How AI and Data Science solve the biggest business challenges of today

About Webinar

Data is the heart of any business. Raw data describes the facts and figures that a company processes every day. Data becomes information after it has been processed. This adds context, relevance and purpose to the data.

Predictions are definitely difficult, but they are not impossible. Using Data Science for your business reinforces the analysis process that results into core business benefits like demand forecasting, effective marketing and increased revenue. Applying Data Science on the scattered data helps businesses to expose variability and optimize their operations. It would help an organization to employ competitive advantages.

Demand forecasting in simple words means prediction of possible demand for a product or a service based on past events and current prevailing trends. While each operation has its own challenges – demand forecasting may be far more sensitive than the others. Fortunately, there are effective strategies to implement and optimize the benefits of demand forecasting.

In this webinar, presented by the experts of Softweb Solutions, we will explore the opportunities that Data Science provides in the field of demand forecasting that any industry can leverage. Our subject matter experts will discuss the business cases of Data Science to better understand how it enables you to improvise, market, price, and plan your sales process.

Agenda

• An Overview on Data Science and Artificial Intelligence
• Why every business needs Data Science and AI
• Business cases for varied industries
• Demo
• Roadmap for implementation
• Questions

Questions & Answers

The following are the answers to the questions that were asked during the live webinar.

Question 1: Should I be concerned about data security while sharing my data?

Answer 1:I would say no. Data security is a major concern when we talk about data science but most companies providing data science services have a secured NDA policy that ensures privacy of your data. Also, any personal information like name of your customers or client’s information is eliminated while processing the data. Only the information that is useful for forecasting is extracted to apply data science on.

Question 2: In what format should the data be?

Answer 2: This is a question that pops up in the minds of most organizations while exploring the field of data science. Data science deals with multiple formats of data. The question, however, lies not in the format of data, but the type of data to be processed upon, meaning, the data should be enough and apt. The data file can be in CSV, XLS or XML format.

Question 3: When we say enough and apt data, how much data is enough data?

Answer 3: As discussed in the webinar too, I’d say data science does not really rely on big data. Also, it is not necessary for an organization to be large sized to carry data science. A consumer centric start-up can also opt for data science. A person tags a location, adds bookmark on a web browser, posts on social media and does ample of other online activities, these all can be utilized for applying data science. It not always have to be quantity of data, the quality matters more. Organizations have to be clear on what are the important datasets that can help them in forecasting rather than focusing on how much data to be used. Your usual CRM data, email data and customer-care call logs can be used for data science.

Question 4: Can we apply machine learning on real-time data even after the model is once trained?

Answer 4:This is a good question. Now, let me give you an example over here. Suppose you have n number of machines that generate data on a regular basis. You have a well-trained data science model set on the data generated by those machines. Now, say you are making progress and you get another machine to join the team. This means you now have n plus one machines that generate data for you. The trained model can adapt to the data generated by the new machine. Even if the previous model was trained to forecast based on the existing or historic data, data science has the capabilities to adapt real-time data on which machine learning can be applied to provide users with set forecasting.

Question 5: How to derive ROI from data science?

Answer 5: Many organizations see the potential in data science but aren’t confident to fully explore its capabilities, giving rise to fears that they won’t get a reasonable ROI. I would again like to give an example for the same. Say you are struggling to decide whether to invest more on social media promotions. Data science does the work for you. By analyzing the data collected from various social channels and applying data science on the same, you’d be provided with a forecast that will help you decide how fruitful would the investment be to you. This would, ultimately, lead you to better ROI plan.

Question 6: How to get started with data science? What should be the first step?

Answer 6: You first need to identify the right dataset that you want to apply data science upon. And for this you need a discrete team that understands the importance of data in your organization and can source that data to the data science service providers.

Your in-house team needs the ability to access and watch data in real time. The team needs to take the data and understand how it can affect different areas of the company and of course, communicate the relevant information to the vendors. A proof of value program would help you better understand the initial approach towards implementing data science.