Stage 3: Data preparation
Once you’ve identified the appropriate data source, the next stage of the data analytics lifecycle is to clean it i.e. making it suitable for analysis. The process involves addressing issues like missing values, outliers and inconsistencies before putting a standardised format into place.
Data integration is a key skill to master here, as there may be multiple datasets that need to be combined to make analysis simpler and easier. For instance, you might need to merge customer demographics with transaction history.
It’s all about reshaping and reorganising data so that it’s structured and useable so that someone analysing it can focus on gathering insights, rather than dealing with inconsistencies. It is a fine art, which is why companies often turn to external experts or platforms like Python and SQL querying and manipulation, setting the stage for accurate analysis.
Stage 4: Data analysis
The fourth stage of the data analytics lifecycle involves delving down into your prepared datasets to uncover patterns, correlations and trends that can inform decision-making. Critical to this stage is what’s known as exploratory data analysis (EDA), which involves summarising and visualising data to understand its underlying structure and distributions.
The key components of Exploratory Data Analysis (EDA) include:
- Descriptive Statistics: Summarizes data with measures like ‘mean’ & ‘standard deviation’
- Data visualisation: Includes histograms, box plots, and scatter plots to spot trends.
- Outlier Detection: Identifies data points that deviate significantly.
- Missing Data: Assesses and handles missing values.
- Feature Engineering: Creates or modifies variables to improve analysis.
- Data Cleaning: Fixes duplicates, errors, and inconsistencies.
In addition to EDA, basic statistical tools like ‘correlation’ help measure how two items of data are related, while ‘hypothesis testing’ checks if certain assumptions are true. For more advanced analysis, machine learning methods like regression (predicting outcomes), classification (sorting into categories), and clustering (grouping similar items) can make better predictions or find patterns hidden in the data.
Stage 5: Model building and validation
Next, we have the model-building and validation stage, which focuses on creating models that can be used to make predictions or uncover insights within the data. Here you’ll be choosing the appropriate model type and algorithms to answer the question being asked. It involves ‘training’ these models with data and validating their performance to ensure reliability.
Models can be predictive (e.g. forecasting future sales) or descriptive (e.g. explaining patterns and relationships in the data). Common types include regression models, classification models, and clustering models for grouping similar data points together.
Stage 6: Deployment and communication
Once the necessary insights have been acquired, it’s time to put them into action in the real world. This requires you to make the results accessible and actionable for stakeholders through Application Programming Interfaces (APIs), interactive dashboards or traditional reports. The provision of this data allows decision-makers to see key insights and track performance metrics in real-time.
At this point, effective communication of your findings is very important and by using visualisation tools like Power BI and Tableau, it’s possible to make complex data understandable, even to non-technical stakeholders. They make all correlations, trends and predictions that much easier to grasp.
Stage 7: Monitoring and maintenance
The last stage of the data analytics lifestyle is to monitor and maintain the deployed models to ensure they continue to deliver accurate and relevant insights. Business environments change, as do data patterns - such as variations in consumer behaviour - which has the potential to impact the efficacy of the models you’re using.
As such, continuous monitoring is essential in the early detection of a decline in model performance. If any of the tracked metrics show signs of degradation, it might suggest that it’s time to retrain the model with new data that reflect how the conditions have evolved.
Feedback loops also play a crucial role in refining both the data and the models. As new data becomes available, it can be fed back into the analytics process, allowing the model to learn from recent trends and adjust its predictions. This process ensures that models stay up-to-date and aligned with your current business needs.
Making use of every byte of data your business creates
The data analytics lifecycle is crucial in turning raw data into the insights you need to make better strategic business decisions. By embracing this approach, it makes you adaptable to change and ultimately more competitive. It ensures that data-driven insights remain relevant, supporting growth, operational efficiency, and informed decision-making across various business functions.
The complex and technical nature of data analytics requires a particular set of skills, which is why the assistance of external experts is often required. As such, any company taking this path must choose the agency it works with carefully, due to the importance of the task.
At MCI, our team possesses the technical expertise required to help companies squeeze every drop of value out of the data they generate. If you’re in need of data analytics expertise you can rely on, why not get in touch with us by filling out our contact form?
Alternatively, take a look around our website to find details of our full range of engagement services or to see our many client success stories.
MCI Content Team
