What is analytic architecture modernization?
Since the relational database first came into existence in 1986, organizations have been continually evolving their analytical architectures.
The “analytical architecture” simply refers to the universe of data available to an organization for analysis and other business/operational purposes. Modernization is the act of ensuring the architecture designed makes use of the best available technology with the ultimate goal of optimizing operational efficiency (cost, ROI, SLA’s, scalability, performance, etc.) and analytical effectiveness (accessibility, uptime, quality, currency, latency, etc.). In the old days, a premium was paid for consistency—data in one part of the organization was the same as the data in another part.
This gave rise to the idea of the Enterprise Data Warehouse, a centralized repository of data, a single version of organizational truth. That made sense at the time because other data storage technologies like scratch, and even ancient punch cards were suitable for data storage, but not very good for data usage. So, the relational database in the form of the Enterprise Data Warehouse was the only game in town.
Today, the technology landscape is radically different. The evolution of data warehousing has been driven by the increasing scale and diversity of contemporary organizations at the high end and the need for cost-effective ways for smaller organizations to enjoy enterprise-class analytical capabilities at the low end. An explosion of available data from both on-premises and cloud-based sources has created massive data lakes of available data for organizations to sift through to find relevant, meaningful information.
The old centralized enterprise database became too slow for large organizations, and it was always too expensive for smaller ones. New technologies have made it possible to push data closer to the user for enhanced analytical effectiveness while providing powerful tools to optimize operational efficiency. New open source platforms like Hadoop enable management of virtually any type of data. More powerful relational databases process terabytes of data in minutes.
Data integration technologies enable governed self-service access to data. Data archiving software helps manage the data load on the entire system. Governance technologies help ensure data are clean, secure, and reliable. Finally, organizations have the opportunity to choose cloud-based deployment options for both their data and their applications, providing an unprecedented means to reign in capital costs.
With so many technology options, an organization is charged with modernizing their analytical architecture in order to manage the massive data lake they are wading into. Finally, organizations have the opportunity to choose cloud-based deployment options for both their data and their applications, providing an unprecedented means to reign in capital costs.
Together, tech solutions and tech-minded, data-driven professionals hold the keys to updating legacy architecture.