By Ananth Chakravarthy, RVP Sales, Denodo India
In the dynamic landscape of business, adaptability is the key to survival, just as it is in Darwin’s “survival of the fittest” principle. To stay ahead in fast-moving markets, organizations must leverage data insights effectively. Data analysts play a crucial role in driving efficiency, creating competitive advantage, and unlocking new opportunities through data-driven decision-making. However, with the increasing complexity of data sources, formats, and protocols, traditional data integration methods struggle to keep pace with business needs. Consequently, companies are turning to logical data management solutions, powered by data virtualization. According to Gartner’s Market Guide for Data Virtualization, an estimated 60% of organizations will implement data virtualization as a key delivery style in their data integration architecture by 2022.
Let us explore the top 5 challenges faced by business analysts and how data virtualization effectively addresses them:
1. Simplifying Data Access: Business users often struggle with understanding data connectivity, formats, and protocols across various sources, making it challenging to change data sources and manage data security. Data virtualization eliminates this complexity by providing a data virtualization layer that resembles a data warehouse in that it enables data access from one location, but it connects with myriad different sources, on-demand, using metadata. Through this layer, business analysts gain easy, secure access to all data sources, regardless of their location, format, or protocol. This enables them to easily establish a logical data warehouse architecture.
2. Freedom from Vendor Lock-in: Semantic models embedded in specific business intelligence (BI) tools can lead to vendor lock-in, hindering the adoption of new BI and analytics tools. Data virtualization liberates organizations by enabling data consumers to use different analytics and visualization tools on the shared virtual layer. The semantic model is centralized in the virtual layer, eliminating the need for costly data model re-writes with each tool change. This enhances business agility, as changes need to be made only once.
3. Optimizing Data Performance: Inadequate query pushdown in traditional BI tools results in dragging large data volumes across the network to the BI server, which leads to poor performance at scale. Logical data management solutions powered by data virtualization, like the Denodo Platform, tackle this challenge by optimizing queries and pushing them down to the data sources. This significantly reduces data movement across the network, resulting in improved performance, even with vast datasets.
4. Maximizing Efficiency: Business analysts and data scientists spend a substantial amount of time gathering and preparing data, rather than focusing on analysis. Data virtualization revolutionizes this process by minimizing data preparation time. Customer feedback indicates a drastic reduction from 70-80% to just 10-20%. This means analysts can dedicate 60-70% more time to valuable analytics work, effectively boosting their productivity.
5. Enhancing Sharing & Collaboration: Semantic models embedded in BI tools limit data sharing and collaboration with users of different analytics platforms, leading to extra work and model inconsistencies. Data virtualization resolves this issue by creating common semantic models in the virtual layer that are not tied to any specific analytics platform. Business analysts gain a single view of the truth, effortlessly shared across users of diverse analytics tools.
To Excel, Embrace Data Virtualization
Darwin’s principles of natural selection apply to business evolution. Data visualization capabilities are essential for achieving and sustaining competitive advantage through business analytics. By leveraging data virtualization, businesses can adapt, thrive, and lead in competitive markets, making smart decisions fueled by data insights.