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Several companies often need to be more compliant with outdated systems which generate, manage, and gather data in legacy databases. These obsolete systems need to offer access to reliable, relevant, and timely data, which costs business productivity and customer experience.
This guide will help you understand modern data architecture, the latest data modernization strategies and techniques, analytics, and business intelligence, which allow them to access, measure, transform, and monetize their data effectively.
Data modernization enables data to ideally among several databases, breaking silos, enhancing data consolidation, and allowing data ready for analytics. Data modernization technique is at the core of digital modernization for any company that looks to leverage its data’s full potential.
Companies need to have an acute sense of the market, which enables them to practice robust data modernization techniques to manage such a large influx of data.
Modern data architecture is not simply about integrating a data lake with a data warehouse but consolidating a data warehouse, a data lake, and purpose-built stores, allowing unified governance and easy data movement.
Based on your industry and organization, a data architect’s daily roles include the following:
Every company’s data is increasing unprecedentedly, from terabytes to petabytes and often exabytes. Legacy on-prem data analytics strategies can only manage these data volumes if affordable. Several companies are using all their data from several silos and collecting it in one place, known as a data lake. This is done to perform analytics and machine learning directly on top of that data.
Otherwise, these same organizations store other data in purpose-built stores to analyze and get quick insight from structured and unstructured data. This data transfer can be “sharing across, “around the perimeter,” “outside-in,” and “inside-out,” as data has gravity.
Data migration is the process of selecting, preparing, and transforming existing data from one computing unit to another. Data migrates between data centers, storage systems, applications, databases, and business processes.Every company’s data migration objective and process are unique. Several factors that must be considered include the impact on business operations, costs, technical needs, timing, data loss potential, compliance needs, and more.Common types of data migration include storage, application, business process, database, and data center migration.
Data ingestion and integration define data movement from one system to another. Data ingestion is defined as the process of putting data into a database, whereas data integration is extracting that same data out of a database and transferring it into another database.Data integration is often crucial when you wish to integrate one organization’s product with another and also when you wish to integrate your internal business processes with external companies.Real-time and batch ingestion are two types of data ingestion. It involves collecting, transforming, cleansing, and consolidating data from several sources into a single source for analysis.
Data cleansing helps to remove data that does not belong to your database. Data transformation helps to convert data from one structure or format to another.Transformation mechanisms are also known as data munging or data wrangling, mapping, and transforming data from one “raw” data type into another for analyzing and warehousing.
Data rules refer to the set of guidelines or constraints that govern the system’s management, storage, and manipulation of data. These rules define data structure, format, integrity, and behavior, ensuring consistency and accuracy. Data rules help maintain data quality, enforce governance policies, and support data-driven decision-making.Polymorphic data stores are database systems or storage mechanisms that accommodate different data types or structures within a single storage unit. Unlike outdated databases that need a predefined schema and a fixed form for all stored data, polymorphic data stores offer flexibility in handling varying data formats.
It refers to designing a visual representation of a part or whole information system to communicate links between structures and data points. The objective is to explain the types of data used and stored under the system, the bond among these data formats, the methods for organizing and grouping, and its attributes and formats.Data models are designed around business requirements. Rules and needs are defined upfront using feedback from business stakeholders such that they can be included in the design of a new system or used in the iteration of an existing system.
Defined as the degree to which the data meets the needs of an intended purpose. Modern data architecture demands the need to examine components of quality data. This includes validity, accuracy, completeness, consistency, and uniformity.Moreover, the definition of data quality differs for every company. For example, for some organizations, data accuracy may be a better indicator of data quality than data completeness. This demands companies define their requirements and characteristics for maintaining data quality across the company.
Data warehousing is a central repository unit in which companies store valuable data, like sales and customer data, for reporting and analytics purposes.It offers insights and guides decision-making through business intelligence. Data warehouses often include a collaboration of historical and current data extracted, transformed, and loaded (ETL) from various sources such as internal and external databases.
It refers to the representation of data and information using maps, graphs, charts, and other visualizing tools. These features allow data professionals to quickly understand a data set’s trends, patterns, or outliers. The data visualization goal is to help drive informed decision-making and to add colorful meaning to an otherwise bland database.
It refers to the ongoing process of allowing everyone in a company to work with data comfortably. To feel confident about data, make informed decisions, and build customer experiences enforced by data.Some expected benefits of data democratization include supporting a modern workforce, empowering employees, expediting decision-making, improving operations, and enhancing customer experience.
Moreover, a complete data fabric architecture can only be achieved with an independent solution. Data and analytics leaders can establish a robust data fabric architecture by combining internally developed and externally purchased solutions. One approach could involve selecting a capable data management platform encompassing approximately 65-70% of the necessary functionalities for integrating a data fabric. The remaining capabilities can then be supplemented with a custom-built solution.
The modern data architecture includes the following pillars:
It is important to note that data modernization initiatives should be driven by a well-defined strategy and aligned with organizational goals. They should also involve stakeholder collaboration and support from leadership to ensure successful implementation and adoption of the modern data architecture platform.
As more companies establish data-centric technologies and democratize their data. More companies are incorporating AI abilities to offer richer analytics and predictive capabilities. This help employees understand why that data matters and what should be done.