6 Phases of the Data Science Project Life Cycle
The importance of integrating data science into your business practice has become increasingly obvious over the past decade.
Data is the key to unlocking the true potential of your business and will help you refine your practices and do away with wasted time and resources. Making sure you handle data science correctly, however, requires expertise, discipline, and organization.
The data science process life cycle takes you through every stage of a data science project, from the initial problem that must be solved to the point at which the solution can offer consistent value to your business.
Without a solid framework, your project lacks the foundation that will guide it towards success; this is one of the main reasons that data science projects fail.
Following the six phases of the life cycle of a data science project is therefore essential to choosing the right data science tools, utilizing your team’s data scientist skills effectively, and maximizing the potential value of the project itself.
The field of data science is constantly evolving. Before the importance of a comprehensive Data science life cycle was first understood, the actual usage and value of a project were never fully interrogated.
The life cycle connects data acquisition and analysis with its purpose, which is derived from your pre-existing business intelligence, and makes sure that every stage in the project is strictly tailored to your business’s requirements, aiding the success of the larger software engineering project that it forms a part of.
It also ensures that the data science process can be refined and improved over time. The data science life cycle is then repeated endlessly; once it is complete, it simply begins again, becoming more efficient and valuable with each revolution.
One of the key benefits of structuring most data science projects using the life cycle is the guide that it provides, even during instances of trial and error.
If, after completing stage three in the cycle (model planning), you discover that your data has not been prepared in the correct way, your data science team can use this newfound knowledge to go back to stage one and improve on the first two phases of the Data life cycle.
By the time you reach stage three again, you will already have added value to your project without wasting time and resources by only diagnosing your mistakes at the end of the project.
This new open-ended attitude towards data science means that your project does not truly have an end date; the data science project framework can repeat itself until your model becomes outdated.
Even when your data insights are deployed, the system must continue to refine and inform any future projects you undertake. Over time, your developing business knowledge can be combined with data science techniques to ensure that every project is more successful than the last.
6 data science life cycle stages
As the first step in the data science process, discovery is also arguably the most important. If you get the foundations right, the final outcome of your project is likely to be of greater value.
Setting out specifications, requirements, priorities, and a definite budget is a must. Defining these things will help you understand whether you have the right resources to take on a project, whether you have collected enough reliable data, and whether the project’s potential value outweighs its cost.
Once this has been secured, your data team can outline the problem your business needs to solve and formulate initial hypotheses to test; in other words, they can define what a successful project will look like.
Begin by asking yourself these questions:
- What is the problem that needs to be solved?
- What form should the solution take?
- What results would constitute ‘success’?
- What relevant data is available to be used for testing, and what data sources would be available for real-time deployment? Do you have a robust data acquisition process? If you do not have enough available data to train your model, would data extraction tools and open source databases prove useful in acquiring new data?
2. Data preparation
Before you can begin testing your hypotheses, your data (taken from your own databases or via data extraction tools) must be preprocessed and conditioned. This involves setting up a sandbox (or testing) environment, and extracting, transforming, and loading your data into your new sandbox in a format that is ready for analysis and model building.
Then, your data can be conditioned, surveyed, and visualized. Data visualization can involve graphical representation and/or customizable dashboards and reports, depending on your chosen tool.
But, with every visualization tool, this step helps you pinpoint and potentially remove anomalies, leaving you with relevant, clean data.
It is at this stage that the relationship between all your different sets of data is established, which will dictate which data characteristics/signals will be useful to your data science model in finding a solution to your problem; thus creating a clear directional path for exploration.
Data cleaning and refining tools can be built into your central database, which would ensure that issues such as record duplication are corrected automatically before they can adversely affect your own data preparation.
3. Model planning
Next, you must choose the methodology best suited to your requirements so that an automated solution to the original problem can be developed.
Your chosen techniques must respond to the different relationships between your data variables, so it’s important to get a really clear idea of the presentation of your data.
Decision trees produced by Explainable AI can be a useful way of predicting possible outcomes and their usefulness, while also signposting missing links and informing any re-evaluation of data sources at this stage.
Exploratory data analytics (EDA), in the form of visualization tools and statistical formulas, help you unlock your data. EDA allows you to identify the main characteristics of your data, manipulate your sources to reveal the correct information, and test your hypotheses and proposed techniques.
Popular tools for EDA include Python, SQL, R, and SAS/ACCESS.
4. Model building
In this phase of the data science project flow, you can determine the training data sets you will use to test your proposed machine learning algorithm.
It should be clear at this point whether you need to rethink the data you are using and if there are gaps that need to be filled. Fast and parallel processing may be required if your tools cannot support the model you have chosen.
A data scientist will often build a baseline model that has proved successful in similar situations and then tailor it to suit the specifics of your problem.
When building predictive models, you will need to use learning techniques such as classification, regression, and clustering to guide your model in dividing up and understanding the significance of your data.
It is better to begin with a simpler model and add complexities with each revision. This will help keep things focused and will encourage you to develop only in ways that will be strictly valuable.
Once the basic model pipeline has been chosen, the potential for a data flywheel becomes clear. In production environments, the most popular tool for model-building is Python.
In more research-based and/or educational projects, tools can include R, SAS Enterprise Miner, WEKA, SPCS, Modeler, Matlab, Alpine Miner, and Statistica.
At this stage, you begin to run your chosen model and deliver the final reports on your model performance findings, as well as any necessary briefings, code, and technical documents.
If your model has worked better than expected, it is at this stage that you can put together a small-scale pilot project outside the sandbox environment, in a real-time production environment, to start tracking real-world effectiveness.
This will reveal any unforeseen constraints that will need to be accounted for before your model can be fully put to use. A suitable API will be needed to begin processing model outputs online, outside the sandbox environment.
6. Communicate results
The results of your project can now be communicated to those involved. They can be compared to the initial hypotheses defined in phase one of the life cycle, to determine whether the data has revealed the expected insights and whether your model has worked in a way that will solve the initial problem.
If your process needs to be refined to improve the quality of your results, you can begin again at phase one with a more specific problem to solve. With each refinement, your model gets closer to being ready for deployment in a real-time environment.
After handover and deployment, the life cycle continues. The efficacy of your model must be continuously monitored and tested to make sure it provides both your business and the consumer value. Data changes rapidly over time, and your model will need to adjust to new trends to avoid performance regression.
Other popular data science life cycles
Different data projects will require slightly different life cycle models, depending on their end goal and the problem they aim to solve.
On the surface, these life cycles follow the SEMMA methodology: Sample, Explore, Modify, Model, and Assess.
There are key differences, however, particularly at the data preparation and model development stages.
Data mining life cycle
If the objective of a project is to acquire data, filter data, or simply increase knowledge of data, the Data Mining life cycle can be employed.
This kind of automated model can help businesses acquire only the type of data they require from their data warehouse, as determined by a specialist list of rules.
1. Business understanding
Similar to the Discovery phase of the data science life cycle, the process for data mining begins with defining what the desired output for the business is, and how the business will define success.
Issues such as risk, constraints, and budget must be addressed, and the business rules must be stored in the Information Knowledge Repository (IKR) alongside any earlier data mining results.
2. Data understanding
Provided the initial collection of data has already occurred, this step includes defining the metadata of the source, exploring the range, scale, and content of the source, and verifying the validity of the source and resulting data.
3. Define objectives
At this stage, the data science team can predict results and define a series of hypotheses based on what they already know about the data. These will later be used to measure success.
4. Select and sample
A selection of data is chosen automatically or manually from the data warehouse to be used in the test environment. This sample should reflect the appearance, or distribution, of the whole data.
5. Pre-process data
The selection of data is manipulated and modified to remove features that are redundant. As the current format of the data is unsuitable for mining, preprocessing techniques such as binning, categorization, mapping, standardization, and scaling are applied to get it into the correct, usable format.
6. Transform data
Data is then transformed (by methods such as reduction or projection) to make sure it fits into the rules of the model. Reducing the complexity of the data helps increase the performance of the final model.
7. Data mining
The main data set is then mined using the model. Popular data mining algorithms include classification, clustering, regression, and sequencing.
8. Model evaluation
The results of the data mining model are then evaluated against the hypotheses designed in stage three. Every correct result is then stored in the IKR, ready to influence future projects.
The data mining model can then be deployed on the live data warehouse. The effects of this must be continuously monitored to reveal any problems or inconsistencies in the model.
Machine learning project life cycle
Machine learning projects aim to find a solution to a problem using an automated algorithm rather than by testing ideas in a real-time live environment.
Potential solutions to complex business problems can be run alongside each other and compared, before they are implemented.
1. Business understanding
The problem must be defined using existing business knowledge, research into business competitors, and previous instances where time and/or money has been saved with automation.
2. Data collection
At this stage, the business must define its data source, whether this is their own data warehouses or third-party sources.
Open-source databases can help with general problems experienced by many businesses, and contain data that has already been through many of the necessary manual processes, which makes the following steps less intensive.
3. Data preparation
This part of the process helps you filter through the huge volume of collected data, getting rid of that which is irrelevant, spotting gaps, and eliminating outliers.
You can then format your data so that your machine learning model will be able to read it. This is one of the most time-consuming steps, as it requires data engineers to process the data using techniques such as EDA.
4. Data annotation
Each of your data samples must be annotated or labelled according to a definitive annotation guideline designed by your machine learning engineers.
5. Model development
Your general model can then be tweaked to meet your business’s specific needs. Then, the experimentation begins, in order to fine-tune the machine learning model until it is producing the desired results.
Hyperparameter tuning methods allow you to test multiple hypotheses to find a solution to your problem. Different potential machine learning models are then compared to one another using a defined set of metrics.
A separate validation dataset will evaluate your model throughout the training process, helping you ensure your results are accurate.
6. Model deployment
The chosen problem-solving model is then deployed and model performance is monitored.
Big data life cycle
The models explained above are not necessarily well-suited to the big, unstructured data of today. For big data projects, this life cycle may be more appropriate.
1. Business problem definition, research, and human resources assessment
The problem must be defined, as well as the potential value of the solution. The solutions of your business’s competitors should be taken into account, but then assessed alongside your comparative human and technological resources.
2. Data acquisition
Data samples are collected from your chosen sources. In the case of big data, this will likely be unstructured and hugely varied in form.
3. Data munging and storage
The data must then be translated into a format that can be understood by your model. This involves converting unstructured data into more structured forms using techniques such as Natural Language Processing.
Anomalies and missing values should also be identified and accounted for. The data should then be stored in a way that means it is easy to retrieve.
4. Exploratory data analysis and data preparation
EDA methods are then implemented to understand and map the data. This should reveal whether the available data is capable of solving the initial problem.
5. Modeling development
Once you have defined your datasets for training and testing, you can begin to trial different models in a similar way to that mentioned above.
6. Modeling deployment
After the chosen model has been evaluated and refined, it can be implemented in the data pipeline, and then must be continuously monitored.
Impact of data science life cycle on business
Data science has the potential to dramatically improve the value and efficiency of your business.
Not utilizing the power of user data in today’s digitized world can lead to missed opportunities for development. But, on its own, data science is not always a profitable investment.
Knowing how to leverage data effectively is essential, and leveraging data effectively requires a tried and tested methodology, such as the data science project life cycle outlined above.
If you would like help implementing any of the ideas mentioned here into your business processes, get in touch with us.