The concept of the data science as a service is in its beginning, but yet starting to gain the momentum. The internet searches have revealed that several companies are selling data science as a service, and there has no definition. Most of these companies are promising to help in handling the big data without saying anything on the procedures. It certainly makes the data science as a service which seems like an unknown quantity.
You can say that the consultant data scientist are so mysterious, that it can be defined as the easy analytics programs which raise the analytics data to the business users within their current workflows. It is often implemented by using the external tools and the software which can be automated the analytics process just by integrating with the organization’s data storage and the business solutions to quickly shape, analyze, and disseminate the information throughout the organization.
Some of the enterprises have already done this today. The Data science as service tools has developed keeping in mind a non-technical worker. By focusing on the user's business needs other than complex statistical models, these tools provide information that can be used in the real time.
Example for this could be: An employee at the call center is supposed to give the best service to the customer's you calls in, which could be very helpful if provided with the basic information like - name, phone number, or email, the representative can see the customer’s recent purchase history. Have you wondered what else he could see more than that? What if the representative can see how typically you rate the company in surveys? Or the recent history? Or the average dollar amount which the customer spends with the company on purchases?
Such type of information can quickly give the call center representative into not only what the customer’s issue is but also the frame of mind of the customer while on the call. It can be taken as an example for rising the analytics into the workflows which is in existence and just one way of that is analytics as service which helps make analytics more accessible to the employees at all levels of the enterprises.
Some of the organizations may have already been doing this till some extend with in-house tools. But when compared to the data science as a service, these in-house tools will fall short, they will not able to make the information available nearly as flexible or as quickly. The Data science as service tools possess the ability to quickly share the information within the service like the process or workflow, it is as quickly as seconds and after that data is collected. And few systems will even have the ability to respond to the questions which are regarding the insights in milliseconds while ensuring that the insights can be acted upon instantly within the external workflows.
The introduction of data science as service tools into employees workflow has given them access to the insights into what they are doing and how can they improve things, it helps the companies in making their employees much more strategic and effective in their work.