Organizations today have invested heavily in their cloud data infrastructure, including data storage and consolidation, for analytical purposes. They understand that being insights-driven is no longer optional for business growth – it’s imperative. Yet, companies fail to optimize their data analytics capabilities due to the limitations posed by their BI tools.
Generating ROI gets more complex in this scenario, and delayed insights often result in uninformed business decisions. Analytics acceleration can help mobilize all their data (historical and current) and uncover even the most hidden insights, never to miss an opportunity. Here are the top seven reasons to accelerate analytical functions in an organization:
Table of Contents
No Limitation on Data Volumes
Once businesses invest heavily in data consolidation and migration to the cloud, scale limitations can hinder their ability to derive timely insights from this data. Analysts may end up losing performance SLAs and incur humongous costs when analyzing this data.
Since most BI architectures fail to work with such volumes, an analytics acceleration platform is a viable addition to their tech stack. It allows users to analyze as much data as needed without breaking performance.
As soon as the enterprise-level data crosses its maximum limits, the dashboards become sluggish, especially when too many users fire queries concurrently. All these queries need a specific amount of runtime processing to deliver the required results, taking minutes and even hours to return.
To remove these latencies, users need an acceleration methodology that delivers instant responses irrespective of data size or query complexity.
Enable Self-Service Analytics
As organizations grow, their reporting needs increase and ad hoc queries require expensive resources to tune, optimize, and write those queries. Often, enterprise data is inherently complex and needs the expertise to run analytics.
This can occupy your data engineering teams, making it challenging for them to catch up with the frequent reporting needs of business users. Analytics acceleration platforms like Kyvos enable self-service analytics for business users. This also helps data analysts and engineers to build optimized data models using an intuitive, code-free UI.
Unified Data View for a Single Source of Truth
When businesses are spread across multiple geographies, timelines, product ranges, and departments, building a comprehensive unified view of such massive data can be complicated. The process involves rigorous manual work to collate data, resulting in siloed analytics and fragmented reporting.
All they need is an advanced platform that not only accelerates analytics but also deals with complex datasets in high volumes to create a unified singular view translated into simple business logic. A universal semantic layer provided by such platforms can unify complex hierarchies and enable drill-down capabilities to go as deep into the insights as needed.
Ensure Faster BI Performance
Despite investing in industry-leading BI tools, dashboards can fail to meet the reporting requirements when a business evolves and data grows across the length and breadth, creating a need for granular analytics. The BI tools slow down under this workload, and there’s no quick fix to resolve the issue.
In this case, an analytics acceleration platform that can supercharge BI tool performance to deal with cloud-scale data will come in handy. The platform can deliver seamless user experiences by pre-processing data models and caching frequent queries to show results within split seconds for every query. Even for high concurrency, it allows democratized data access to maximize cloud ROI.
Optimize Cloud Costs
With higher data volumes and cloud consumption, analytical costs can explode, especially when more queries are run separately on similar datasets but by different user groups.
An advanced analytical ecosystem helps balance and control these costs with optimum resource utilization, even for complex and multidimensional queries. Smart aggregation with pre-processed queries and load-based scaling can reduce runtime costs for firing as many queries as needed.
Analytics acceleration is critical to managing excessive BI workloads when data volumes and the number of users grow in an organization. To stay ahead of the curve, businesses need to invest in a fast and effective acceleration platform that works with all BI tools and data warehouses to deliver sub-second responses while enabling easy governance and unified data views.