Today, business leaders across the world are struggling to make effective use of data and analytics. A key impediment is that the data remains inaccessible in the appropriate formats. An enterprise's attempt to continuously transform its business processes can only be effective if it has the appropriate "Data Strategy" that covers data organization & management, processing, storage & analytics, governance, and security; with the ability to help identify actionable insights as well as trigger downstream actions in the business processes leading to increased, and efficient business execution at lower costs.
Today, the output expected from data has grown immensely, while businesses over the past decade have indeed achieved a certain level of data maturity, today’s environment and everchanging client expectations have led to significant complexity and the need for modernization. Data modernization (modernization of data strategy, data cloudification, managed operations, data ops, etc.) is now the inevitable next step for enterprises. It's time for enterprises to Reimagine their data strategies.
Data strategies have moved from the conventional data warehousing (on-premise silos) to data lakes (initially on-premises and then on-cloud) and now to data lakehouses. While data lake breaks the silos and brings in unification, and it has limitations. Data lakehouse addresses these by offering a cloud-managed ecosystem of lakes and warehouses. The most modern data strategy however is a "Data Mesh" that integrates machine learning (ML) and artificial intelligence (AI) to offer augmented data management and analytics.
My detailed discussions with the business leaders of various organizations have led us to realize that business must modernize their data strategy to upgrade to a data mesh setup that will include/ leverage the following:
- Augmented Analytics: Augmented analytics helps your team transform large data sets into smaller, more digestible information through statistical and linguistics technologies. A combination of ML, AI, data insight, and augmentation explores how analytics can be built, consumed, and shared with your users
- Augmented Data Management: Augmented data management is the application of AI to enhance or automate data management tasks. It has the ability to support data talent, such as the above-mentioned data scientists, with time-consuming and data-intensive tasks which might normally be done manually
- Continuous Intelligence: A design pattern in which real-time analytics are integrated into business operations, processing current and historical data to prescribe actions in response to business moments, and other events
- Data Fabric: A design concept that serves as an integrated layer of data and connecting processes. A data fabric utilizes continuous analytics over existing, discoverable, and inferences metadata to support the design, deployment, and utilization of integrated and reusable datasets across all environments, including hybrid and multi-cloud platforms
Therefore, the Recommended Strategy for Data Modernization is:
- Enterprise Data Lakehouses/Data-Mesh: Structure enterprise data as cloud-based lakehouses or data meshes, where data at different levels of analysis (raw, bronze, silver, gold, platinum) is discoverable, available as Data-as-a-Service, accessible via APIs, and managed governance & access management
- Migration to Cloud Data Platforms: Building entire end-to-end (ingestion, ETL, storage, compute, data analytics - basic and advanced) data engineering on cloud hosted big-data ecosystem of managed ETL tools and data clouds like Snowflake, Azure Synapse, and Google BigQuery
- Discoverable & Self-Serving Analytics: Create and manage data hubs where data is discoverable, self-serving, and offers self-serving BI. Create self-serving workbenches for data analysts and data scientists. Integration of ML/AI platforms with data hubs. Building simple and advanced modeling capabilities
- Advanced Analytics (ML/AI/Cognitive): Build analytical setups that enable enterprises to leverage the power of ML and AI to predict outcomes, bottlenecks, probable operational failures, missed & hidden opportunities, and even apply AI-based corrective actions automatically to optimize business processes
Organizations should continue to evolve their data architecture and look to industry trends and exciting new technology to help them compete. Continuous modernization requires that organizations take a comprehensive view of their application and infrastructure environment. Realizing the benefits of data modernization requires thinking beyond just applications and infrastructure and expanding to consider how applications impact and are impacted by business processes, new and rapidly changing data, a DevOps culture and tools, cloud infrastructure, continuity, and deployment options.
