The collection of data is growing, and this is something that by now we should all be able to recognise, but collecting data is only one piece of the big data puzzle and it doesn’t necessarily bring in money.
In-fact businesses are spending a lot of money on storing that data they’ve collected and it’s about time that they start getting some return on their investment.
Monetising data can be done in a number of different ways including selling the data or analysing the data to influence business decisions leading to more successful outcomes.
First, it is necessary to better understand the data in order to see where the value may lie. Whether the data is from call centre logs, social media, sensors in stores or any other area it is important for the data to be structured in a way that allows for the extraction of relevant insights.
In order to find the relevance of the data it is necessary to ask questions of it; whether there is enough of it and if more is needed, what that data can be applied to, and if there is a certain business problem that could be addressed through the use of data.
These are just some of the many questions that can be asked of data, it is something that is personal to your business and its goals.
Analytics tools, data mining, predictive modelling and data science can be beneficial to the organisation in performing complex correlations on data in order to gain business insights.
There are plenty of tools out in the tech world that can be used, many of which can be, or already are, integrated with core business tools such as the CRM or ERP system. Salesforce provides this through Wave Analytics while SAP offers BusinessObjects on its Business Intelligence platform.
Selling the data is another way to make money through the valuable data that the business has collected.
A service such as the NASDAQ Data On Demand service is one service that can be used. The cloud computing solution is built upon the Xignite DataonDemand platform and is designed to provide easy access to large amounts of historical data for NASDAW, the New York Stock Exchange, OTC Bulletin Board, Pinksheet and other regional-listed securities.
In this service data is available for purchase online and is made available in XML or CSV files so that it can be analysed.
Thomson Reuters is another company that offers data services through its Data Feed Direct which is a fully managed service that provides full-tick feeds and integrates with Elektron Real Time and Thomson Reuters Enterprise Platform.
The company also offers Market Data with global coverage of equities, commodities & energy, fixed income, foreign exchange and money market data. It includes reference data covering major markets and instruments with over 5.1 million live records; 450 markets and seven million active quote records split across asset class.
This is essentially Data-as-a-Service and it can be used to both buy and sell data. One company that is making big plays in the market area is Oracle with its Data Cloud.
The Data Cloud provides a solution for Data-as-a-Service, which the company says is the world’s largest third party data marketplace. It gives marketers the opportunity to buy consumer data for purposes such as marketing and advertising.
The service includes the Oracle ID Graph, which connects together various Ids associated with digital consumers across channels and devices in order to give a fuller consumer picture, this can then lead to more focused and personalised marketing and advertising.
Big Red recently acquired the company AddThis, which readers may recognise above the image in this article. It offers features that allow people to share stories to sites such as Facebook and Twitter.
The sharing button has activity data for 1.9 billion monthly unique visitors and over 15 million mobile and desktop web domains. What this means for Oracle is that it has access to a very large data source that it can sell on to marketers and advertisers.
It all seems very simple and straight forward but there is a piece of best practice advice to follow. Cory Treffiletti, VP, Marketing, Oracle Data Cloud, told CBR: "When it comes to best practice, there’s only one piece of advice: go with a reputable DaaS provider. Purchasing data shouldn’t be a complicated process; your provider should handle the complexity themselves. If there is a ‘best practice’ it is to research your provider carefully, as not every vendor is the same."
For Oracle, the way to use its service is either to buy the data itself and to activate it within relevant platforms, this could be a Demand-side Platform, ad exchange, or site optimisation or it can be bought through DSP’s.
There are also things as a data purchaser that should be watched out for, one of those is full transparency, Treffiletti says that it is necessary to know where the data comes from and whether it has been properly licensed for use.
It is also important that the business identify individual but anonymised users across every channel and every device that they use, this enables users to create consumer profiles across the full range of platforms they use. However, with regulations such as Safe Habour/Privacy Shield and the upcoming European General Data Protection Regulation it is necessary that the regulations are followed so that data is not misused.
The final point that Treffiletti raises is that: "When they are evaluating different DaaS providers, users need to be satisfied that their partners can provide the scale and the transparency to be truly effective."
The world of data doesn’t need to be complex and businesses don’t have to waste money by storing masses of data that isn’t being used. Improvements in the simplicity of analytics tools can help the business to gain valuable insights providing one avenue to effectively monetising the data.
One of the other paths to follow is that of Data-as-a-Service, both if more data is needed and if selling your valuable source of data is the way to go.
It isn’t necessarily a choice of one or the other, but whatever decision is made the most important thing is that the business is monetising its data.