Enhancements of businesses can be quite a tricky process. Before taking decisions including monetary factors, background information from all possible sources is significant. Traditionally, companies and individuals made use of conventional market data provided by stock market indexes and other industry-specific limited resources. Thanks to the new expansions in Machine Learning and data, smart businesses are understanding the importance of alternative data sets for an investment decision. 

AI-grounded technologies such as Machine Learning and Natural Language Processing are used to extract data points from one of the large data sets in the world; the web. By training an AI model to decode information from vast unstructured data sets, you will be able to make wise decisions in business and trade. In a survey by Greenwich Associates it was stated that, 95% of professionals involved in trading believe that alternative data will become more important to the trading process.  

James Bell, head of AI and Machine Learning for Dow Jones, spoke on a panel about Alternative Data and offered guidance on evaluating data sets to spot new opportunities and growth of revenue. He noted, that finding data, which was difficult in the past has flipped and companies are deciding between dozens of purchasable data sets as a solution to the problem.

AI and Alternative data

According to the report by Greenwich Associates, “74% of firms has agreed that alternative data has started to have a big impact on the institutional investing, while nearly 30% of qualitative funds attribute at least 20% alpha to the alternative data”. However, finding the required data is only the basic step. To study and make use of this vast sea of information, it needs to be analysed efficiently. Usually, these data would be unstructured and will not follow any particular pattern. Hence, the business personnel and investors have a growing need for talent and technology including analytics platforms, tools for testing, fluid data structure, and data science teams to help them with the process of data management. 

Thus comes to being the significance of advanced analytics and AI algorithms, such as Machine Learning and Natural Language Processing. Machines have the capability to process events at roughly 2000 times the speed of human beings. It can digest vast datasets and work around the clock. During the process of investment, AI-enabled data can increase the volume of idea generation. This increase in data, when combined with the power of the computer can act as an aid to the managers in developing long-lasting competitive notions for their business. 

Steps in Alternative Data Analytics

There are variety of sources for Alternative data. It includes financial transactions, sensors, mobile devices, the Internet and Social Media. To analyse these wide sets of data and extract the required information, the process can be quite long. Mentioned following are the stage of Alternative data analytics:

1. Getting data: By getting data, it means that one needs to copy the data from the Vendor source to where you want to analyse the data
2. Ingesting Data: In this stage, the data format is altered and converted into a machine-readable format. This can be done by grasping OCR to convert unstructured data which might be in PDF or image formats into structured machine-readable texts.
3. Loading data: The data converted is imported onto your analytics tools. This can be done manually or using screen mapping elements.
4. Preprocessing data: Clean, filter and transform data, making it ready for the modelling process.
5. Modelling: This is where you apply your analysis, derive predictions and draw conclusions. One can make use of AI algorithms such as Deep Learning for this process.
6. Presenting: To present the insights drawn, one can make us of digestible formats such as figures or tables.

For most industries, data is the new oil. People will be empowered by data-driven platforms to conduct better investment research. Data providers now have the power to assist the industries by making Alternative Data and AI the drivers of future investment research. The use of AI-driven tools will make the data analytics effective, resourceful and time-saving. The use of AI and alternative data in business will help companies build a better business strategy.