Chances are if you’re reading this, you’ve heard the phrase “data is new oil” coined by Clive Humby, the British mathematician who was also the architect of Tesco’s Clubcard loyalty reward scheme. Although it might have become something of a cliché, it was an even more appropriate expression than when it first heard.
Poisonous and dangerous oil in its crude form; need to be refined before it becomes valuable; and the same goes for data. Failing to control it, eliminate duplicates, clean it up, or analyze it in context, will only be the basis for bad decisions. What is needed is data that is applied with a purpose. Do all those things and you have a platform for data as a service aka data as a service (DaaS).
If data is new (crude) oil and the “as a service” model takes over, not only technology but also the subscription economy ; we rent rather than buy goods and services, then combine the two phenomena in DaaS it must really be something. DaaS is the nirvana of analytics, a constant source of data available on demand from multiple internal and external sources in the cloud .
DaaS typically manifested in mash-ups and composites with metadata and policy management that add extra power and control. You can compare it with the film Minority Report; data is visualized in real-time , animating dashboards that benefit their owners by anticipating events, not just reacting after them. This is a new wave of event driven architecture where previous events automatically trigger actions based on known trends, which helps us predict what will happen next.
However, DaaS It is also guaranteed with access to multiple sources , both internal and external. Think of an example of utility. The water company may receive information about the leak from an analysis of social media sentiment. The Twitter complaint helped him identify where the leak had occurred and by triangulation how widespread the problem was. The utility company could then solve the problem by using geofencing to determine which technician was in the nearest location to stop the leak.
Companies are naturally looking for useful data but governance and compliance guidelines are needed to avoid violating data privacy rules or scaring customers off (insecure). Data virtualization, the modern data layer eliminates bottlenecks, helps ensure that data is accessible without compromising security by moving it elsewhere.
Another challenge is finding data scientists and chief data officers to deploy DaaS. However, the good news is that we are seeing more graduates emerging, more data mentoring, communities of practice, data mastery, as well as established best practices and case studies to rely on.
By combining data and bringing it to the people who need it on demand and in near real-time , we can answer questions very quickly. When is traffic worst near where I live? Where are the worse cases of COVID-19 and who is most affected – male or female, young or old? How many crimes are there in my neighborhood and what kinds of crimes are being committed?
Almost all questions can be answered if you have the data. Data drives insights, they drive smart decisions, and those smart decisions build the platform for commercial and operational success. Using the API, we can create the ” mash-up monster ” we need, but to do that, we need a smart workflow and we need to avoid data silos. DaaS driving revenue, profitability, strategy, and efficiency. So, every company needs to catch up, become data-infused , apply tools and data scientists who can interpret the data and explain it in a way that is interesting, even for non-specialists.