AIMultiple ResearchAIMultiple Research

The Ultimate Guide to Alternative Data Analytics in 2024

Updated on Jan 12
4 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

View Full Profile

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.

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.

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:

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:

Find the Right Vendors
Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources:

AIMultiple.com Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

To stay up-to-date on B2B tech & accelerate your enterprise:

Follow on

Next to Read

Comments

Your email address will not be published. All fields are required.

0 Comments