Getting serious on Data Monetizing & Data Intelligence

July 31, 2017

 

Do the test and open three surveys of either IDC, Gartner or Forbes: ... you'll immediately get overwhelmed with the messaging of our data driven economy .... or, how 78% of today's decision-taking in leading enterprises is data driven...or, how data is the new gold for them ... or ... well, anyway .... ever thought how these leading enterprises got to that stage? ... ever thought what's behind their initiatives on taken data driven decisions .. ?

 

Action upon insights: Data Monetizing

 

Stepping back on the above question, is basically thinking around the action these enterprises take upon there existing insights. In the end, that's what it's all about: doing something valuable with the analytical insights you gather using business analytics. Making value out of those insights is what we call Data Monetizing or often mentioned as Data Intelligence. I am not going to explain here how to do so, because I can't: the what, why, when and how of taking-action upon available insights depends from enterprise to enterprise. Moreover, probably at least two "libraries of books" have been written about these topics. The point I do want to make is the emphasis enterprises today put in Data Monetizing initiatives. Most of them are getting in place with modern and decent analytics which are strong prerequisites of data monetizing:

 

  • having insights available Live in real-time

  • having insights available onto the transactional detail

  • have an agile analytics environment that allows for interactivity and easy data blending

 

Inspired by the data driven economy and disruption all around us, enterprises initiate data monetizing projects to prevent from being disrupted themselves. They absolutely need to do so to gain competitive advantage or find new markets to penetrate.

 

See the wiki on data monetizing here.

 

Data intelligence is the analysis of various forms of data in such a way that it can be used by companies to expand their services or investments. Data intelligence can also refer to companies' use of internal data to analyze their own operations or workforce to make better decisions in the future. Business intelligence, mining or online analytics are all types of data that companies gather for data intelligence.

 

 

 

SAP Data Network

 

As a cloud company driven by in-memory technology, SAP is pretty class-leading in data monetizing initiatives. This is done via the SAP Data Network. There is a really good white paper on SAP Data Network services and products via this link. If you take a closer look at the three focus areas of SAP Data Network, you'll find following:

 

  1. Live Insights: Of course SAP manages massive amounts of data in their cloud applications (FieldGlass, Concur, Analytics Cloud ... to just mention a few). By aggregating this customer data onto benchmark data - while respecting privacy of the data by all means (!) -, SAP is able to make insights actionable. A nice example is Workforce Insights using the FieldGlass data of their customers, as explained below in this article. It allows to review your company data-points versus the industry benchmarks as derived from the aggregated data. The idea is very innovative if you'd ask me. There is also a similar application on top of SuccesFactors, and one is built for aggregated Concur data. 

  2. Live Customer Cloud: the SAP Data Network also builds connected data hubs between businesses to help both to gain greater value form data. Example? ... well, you could imagine that the insurance industry will be very interested in benchmark data of the healthcare industry. How valuable would it be for them to know what percentage of cured hemophilia have a tendency to resume to their disease? They might want to adjust their premiums. Another example is car repair companies who probably would love to have detailed insights in the summer-travel behavior of people using a car. The SAP Data Network has a team of scientists, technology experts and business strategists that can help customers to build these kind of hubs. There is a nice video on Live Customer Cloud here. A pretty impressive use case was with Schindler as you can see in this video.

  3. Live Data Store: the SAP Data Network team is also building world class ecosystems for enterprise data. Using state of art API's to connected data sources and networks, they create a platform of data assets using machine learning to action upon insights and outcome.

 

Of course the above is just a summary of how data monetizing and data intelligence is handled by SAP and you should definitely consult the whitepaper for a more comprehensive picture. You can already imagine that "making insights actionable and create value of out of it" affects more than only technology. Think like:

 

  • a winning data strategy & data monetizing approach

  • ensure the customers culture, people and processes are aligned on the data monetizing strategy

  • help organizing the required eco system to collaborate and interact with other businesses

  • assist and guide customers in choosing, using and applying the best technologies for their data monetizing strategy

  • automate with Artificial Intelligence (AI): leveraging automation and AI allows data-heavy processes to run in a virtuous cycle of constant improvement

 

 

 

 

 

 

 

Workforce Insights

 

Of course you like to see examples if I talk data monetizing or data intelligence. And right you are, since samples often tell more than words.  Using Live Insights with FieldGlass ((1) from above list), the SAP Data Network aggregated customer data onto benchmark data. It allows the user from below example to exactly judge what to pay for a JAVA developer, where best source that person, and in the same time compare those insights against their own live FieldGlass data. Definitely click the below video to see it working. Innovative! 

 

 

Video: Workforce Insights with SAP Data Network

 

 

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