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If you are a CXO, Director, Head of Engineering, or Senior Product Manager, you are most likely dealing with various aspects of technologies like Data Science, Machine Learning, Artificial Intelligence, Deep Learning in your work. While all these technologies are interlinked and used interchangeably, it’s essential to understand the business implications of each technique and what kind of solutions and insights you are looking to generate for your business.

GS Lab | GAVS conducted a webinar on Demystifying Data Science, AI, and ML to explain the differences between Data Science, AI, and ML, how each of them can be brought to use in business scenarios, and where they can be a game-changer. The webinar also deep dives into understanding if Data Science and Machine Learning augment the organization’s existing Analytics portfolio investment. This blog captures key discussion points and takeaways from the webinar.

Mr. Kunal Shah, Senior Marketing Manager at GS Lab | GAVS, moderated the session. The panelists were Mr. Vineet Raina, Chief Data Scientist and Architect from GS Lab | GAVS with over 15 years of experience and two patents to his name; and Mr. Srinath Krishnamurthy, Principal Architect at GS Lab | GAVS, who is TOGAF 9 certified and an expert in data warehousing, data architecture, and scalable cloud-native architectures.

Understanding AI, ML, DS, and DL

Artificial Intelligence (AI) is an extension of human intelligence that uses sensory inputs such as vibrations, chemical compositions, and audiovisual inputs to take decisions. There are two methods of AI – the rule- based approach and the data science approach. While using AI, these approaches will require different processes. Consider the example of filtering spam mail. In a rule-based approach, considered a more traditional approach, the developer has to implement complex rules to detect spam emails and constantly update code as and when rules evolve. However, in the data science approach, AI compares emails with previous spam mail and learns from available data.

Data science is extracting actionable insights from data collected through various data channels. Analyzing past data to predict future sales, interpreting customer similarities based on available data to recommend products, or learning from past hotel bookings to suggest better tariff are some real-life examples of data science.

The data science process starts with data capture and moves to data preparation. Once the data is prepared for analysis, ML learns from the data to give an output. The three types of Machine Learning are Supervised, Unsupervised, and Reinforcement Learning.

For ML to give the right output, data science is imperative. Machine Learning analyses the data and comes up with the solution or output. To do so, it builds a model using the data that is available to it. Model in ML is the simplified version of the data. Some of the types of ML models include linear regression, decision trees, and random forests.

ML is highly trainable and can be done through simple training, performance evaluation, and train-test split. Consider the example of employee experience and salary. If the data fed is about the salary and experience of various employees, then the model can arrive at an equation that shows the correlation between salary and experience. This model can then be used to predict unseen entities.

Deep Learning is a subset of ML. It uses neural networks that run over clusters of multiple machines. Different types of neural networks – forward and recurrent – can be used to predict outcomes accurately.

Impact of Data Science on Business

Before looking at the business implications of data science, it is important to understand how it can be used in a business scenario. Broadly, there are three ways in which data science can be integrated into an existing business. First is data science enablement by integrating DS capabilities into a product. Second is the use of data science in the product development process. Third will be data analysis, where data scientists analyze existing data to generate insights to improve processes and business.

With data coming from various IoT sensors and social media, businesses can leverage DS to forecast future values based on past sensor data, detect anomalous signals which could be signs of faults in a device, capture insights by analyzing usage data, detect fraud using past labeled data, and predict failures based on past data.

However, there are also a few common misperceptions when it comes to DS. There is an assumption that having data is enough to predict anything. While DS is about data, it is also about usable data. Similarly, there is an expectation that a good predictive model can be prepared in a week. ML using the DS approach is about feeding the system with enough relevant data over a period of time.

Key Takeaways

To use Data Science for your business, capturing relevant and good-quality data is important. Since everything is data-driven, identify a domain expert who understands the domain and data. Once you have them on board, bring a data scientist to work in tandem with the domain expert. Instead of investing everything in Data Science, we recommend having a small discovery phase focusing on ROI. Investment in a full-fledged data science project can then be done based on outcomes.

This blog offers only a high-level gist of the webinar. Here, you can watch the entire discussion, including the poll questions and the experts’ take on audience questions. GS Lab | GAVS periodically organizes insightful webinars with our tech leaders, the leadership team, and industry thought leaders to explore current and emerging trends. To watch our other webinar recordings, please visit

GS Lab | GAVS’ Data Science capabilities help businesses realize the full potential of AI/ML right from knowledge discovery, forecasting, decision making to end-user engagement. We will help you choose the algorithms and realistic models best suited for your business needs. You can find more information on our AI/ML offerings here.