Why Data Science Doesn't Respond Well to Agile

It is the time when we have our hands on all kinds of technologies. But we are still going after new technologies, as we know it is a never-ending race. With that, we are also trying our luck with existing technologies. We are combining two or more technologies to make something new and more powerful. It is like pairing technologies in a way that they overcome all the flaws of each other.

The two most talked-about technologies are data science and agile. We often hear about both of them, and sometimes it is about how to combine them. Data science is the technology that is all about processing the data in a way that we take something valuable out of it. Agile, on the other hand, is all about software development in a way that it is all automated, and takes lesser time than usual.  We will get to the point where we will see how they work together if they do. But before that, let's look into both of them a little more deeply.

Data Science

Stating the exact definition of data science would not be possible. We can say that it is the practice of using statistics and algorithms to bring out valuable insights from a large amount of data. This data can be about business or anything else. We can use data science to work through the data to predict the possible outcome. The professional who performs this task with or without a team is called a data scientist. There are tools available to make this task easier, and a data scientist is supposed to know how to work with them.

Agile

Agile is the methodology that is we use in development, mostly in software development. It is the practice of managing the project in a way that the complete project is divided into parts, and things are happening iteratively. Planning, execution, and evaluating are fundamental parts. In Agile, collaboration is very essential, within the time and with the stakeholders as well.

Agile is the methodology that was developed in 2001 by a team of 17 people. There are multiple frameworks of Agile, such as Kanban, Scrum, and many more. It is the best way to fulfill the costumer's needs.

Reasons, why Data science and Agile don’t work well together

We can say one thing for sure that both of these fields are highly technical, and even the skill sets are almost the same for both. The difference starts when we go towards applying those skills.

For a data scientist, for most of his career, he uses his skills to work through data, to pull out valuable information. He then applies those insights into the business to make it more efficient. Let's say a data scientist is all about analysis with a little touch of programming.

In Agile, on the other hand, a software engineer uses his skills to create different software and systems that have some specific purpose and are friendly to users. If we see, there is a little overlapping of the two fields, as there is an analytical touch in this.

Let's look into it like, when you are using Google chrome to search something, here chrome is a product by a software engineer, and the algorithm that finds you your search results is a data science product. If you want to know more deeply about the relationship between data science and agile, you can join our training program. Let's see why data science and agile do not work together that well.

Methodologies

We can find a little bit mix-up between data science and agile, but when it comes to methodologies, they are all different. Let's look into it.

• Data Science

As we know, the work of data science is dependent on the pipeline. In the pipeline, there are different phases. If a person is responsible to gather data from various sources, clean it, and load it onto the database, this person is called a data engineer. Likewise, if a person is responsible to build the data model analyze it, he is called a data analyst. This way, every phase of the pipeline should be performed accurately.

• Agile

When we talk about agile, it depends on the software development life cycle (SDLC). They follow this for the development and maintenance of the software. It involves all the steps like planning, implementation, testing, documentation, deployment, and maintenance. To develop a software or an application, SDLC is the best way and agile recommends it.

Approaches

If we talk about approaches, data science and agile is a bit off there. This is another reason why these two do not get along with each other. Let’s look into it.

• Data Science

Data science is a field that is all about data being processed. The approach of data science is simple, collect the datasets and analyze them to understand a problem and to find a solution. The training that is required to do this, you can get it by taking data science training classes.

• Agile

Agile is a field that approaches a problem with the features it already has, like methodologies and frameworks. An example of this is the waterfall, that agile model completes all the SDLC phases to find the solution to a problem.

Different Tools

If we talk about doing the job efficiently, both of these fields have a wide range of tools for that. Which are different than each other. Let's look at some of them.

• Data Science

In data science, the tools that we have are based on data modeling, data analytics, and machine learning. Some of the good examples would be Hadoop, MySQL, MongoDB, and many more.

• Agile

When we talk about tools in agile development, these are mostly about designing, testing, and implementing software. To write the code and test it, we have the visual code studio, Emacs, and Vim. To design the web development application, we have python's Django and Ruby.

Well, these are factors that do not let these two fields go along with each other. Data science and Agile, they look similar and they should work well together. But the reality is something else. We cannot say anything for certain as we know anything is possible in the future because we're on our way to achieving great things keeping in mind the rise of technology. That's all from our side, but you can contact our experts for anything you want to know.