The Changing Face of Vendor Analytics

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Our most recent vendor project was an interesting change in direction compared to several vendor related projects we have previously worked on. We were asked to build out a vendor reporting capability that went beyond simple spend analytics and also brought in data from online sources such as Twitter, Google, Bloomberg, Reuters and Facebook.

This project brought forward interesting trends not just in the area of vendor analytics but also in how datasets that underpin traditional reporting areas such as sales and budgeting are likely to expand in scope to include more and more data from online sources.

Some companies are constantly meeting vendors and they need to make sure that they are asking the right questions and signing off the correct deals in these meetings. For this they need their staff to understand more than just the historical spending with the vendor. They need to know how that company is represented in the news, what key events the vendor has been involved in, such as mergers or financial results, and what people are saying about them.

Historically BI solutions have focused on summarizing and visualizing the internal data side of the business – sales, spending, CRM… Users would then supplement this with their own knowledge and research of customers, competitors and suppliers to build-up an understanding of their environment. Recently however, our aim was to improve how users gather information from research. In order to achieve this, a BI solution needs to capture a wide variety of data sources which are then analysed, aggregated and presented back to the user in an easily understood way. Then, by automatically combining this with spend data you can also allow users to better understand the relationships between datasets.

New role of BI Solutions

In order to pull all this together, there are 4 key areas that need to be built out:

    • Text mining – you need to find a way of summarizing the large amounts of unstructured content that are brought in from online data sources – after reviewing several options we went with AlchemyAPI.
    • Data mashing – a more traditional database layer is needed to combine summary results from the unstructured data with internal vendor spend data – for this we stuck with SQL Server.
    • Reporting layer – To deliver the solution we used Tableau to create a series of reports that allowed users to interact with the combined data.

 

Our final architecture looked something like this:

Data flow architecture

 

This project has led to several useful findings:

    • Overall the area of vendor analytics is enhanced by blending the spend data with online data sources. Events such as a vendor being acquired by another company, a successful project collaboration or a sales event need to be visible by the output from a tool.
    • The ability of AlchemyAPI to mine insights from text content is critical. This includes sentiment analysis but also tackles entity extraction – the process of relating people, places, companies and events to articles.
    • With AlchemyAPI you don’t have to store the content of every article (which is also why we chose it as the best tool for text analytics). You can simply send AlchemyAPI the URL to the relevant article and they analyse the content – other solutions require you to capture the full content or an article and send it to their applications.
    • ElasticSearch delivers what is needed from a NoSQL database with its flexibility to store and analyse large scale unstructured data from multiple sources. Its ability to allow multiple processes to collate and analyze data, simultaneously in real time, gives it significant advantages over other data storage solutions.
    • ElasticSearch delivers what is needed from a NoSQL database with its flexibility to store and analyse large scale unstructured data from multiple sources. Its ability to allow multiple processes to collate and analyze data, simultaneously in real time, gives it significant advantages over other data storage solutions.
    • Having built several solutions in Tableau we are aware of its traditional strengths. However, for this kind of project it is the ability to store web links in a dashboard which users can then access that is particularly useful. So if a spike in negative sentiment occurs for a supplier, a user can quickly navigate from a trend chart in Tableau to a summary of the articles content, again stored in Tableau, to ultimately to the most useful articles online.

 

In conclusion, we found that the area of vendor analytics can be enhanced by combining traditional spend data with online content. The process of combining unstructured online data with spend and sales data is likely to become the norm in future BI developments as companies seek to fill in the gaps that internal data cannot answer on their own.

 

Author: Angus Urquhart

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