Analytics as a service: Why more enterprises should consider outsourcing

Joey Lei Contributor

Joey Lei is director of service management at Synoptek. With more than 14 years of experience in engineering and product management, Lei is responsible for the development and growth of the Synoptek service portfolio and solution development with strategic technology alliance partners.

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Debbie Zelten Contributor

Debbie Zelten (SAFe(R) 4 Agilist, SAFe Scrum Master, CSM, LSSGB, PMI-ACP) is the director of application development and business intelligence at Synoptek. She has over 20 years of experience in implementing software and data analytics solutions for companies of all sizes.

With an increasing number of enterprise systems, growing teams, a rising proliferation of the web and multiple digital initiatives, companies of all sizes are creating loads of data every day. This data contains excellent business insights and immense opportunities, but it has become impossible for companies to derive actionable insights from this data consistently due to its sheer volume.

According to Verified Market Research, the analytics-as-a-service (AaaS) market is expected to grow to $101.29 billion by 2026. Organizations that have not started on their analytics journey or are spending scarce data engineer resources to resolve issues with analytics implementations are not identifying actionable data insights. Through AaaS, managed services providers (MSPs) can help organizations get started on their analytics journey immediately without extravagant capital investment.

MSPs can take ownership of the company’s immediate data analytics needs, resolve ongoing challenges and integrate new data sources to manage dashboard visualizations, reporting and predictive modeling — enabling companies to make data-driven decisions every day.

AaaS could come bundled with multiple business-intelligence-related services. Primarily, the service includes (1) services for data warehouses; (2) services for visualizations and reports; and (3) services for predictive analytics, artificial intelligence (AI) and machine learning (ML). When a company partners with an MSP for analytics as a service, organizations are able to tap into business intelligence easily, instantly and at a lower cost of ownership than doing it in-house. This empowers the enterprise to focus on delivering better customer experiences, be unencumbered with decision-making and build data-driven strategies.

Organizations that have not started on their analytics journey or are spending scarce data engineer resources to resolve issues with analytics implementations are not identifying actionable data insights.

In today’s world, where customers value experiences over transactions, AaaS helps businesses dig deeper into their psyche and tap insights to build long-term winning strategies. It also enables enterprises to forecast and predict business trends by looking at their data and allows employees at every level to make informed decisions.