A primary concern of digital businesses today is the reliability of data. Many business users are still judging the ultimate value of data-guided analytics with a certain degree of skepticism, which can only change over time with delivery of solid benefits. The reliability of business data comes from a well-designed and well implemented Data Architecture. Very simply, the Data Architecture is the blueprint for managing data in an organization, which contains the specific data collection and access controls, Data Governance mechanisms, data security loops, and other Data Management policies to ensure that organizational data professionals have access to high quality data at their fingertips.
Comprehending Data Architecture
The term Data Architecture is typically defined as those specifications that collectively govern the “descriptions of existing state, definitions of data requirements, guidance of data integration, and control of data assets.”
Simply put, Enterprise Data Architecture indicates a collection of standards, rules, policies, and procedures that govern how “data is collected, stored, arranged, used, and removed” within the organization.
What is Data Architecture? defines Data Architecture as the “bridge that connects business strategy with technical execution.” This explanation is reinforced by a quote from Data Architecture Report, which says “Data Architecture is as much a business decision as it is a technical one, as new business models and entirely new ways of working are driven by data and information.”
Gartner emphasizes information infrastructure, which includes technologies that “describe, organize, integrate, share, and govern data.”Although Data Architecture and Information Architecture are used differently within a business framework, the underlying Data Management principles are similar. The significant point is that with an evolving Data Architecture, the underlying technology has to mature and respond appropriately to the changing systems within an organization.
Why Even Bother with a Sound Data Architecture?
8 Steps to Building a Modern Data Architecture offers a convincing argument for investing in a strong organizational Data Architecture. This author believes that as the Data Architecture provides a “roadmap to follow,” it is imperative for data professionals to take a planned approach to designing and building the right architecture that addresses the current and future Data Management needs of the organization. The article suggests that without the right Data Architecture in place, an organization can run into data anarchy.
The article Data Architecture vs. Information Architecture provides a comparative review of, where the author uses technology models to explain the differences between the two.
The Recent Problems in Data Management
Here are some common problems faced by data professionals across industry sectors:
inability to handle the speed and volume of multi-source data, especially IoT
inability to find a single technological solution to collect, store, and
organize data from disparate sources.
inability to handle Big Data projects with a single database technology.
- The increased adoption of cloud platforms and cloud infrastructures has raised concerns regarding data security and Data Governance.
Data Management Trends in 2019 states that businesses currently expend too much time, money, and energy managing their data, while investments in Data Analytics are neglected. The recent emergence of Big Data, IoT devices, and streaming data have added to the Data Management headaches, and now businesses are singularly focused on Data Governance and security while the cost-to-analytics is not even considered. The author suggests that companies need to invest equally in Data Architectures, Machine Learning, cloud analytics, and self-service BI platforms to derive the highest business benefits from their data infrastructures. This is where modern Data Architectures come in, which must be conceptualized and designed based on the rising Data Management needs of an organization.
Data Architecture Trends to Watch in 2019
Leading industry experts expect the following Data Architecture trends for 2019:
- Converged data platforms will accommodate data from widely disparate sources. The converged data platform will also enable data professionals to mirror the data repository from one data center to another. The specific benefits of converged data platforms are outlined in the article 7 Essential Technologies for Modern Data Architecture.
- The rise of cloud-based Data Architectures, as cloud service providers continue to offerInfrastructure-as-a-Service (IaaS) along with Platform-as-a-Service (PaaS) to fulfill the growing demands of businesses of all shapes and sizes. DATAVERSITY®’s Cloud Architecture and Cloud Computing Trends in 2019 describes this.
- Although the standard practice this year will be to combine several database technologies, the RDBMS will rule the roost, especially in Big Data projects.
- Managed data warehouses: The changing data warehouse architecture will be a critical component of the enterprise Data Architecture, where ETL and warehouse services will be delivered by outsourced vendors.
- Hadoop and Spark will continue to replace the data warehouses, again thanks to the rise of Big Data.
- With the emergence of AI, Machine Learning, and real-time analytics, “more speed, scale, and flexibility” will be expected from the organizational Data Architecture, according to Top Trends in Modern Data Architecture for 2019.
- With GDPR in action, more Data Governance and data security are in the cards. The DATAVERSITYwebinar, Trends and Predictions for 2019 explain how general Data Management trends of 2019, especially Data Governance and data security, will impact Data Architecture.
- Due to the business demand of speed-of-execution, in-memory databases may be more commonplace than before.
- The data lake will be the “preferred” mode of data repository in 2019 Data Architecture, which is viewed as an umbrella model comprising data warehouses, data marts and so on.
In the webinar Emerging Trends in Data Architecture – What’s the Next Big Thing?, Donna Burbank explains how modern Data Architectures are meeting the demands of converging technologies, high data quality, and Metadata Management.
The changes in Data Architecture are certainly as complicated as the changes going on within the Data Management industry as a whole. With so many emerging new technologies and the increasing complexity of dealing with enormous volumes of data, hybrid architectures are becoming the norm, rather than the exception. Such trends will continue to become more pronounced as we move into 2019 and beyond.
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