departments. 

But how do you prepare data for analysis and ensure it’s relevant and of sufficient quality? This article gives a detailed answer by discussing what is data lifecycle management (DLM)? And what it consists of, the connection between BI and DLM, why these two processes are inseparable, and how your operational efficiency depends on how you process the data your business generates. At the end, you’ll also get a checklist to evaluate your data and BI maturity. But first, let’s define data lifecycle management.

Section contents:

• Seven stages of data lifecycle management you should follow
• What roles should be involved in DLM
• What issues you can prevent by establishing an organized DLM process

DLM is the process of managing, organizing, and securing data at different levels, from creation or collection to archival and deletion. This process gives you complete power over your data and allows you to track the entire data flow. 

With the help of DLM, you can develop a structured approach to data management and optimize memory use by deleting data assets that are no longer needed. In addition, you’ll learn which datasets are important and which you lack for streamlined decision-making and incremental business expansion.

Key stages of data lifecycle management

What is the data lifecycle? The best way to understand it is to discuss its main stages. In this section, we cover seven critical DLM stages. Depending on your company’s unique business needs, they can involve different tools and steps.

Stage 1. Data creation or collection. The first stage of the data lifecycle management process is collecting the raw data necessary to enable software development or set up specific business processes. At Yalantis, we’ve composed a few common rules for working with raw data:

• Establish solid communication between teams. Proper collection of raw data requires strong relationships between data and technology teams and other departments. These relationships allow data specialists to get to know owners of different datasets and collect data only after receiving the owner’s permission. Plus, with the help of data owners, data engineers can easily validate data sources and learn whether they contain quality datasets.
• Set requirements for receiving data. Data and BI engineers at Yalantis can dictate the characteristics that raw data should possess for a streamlined data collection process. These requirements vary depending on the business needs but could be, for instance, a requirement for collecting unique data without duplication.
• Ensure a constant feedback loop. Our data engineering teams constantly keep in contact with the client during software development to ensure that we use only relevant datasets, as raw data can quickly lose its relevance. By staying in touch with the client, our data engineering company can also accurately define all data processes that align with business objectives to ensure that during the data analytics stage, businesses generate only valuable insights.
• Use a multi-tool approach to data management. We prefer this approach, as it helps businesses stay flexible and smoothly collect data of different types from various sources. Depending on project requirements, we use Apache Kafka for real-time data streaming, Amazon Simple Storage Service (S3) to ingest data of different types into a data lake (advantages of a data lake), and connectors to integrate with various databases. For instance, for our client Meroxa, Yalantis Go developers quickly built quality data connectors with their unique input.

• Optimization. It’s also critical for us at this first stage of DLM to create unified approaches and templates for data extraction processes to save time on collecting raw data. This way we can also simplify and accelerate the onboarding of new specialists to similar projects.
• Data accessibility. We request access to all critical data sources and datasets so we have all the data we need to move to the next DLM stage.

Stage 2. Data processing. At the data processing stage, the Yalantis data engineering team:

• investigates collected data to validate its quality and relevance
• cleans the data to ensure it’s consistent, includes critical values, and doesn’t contain inaccuracies and duplicates
• changes the data format if necessary to fit the data storage system

To enable efficient data processing, we use Apache Airflow, Apache Spark, or Tableau Prep. Apache Airflow helps in orchestrating complex data workflows and optimizing extract, transform, and load (ETL) processes. The latter can be managed and monitored as directed acyclic graphs (DAGs). Apache Spark is best suited for large-scale and real-time data processing. Tableau Prep is useful for effective data processing if a business uses Tableau as a BI visualization tool.

Stage 3. Data storage. After data is collected and processed, the Yalantis data engineering team can ensure it is properly stored in the chosen data repository. For instance, this could be a database (Amazon Relational Database, Azure SQL, PostgreSQL), a data warehouse (Amazon Redshift), or a data lake (Amazon S3). The choice of data storage should depend on the systems you already use at your company so that any new solution for storing data doesn’t disrupt your existing data management flow.

Stage 4. Data analytics. This is a stage when the BI team can use your data to derive insights and support decision-making. At this point, we can work with BI tools such as Tableau and Amazon QuickSight. Depending on your company and project maturity, we can implement advanced analytics solutions based on machine learning services (ML), such as predictive and prescriptive analytics as part of our data science services.

Stage 5. Data sharing. A strict data security policy and data protection controls should be followed during this stage to ensure your data isn’t compromised. For instance, at Yalantis, we encrypt data at each stage of the data lifecycle and comply with role-based access controls to ensure that only authorized users have access to business data.

Stage 6. Data archival. Data owners decide which datasets are no longer relevant and can be archived.

Stage 7. Data deletion. This is the final stage of the data lifecycle, which involves deleting datasets and freeing system and server capacity for other data that can be of more importance to the business.

A responsible approach to data lifecycle management is critical at Yalantis. As a data and business intelligence services provider, we believe that properly setting up data lifecycle management has a strong influence on business efficiency.

Critical roles and responsibilities for implementing a robust DLM strategy

The people involved in organizing the DLM flow can vary depending on the project you’re working on and your evolving business requirements. But such roles as CDO and data and BI engineer are critical to ensure that DLM is woven throughout your entire organization. Organizing a DLM process requires close collaboration of all the specialists mentioned below. Some you should hire full-time, and some can be part-time or outsourced partners.

Chief Data Officer (CDO): Permanent role. The CDO is responsible for orchestrating the entire DLM process, assigning other critical roles for this process to run smoothly, and reporting to other C-level executives on the data condition and data analytics capabilities.

Chief Technology Officer (CTO): Permanent role. The CTO is responsible for making technological decisions regarding DLM. The CTO can also be a data owner of certain data assets and can grant access to them when requested.

Data architect: Permanent/outsourced role. The data architect builds data architectures that align the organization-wide DLM strategy with the company’s business goals, taking into account functional and non-functional requirements digital systems should have.

Data and BI engineer: Permanent/outsourced role. This role can be filled by two separate specialists or only one, as it is at Yalantis. Such a specialist is responsible for running the DLM process, ensuring a prompt data flow during every stage, and defining how businesses can benefit from analyzing datasets. 

Data analyst: Permanent/outsourced role. Data analysts work to visualize data, compose reports and dashboards, and efficiently unite IT and business teams.

Compliance officer: Permanent role. The compliance officer is responsible for ensuring that data is secure and complies with industry standards, laws, and regulations at all stages of DLM.

Change management specialist: Permanent role. The change management specialist oversees implementation of data technologies, addresses employee concerns regarding changes in their practices, and assists top management in rolling out a data-driven culture at the organization.

Risks that the DLM strategy helps you avoid

Thinking through your company’s DLM flow in detail is beneficial in many ways and helps you avoid the following pitfalls:

• Increased costs for storing irrelevant or duplicated datasets. Even though digitization initiatives enable businesses to generate large amounts of data every day, companies may store datasets that are either unnecessary or too outdated to bring any value. This results in increased costs for data storage and maintenance. With a well-thought-out DLM strategy that fits your business model, you can get rid of these datasets and store more relevant data instead.

• Insecure data sharing within and outside the company premises. With a regulated data flow, you can ensure that data is securely exchanged within and outside your company. At Yalantis, we follow a security-by-design approach to the data lifecycle, which begins with a properly established system of access and permissions to sensitive data at the data collection stage and continues with setting up robust security controls during the following stages.

• Accidental loss of business-critical data. If you’re aware of how and where your business data is stored and organized, you can be more confident that it won’t accidentally get lost or misused to harm your reputation.

• Data analysis with poor-quality data. If you begin data analytics with insufficient datasets that, for instance, are missing crucial values, this can lead to ambiguous insights and affect decision-making. As part of your data lifecycle management framework, you can implement data processing practices that ensure only data of sufficient quality moves further in the DLM funnel.

• Data silos that make it difficult to retrieve critical data. Developing a unified approach to data collection within your organization can help you avoid the constant struggle of retrieving data from multiple siloed data sources. 

As we’ve already learned about the essence of data lifecycle management, we can move on to its connection with business intelligence and insight generation.

Section contents:

• How you can benefit from BI adoption based on well-established DLM
• How to solve issues that prevent you from smooth BI implementation 

Business intelligence is about using data in a way that addresses business needs. Choosing a successful method of data usage is what differentiates a skilled BI engineer from an inexperienced one. 

But how can a BI engineer successfully choose the right method of data usage?

A skilled BI specialist should be well-versed not only in the technological aspects of BI implementation but also in data lifecycle management. DLM plays a crucial role in increasing the efficiency of business intelligence and data analytics services and solutions. Although BI professionals can generate charts, tables, forecasts, and dashboards without DLM, the lack of reliable data lifecycle management can lead to problems with data quality, consistency, and relevance. In contrast, a well-integrated DLM strategy ensures that the data used in BI processes is accurate, reliable, and aligned with business goals, thereby maximizing the value derived from BI analysis.

Business benefits of DLM featuring BI

Combining DLM and BI efforts can help your business achieve the following benefits:

Eliminate manual data queries and optimize time and resources. A modern DLM strategy inadvertently involves the automation of certain processes such as data collection and data processing. Automated DLM stages speed up data ingestion into BI tools, which can time-efficiently generate BI reports and dashboards and regularly show them to you, saving you, as a top manager, from hours of manually searching for the right information across departments and helping you optimize expenses.

Enforce a data-driven culture for different levels of decision-making. Adopting a data-driven approach to decision-making is an important step every business should take, even at its inception. When making decisions, there is a tendency to merely rely on the opinions of experts due to their experience and authority, but this isn’t ideal if you strive to grow your business, generate incremental revenue, and attract more customers. For instance, a C-level manager may have one vision of their product’s target audience, whereas a BI report can show a different audience that could bring even more revenue.

Quickly compare datasets for a comprehensive analysis of your organization’s effectiveness. Easily accessible high-quality data and BI solutions built on top of it allow for quickly decomposing all key business processes, validating their success, and defining issues or bottlenecks, such as by comparing budgets for two different projects and defining which is used more effectively.

Validate business ideas. As an evolving and ambitious business, you may often have ideas that need to be tested and validated before they’re implemented. Data that is properly collected and visualized in a convenient dashboard is exactly what you need in this case. 

Possible use case: Your organization wants to test the performance of the referral program for your digital banking solution. In this situation, the BI team:

1. checks where and how data about referrals is collected and stored
2. validates this data to define whether it’s sufficient and confirms that there are no fraudulent referral activities

These results can then be presented to you in a clear and transparent dashboard.

Common issues with BI adoption strategies and how to avoid them

Despite the variety of BI benefits for your organization, there are also typical issues that you can promptly solve in collaboration with qualified BI specialists and data engineers. Take a look at the issues below; probably, some of them resonate with you:

Irrelevant BI reports don’t form the big picture of the business’s state. Generating reports and dashboards that don’t add any value to your business is a waste of time and resources. This can occur if company management doesn’t think through KPIs to measure with business intelligence. For example, you may consider that financial KPIs such as gross profit and net profit margin are enough to define your business efficiency. In reality, you may need to consider many more KPIs, such as employee productivity, customer satisfaction, and conversion rate that a skilled BI engineer can help you identify, track, and interpret.

Lack of trust in data used for generating BI reports. If the DLM process isn’t properly set up, you can’t be sure that you’re using relevant, high-quality, and consistent data for your BI initiatives.

Fear of change and old practices as a bottleneck to maximizing BI potential. It gets complicated to implement BI when an organization gets used to a certain workflow, such as manually compiling reports in Excel, or when one person’s opinion matters more than a report based on data. As a result, siloed data lives in employees’ own spreadsheets, each in its own unique style. Plus, new technologies and digital initiatives may evoke fear of change among employees. Such resistance across the organization can deprive the business of crucial insights.

Lack of clear communication on BI value from top management. BI implementation should be justified by top management in an outward and clear format so that all employees see its potential. When employees understand the value and purpose of BI, they are more likely to embrace it and use it effectively.

Solutions to BI adoption issues

1. Adopt a change management framework that clearly states the value of BI for both the business and employees, includes onboarding materials for employees, and shows how to address concerns and issues when such occur.
2. Assign a change management specialist to facilitate BI implementation. We’ve already mentioned the importance of such a specialist for implementing a DLM strategy, but a change management specialist can be in charge of any new initiatives you’re planning to carry out at your company.
3. Adjust the DLM strategy to match business needs and build trust in data among your BI engineers so they’re sure that BI reports and dashboards are relevant and reflect the current business state.
4. Select BI tools and data technologies that build trust to ensure your data teams know how to use them effectively to gain the most advantage for your business.

By streamlining your data lifecycle, you can get on track with BI and start generating insights that will move your business forward. You’ll advance much faster with BI than without it, as you’ll make decisions backed by relevant datasets and not just by someone’s opinion or by blindly following industry trends.

How DLM can foster BI evolution in your organization and help modernize and innovate your business

A 2023 Data and Analytics Leadership Annual Executive Survey among 1,000 C-level executives shows that in 2023, 87.8 percent increased investments in data initiatives such as data modernization. This indicates that mature and modern businesses highly value their data, how they use it, and what technologies they leverage throughout the entire data lifecycle.

Becoming more mature in terms of data management and analytics allows you to significantly speed up your business processes. For instance, with the help of automation and BI tools, a single BI engineer in collaboration with only one accountant can substitute a department of five accountants working merely in spreadsheets. In this way, you can reduce costs, optimize resources, grow, and quickly assess your current business condition as well as predict future performance.

For Yalantis, BI and data are inseparable, and we often combine data engineering and BI capabilities for better business insights. What BI and data stand for at Yalantis:

In the context of a data-driven culture and business decision-making, we consider such important aspects as security, velocity, quality, and accessibility of data, as only after fine-tuning all of these aspects is it possible to generate valuable insights.

DLM and BI services Yalantis provides

We’ve been providing data and BI services for a wide variety of businesses across industries including FinTech, healthcare, logistics, real estate, and manufacturing. For instance, we’ve helped a US-based neobank efficiently define fraudulent activities and generate deep-dive insights about customer behavior to improve and personalize their experience. As a result of our cooperation, we helped the company:

• decrease fraud-related financial losses by 40 percent
• achieve a 5 percent increase in the user adoption rate

Yalantis’ data architects and BI and data engineers can:

• Analyze current DLM practices. Analysis is necessary to define possible issues with your data lifecycle and come up with an improvement plan.
• Create a full-blown data ecosystem. This means thinking through every detail of the data lifecycle and developing a clear schema showcasing how data is collected to how it gets archived or deleted.
• Continuously maintain each DLM stage. Our data engineers not only set up DLM processes but also constantly ensure they function as needed and make updates or changes when necessary or requested by the business.
• Compare BI tools and perform advanced competitor research. We aim to ensure you’re ahead of the game in your industry. When selecting BI tools, we take into account not only your needs and resources but also what your competitors use. 
• Develop a BI implementation roadmap. To ensure smooth BI implementation that won’t disrupt your business workflow, our data team develops a detailed plan tailored to your business needs and organizational structure.
• Integrate advanced analytics and data science capabilities. If you want to derive value from such solutions as predictive analytics, machine learning models, generative AI algorithms (generative ai software development), and chatbots, we can help you with their integration, maintenance, and troubleshooting.
• Onboard and train employees. We can conduct regular knowledge-sharing sessions with your in-house team and our experts and prepare detailed training materials and documentation to help you streamline user onboarding to new solutions.

Business intelligence and data lifecycle management go hand in hand in a modern, mature, and growing organization. You shouldn’t rush into business intelligence without ensuring that your data lifecycle is properly organized. If a business lacks a unified data collection flow, doesn’t know precise data owners, or is missing a data processing stage altogether, their BI initiatives will most certainly fail. 

To be successful, you should move towards a holistic approach that seamlessly combines BI and DLM. This requires recognizing that BI isn’t just a tool or a set of technologies. It’s a strategic initiative deeply intertwined with how an organization manages and leverages its data throughout its lifecycle.