Data Science Lifecycle: From Business Objective To Product

Data Science Lifecycle: From Business Objective To Product

Data Science: As digital transformation advances in the corporate world, demanding information-based responses and experiences from companies, data science becomes essential to support the business, from operations to business strategies.

However, when deciding to enter the data universe for good, many organizations ask themselves: where to start? What will be the lifecycle of a data science project?

After all, there are so many technologies and tools used for the construction and development of projects in data that, for some managers, it may seem complex. And he will be correct. Being a science, data science requires high doses of experimentation and patience to reach the expected results.

Why Bet On Data Science In Your Company

Data science has become one of the main trends in the data market in just a few years. Precisely because it allows managers to use the information they generate to obtain answers about their business and, thus, have a more strategic view of the activities.

Applied in different segments and with infinite possibilities of use, the study of data is no longer used only by innovative companies. It has become necessary for organizations wishing to remain in the business.

However, before betting on this solution, you need to prepare your company as a manager. Therefore, it is essential to ask: Is your organization prepared for data science? If the answer is yes, don’t waste any more time! Using data intelligently is a great way to guide your decision-making.

After all, many data science deliverables are:

  • Information that will feed a BI.
  • A facial recognition solution.
  • A recommendation system.
  • A model for document analysis.

Thus, in addition to helping to predict demand and identify customer behavior patterns, data science usually increases consumer retention by enabling the company to customize its products and services based on the information collected.

This streamlines the operation and, at the limit, refines the strategy and, thus, improves the financial result of the business. Because by optimizing the process and increasing the performance of the company as a whole, data science reduces organizational costs.

Stages Of An Efficient Data Science Project

To help managers understand all stages of data science implementation in the organization, we have listed, step by step, an efficient journey of construction, development, and completion of a data analysis project.

Step 1 – Understanding Your Goal

The first step to investing in data analysis is understanding your primary objective with this project. In this step, answer the questions:

  • What will be solved (problem, evolution, or improvement?); 
  • How will the solution be delivered (API, dashboard, dataset, etc.?);
  • What are the expectations regarding the solution (delivery time, model accuracy, KPIs, etc.?);

Step 2 – Obtaining Data

Having defined the objective and what you want to improve with data science, the data discovery is carried out, that is, the survey of which data will answer your objective. This data can be stored in different places and formats, inside or outside your organization, in systems or databases, in audio, videos, or texts.

Step 3 – Data Preparation

After the discovery stage, we collect this data. For this, data extractors, such as web scraping, web crawler, and data listening, are created. It would help to verify that your data complies with data security and privacy regulations.

Step 4 – Data Structuring

As soon as we have the data in hand, the data is structured so that it is stored and accessed in a simple, safe, and transparent way. A point of attention when collecting, preparing, and manipulating your data awareness is that everything is done with the utmost care not to introduce unintended trends or undesirable patterns.

Step 5 – Data Storage

The next step is to properly store the data in a data warehouse, data mart, dataset, or data lake. Creating useful visualizations, such as graphs, for your dataset is an excellent way to explore and communicate your findings along the way and always have them on hand.

Step 6 – Data Maintenance And Updating

The data engineering part is over with adequately cleaned, organized, and constantly updated data. And then, the exploration phase begins.

Step 7 – Data Conversion

In this phase, the data will be fine-tuned so they are all in the proper format for your project. 

Step 8 – Filtering The Data

The next step is filtering, performed to reserve precisely the data you need for the project. 

Step 9 – Data Mining

Data mining is performed to look for behavior patterns and conduct various mathematical and statistical calculations to process the data.

Step 10 – Data Representation

In this step, several representations of the patterns found are made. The model helps evaluate the patterns’ consistency statistically and decide if they are ready for consumption or if it is necessary to go back to the previous step.

Step 11 – Data Refinement

Here, the need for further adjustments is verified. If you don’t have any changes to make, the data is ready to be used.

Step 12 – Application Of The Dataset

You should already have the data prepared to be applied in machine learning, deep learning, or any other algorithm at this stage. However, to ensure that the data is aligned with your business, you must finalize.

Step 13 – Finalization Of The Project

At this stage, parameters will be added, so that directors and managers can obtain safe answers in line with the project’s objectives.

Step 14 – Delivery Of The Project

If the project is carried out by a partner company specialized in data science, the delivery can be carried out in a database, files, or even an API.

Step 15 – Presentation And Interaction

For you to consume this information and measure the results in the future, the project can also be presented in a BI, executive dashboard, PDF distributor, email, or any other way. 

Step 16 – Solution

To close this first cycle in data science, we recommend that you schedule a meeting with the company that developed the project and follow all the steps to assess the results at this stage.

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