How to Find Your Perfect Data Combination Using Alternate Data
Almost every day you interact with data (even if you don't realize it!).
Every time we interact with technology, such as your phone or a credit card, data is generated. Sometimes that data doesn't go anywhere, but often companies store and analyze that data to better understand behavior.
Frankly, due to the abundance of raw data being created, organizations struggle to understand the best ways to deploy it and use it to make better business decisions and come up with stronger assumptions, thereby increasing accuracy and business efficiency in a number of areas. There are problems that were intractable in the past that can now be studied, explored and solved based on the types of data created every day through social, personal and professional interactions.
What is Data as a Service?This exponential creation of data from technology has given rise to an entire category of business services, called data as a service (DaaS).
Data users have not only realized that data needs to be created more efficiently, but it also needs to be tracked, stored, analyzed, manipulated, shared and sold. The existence of such a large amount of data required a larger infrastructure to maximize the potential of the data.
Basically, the DaaS industry aims to deliver and distribute data through a network. Usually, data will be stored in data warehouses and made available to users through cloud computing. DaaS is a service that is growing exponentially due to its ability to increase ease of use and speed of access for businesses.
Given its newness, DaaS companies are pricing their data offerings in different ways, but the most common right now is volume-based pricing, which ultimately sets the price of data based on the megabyte size of the sets desired data used and provided.
However, costs tend to vary depending on the type of data a business uses, how often they need to access it, and what they hope to get out of it.
Tip: Some notable examples of DaaS providers can be found in this list.
Data use cases in different industriesData has always played a role in business (think a hand-tracked ledger!). But today, with the sophistication of data, we see every industry favoring data that suits their needs in unique ways. Even when the same data may be used by two different parties, it may be used for very different reasons and therefore stored and manipulated in unique ways.
For example, we have the supply chain and logistics industry that uses all sorts of data metrics on their transportation methods to overcome bottlenecks. In contrast, the marketing industry could use demographic and firmographic data to create the perfect outreach methods.
"Precursors gain efficiencies by intelligently collecting and leveraging this data. So the rest of the industry is trying to keep up/catch up."
- Felipe Torres, Account Executive, Investors Solutions at G2
Investment management is one of the core areas of DaaS. These organizations are the ones writing the checks to fuel the exponential growth of the DaaS software industry, and investors are beginning to foster and build their own data systems to ensure that their investments are truly the best use of funds with the best returns for their investors.
"It's a very competitive investing landscape and you need data insights to find better companies, build conviction, and help their businesses with data."
- Sean Saint, Head of Investor Solutions at G2
In this article, we will explore a population called institutional investors. By definition, these organizations invest money on behalf of their clients – think hedge funds, private equity investors, venture capital and investment banks.
A significant amount of capital flows through these markets, and historically, decisions made by these parties have relied primarily on financial data and human-to-human interactions. But as expectations and the size of checks have increased, so too has the need to have absolute confidence in each selected investment.
The days of intuition are over (although any good investor will probably tell you that intuition still matters). Instead, the prevalence of alternative data sources has dominated this market.
Traditional data vs alternative dataInvestment data can be divided into two categories: traditional data and alternative data.
Almost every day you interact with data (even if you don't realize it!).
Every time we interact with technology, such as your phone or a credit card, data is generated. Sometimes that data doesn't go anywhere, but often companies store and analyze that data to better understand behavior.
Frankly, due to the abundance of raw data being created, organizations struggle to understand the best ways to deploy it and use it to make better business decisions and come up with stronger assumptions, thereby increasing accuracy and business efficiency in a number of areas. There are problems that were intractable in the past that can now be studied, explored and solved based on the types of data created every day through social, personal and professional interactions.
What is Data as a Service?This exponential creation of data from technology has given rise to an entire category of business services, called data as a service (DaaS).
Data users have not only realized that data needs to be created more efficiently, but it also needs to be tracked, stored, analyzed, manipulated, shared and sold. The existence of such a large amount of data required a larger infrastructure to maximize the potential of the data.
Basically, the DaaS industry aims to deliver and distribute data through a network. Usually, data will be stored in data warehouses and made available to users through cloud computing. DaaS is a service that is growing exponentially due to its ability to increase ease of use and speed of access for businesses.
Given its newness, DaaS companies are pricing their data offerings in different ways, but the most common right now is volume-based pricing, which ultimately sets the price of data based on the megabyte size of the sets desired data used and provided.
However, costs tend to vary depending on the type of data a business uses, how often they need to access it, and what they hope to get out of it.
Tip: Some notable examples of DaaS providers can be found in this list.
Data use cases in different industriesData has always played a role in business (think a hand-tracked ledger!). But today, with the sophistication of data, we see every industry favoring data that suits their needs in unique ways. Even when the same data may be used by two different parties, it may be used for very different reasons and therefore stored and manipulated in unique ways.
For example, we have the supply chain and logistics industry that uses all sorts of data metrics on their transportation methods to overcome bottlenecks. In contrast, the marketing industry could use demographic and firmographic data to create the perfect outreach methods.
"Precursors gain efficiencies by intelligently collecting and leveraging this data. So the rest of the industry is trying to keep up/catch up."
- Felipe Torres, Account Executive, Investors Solutions at G2
Investment management is one of the core areas of DaaS. These organizations are the ones writing the checks to fuel the exponential growth of the DaaS software industry, and investors are beginning to foster and build their own data systems to ensure that their investments are truly the best use of funds with the best returns for their investors.
"It's a very competitive investing landscape and you need data insights to find better companies, build conviction, and help their businesses with data."
- Sean Saint, Head of Investor Solutions at G2
In this article, we will explore a population called institutional investors. By definition, these organizations invest money on behalf of their clients – think hedge funds, private equity investors, venture capital and investment banks.
A significant amount of capital flows through these markets, and historically, decisions made by these parties have relied primarily on financial data and human-to-human interactions. But as expectations and the size of checks have increased, so too has the need to have absolute confidence in each selected investment.
The days of intuition are over (although any good investor will probably tell you that intuition still matters). Instead, the prevalence of alternative data sources has dominated this market.
Traditional data vs alternative dataInvestment data can be divided into two categories: traditional data and alternative data.
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