In the environs of information technology, data has gradually acquired an intrinsic value. Over the last few years, we are seeing the rise of a new data economy that is commanded by data-driven products and new analytics techniques. New data vendors are emerging and creating a robust data marketing ecosystem. This ecosystem is supplied by data from educational, healthcare, and business domains. The data fabric is further supplemented by some of the best data science courses online. In this article, we take a look at the various facets of the analytical data ecosystem.
The analytical data ecosystem
The analytical data ecosystem has four main stakeholders whose functions are connected to each other. Let us examine this interconnected web in detail. The first component of this analytical data ecosystem is the data-driven devices. Various smart devices that are connected with each other via a common network generate voluminous amounts of data that need to be processed in real-time. For instance, various types of smartphones get connected to each other when the user grants access to data as well as a location including other personal details. This sensitive information needs to be stored and processed in a secure way to prevent its misuse. Similarly, the customers who shop online using their credit and debit cards are at an increased risk of online theft. A secure data analytical network can ensure the continuity of these transactions as well as the safeguarding of the personal details of customers.
The second important component of the data ecosystem is data collectors. One of the most important examples of data collectors includes the RFID chips which gauge the movement of vehicles through specific routes and collect toll taxes. These data collectors also gather information like vehicle numbers so that we can keep a track of traffic movement and regulate it accordingly. Similarly, data aggregators act as the third important component of the data ecosystem. Data aggregators are also one of the core entities of the internet of things. This serves as a repository for data compilation, processing as well as for analytics. The fourth important entity that we describe is the data users and buyers. In the age of the data economy, data is nothing less than a virtual currency. As such, different organizations are involved in buying and selling various data sets. Various E-Commerce organizations buy data from retail banks so that a large number of customers can be targeted with new products and recommendations.
The prime stakeholders of the data ecosystem
There are three prime stakeholders of the data analysis system. The first stakeholder includes data enablers. This group is primarily concerned about the support and execution of analytical projects. It also helps in the management of data architectures as well as the administration of the database management system. The second prime stakeholder in the data analytical system includes the data professionals. This group is blessed with the knowledge of statistics and machine learning and can carry out advanced analytics with a lot of ease. This group is primarily concerned about the data analytics of the market economy and contributes to business organizations, financial management as well as other managerial functions. The third prime stakeholder in the data analytical system is the analytics team. The main function of this team is to process large volumes of structured and unstructured data with the help of various analytics tools. Experts in this field include data scientists, mathematicians as well as economists.
The importance of data scientists in the analytical ecosystem
Data scientists perform very important roles in the analytical ecosystem. Various types of complex business analytic challenges are directly handled by data scientists. These professionals are aware of the entire data ecosystem and can diagnose various kinds of flaws that hamper the growth prospects of a business. Data scientists also help in the design, development, implementation as well as deployment of various kinds of business models. They do this by using various data mining and big data analytics techniques. Data scientists also help to develop deeper and actionable insights into prospective business ventures that can yield a higher return on investment in the near future.
The important skills of data scientists related to quantitative and technical aspects also help the business organization in developing the future roadmap for the purpose of expansion. The communication, collaborative, and critical thinking skills of a data scientist help in the functioning of various business teams as a single unit.