Data science as a service has started to gain popularity though it’s in its early stages. Statistics have revealed that numerous companies are selling data science as a service even though there is no clear definition of the service.
These companies are promising to assist their clients in handling massive data volumes, but they have not developed procedures on how to make that happen. This makes people see data science as a service like something very strange.
However, data science as a service can be defined as a user-friendly analytics tool that enables its users to get analytics data within their businesses. Data science as a service can be implemented using external tools and software.
These tools can integrate an organization’s data storage and business solutions through an automation process. Businesses can then analyze, redesign and distribute the information.
There are some public companies which have already adopted the tool. Feature in data science as a service have been designed to suit users of all manner, including the ones with minimum technical knowledge.
These tools focus on business needs of the user rather than technical statistical needs and then return information which can be used in real-time.
For example, a call center agent can only give a customer who has called in the best services by entering the customer’s basic information into the system. This can assist the agent to see the customer’s recent buying patterns. But, what if the agent can get more information than that?
The information can assist the agent not only to understand the customer’s issues but also try to figure out what’s in the mind of the customer.
This is one of the many ways in which data science as a service can be integrated into to businesses to provide analytics to all employees at the different level in an organization.
Some organizations are already using in-house tools to perform these operations. But these tools cannot be compared to data science as a service since they are less flexible and slower thus don’t avail the information within the required time.
Data science as a tool, on the other hand, can shape the information as the process goes on or in case the workflow changes or within seconds after the data is collected.
When you introduce data as service tools to your employees’ daily schedules, they get to know their daily targets and the improvements they need to make. This can make the employees more efficient and strategic in the workplace.
Transform Your Team with Data Science as a Service
If you decide to hire someone one to be working in data science and processing data, that won’t help your organization. However, by employing data science as service tools in your business all existing workflows can be integrated, you should understand how this works in meeting the daily needs of your business.
CMOs depend on technology to get what data science as a service tools provide at the highest level – that is, a better understanding of the customer’s needs. Recent studies indicate that soon CMOs will increase their spending on technology.
We should also understand that the CMOs are not just the ones requiring data insights; also, other people within the organization do require these insights.
There are new tools that are used to make data accessible to all employees at all levels within an organization. These tools have user-friendly interfaces, questions that can be reused several times and dashboards which are not complex.
These features allow the employees to get answers from the company’s data when they run the questions. They don’t necessarily require knowing how to run the statistics individually.
Businesses can use data science as a service to help them move from one level to another, as the tools have the ability to integrate data from different places in the business while allowing employees to access the data and its analyzed results.