Artificial intelligence (AI) has become the most superior in technology innovation, leading activity around industries such a way that hasn’t noticed since the dot.com era of the ’90s. Companies such as General Motors and McDonald’s are spending billions of Dollars acquiring fledgling AI companies highlight that this is not an ordinary tech fad. Business sector companies are recognizing the disruptive potential and existential threat as well as rapidly ramping up investments in AI and advanced data analytics. Focusing on specific business scenarios, expectations of these expensive investments are high awaiting early and impactful outcomes.
The Obstacle in Achieving Real Business Value with AI
Delivering meaningful results from complex projects is indeed a challenging task. As presented in the Gartner blog, only about 20% of such projects offer tangible business value- a sophisticated statistic that is expected to continue through 2022.
Even with high investment and potential, many AI and advanced analytics projects often miss the mark when it comes to demonstrating real business value. One of the possible reasons could be a lack of clarity while structuring the idea of how business value will be created. Value cannot get delivered until an investment is used to garner and apply insights, trigger actions, and measure impact.
In short, the business value is driven by the data analytics value cycle.
3 Key to Successful Analytics-driven Organizations
Business and technology leaders across world-class AI-driven organizations recognize the importance of optimizing the analytics value cycle. They access their analytics capability investments in three key objectives to do so:
Accelerating Cycle Time
What counts for the business is demonstrable returns, and completing a full cycle serves value. Wherein for several organizations, the average cycle time is measured in weeks or months, leading organizations aspire to days or hours.
Efficiency can be increased by turning and automating a manual process, maximizing utilization of resources, and eliminating choke points.
Although data drives the cycle, it’s exposed to risk and costly error. Key compliance goals reduce data sprawl, streamlining secure and compliant access control processes, and management of the data life cycle at scale.
How to Accelerate Analytics Success
Many customers across a diverse range of industries and use cases require three foundational capabilities for achieving and sustaining success at scale:
Data as a Service
A governed data catalogue and management service are efficient in supplying reliable, secure, policy-compliant data for data science, analytics, and application development.
Analytics Platform as a Service
It provides customized model development environment equipped with the required tools and infrastructure resources such as CPU/ GPU, storage via an automated on-demand, self-service interface.
AI-Enabled APP Service
It is a common framework to publish packaged models such as APIs, deployment code, test harnesses, and enablement materials to an internal marketplace that allows application development teams to integrate and operationalize analytics models rapidly into production applications.
When equipped as an integrated set of services on modern software-defined infrastructure, these potentialities are the key to stimulating the analytics value cycle and improving speed time to value. AI is a real competition in terms of driving growth, profits, disruption, and consolidation. Speed maintains the essence. As it’s a digital era, the fast gobble the slow.