Data and analytics leaders are increasingly undertaking the modernization of data analytics and business intelligence plans. Tangible benefits are being gained by organizations by implementing data science initiatives as they address assumptions of analyses, data cleaning, and transformations. As a result, investment in data science solutions continues to grow.
When pondering over the implementation of data science in their business, an organization must know about the problem at hand and the existing analytical maturity of the company to solve it. The business should take note of the availability and the scale of skilled staff in the IT department and the time that will be taken by them to come up with a solution. Before adopting a particular data science plan, organizations should think of the fact whether the need is urgent or business-critical. Furthermore, it is pertinent to take care of whether specialized and readily available tools, called packaged application, are available or not before going for a data science plan.
The right solution paths for implementing data science
There are three solution paths for implementing data science in your enterprise. Let’s discuss them one by one.
Build a solution using data science platforms, primarily through in-house projects
A highly skilled staff, with a deep understanding of how to build advanced analytics solutions based on data science platforms, is of utmost importance. Moreover, resources and tools that range from general-purpose programming languages to more user-friendly and easy-to-use dedicated development environments provided by SAS, KNIME, Dell, IBM, and RapidMiner are of primary importance.
Buy packaged analytics applications, even though they may require some adaptation
Vendors provide dedicated packaged analytics application software for properly defined and prototypical domains that solve a specific problem. Such a software segment is also called as off-the shelf analytics application. Price optimization, social network analysis, marketing campaigns, condition-based monitoring, and fraud detection are some of its examples.
Outsource building a solution to an analytics service provider or freelancer
There are four varying categories among the different service providers who claim to build data science solutions. The first category consists of global IT service providers, such as Capgemini, Deloitte, Cognizant, and Accenture. The second category consists of diversified analytics service providers, such as Blue Yonder, ZS associates, Mu Sigma, and Fractal Analytics to name a few. The third category consists of specialized data science service providers, such as KDnuggets. And last but not the least, the fourth category consists of crowdsourcing platforms like Experfy, Kaggle, and Topcoder. Myriad teams and individuals select the kind of data science problems that they want to work on by enlisting their talents on these marketplaces. Once they are enlisted, interested companies select the talent, based on their technical expertise and past experience.
A structured and intelligent plan is required for implementing data science in your business. This helps gain a significant competitive advantage over other organizations. A good plan, executed in a smart way, helps companies grow exponentially and become industry giants.
