The world is moving towards complete digitization. With everything going digital, we are producing more data than ever. However, we are wasting this vital data without knowing its worth. In the right hands, this data can be a gold mine. It's because you can extract tons of game-changing business insights by simply passing this data through a comprehensive data science life cycle and applying data mining techniques.
Data is, without a doubt, one of the most crucial elements for a business. That’s precisely why the field of data science is growing at a rapid pace, and modern-day businesses are investing heavily in it. After all, data science can help businesses make well-calculated decisions in a timely manner to stay ahead of their competition. However, the concepts of data science can be complex and confusing, especially if you are new to the field. This is why learning about the data science life cycle is a good idea.
Basically, the data science life cycle brings structure and order to an otherwise dynamic set of processes. Moreover, it breaks the entire data science process into multiple, easier-to-understand steps. In this article, we are going to discuss everything you need to know about the data science life cycle, including what it is, as well as the steps and phases involved in a typical data science life cycle. Furthermore, we are going to briefly discuss some of the most commonly used data science life cycles. So, without further ado, let's dive into it!
What Is Data Science?
Before we get into an in-depth discussion on the data science life cycle, let’s talk a bit more about data science itself. Put simply, data science is an extensive field that combines various skillsets in order to extract useful information from datasets. It uses various techniques to find patterns and analyze data to form predictable and actionable models. Data science is incredibly relevant in the modern era since organizations of all sizes have access to a large volume of data.
This data comes from all kinds of sources, including sensors, the internet, and even our smartphones. On its own, this data doesn’t tell us much. However, if analyzed carefully, these chunks of data can reveal patterns and correlations that can enhance decision-making and help organizations in a number of ways. The data science life cycle breaks down the entire process of collecting and analyzing data into multiple phases. This helps streamline the entire data analysis cycle.
Data Science Life Cycle:
Data science is incredibly useful for organizations. However, in order to make the most out of it, companies need to develop a proper understanding of the overall lifecycle and all the phases involved in it.
Apart from having a proper understanding of the data science life cycle, companies also need to have the proper infrastructure for building and maintaining data science models and products. This is a whole other topic on its own, and we will soon cover a detailed article on it. Right now, let’s focus on understanding the data science life cycle itself. So, a general data science life cycle consists of 5 phases:
Data Investigation and Cleaning
Minimal Viable Model
Deployment and Enhancements
Data Science Ops
General Data Science Life Cycle:
Every dataset is unique in its own way. This means that the analysis approach for every data science model/product is different. This can make it hard to manage multiple data science projects. The life cycle helps streamline project steps by providing a general framework for people to follow. Let’s discuss the 5 phases of the general data science life cycle model in detail!
1- Problem Definition:
The first step of the data science life cycle is very important since it helps establish the end goal of a project. This phase is usually overseen by project managers, which leads to a lack of results. The problem definition stage begins by stating the problem that a product/model is trying to solve. Clearly knowing the purpose of a model/product helps bring all stakeholders on the same page and manage expectations. Once the end goal has been defined and all relevant research is completed, a clear-cut project plan is developed. After this, the team can move on to the next steps in the data science project life cycle.
2- Data Investigation and Cleaning:
After developing an overall scope for the project, the next step involves getting access to a relevant set of data and preparing it for processing. Data can be obtained from numerous sources, including a company’s own databases as well as third-party sources. Once data has been obtained, the next step is to verify its quality and relevance. During this phase of the data science life cycle, the dataset is cleaned and sorted. Oftentimes multiple datasets need to be merged in order to obtain data that is relevant to the project. During this phase, a dataset is reviewed and revised multiple times before moving on to the next steps in the data science life cycle.
3- Minimal Viable Model:
Once your data has been finalized, the next phase involves conducting a test run before actually developing an entire model/product. A minimal viable model helps identify whether the data and overall project is producing the results that are needed. Test results enable data scientists to make adjustments as needed before developing and launching a complete product. Data scientists make use of various techniques to run tests on their models before going into full development.
4- Deployment and Enhancements:
Data models are only useful when they have been properly deployed. At this stage of the data science life cycle, the model is built fully and can now be shared through relevant channels. Deployment can take place on a small scale or across a network of millions of users. Even after deployment, the model needs to be “enhanced” in a number of ways. This is done by continually repeating the first 3 phases of the data life cycle.
5- Data Science Ops:
Once a data science product has been deployed, it needs to be maintained and sustained. This is where the 5th phase of the data science life cycle kicks in. Just like any other digital solution, data models are integrated with a variety of software systems. In order to sustain the data model, the software needs to be continually managed. Ongoing operations include software maintenance, data management, stakeholder management, and other routine tasks that ensure the overall product performs smoothly.
Modern Data Science Life Cycles:
The model that we’ve been discussing so far is a general framework. Modern data scientists have derived a number of different data science life cycles based on this framework. As each project is unique in its own way, different life cycles are used for approaching different projects. Let’s take a look at some of the more popular life cycle models.
OSEMN stands for Obtain, Scrub, Explore, Model, and iNterpret. This version of the lifecycle is a more comprehensive iteration of the data science life cycle. It covers the entire process from end to end.
2- Microsoft TDSP
TDSP stands for Team Data Science Process. This model combines agile methodology with the data science life cycle in order to develop models at a fast pace. This version of the life cycle often makes use of Microsoft Azure, but it isn’t limited to this platform alone.
3- Domino Data Labs Life Cycle
This version of the life cycle is quite similar to the general one. It has all the same stages; the only thing different about this life cycle is its addition of a 6th stage: the research and development stage. This stage involves further research that helps data scientists further refine their data and planning before developing the model.
Other Popular Data Science Life Cycles:
As we mentioned earlier, building data science models is incredibly dynamic as every project has its own unique points. Due to this, data scientists have to come up with a variety of ways to work on data models. That's why there are a lot of variations of the data science life cycle. Here are some other notable life cycles.
1- Knowledge Discovery in Database (KDD) Process:
The KDD process is usually applied in situations where data mining is required. This process involves processing raw data in order to find patterns and meaningful information.
2- Sample, Explore, Modify, Model, and Assess (SEMMA):
The SEMMA process focuses primarily on testing out data before using it to build a model. This approach takes a test portion from the data that is large enough to provide accurate results but not so large that it’s hard to manipulate. This portion allows you to run quick and reliable tests on a dataset.
3- Cross-Industry Structured Process for Data Mining (CRISP-DM)
The CRISP-DM approach is quite similar to the general framework as well. It consists of 6 phases that data scientists can refer to throughout their data science project's life cycle.
Data science is a rapidly growing field with a lot of potential. Large and small organizations can benefit greatly from investing in this technology. By the end of this article, you should have a better idea of how data science models are planned, built, and maintained. Managing any data science project requires a diverse pool of skillsets and a deep understanding of data science models. If you don’t have an in-house data science team, you need to partner up with a highly reputable agency having expertise in data science. That’s where we come in.
Here at IIInigence, we stand proud as one of the best data science, mining, and analytics agencies in the USA. We can help you make the most out of data science just like we’ve helped numerous other companies develop, enhance, and maintain data science projects. We have gathered highly experienced and qualified data scientists from all over the world who can help you develop a custom data science life cycle that will help you reach your end goal efficiently. Get in touch with our experts for a free consultation and see how we can help you fulfill your dreams.