Unexpected situations like the COVID-19 pandemic and the ongoing macroeconomic atmosphere are a wake-up call for companies worldwide to accelerate digital transformation exponentially. During the pandemic, when lockdowns and social-distancing restrictions transformed business operations, it quickly became apparent that digital innovation was vital to the survival of any organization. Why? The dependence on remote internet access for business, personal, and educational use elevated the data demand and boosted global data consumption. Additionally, the increase in online transactions and web traffic generated mountains of data.
Companies realized their aging enterprise data warehousing solutions could not manage the enormous workload. Innovative organizations sought modern solutions to handle larger data capacities and attain secure storage solutions to meet consumer demands. One of these advances and the associated business success and competitive advantage that came with it included the accelerated adoption of modernized data warehousing technologies.
Why Data Warehousing Is Critical to a Company’s Success
Data warehousing is the secure electronic information storage by a company or organization. It creates a trove of historical data that can be retrieved, analyzed, and utilized to create reports designed to provide insight or predictive analysis into an organization’s performance and operations.
Data warehousing solutions drive business efficiency, build future analysis and predictions, enhance productivity, and improve business success. These solutions categorize and convert data into readable dashboards that anyone in a company can analyze. Data is reported from one central repository to enable management to make meaningful business insights and faster and better decisions. By running reports on historical data, a data warehouse can clarify what systems and processes are working and what methods need improvement. Data warehouses also provide the base architecture for artificial intelligence (AI) and machine learning (ML) solutions.
Benefits of New Data Warehousing Technology
Companies require additional resources and people to process enterprise data. Most enterprise legacy data warehouses will support only structured data through RDBMS databases. It is nearly impossible to achieve business efficiency and agility with legacy tools that contribute to more inefficiency and elevate costs.
Managing, storing, and processing data is critical to business efficiency and success. Everything is data, regardless of whether it’s structured, semi-structured, or unstructured. Modern data warehousing technology can handle all data forms.
Significant developments in big data, cloud computing, and advanced analytics created the demand for the modern data warehouse. Today’s data warehouses are different from old single-stack warehouses. Instead of focusing primarily on data processing, as legacy or enterprise data warehouses did, the modern version is designed to store tremendous amounts of data from multiple sources in various formats and produce analysis to drive business decisions.
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Modern Data Warehousing Solutions to Consider
A superior solution for companies is the integration of existing on-premises data warehousing with data lakehouse solutions using data fabric and mesh technology. Combining these technologies creates a modern data warehousing solution for the long term.
A data lakehouse contains an organization’s unstructured, structured, or semi-structured data, which can be stored indefinitely for immediate or future use. This data is used by data scientists and engineers who study data to gain business insights. Data lake or data lakehouse storage costs are less expensive than those for associated with an enterprise data warehouse. Further, data lakes and lakehouses are less time-consuming to manage, reducing operational costs. Databricks on Amazon Web Services and other emerging technologies offer an excellent next generation of data warehouses and data lakes to leverage analytics and artificial intelligence to achieve better business outcomes.
Data fabric is the next-generation data analytics platform that solves advanced data security challenges through decentralized ownership. Typically, organizations have multiple data sources from different business lines that must be integrated for analytics. A data fabric architecture effectively unites disparate data sources and links them through centrally managed data sharing and governance guidelines.
Many enterprises seek a flexible, hybrid, and multi-cloud solution based on cloud providers. The data mesh solution pushes down the structured query language (SQL) queries to the related RDBMS or data lakehouse by managing the data catalog, giving users virtualized tables and data. With data mesh technology, data is never stored locally, which is an advantage for a business. A successful data mesh solution will reduce a company’s capital and operational expenses. Cloud Pak for Data is one example of a data fabric and data mesh solution for analytics. Selecting a data fabric and a data mesh, or choosing another data approach, depends on a company’s goals and how to get the best value out of their data.
Cloud technology has emerged as the preferred platform for AI capabilities, intelligent edge services, and advanced wireless connectivity, among other services. Many companies will leverage a hybrid, multi-cloud strategy to improve business performance and success and thrive in the business world.
Best Practices for Adopting Data Warehousing Technology
Data warehouses are at an exciting point of evolution. The global data warehousing market is estimated to grow more than 250 percent in the next five years. Companies will rely on new data warehouse solutions and tools that make them easier to use than ever before.
The increased volume of data makes it a priority for businesses to modernize their data warehouses to remain competitive in today’s market. Data warehouse modernization includes extending the infrastructure without compromising security. This strategy allows companies to reap the advantages of new technologies, inducing speed and agility in data processes, meeting changing business requirements, and staying relevant in this age of big data. Businesses need valuable insights and real-time reports. Enterprise or legacy data warehouses cannot keep pace with modern data demands.
Cutting-Edge Technology to Keep Up with Constant Change
AI and other breakthrough technologies will propel organizations into the next decade. Data consumption and load will continue to grow and provoke companies to discover new ways to implement state-of-the-art data warehousing solutions. The evolution of digital technologies and connected devices keeps companies afloat, an unimaginable feat 20 years ago.
Essential lessons arise from an organization’s efforts to optimize its enterprise/legacy data warehousing technology. One vital lesson is the importance of making specific changes to modernize technology, processes, and organizational changes to evolve. This knowledge will be critical going forward since the rate of change will continue.
About the Author
John Thangaraj is a senior brand technical specialist with more than 17 years of experience. He is an expert in OLTP database and data warehouse infrastructure architecture, relational and dimensional data modeling, logical and physical database design, data architecture, data integration, SQL optimization, etc. For more information, contact John at firstname.lastname@example.org or on LinkedIn.
Disclaimer: The author is completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE’s position nor that of the Computer Society nor its Leadership.