Data Science Trends We Will Frequently Encounter in 2021
With the data-driven transformation, data analysis methods, must improve themselves to understand and evaluate the exponentially increasing data world. Even if these terms are already used in the sector, they will be heard more frequently in 2021. In this article, we define these terms briefly.
“Data Meshes”, “Data Democratization”, “Continuous Intelligence”, “DataOps” and “Augmented Data Management” … These terms define new trends of data analytics. These trends are actually not new inventions; they refer updates of existing data science technologies.
Let’s get to know these trends more detailed.
Excessive data is accumulating with great acceleration and radical changes may occur even if your organization is still into data-driven transformation. For example, centralization of data and analysis them only through one unit are old methods. Thanks to Data Meshes, data that are kept in multiple data warehouses are brought together. Traditional monolithic models process data consumption, storage, transformation and output in one central data lake. However, distributed data between different locations and organizations can be bounded with data meshes. With this structure, it is possible to provide the opportunity to work collaboratively. Data meshes ensure that data is highly accessible, easily discoverable, safe, and interoperable with applications. Additionally, it is possible to connect cloud applications with internal data or sensitive data on cloud. Virtual data warehouses or data lakes can be created for analytical and machine learning training without combining them into a single repository. Thus, application development teams can be provided with ways to query data in various data stores without worrying about “how” to reach this data.
What an interesting definition is the democratization of data, isn’t it? Here is the basic definition of “data democratization”: making digital data accessible for any user and it is not required the participation of IT department. Consider how decision-making processes will accelerate if data can be easily accessed and interpreted by everyone without the need for a central unit. In addition, imagine that how this acceleration affects you in the competitive environment. Data can be scanned in the same natural language just like searching in any search engine from now on. Data creators, data analysts and data consumers in the team can work on the data collaboratively with a catalogued list of all data. It becomes much easier for users to access and understand data. Therefore, decision-making process speeds up for each unit and discovering new opportunities is much easier. With “accessible data” in data-driven organizations, anyone within the organization can easily access and manage data. They can work collaboratively to make effective business decisions.
Continuous Intelligence (CI)
It is a modern machine-oriented approach that accelerates analysis processes by using augmented analytics, event stream processing, optimization, business rule management and machine learning. Thus, CI provides reaching whole data quickly regardless of the number of data sources and volumes. It essentially combines data and analysis with transactional business processes and other real-time interactions. Therefore, CI automates and makes operations continues. In this way, these systems can process high volumes of data quickly and protecting people from overload. However, to take advantage of the system professionally, data and analysis teams should work with application architects, application developers, business process management analysts and business analysts collaboratively.
DataOps (Data Operations)
DataOps is a collaborative data management application focuses on improving communication, integration and automation of data flows between data managers and data consumers in an organization. DataOps is not a product or solution, but an automated and process-driven methodology used by data and analysis teams to improve the quality of data analytics and reduce cycle time. We can also define it as an agile approach to designing, implementing and maintaining distributed data structure that will support a wide variety of open source tools and frameworks. In fact, the purpose of DataOps in general is to create business value from big data. It encourages people in the business processes to work with data engineers, data scientists and data analysts by using automation technology during the editing, delivering and management of data distribution processes. Therefore, organizations’ data are used in the most flexible and effective way to achieve positive business results.
Augmented Data Management
We know that machine learning (ML) and artificial intelligence (AI) are used for making self-structuring and self-adjusting processes common. These processes automate most of the manual tasks and allow you to take advantage of data without being a data analyst. ADM systems provide “self-regulation and self-adjustment” for information quality, integration, metadata management, master data management and database management. System also uses AI and ML to procure these operations. ADM products can examine samples of large operational data including real queries, performance data and diagrams. It can determine their proximity to existing data and cases. Moreover, system alerts other automated systems about new data is available and it is a valid candidate for inclusion.
So what makes them trending?
The data pool is growing exponentially with the increase of available database and data sources. Increasing demands on data and companies’ determination of data-driven strategies now require more “smart” and “agile” systems and methodologies. Continuous improvement is also required for more accurate analysis of the growing data pool. Growth and movement of markets and competitive environments are effective in the development of analysis systems. In fact, the common point of all these trends is the purpose to make data analysis as a standard part of our business processes by making data easily accessible and manageable. In other words, data analysis is now becoming a standard operation for all professions in all business sectors.