Rudi Dreyer, Director of Business Environment, 4Sight Holdings.
Businesses are experiencing a data explosion in the wake of widespread digitization and migration to the cloud, as organizations respond to powerful trends around remote working, mobility and business modernization.
Business success in the modern digital economy increasingly depends on a company’s ability to manage and analyze this data, and the ability to securely access critical information from anywhere, at any time. moment.
The powerful insights Big Data can provide enable business leaders to improve decision-making, identify market trends and opportunities, predict and respond to changing consumer behaviors and preferences , and generate operational efficiencies and cost savings.
Ultimately, the strategic exploitation of big data will ensure that businesses remain relevant, viable and competitive. As such, data has become the lifeblood of any successful modern business, making it the most valuable asset of any business.
However, the effective management and use of data within the organization transcends the technology stack. Beyond databases and data analytics tools, modern data-driven enterprises need a sustainable strategy that applies agile methodologies and robust data management architectures to realize the full potential of their businesses. data.
For example, many companies rely on business intelligence reports from ERP systems to inform decision-making and move the business forward. However, this reliance on the reporting function often ties companies to legacy ERP systems, reducing their ability to adapt to change or compete with more nimble and nimble competitors.
More importantly, the organization becomes dependent on the implementation of new systems to maintain business continuity. Any system failure or outage will cut the business off from a critical data source system, which is the antithesis of a sustainable big data strategy.
In response, more and more companies are designing, implementing and managing data warehouse strategies in their operation using various methodologies with the aim of modeling information flows and maintaining business continuity.
The Kimball methodology is an increasingly popular option among businesses in South Africa. This data warehousing methodology applies a bottom-up approach to enable companies to create predefined processes and implement practices that extract data directly from the source.
However, Kimball’s approach has limitations. Specifically, companies must apply these processes and practices on a system-by-system basis. This methodology requires a complete overhaul when systems change, which is unsustainable given the dynamics and complexity of the modern enterprise IT environment.
In response to this challenge, Data Vault 2.0 has emerged as a much more relevant and efficient methodology. It provides a refined set of rules, best practices, standards, and process designs that guide how companies should design a data warehouse strategy and classify, catalog, and standardize data across the operation to create new ways of working.
This methodology combines agile delivery with operational optimization of people, processes, systems, data and technology to better manage unstructured data and integrate big data to create a massive enterprise data vault. , regardless of the number of systems in place.
The resulting independence from the source system provides an agile, adaptable and sustainable data strategy that does not require redesigning each time an operational or IT system changes. And any new system just adds more data to the data vault history.
Additionally, consolidating data within a data vault architecture creates opportunities to implement data hygiene practices, as only the source data in the system that offers the most accurate data point and highest quality are used when multiple instances exist.
This ultimately creates a single instance of the best quality data source for use across the enterprise, coupled with an auditable lineage that allows data warehousing teams to track changes and history.
Importantly, this “sanitized” consolidated data is the foundation upon which to build powerful data visualization and business intelligence capabilities.
The ability to instantly access relevant and accurate information in their preferred format enables business leaders to transform the role of data in the business from a hindsight to offer deep and meaningful insights that can have a positive impact on real-time operation.
Layering intelligence on high-quality historical and real-time data can unlock forecasting capabilities that create organizational foresight to inform future strategic decisions.
The Data Vault 2.0 methodology also supports scalability across the enterprise, with the ability to take a piecemeal approach to implementation to meet unique business needs. Rather than investing in an end-to-end data warehousing project, which can take up to three years, companies can implement the project in phases, focusing first on high-power source systems. priority and high impact to immediately start realizing value.
The business can then allocate the resulting ROI to fund the next phase of its data warehousing strategy. Over time, the company will eventually achieve its end-to-end big data vision through a self-funding model, rather than the traditional upfront capital expenditure model.
And once implemented, the Data Vault 2.0 methodology will unlock additional opportunities to create operational efficiencies and drive cost savings across the business.
For example, companies can leverage the methodology to automate data extraction, capture, and conversion functions to reduce the risk of human error.
It can also identify additional areas for potential savings. For example, the architecture can identify potential efficiencies in database consolidation, which can reduce software and support costs.
Businesses can also automate the data scanning function to move the onerous manual data entry process away from staff, freeing up their ability to focus on higher value tasks within the organization.
Ultimately, the Data Vault 2.0 methodology is an extremely effective and efficient way to design data warehousing projects, the success of which has become a strategic imperative in an era characterized by increasing volumes of data across multiple systems. heterogeneous sources and terminals.