The global market for data warehousing is expected to grow to $34.7 billion by 2025, according to a recent report from Allied Market Research. That’s nearly double the $18.6 billion it was worth in 2017.
What fuels investment in enterprise data warehouse development? Cloud data warehouse technology has given rise to innovative systems and practices that increase efficiency and reduce costs across company functions. Today, departments like marketing, finance, and supply chain operations benefit from a modern data warehouse as much as the organization’s engineering and data science teams.
In this blog post, we list five business priorities that fuel increased investment in modern enterprise data warehouse development.
1. The need to access and act on data in real time
Today, businesses can process data and detect signals in real time that traditionally had much higher latency. Knowing stock levels at retail locations, for example, enables a retailer to respond to customer trends and supply chain issues before they adversely impact the business. Better yet, by combining a real-time view of supply chain data and weather, that retailer can restock stores running low on sunscreen before a heat wave arrives.
Modern data warehouses make data viewable and actionable in real time by favoring an extract-load-transform (ELT) approach over the once ubiquitous extract-transform-load (ETL) paradigm, in which data was cleansed, transformed, or enriched on an external server prior to being loaded into the data warehouse. With an ELT approach, raw data is extracted from its source and loaded, relatively unchanged, into the data warehouse, making it much faster to access and analyze.
2. The search for a holistic view of the customer
Historically, the information a company had about its customers was siloed: data from one source would be in a data mart, data from another source, in a data lake, or stored in an on-premises legacy system. Without an easy way to connect the dots, it was difficult to ensure that high-value customers were getting the best possible experience.
The promise of a data lake strategy is that all company information – structured, semi-structured, and raw data alike – can be quickly and easily queried from one place. With that approach, an enterprise data warehouse can enable a complete view of the customer, helping to improve campaign performance, minimize churn, and, ultimately, to grow revenue. An enterprise data warehouse also facilitates predictive analytics, where teams use scenario modeling and data-driven forecasting to inform business and marketing decisions.
3. Understanding data lineage to ensure regulatory compliance
In large organizations, it’s often difficult to find out the provenance of specific data. This can be particularly problematic for finance and accounting teams when conducting audits. Their only recourse traditionally has been to file a support request, which can be costly and slow. A modern enterprise data warehouse enables data customers to audit and vet data sources directly and find errors quickly.
A modern data warehouse can also enable compliance with the EU’s General Data Protection Regulation (GDPR). Without a data warehouse in place, a company would likely have to set up a laborious process to comply with each GDPR request. This would involve several functions or business units searching for relevant PII data. With a data warehouse, there is essentially just one place to look.
4. Enabling non-technical people to query data quickly and cheaply
Developing a data warehouse can also benefit non-technical employees in job functions beyond marketing, finance, and the supply chain. For example, architects and store designers can improve the customer experience within new stores by tapping into data from IoT devices placed in existing locations to understand which parts of the retail footprint are most or least engaging. Global facilities managers can base their decision-making on whether to expand plants or shift product lines on a robust set of information, including hiring and employee retention data, in addition to standard metrics like cost per square foot.
5. The need to bring data together into a single location
Many data sets today are simply too large to transfer and query quickly and cost-efficiently. To curb costs and latency, companies use regional clouds. And those with a multi-cloud strategy–81 percent of organizations, according to research–end up with data spread across platforms from competing cloud providers. Eliminating these roadblocks is a priority for organizations that strive to be truly data-driven.
Best-in-class data warehousing technology will enable companies to store data across different regions and cloud providers, and query it as though it were a global unified data set.
To learn more about how your company can harness these trends, download our ebook on the Five Best Practices for Developing a Data Warehouse.
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