The Ultimate Guide to Alternative Data Analytics in 2024

Alternative data is different than its traditional counterpart because it’s not gathered from conventional sources such as Security and Exchange Commission filings, financial statements, press releases, or management presentations. Alternative data is instead collected en masse from sources such as credit card transactions, sensors, social media feeds, and email receipts.

To be able to make use of these extensive gathered data, analytics tools are required. Analytics tools help investors extract actionable, and financially relevant, insights that would aid them to make educated guesses about the state of the business project that they will be investing into.

In this article, we will explain the steps involved in alternative data analytics, list the requirements that an analytics tool should meet, and at last showcase the top seven tools that are on the market today.

What is alternative data analytics?

Alternative data analytics is the systematic computation, discovery, interpretation, and extraction of meaningful patterns in alternative data, which is derived from a wide array of sources.

We have a data-driven list of analytics platforms vendors, in case you are interested in leveraging one for your business.

What are the steps in alternative data analytics?

Alternative data analytics consists of 6 main steps, including:

1. Getting data

Copy data from the vendor to where you want to analyze the data.

2. Ingesting data

This might involve changing the format of the raw data to make it machine readable. This can be done by leveraging OCR to convert unstructured data in PDFs or images into structured machine-readable text.

3. Loading data

Import data onto your analytics tool. This can be done manually or automated via RPA bots which leverage screen scraping to replicate users’ interaction with GUI elements.

4. Preprocessing data

Clean, filter, and transform the data to make it ready for modeling.

5. Modeling

Apply your analysis, make predictions, and draw conclusions. This could be done manually or by leveraging modeling tools and AI algorithms such as deep learning.

6. Presenting

Present your insights and conclusions in a digestible format such as figures or tables.

Should you have your own alternative data analytics tool?

Not necessarily.

Having an analytics tool with an educated team to make the most use of it requires capital, know-how, and a strategic need of building an internal group. But businesses can still have access to, and gain utility from, alternative data without laying down an expensive infrastructure.

There are third-party vendors, such as UBS’ Evidence Lab, Omnisci, SafeGraph, and Eagle Alpha, who specialize in collecting, analyzing, and selling alternative data to interested clients.

Regardless, this article will act as a guide for investors who would like to learn more about the process as a whole.

How to convert unstructured to structured data?

Unstructured data is any kind of qualitative data (i.e. audio, video, images) that is not a typical format. Unfortunately, because alternative data includes a variety of data types, most of it is unstructured. Transforming these to a structured format requires a data extraction tool with optical character recognition (OCR) to automatically identify, extract, and summarize meaningful information for faster decision-making.

Some of the best tools on the market capable of completing this transformation are RapidMiner, MonkeyLearn, IBM Watson, and Amazon Comprehend.

What programming languages is used for alternative data?

Programming languages are machine-readable words and texts. The top programming languages today are:

Python

It has built-in support for machine learning, allowing data analysts to run complex algorithms directly on the data without having to sample. It’s quickly becoming the ubiquitous language for all but the highest performance-hungry Machine Learning tasks.

R

It’s slightly more scientific than Python, but is often the fastest language for prototyping data products. It has more data science community support Python, enabling algorithms to simply be downloaded rather than developed from scratch.

SQL

Structured Query Language (SQL) has become a popular programming language for managing data. Although not exclusively used for data science operations, knowledge of SQL tables and queries can help analysts deal with database management systems.

What are the top analytics tools for alternative data?

There is no such thing as a one-size-fits-all technology stack. Having said that, it’s not a bad idea to have a strong bias for technologies that enjoy mass adoption. Some of the popular tools on the market today are the following:

Tableau

It’s an end-to-end analytics platform that allows you to prep, analyze, collaborate, and share your big data insights. Tableau excels in self-service visual analysis, allowing people to ask new questions of governed big data and easily share those insights across the organization.

Spark

Spark has massive community support, full documentation, and accessible libraries in Python, R, and Java. Built-in support for ML means data analysts can run complex algorithms directly on the data without having to sample.

SQL Client

It accounts for the majority of the heavy lifting in data operations. As a common denominator for all data analysts, it’s the bread and butter of sorting out structured data.

Excel

As an everyday office tool that does not require inputting lines of code, Excel is a good, basic option for data analytics. Excel provides many beneficial tools and features for organizing and accessing data.

Elastic Search

Although it’s more of a search engine than an analytics tool, it excels at extracting information from documents containing dated events and can help an investment team find quick answers in vast datasets, spreadsheets, and research documents.

For more on alternative data

To learn more about alternative data and its use cases, read:

• Top 10 Use Cases of Alternative Data for Investment Projects
• Alternative Data: Make Better Investments Than Competitors
• Top 5 Sources of Alternative Data

Finally, if you believe your business would benefit from an alternative data source, we have a data-driven list of vendors prepared.

And we can help you find the provider for your business: