Machine learning (ML) and artificial intelligence (AI) have evolved from industry buzzwords to strategic tools that companies are implementing to transform data into actionable insights. This is evidenced by a recent study that found 97% of large organizations are making investments in big data and AI initiatives. While these statistics are encouraging, there’s a sizable leap from technology purchase to deployment, and many organizations can fall victim to common pitfalls along the way. Here are three fundamentals to consider when implementing AI, ML and predictive analytics.
Understand your internal data infrastructure
Early adopters of popular technologies, like AI and ML can easily fall victim to hype. The most fundamental step toward realizing the promise of new innovations is to understand your internal data infrastructure and align it with business needs. Although the market supports broader flexibility in terms of data access and analytical outcomes, taking advantage of AI and ML requires an understanding of the data, where it resides, what related data is required and, finally, what initial business questions exist. Otherwise, it’s impossible to know the tools you need or where to begin to gain the greatest value from the deployment. And, how can you convey the value of data analytics to key organizational decision makers if you haven’t made a proper business case? To begin, you need a detailed understanding of your current infrastructure. Only then will you be able to ask the right questions of solution providers, and ensure they support current and future requirements. These questions can include API support for disparate data sources, industry expertise for algorithm development, and support for SLAs and data latency.
Know the resources you need to be successful
AI and ML are most commonly used to build predictive models that organizations can apply to create rules for everything from identifying anomalies and determining probabilities to making personalized recommendations for consumer products and services – the sky’s the limit. However, creating these models requires a significant investment in resources (people, time, hardware, etc.). Organizations should ensure they have the data diversity and volumes required to develop relevant scenarios that can be built upon over time. While most businesses rightly assume they have enough data for complex analytics, it’s certainly something to discuss with your solutions provider ahead of time. Secondly, do not underestimate staffing. You’ll need the right people involved who understand both the technical and the business requirements of AI and ML. Without proper manpower, the technology is never going to be utilized. Finally, agree upon a set of outcomes that can be measured (i.e. initial pre-defined metrics) to make sure AI and ML projects are on track.
Establish goals that are realistic
As discussed above, it’s critical that organizations be pragmatic in setting benchmarks and goals for AI and ML implementation. This requires an understanding of the desired business outcomes, such as sales objectives as well as how the AI and/or ML initiative will support the corporate mission and vision. Too often, organizations focus on the technology to make sure that everything works (algorithms, etc.) but overlook the desired business outcome and miss the entire point of deriving value from the analytics. And, don’t be afraid to get some help – outside experts can assist in objectively identifying organizational needs to properly implement an AI/ML strategy and setting realistic goals.
Implementing new technologies like ML and AI is not a matter of simply extending current strategies or infrastructures. To be successful, organizations must gain a comprehensive understanding of their data framework. By taking these key steps, you can position your organization to not only realize, but maximize the business value of these technologies.
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