We’re often asked to manage or present a single aspect of data when working on data and analytics projects. You may be working on how to get data from one system to integrate with data from another system to solve a specific data problem, or you may be tasked with dealing big and messy database and visualizing it for the sake of decision-makers with the information they need. But working with data is not so simple, as you need to understand the entire life cycle of your data.

Defining the Data Life Cycle

The way we see it, the data lifecycle exists in five phases. First, you gather your data, then you transform it into something useful, then you present and interpret your data, and finally maintain your data so that you can use it again. Visually put, the data lifecycle looks something like this:

                                                                                         Source: Excella

Let’s see what do all these things really mean?

Phase 1: Data Collection

This includes the collection of data across structured, semi-structured, and unstructured sources including website data, operational systems, and social media data. It’s important to identify where your data resides and how best to capture it, whether working with Big Data or traditional structured data sources.

Phase 2: Data Integration & Transformation

The foundation that enables easy and swift access to information for your end users is the Quality integration of your data assets. A data integration strategy is required to ensure data quality and consistency, even with the advanced capabilities of data tools to bring data together on demand. The processes that are repeatable, automated and able to be extended to meet future business needs are the provided best solutions.

Phase 3: Data Presentation

Your data is ready for its unveiling! You can uncover key metrics that will inform you of the current state, trends, and exceptions, through different methods of presentation. Findings should be presented in the most effective format and are often built using popular Business Intelligence tools and formats including exception reports, scorecards, historical trend reporting, operational reports, executive dashboards, and tailored web visualizations.

Phase 4: Data Interpretation

The initial interpretation of what the data is telling should be easy and obvious. Data Science is the practice of deriving insights from data when you will want to dig deeper and explore data using statistical methods. Data Science can gain insights from data and can encompass statistical analysis, machine learning, text analytics, predictive analytics, and more.

Phase 5: Data Maintenance

Maintaining the consistency and quality of data factors ensures that data remains functional long term. Some tactics include data quality thresholds and alerts, data integration breakpoints, and audit reports that can be built into data integration designs and promote data standards and data consistency. Data Governance can include building master data repositories, selection and deployment of data quality tool suites, and creating and implementing data privacy strategies.

Why is Understanding the Data Life Cycle Important?

When the data is presented to us in a report or dashboard, only one phase of the data lifecycle is seen. You’ll get data, but it may be more difficult to digest if you skip data integration steps. In reality, we advocate that the practices of data standards and data quality are embedded throughout the design, build, and deployment of every delivery.

In our data-driven age, with volumes of data growing so rapidly that the ongoing health and well-being of our data becomes critical. A holistic view of the data lifecycle is required, by avoiding common pitfalls to achieve the omnipresent goal of truthful data using proven practices.