Data Science has revolutionized the way products and services are developed to simplify strenuous real-world tasks. With Data Science, organizations can eliminate Fraud, improve Decision-Making, and automate Recommendations. However, developing novel Data Science Products and Services from scratch requires a colossal amount of resources.
Building Data Science solutions is not a cakewalk since it involves hiring the right experts, defining problems, obtaining data, and developing production-ready models. Consequently, companies embrace Cloud-based solutions for their Data Science requirements.
In this article, you will learn about Data Science and Data Science as a Service. This article also highlights the various components and challenges associated with Data Science. At last, you will explore various types of Data Science as a Service. Read along to gain insights about Data Science as a Service.
Table of Contents
To get a complete understanding of the blog, you need to have a basic understanding of the following concepts:
- Understanding of Big Data
- An idea of Data Analytics
Introduction to Data Science
Data Science is the process of leveraging Big Data to analyze and gain insights for better decision-making. It also involves building Machine Learning models to automate a wider range of tasks. Today, there is an abundance of data that allows companies to understand business challenges better and mitigate problems with superior Machine Learning models.
To read more about Data Science refer to Ultimate Guide to Data Science Simplified 101.
Introduction to Data Science as a Service (DSaaS)
There are several challenges that will be discussed further in the article that companies have to overcome for effectively building and implementing Data Science solutions. To avoid several problems, including the lack of skilled professionals in the market, companies embrace tools that can be used by most professionals and quickly meet business needs. This not only expedites the business processes but also reduces operational costs.
Companies often shell out money while deploying new Data Science solutions as most models do not go into production. In addition, unlike traditional methods of building Machine Learning-based products from the ground up, Data Science as a Service (DSaaS) allows organizations to plug and play to start gaining the return on investment from the right go.
To understand how Amazon Web Services (AWS) supports DSaaS, read Data Science AWS Simplified.
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Components of Data Science
Data Science consists of a broader range of processes that requires different types of tools and expertise to accomplish business objectives. Here are a few components of Data Science:
1) Understanding Business Problem
Before developing Data Science solutions, organizations must define business problems to device model-building strategy. Without a well-defined business problem, it is challenging to accomplish Data Science goals. As business problems can range from insights to automating tedious tasks and releasing new innovative products, the solutions will be different for every business. Understanding what your business needs and defining it is critical for getting started with Data Science.
2) Collecting and Storing Data
After defining business problems, the next step involves Data Collection and Storage. More often than not, organizations are required to collect data from different sources, which involves Web Scraping and API expertise to gather information. Data is then stored in either a Data Lake or Data Warehouse based on the requirements. While a Data Lake acts as a central repository for Structured and Unstructured Data, a Data Warehouse is used for Structured and Semi-Structured Data for Data Analysis. Techniques like ETL (Extract, Transform, and Load) are crucial to collect and store information.
3) Processing Data
Data Processing can be carried out in several ways to obtain desired results. Data Analysts use Big Data to find insights and generate reports for allowing decision-makers to make effective decisions or processed by Data Scientists to create Machine Learning models for enhancing business operations. Both the process requires varying infrastructure and tools for streamlining the workflow for Analysts and Data Scientists.
Challenges in Data Science
Although Data Science has been in the market since 2008, organizations started embracing it only in the last few years. The proliferation of Data Science was pushed due to the development of Computation Power in recent years. Today, organizations can process complex Neural Networks with ease to empower developers to create state-of-the-art Artificial Intelligence-based solutions. However, implementing projects that are heavily reliant on Data Science is not straightforward. According to a report, 87% of Data Science projects never make it to production. There are several reasons why a project fails, including lack of proper Data, Planning, Skills, and Tools.
1) Absence of Desired Data
Machine Learning is heavily dependent on Data Collection to solve complex business problems. For instance, a Self-Driving Car requires data of every possible scenario to train the models for garnering superior outcomes in real-world situations. However, the lack of data impedes Autonomous Vehicle Development since it is challenging to accomplish Level 5 Autonomy, a stage where vehicles do not require any supervision.
2) Lack of Proper Planning
Most organizations fail to plan the Data Science roadmap because every project requires a different approach. Data Science is not one size fits all; every problem has to be solved based on specific business objectives. Planning in Data Science involves evaluating the right Tools, Methodologies, and Expertise.
3) Limited Skills in the Market
The demand and supply of Data Science professionals are entirely imbalanced. This is mainly because there were only a few universities that provided Data Science courses. Consequently, there are limited skilled professionals in the market. Companies often struggle to attract the right talent to implement or blaze their trail for developing exceptional AI-based solutions.
4) Deploying the Right Algorithm
With the rising complexities due to the ever-increasing Big Data collection, companies are forced to continually embrace new techniques to handle and process a colossal amount of information. As a result, decision-makers have to evaluate numerous techniques and implement the most desired algorithm. These newly deployed algorithms would then require new expertise among the team members, requiring a lot of effort and potential workflow changes.
Types of Data Science as a Service (DSaaS)
In this section you will explore some types of Data Science as a Service (DSaaS) that organizations embrace:
1) Data Science as a Service: Data Collection And Transformation Tools
Several no-code or low-code Data Science solutions are available in the market. They help companies automate the end-to-end process of extracting data from different sources and storing it in the desired format. ETL tools remove manual efforts while ensuring Data Integrity across the departments.
2) Data Science as a Service: Data Analytics Tools
Over the years, Data Analytics Tools have removed the tedious process of writing codes for insights generation. Today, you can drag and drop to quickly process information for making informed decisions. Data Analytics Tools like Power BI and Tableau have not only simplified Descriptive and Predictive Analytics but also Sentiment Analysis with Text Data.
3) Data Science as a Service: Recommendation Systems
One of the most highly used Data Science solutions is Recommendation Engines. These systems allow companies to offer a personalized experience to customers. Highly used in Media, Entertainment, and eCommerce companies, Recommendation Systems are very complex in nature. Building Recommendation Systems from scratch would take several months and would require constant monitoring, resulting in increased operational costs for many companies. With several available industry-specific Recommendation Systems providers in the market, organizations can leverage solutions that would require little to no tuning while implementing.
4) Data Science as a Service: Chatbots
Today, Chatbots are everywhere and are probably the most widely used DSaaS. Chatbots are assisting companies in providing better customer service at scale with almost no human interaction. Developing Chatbots require expertise in Natural Language Processing and numerous datasets for training Virtual Assistants. Chatbots are the most accessible plug-and-play data solutions available for all types of organizations.
5) Data Science as a Service: Computer Vision Systems
Computer Vision solutions are being used for Identity Verification, Extract Information from Documents, Find Defects in Physical Products, and more. Pre-built Computer Vision models can be used within companies to speed up the business process that involves Verifications and Digitizing physical documents.
6) Data Science as a Service: Fraud Detection
Fintech has witnessed a revolution in recent years due to the advancements in the Data Science landscape. Financial Transactions can be verified for their genuineness with Machine Learning models automatically, which was usually carried out manually. Since the Fraud Detection process has been automated, millions of transactions are being processed within seconds, leading to the Fintech revolution. Fintech firms can embrace off-the-shelf Fraud Detection solutions to abide by the rules in the highly regulated industry.
7) Data Science as a Service: AutoML
While developing Data Science solutions, Data Scientists spend a lot of time evaluating different models to gain the best results. This slackens the workflow since it is a manual process. AutoML solutions in the market are critical to recommend the right algorithms for your Data Science projects. Although there are huge advancements in the AutoML, it still is in a nascent stage. Nevertheless, it still enhances productivity in Data Science projects.
Limitations of DSaaS
While DSaaS can reduce operational costs and turnaround time for implementing a new initiative within organizations, it has several drawbacks.
- One of the most prominent issues is that not all solutions would cater to your business-specific needs. In such cases, you will have to develop solutions from scratch. Therefore, you cannot always rely on existing tools for all your Data Science requirements.
- In addition, since DSaaS are mostly Cloud-based, you will have to, on numerous occasions, provide your data to the tool provider, which might compromise Data Privacy. As a result, you should not embrace DSaaS for every requirement.
In this article, you learned about Data Science and Data Science as a Service. You also gained insights into the various components and challenges associated with Data Science. Moreover, you explored various types of Data Science as a Service.
DSaaS is gaining popularity among organizations to streamline the entire workflow of Data Science initiatives. As the DSaaS landscape matures, organizations will have more options available to reduce the dependency on experts and maintenance for enabling Data Science Infrastructure. In the future, DSaaS will revolutionize the way organizations implement Data Science for business growth.
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