Business Tips | Data Strategy

When data enters a database, one of two things can happen: it’s either scrubbed, organized, and used, or it grows stale and outdated. Data Lifecycle Management (also known as “DLM”) is the process by which organizations gather, store, and use data from inception until the moment it becomes obsolete. In other words, the data lifecycle is a sequence of events every piece of data goes through on its journey from collection to eventual archival or deletion.

In an ideal world, data lifecycle management is automated. The logic and systems in place should carry the data through its relevant and effective stages. Getting your data management system to a point where DLM is automated takes careful planning, though. Even then, DLM is never truly over. Just like any piece of your data management process, a “set it and forget it” mentality will only get you so far.

That’s where you, the data manager, comes in. Whether you’re looking to increase sales or refine your logistics process, data lifecycle management is something every organization needs to consider, create, and constantly refine. Here’s how:

Plant, Cultivate, and Harvest Your Data: The Data Lifecycle

cultivating, harvesting, and growing data

The data lifecycle breaks down into five distinguishable parts. From collection to maintenance, each step requires a plan, a process, and care. Before you embark on your DLM strategy, take time to understand what it will encompass and what each phase in the lifecycle needs to accomplish.

1. Collecting Data

A piece of data enters DLM the moment you collect it. Most organizations pull a myriad of metrics from many sources. In fact, each department of your company probably needs data from multiple, unique sources. This might include website traffic data for your marketing department, sales metrics for your sales team, or employee engagement data for your operations managers.

2. Integrating and Scrubbing Data

Now that you have the data you need, it’s time to scrub and integrate it. This is the phase where the data is organized. Additionally, you’ll need to set up a process to make sure the data you integrate is clean (accurate and consistent). During this step, focus on creating a process to acquire your data effectively and impose safeguards to ensure it is error-free.

3. Presenting Data

Presentation brings your data to life. During this phase, you’ll take the scrubbed and organized data from step two and give it an audience. Practically speaking, “data presentation” simply means the data is available to users in a polished, user-friendly manner – such as a dashboard. By presenting your data via data visualization, you position it to provide insight to its audience during the next phase: interpretation.

4. Interpreting Data

When it comes to data, insight is impossible without interpretation. Now that you’ve served your data to the people who need it, it’s time for stakeholders to use it. This step is when data becomes truly useful and empowers viewers to make informed decisions and enact the necessary change in your company. Whether you’re managing the sales pipeline or streamlining product delivery, data interpretation helps your organization make smart choices and answer the question, “What do we do with this information?”

5. Maintaining Data

Data maintenance is an ongoing process designed to keep your data relevant and useful through the duration of its lifecycle. Consistency is the primary objective. This could include a wide array of strategies, such as privacy settings, integration breakpoints, and alerts for certain data thresholds. In short, data maintenance should set a standard for your data and equip your team with the tools needed to preserve it across all reporting.

Read next: Building Better Data Through Teamwork

How a Holistic Approach Can Change Your Organization’s Relationship with Data

Holistic approach to organizational data

With the exception of data scientists, most people in your organization will only see data at a specific phase in the lifecycle: when the data is relevant to them. On the one hand, you don’t want to burden stakeholders with too much backstory. After all, their main goal should be to interpret and utilize data. On the other hand, instilling the importance of DLM in every facet of your organization can propel it toward a deeper appreciation for data-centric decisions.

You don’t have to be a nutritionist to understand that good, healthy food is important – but you can only value clean eating if you appreciate the value of healthy ingredients. In the same way, every member of your organization should understand the importance of data and DLM so they can realize its full potential.

Tips to Speed Up DLM with Data Visualization

tips to speed up data lifecycle management with data visualization

Data visualization is an integral part of your data lifecycle management strategy. Here are a few ways you can use data visualization to improve DLM:

  • Don’t wait until phase three to start visualizing your data. In other words, start thinking about data visualization from the very beginning of the DLM process. By keeping your final objective in mind (a useful dashboard), you’ll gain a better understanding of the steps you need to take to get there.
  • Visualize your data management lifecycle. Storyboarding is a great way to get a big picture perspective of your plan. In fact, one of the first steps in the DLM process should be to create a visual representation of the lifecycle. By doing this, you’ll safeguard the project against any plot holes in your data narrative.
  • Emphasize the importance of a great dashboard. Remember: the purpose of data visualization is to make data easy to understand and accessible to everyone in your company. Your dashboard is where stakeholders digest and interpret data, which puts data visualization at the crux of your DLM process.
  • Don’t be afraid to shake up your data visualization. No process is perfect – at least not forever. Set aside recurring time blocks to reassess and refine your data visualization. Even if your dashboard was perfect three months ago, revisit it to ensure users’ data requirements are the same. If you need to make adjustments, do so and set aside more time to repeat the process in the future.

At iDashboards, our data visualization software can help you define, maintain, and refine your data lifecycle strategy. We love data – and we love helping organizations like yours realize their full potential through seamless data reporting. Click here to set up a personalized demo.

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Aziz Sanal Principal Technical Consultant @iDashboards

As a Business Graduate and tech professional, Aziz has become an expert in the information technology space with a proven record in the areas of programming, system analysis, consulting, system implementation, support, and training. Currently, Aziz trains iDashboards clients and helps them visualize their data in an exceptional way.


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