Data Science as a Service: what is it and what are the benefits?

Data Science as a Service can help companies leverage their data, improve decision making and business process efficiency.

However, creating a data-driven process is no easy task . Mainly due to problems with culture , people , assessments and technology .

And this is when Data Science as Service (DSaaS) comes into action, allowing companies to access and use data more efficiently and effectively.

But after all, what is this? How it works? What are the benefits?

These and other questions will be answered below.

Good reading! 🥸

What is Data Science as a Service?

Before knowing what Data Science as a Service is , we need to explain what data science is .

Data science's main objective is to improve the decision-making of organizations, governments and people through data analysis . Therefore, it studies “ the data life cycle ”. That is, it addresses the principles, definitions, algorithms and processes necessary for the extraction and interpretation of data spread across large sets.

Data driven organizations have their data cycle organized and structured . This work requires investments in people, culture, technologies and processes . But not all companies can allocate resources for this purpose, but this is not a problem: Data Science as a Service emerged to fill this gap.

Now yes, Data Science as a Service is nothing more than outsourcing data science services . This concept was born in the United States and became popular given the great market demand for data science professionals.

It is an accessible service, with reduced costs , that democratizes data science for companies of all sizes. And it was created with one goal: to help companies of all types make better data-driven decisions .

Our mission is to minimize the "I think" in managers' vocabulary and spread the data culture , in a scalable and successful way , to all organizations.

How does Data Science as a Service work?

Data Science as a Service aims at data transformation , that is, the transformation of data for data driven decision making .

Each company handles its data in a unique way. Therefore, we adapt and personalize all projects according to the reality of each organization.

Our methodology can be summarized in three words: past, present and future. The past, recorded in historical data, teaches you to visualize the present to predict business trends.

Now, understand how the data cycle involving the 5 steps works .

1. COLLECTION

First, data from various sources and formats is collected according to the data model and using extraction, transformation and loading (ELT) and data acquisition techniques.

2. STORAGE

Then, the data is organized, prepared and stored in a robust and secure structure, known as a data lake or data warehouse .

3. VIEW

At this moment, managers start to identify, visualize and filter trends in data through BI dashboards and intelligent reports.

4. MODELING

The next step goes beyond visualization. It includes the development and validation of custom machine learning models with statistical modeling and artificial intelligence methods to predict and anticipate specific business problems.

5. DEPLOYMENT

Now is the time to operationalize the models created and validate the entire data cycle. For this, intelligent applications, such as dashboards and personalized apps, are implemented. This way, managers can apply the benefits of predictive modeling in real time using an intuitive and personalized interface to overcome challenges and predict trends.

Companies that go through this process transform large volumes of data into insights and business opportunities.

What are the benefits of Data Science as a Service?

There are countless advantages of being data driven with Data Science as a Service . Companies can:

• Rethink your data culture , overcome the fear of change and adjust to digital transformation.
• spend less time on training professionals and data analysts .
• communicate data and metrics in real time to all organizational levels, ensuring accessibility for decision making.
• invest less in systems, software and hiring data professionals.
• transform data into business assets to improve products and services , working with the customer base to increase revenue.
• reduce operational costs and increase sales with assertive decision making.

Do not know where to start?

No matter the size of your company, if you want to accelerate its growth and make it more profitable, Indicium can show the way to your results with the implementation of the Data Science as a Service solution .

Contact us by clicking here and find out how DSaaS can be useful for your organization.