You may have already heard the expression “data is king”, and in the world of business, it really is. The insights that are held within your company’s digital information can help enhance your operational processes and boost overall performance. Thus, it’s no surprise that data science adoption is growing across industries.

As the understanding of the technology’s power grows, so does the interest of business leaders in learning what stands behind successful data science solutions. After all, when you are managing a company and planning to invest in a new initiative, it’s always good to know what to expect.

So, in today’s post, we’ll focus on explaining the role of data science in business, how its implementation can boost growth, and what the data science life cycle phases might look like during your next project. Let’s get into it.

What is Data Science?

What is Data Science?

In essence, data science is a combination of domain expertise, computer science, mathematics, and statistics that helps extract meaningful insights from data. Additionally, it incorporates complementary disciplines like data mining, artificial intelligence, machine learning, and even cloud computing.

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All these technologies come together with the ultimate goal of improving efficiencies, identifying new business opportunities, and outpacing competitors. Hence, the typical benefits of data science implementation include:

Now that we’re on the same page about that, you might be asking yourself, how you can apply data science to your business and what is the data science life cycle then? In short, it is simply the process of building and maintaining a data science solution.

Projects may vary in software development methodology depending on the industry or due to the specific goals and requirements they have, but there are certain stages of the data science life cycle that will likely remain unchanged from one initiative to the next. That’s precisely what we shall talk about in the next section.

Phases of the Data Science Life Cycle

As previously mentioned, the life cycle of a data science project may vary somewhat on a case-by-case basis. After all, some companies require a minor data science implementation while others are looking for enterprise-wide deployment. Whatever the case may be, the following six steps are the main ones that you can expect your IT team to go through.

Phases of the Data Science Life Cycle

1. Objectives Definition

Pretty much any custom software development project starts with understanding the business problem a company faces. What challenges need to be solved? How can data science implementation help in the company’s unique case? These are the questions that any data science project is bound to start with.

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Once a problem is identified, it’s time to pinpoint the objectives of your initiative and the requirements of the final solution. It’s a good idea to document the following elements so that you can always come back to them during and after development:

Once this stage of the data science life cycle is done, the IT team can move on to looking at your data and determining the next steps.

2. Data Preparation

This next step is likely one of the most crucial within the data science development life cycle. Without quality data, you’ve got nothing. Hence, it’s essential to not only collect the relevant digital information but also cleanse and prep it for use within a data science model.

Identify sources. First, your team will likely identify the various data sources you have that are relevant to the project. This may include web server logs, details from CRM platforms and other internal software you have, or even information from publicly available libraries like the US Census.

Collect data. After identifying relevant internal and external data sources, the team will collect the required data via web scraping, with the help of API technologies, or by using a repository with premade datasets.

Cleanse data. Once the data is obtained, it’s time to explore and clean it. At this phase of the data science life cycle, your team removes duplicates, converts data into a single format, deals with missing values, and looks over any outliers that may be present.

Read up on how we Automated Data Cleansing for an Executive Search Firm

Data preparation is often the most time-consuming aspect of the data science implementation methodology and one that definitely should be approached seriously.

Visualize preliminary data. Finally, as soon as this type of work is complete, the digital information can be visualized via intelligent dashboards and presented to key stakeholders for discussions about preliminary findings.

3. Analytical Algorithm Development

Finally, the next step of the data science life cycle is all about building the data model. At this point, the digital information is taken as the input so that the preferred output can be prepared.

As you can probably imagine, there’s a variety of data science use cases across industries. So, depending on the kind of problem you have, the most suitable type of model needs to be selected to acquire the needed results.

Most of the goals you’re trying to achieve can be accomplished through statistical analysis, classification, regression, clustering, or anomaly detection. Each of these methods has universally known state-of-the-art approaches. In this regard, experienced data scientists will help you navigate the options and settle with the right one that suits your unique use case.

Remember, it’s important for the model to deliver accurate results and have generalizability. Otherwise, it won’t be suitable to run on new data. For this reason, testing the model is an absolute must.

4. Model Testing

To ensure that the final data science product actually benefits your business, it’s important to test the developed model prior to deployment. That’s the only way you can assess quality and result accuracy before implementing it into your IT infrastructure.

Model evaluation can be done through the hold-out method or cross-validation. During the former, the dataset has to be divided into two subsets, with one being used for training and the other for testing.

On the other hand, during cross-validation, generalizability is assessed by splitting the dataset into ‘k’ groups and using one for testing and the others for training. Then, this process is repeated so that each of the groups has been used for testing.

Model Testing: Cross-Validation

Overall, cross-validation is preferred for model testing over the hold-out method. This is because it accounts for more variance between splits and is more dynamic. It’s good to keep this in mind, however, experienced data scientists will help guide you through this step of the data science methodology.

5. Solution Deployment

Following extensive model testing, it’s time to implement your data science solution in the preferred channel and format. At times, you may want to deploy it to a set of users or into a test environment, once again it all depends on what your project aims to do. Some require the tool to be introduced in a limited capacity as an MVP app until it is proven to be successful, before rolling out full-scale implementation.

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This is the stage of the data science life cycle that stakeholders have been most looking forward to. Yet, it’s important not to rush the process.

Like we said, deployment will look different for every software initiative. For some, it may be as simple as getting the model output onto a Power BI dashboard. For others, it will entail deploying it to the cloud or embedding it into a mobile app for hundreds if not thousands of users.

By the end of this, your data science model can finally work with a real-time inflow of data and generate the outputs you were looking for.

6. Support

Finally, the phases of the data science life cycle conclude with performance monitoring and support. At this stage, results and feedback from users are collected to evaluate if there are any immediate tweaks that are necessary.

Often, the development team will continuously support the implemented solution and refine it to keep up good performance standards. After all, you wouldn’t want to spend all this time and money on a new piece of software only to let it deteriorate with time.

Additionally, it is at this point that you might want to assess whether the final solution has solved the initially set-out challenges and what is the effect of its implementation on the bottom line.

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Data science can deliver a multitude of advantages to organizations across industries. So, it shouldn’t be overlooked as a worthy IT initiative. Now that you’re familiar with the six main phases of the data science life cycle you will be better prepared to start your next project.

At Velvetech, we pride ourselves on the successful data science services we have delivered over the years and would be happy to share our expertise with you. So, if you’re looking to get some help on your next IT undertaking — get in touch with us.

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