The data life cycle describes what system analysts, data analysts, analytics leaders, and data scientists do with the data they collect. Zoom in to understand the importance of each phase. Then discover efficient ways to maximize your data’s potential throughout every step. Here is what you need to know.
The data life cycle is essential in ensuring the seamless flow of information. Understanding the seven phases of the data life cycle will help companies achieve goals without damaging a project’s integrity or threatening its confidentiality. This sequence of stages becomes even more crucial when data volume expands or teams gather data from multiple sources.
Of note here is that there is a healthy diversity of opinions in the numbering and classifying of data cycle stages. Credible data domain experts gravitate to either the 5-phased or the 7-phased approach and sometimes define new permutations altogether. These approaches do not reduce the veracity of the cycle and simply demonstrate the depth of interest in the ever-evolving field of data management. In an effort to be as thorough as possible, I will outline the 7 stages in this blog. Your opinions may vary.
The process of transforming data to usefulness is both a science and an art. There is a healthy variety of nomenclature for role definitions and title separations in the duties involved in data management. These often align with the scope of the organization, with larger organizations using more separation of titles, while smaller organizations may, for example, title all data workers as data scientists. While domain expertise is crucial, an entrepreneurial practitioner may wear multiple hats like data analyst, data scientist, business analyst, and data Engineer. This article uses roles loosely in that enterprising context.
So, what is the data life cycle, and why does it matter? Are all seven phases critical, or can you skip some to save time and resources? We’ll take an in-depth look at the different cycles and then discuss ways to maximize them. Let’s get started.
Table of Contents
A data life cycle is the sequence of steps your data goes through to become usable information. It defines a data manager or data scientist’s actions when capturing, analyzing, and archiving data. The phases include everything from initial collection to eventual deletion. This life cycle also aligns with industry best practices for managing data assets.
Most data have a shelf life that determines their relevance and usefulness. Teams must decide which data units they want to gather for various purposes. Then they have to analyze it, compress it, and dispose of the leftovers. This data cycle helps ensure accountability and confidentiality despite broad collaboration.
The specifics can vary between projects and teams, but standard policies govern all of its phases. This helps maximize the value of collected data and can streamline analytics. Data life cycle management is also essential because it supports innovation and promotes cooperation while protecting data integrity.
Examples of the data life cycle look different depending on the lens, and each phase has a unique approach. For instance, suppose data arrives at an organization through standard data entry platforms, known as sources of record; a data scientist must examine the source, determine its relevance, and do comparative searches before moving to the next step.
Another example is if a company wants to use its data to enhance marketing campaigns. This typically involves analyzing data after dissemination to calculate its impact on specific groups. In some cases, companies are also forced to destroy their data after using it. Because of that, they are enduring each cycle phase is essential.
The differences and similarities between the data life cycle and data analytics can be tricky to understand. The reason is that there is some overlap. Here is how it looks when you zoom out:
- The data life cycle describes the seven stages that transform data into useful information. It is used primarily to help teams make informed decisions.
- Data analysis is one of the data life cycle phases. Teams use it to determine the relevance of information using various tools and algorithms.
Analyzing is inarguably critical regardless of the project. However, so are the other six phases. Talk to a data scientist for more information on how data analysis relates to the data life cycle.
Data, by itself, plays a crucial role in our lives. It comes from everywhere, including places you might not expect. Meanwhile, many prefer that their data be kept confidential or used in specific ways. This can make collecting, cleaning, and utilizing it tricky.
The information powering organizations has a lifespan, and someone needs to manage it. Data life cycle management helps ensure that information flows seamlessly from start to finish. It can also protect stakeholders, investors, and executives from liability. DLM shields teams and encourages innovation under an ethical umbrella.
Parts of your cycle can also invite involvement and feedback. That’s because stakeholders are responsible for the DLM throughout every phase. This promotes meaningful interactions during early development and timely follow-up after deployment.
Understanding the data life cycle and its importance begins with looking at each phase. These are particular actions taken to process, progress, or present data. Some teams use DLM to create impactful visualizations, presentations, and reports. How you employ it depends on multiple factors.
The specifics hinge on why an organization, company, or team needs the data. Their field can also play a role in when or how the information gets collected and shared. However, a standard cycle model includes these seven phases:
The first data life cycle phase is data collection. This describes a tailored process of accumulating and measuring information. Teams can specify interest and objective variables to determine which data units to examine or ignore.
A data scientist will establish a systematic approach to collecting, ensuring accuracy from the beginning. Their efforts can also enable informed collaboration when determining research criteria in later phases. Develop or test hypotheses and evaluate the conclusions during this step.
Data collection is a vital component of DLM because it supports intuitive fields of study, including science, business, humanities, and manufacturing. The techniques could vary by discipline, but the goal is to ensure accuracy and avoid dire consequences like these:
- Not being able to answer crucial questions
- Being unable to repeat effective strategies
- Presenting inadequate or invalidated conclusions
- Wasting precious resources on distorted findings
- Compromising the company or team’s integrity or effectiveness
In extreme cases, skipping the data collection phase of a data life cycle can cause harm. Discuss the available methodologies to gather the most helpful information and use it correctly.
The data input phase starts the engine by providing the information and metrics required for accuracy and substantiality. In other words, this process gives the software something concrete to think about. It helps further define your goals and reveals pockets of missing data crucial to your project.
External data collections require sophisticated programs and due diligence to input with exactitude. Therefore, DLM can involve machine learning tools or AI to streamline the process and eliminate errors. This data life cycle phase also includes entering information into a storage system or secure database for later use.
The processing part of the data life cycle gets the data ready for use by various team members. Their methods can entail converting raw data into a machine-readable format or manually formatting output for specific purposes. Anything involving computer technology to perform defined operations falls into the data processing category.
Data scientists follow a regimented formula to accurately process information and ensure comprehensive data collection. Many teams collect the data and filter it during the processing phase. They also analyze it to check its useability before collating it into different groups. However, the timelines, questions, and expectations can vary depending on several factors.
DID YOU KNOW: There are four standard data processing techniques, including conditionals, searching, comparing, and counting.
Data output provides the quantitative summary of a data science activity, so it’s essential to a data life cycle. It often anchors the research and analysis portion of DLM, offering valuable insights for efficient analytics. You can also use it to present data in various formats for better comprehension by others.
This phase allows a data scientist to study information and tailor metrics to unique circumstances. Researchers can also manipulate it based on specific criteria for in-depth testing and precise translations. It helps teams make sense of otherwise meaningless raw data. At this cycle stage, you can produce meaningful outcomes in different forms, such as audio, video, digital, textual, and print.
Data storage is a method of keeping records of your data collections, techniques, and outcomes. It usually involves maintaining a secure database that team members can easily access. However, the biological molecules of DNA and RNA are technically data storage mediums. Therefore, your team’s storing preferences might differ.
The most commonly used options for a complete data life cycle can include the following:
- Cloud Storage
- Cloud Backup
- Optical Media
- USB Flash Drive
- Written Records
Unfortunately, the data storage process you choose can determine timelines, accuracy, security, and availability. Some storing options could also alter systems integrations, processing speeds, costs, and project integrity. Talk to an experienced data scientist for essential storage tools and tips.
This part of the data life cycle is highly regulated. It describes the transmission of statistical findings and other information to targeted audiences. Organizations can use multiple tactics to disseminate their data across relevant platforms, including social media, print publications, digital communications platforms, and in-person presentations.
Data dissemination lets you share crucial findings with others inside or outside your core team. This can motivate stakeholders, inspire investors, and spark change within specific groups. The cycle advantages here are evident elsewhere also. Teams can publish policy briefs without stress, show outcomes at conferences or meetings, and demonstrate efficacy before integrating solutions.
Data archiving and deletion are delicate processes requiring excellent due diligence and quality control. This phase of a data life cycle involves putting research findings into long-term storage or getting rid of outdated information. Teams can store data in a separate database or erase it when finished. This helps ensure lasting data retention to protect interests and foster further innovation.
Other benefits of archiving data vs. deleting it can include lowered project costs, better efficiency, increased productivity, and boosted storage capacity. Guard your storehouses against data breaches and other security concerns here. Ensure complete backups and make recoveries run faster with an organized archive. This will conclude the cycle and free up space for future endeavors.
NOTE: Some data must be retained to comply with industry regulations. Check with your data science team for more information on specific expectations and data storage best practices.
Getting the most out of the seven data life cycle phases requires a meticulous approach founded on regulatory compliance. However, your team might have other goals to achieve. DLM is typical for data science, business intelligence, and analytics. Still, you can also maximize its potential for your field or audience.
Here are four steps you can take to make the most of any cycle:
- Determine the process. This means clearly defining what you want to do with the data before you collect it. Further, decide the necessary data types, their importance, and where you’ll use them.
- Create and keep a strategy. Data life cycle management means developing and executing plans to help safeguard your databases. Protect crucial findings, shield intellectual property, and adapt to evolving environments to remain competitive.
- Choose the best tools. Managing massive data volumes can be overwhelming, even for sophisticated software programs. Pick data processing tools to help you stay organized, efficient, and collaborative throughout each data life cycle phase.
- Process the feedback also. Present your findings but also track the output and response. You can re-feed the information into your system for a layered cycle that reveals unique insights.
Data life cycle management (DLM) uses a policy-based, systematic approach to collect and disseminate information. The process lets teams develop compelling campaigns, define relevance, and target specific audiences. Meanwhile, cycle best practices can also secure critical databases, prevent data breaches, and support innovation.
Capture, analyze, and archive crucial information using all seven phases of the cycle. Build an iron-clad strategy for collecting and evaluating inputs. Then use the available insights to maximize your data’s potential and rise above the noise. Talk to a data scientist for more information and tailored tips.