Artificial intelligence (AI) is the hottest area of technology innovation right now, driving activity across industries in a way that hasn’t been seen since the dot.com era of the ‘90s. Seeing the likes of General Motors and McDonald’s spending billions of dollars acquiring fledgling AI companies highlights that this is no ordinary tech fad. Companies across every business sector are recognizing the disruptive potential and existential threat, and rapidly ramping up investments in AI and advanced data analytics. Whether part of a broad corporate strategy, or focused on specific business scenarios, expectations of these expensive investments are high, with business teams looking for early and impactful results.
The Common Hurdle to Achieving Real Business Value with AI
Delivering meaningful outcomes from these complex projects, however, is a challenging task. As noted in a recent Gartner blog, only around 20% of these projects deliver tangible business value – a sobering statistic that they expect to continue through 2022.
With such high levels of investment and potential, why do so many AI and advanced analytics projects miss the mark when it comes to demonstrating real business value?
All too often we’ve seen it’s because these projects begin without a clear idea of how business value will be created. However impressive or intricate a predictive model or data lake may be, value isn’t delivered until that investment is used to garner and apply insights, trigger actions, and measure impact.
In other words, business value is driven from the data analytics value cycle.
The Analytics Value Cycle – Simple Concept, Powerful Impact
The premise for the Analytics Value Cycle is simple: Data drives Insight. Insight drives Action. Action generates Data. Repeat.
And, the concepts behind each stage in the cycle are simple. As always in analytics, everything starts with data. Let me explain:
- Data is the fuel that drives the cycle. It’s foundational to organizational learning, business improvement, and competitive differentiation through advanced analytics and AI; data capital, encompassing all digital data – structured and unstructured – is the most valuable component of many modern enterprises.
- Insights are derived from the data in the form of predictive models and algorithms. These facets are the ‘secret sauce’ – the intellectual property – that allows the business to capture and monetize the unique nature of their enterprise data assets. These insights generate action once they are integrated and deployed as features in AI-enabled applications.
- Actions are triggered and captured via AI-enabled applications. This is where the value of advanced analytics and AI is realized – be it in a new mobile app feature, an improved fraud detection algorithm or recommendation engine, an edge-based sensor-driven action, or any of the multitude of other use cases that AI and advanced analytics are applied to today.
- Actions generate new data, which is collected, analyzed, and used to validate the value of the advanced analytics and AI project. It drives new insights, improves existing models, and continues to build business value.
The key point is this: value is only created once a full analytics value cycle is complete. As the cycle repeats, new capabilities build on the prior learnings and successes. The faster each cycle completes, the faster an organization gains value from its analytics investments, and the faster these capabilities compound to build a competitive advantage.
3 Key Goals for Successful Analytics-driven Organizations
Business and technology leaders of world class AI-driven organizations understand the importance of optimizing the analytics value cycle. To do so, they assess their analytics capability investments around three key objectives:
Accelerating cycle time. Seeing demonstrable returns is what counts for the business and completing a full cycle is what delivers value. For many organizations a typical cycle time is measured in weeks or months. Leading organizations aspire to days or hours.
Increasing efficiency. Tuning and automating manual processes, minimizing duplication of effort and data, maximizing utilization of resources, and eliminating choke points.
Improving compliance. Data drives the cycle, but it’s the greatest point of risk exposure and costly error. Key compliance goals include reducing data sprawl, streamlining secure and compliant access control processes, and management of the data life cycle at scale.
Delivering to these objectives requires taking a holistic, end-to-end view of the analytics value cycle – a view that includes the technology, processes, and people involved at every step.
How to Accelerate Analytics Success
To that end, at Dell Technologies Consulting, our approach for helping customers develop their data analytics capabilities is based on considering the entire data analytics value cycle as an integrated series of processes – a single value stream. For many customers, across a diverse range of industries and use cases, the same three foundational capabilities are required for achieving and sustaining success at scale:
- Data as a Service – A governed data catalog and data life cycle management service capable of supplying trustworthy, secure, policy-compliant data for the purposes of data science, analytics, and application development.
- Analytics Platform as a Service – A service to deliver customized model development environments (“sandboxes”) equipped with the needed tools and infrastructure resources (CPU/GPU, Storage, etc.) via an automated on-demand, self-service interface.
- AI-Enabled Application Service – A common framework to publish packaged models, including APIs, test harnesses, deployment code, enablement materials, and more, to an internal marketplace that enable application development teams to rapidly integrate and operationalize analytics models into production applications.
When deployed as an integrated set of services on modern software-defined infrastructures, these capabilities are key to accelerating the analytics value cycle and speeding time to value. AI is a true competitive differentiator, driving growth, profits, disruption, and consolidation. Speed is of the essence. It’s a digital world, and the fast eat the slow.
To explore innovative solutions for advanced analytics, AI and machine learning or for more information on Dell Technologies Consulting services around value stream mapping, and accelerating the analytics value cycle, visit Dell Technologies Data Analytics Implementation Services and Dell Technologies ProConsult Advisory Services or reach out to Rob Small [or comment below].
What steps is your organization taking to accelerate the analytics value cycle and become successfully analytics-driven?
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