The recent push of organizations worldwide to drive self-service business intelligence (BI) has enabled them in turning impending challenges to business needs. There is a constant search for solutions to tackle the problem of embedding machine learning (ML) models in major analytics platforms. While embedded models offer greater control and flexibility in data analysis, confronting native machine learning intelligence of these analytics platforms has brought new challenges and opportunities.

Assessing the risks involved in the self-service ML is of paramount importance to the industry, as technology proliferates and gains widespread popularity and access. A major problem that remains is increasing data volumes that make average users go paranoid. The search for solutions to address these issues is highly critical for organizations to move their processes forward and make them future-ready. 

The Boons of Cloud-Native Business Intelligence/Managed Analytics Platforms

Proliferation of Big Data has provided an impetus to ML and AI research since years in the global business climate. This has resulted in some revolutionary breakthroughs in data science. Easy availability of affordable storage technologies and business data have triggered the adoption of data-driven business intelligence and smart analytics across organizations. The advent of cloud-native Business Intelligence or so called managed analytics platforms has spared SMEs from high investment into their in-house data centers. 

A growing plethora of businesses have begun resorting to cloud-based data analytics and IT solutions to meet their data management requirements. Organizations move their analytics workloads to the cloud with multi-faceted goals in mind. These involve access to scalable storage, better security, and completely-managed analytics capabilities, which make more business sense on the cloud. Several layered components are involved in a full data architecture and modern data warehouse solution, each serving a distinct purpose.

The reason behind applying layers of architectural components is to take all complexities across source systems and give businesses a single and concise source of truth. Managed analytics is set to conform data, detect quality issues, and flag the challenges to be addressed. The IT team can perform data validation, performance tuning, testing, and creation of quality controls with the aid of managed analytics.

Key Approaches to BIaaS/Managed Analytics  

Internal Data-based BIaaS

This approach depends solely on data gathered from an organization’s systems and provides insights into business processes, performance, customers, etc. The prime benefit of this approach is the plethora of data available for analysis. However, the organization is confined to their internal wisdom, lacking external data to distinguish themselves.

External Data-based BIaaS

This approach has been frequently followed by organizations to run strategic analysis on the market or to run social media analytics. The main challenge faced while working on the external data is the onus to ensure data quality. This often stems from multiple disintegrated sources of data and there is a possibility that it may comprise discrepancies and errors.

Hybrid Approach

This approach depends on the leverage of data retrieved from inside as well as outside an organization. Using this approach, an organization can see the bigger picture that engulfs both the market perspective and their internal operations. This is why a hybrid approach has witnessed the most widespread acceptance among organizations around the world. Organizations mostly derive data from various sources, with the number of data sources being proportional to the size of an organization.

To sum up, traditional analytics solutions that run on a sophisticated infrastructure provide considerable cost savings, post-migration to the cloud. On the other hand, organizations must ensure that they don’t compromise their budget due to the overuse of instances. Having a business intelligence or managed analytics solution in place significantly supports organizations that strive for informed decision-making.

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