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Getting Deeper With Advanced Analytics: Become A More Data-Driven Company With The Help Of AI

Jerry Levine is Chief Evangelist & General Counsel at ContractPodAi. He helps guide global client success and shape overall product vision.

Over the last 30 years, observers have watched artificial intelligence (AI) go from the proverbial "spring blooms" to "winter doldrums." After all, the technology has been early in its lifecycle: moonshot projects, like autonomous vehicles and drone deliveries, have yet to become an everyday reality, and many companies have so far only undertaken pilot projects involving AI.

But there are higher expectations—if not higher economic returns—for AI in 2022. What is becoming clear is that AI-driven technology has the potential to not only support specific tasks, but also facilitate whole-business functions and processes. AI analytics, in particular, is increasingly being used for important data-crunching work and, in many cases, business reinvention altogether.

In their 2020 book Competing in the Age of AI, Marco Iansiti and Karim R. Lakhani explain that “analytics systematically convert internal and external data into predictions, insights, and choices, which in turn guide or even automate a variety of operational actions.” All of this, they add, is transforming the role of senior leaders: "Management as supervision, especially of employees performing routine tasks, is finally over. In an AI-powered operating model, managers are designers, shaping, improving and (hopefully) controlling the digital systems that sense customer needs and respond by delivering value.”

Iansiti and Lakhani's vision may be a little bit futuristic. But whether you are a manager or executive, the following primer will help you use AI analytics beyond streamlining operational processes and improving products and services—to make far better business decisions.

Defining Advanced Analytics

Advanced analytics is a term for leading-edge techniques that use AI and machine learning. Whereas data analytics refers to insights drawn from raw data, advanced analytics refers to unique insights collated from previously untapped or unstructured data. The latter, whether autonomous or semi-autonomous, makes use of mathematical algorithms or formulas to recognize patterns, predict outcomes and their associated probabilities, and offer recommendations. According to Gartner, advanced analytic techniques include data or text mining, complex event processing, pattern matching and visualization, as well as semantic, sentiment, and network and cluster analysis.

AI and ML thrive on this accumulated data. As models continue to evolve, the amount of data they learn from increases, and as algorithms get better, companies use even more data and create even greater business value.

Knowing The Benefits Of Advanced Analytics

Today, complex business decisions are supported by a comprehensive understanding of company operations, workforce capabilities, product and service performance, and customer behavior. Thanks to their advanced analytics investments, companies with the most AI maturity can expect to see substantial improvements within their organizations. It grants them the capabilities to consolidate their data to create a single version of the truth about their business, increase visibility and transparency into business operations, and facilitate day-to-day business decisions in real time. From there, those organizations are better able to tailor customer experiences and mitigate the risk of customer churn—not to mention provide a foundation for AI and ML development.

The specific benefits of leveraging this technology include the following:

• Increased capacity for end-to-end supply chain visibility

• Delivery of quicker and more refined, detailed and accurate insights

• Acceleration of hypotheses and prototyping, and responses to future events

• Provision of evidence-driven guidance during times of uncertainty

• Elimination of duplicate data modeling

• Better identification and addressing of threats like security breaches

Making Advanced Analytics Work For Your Company

The changes advanced analytics have brought to industries are growing in scope and magnitude, and are happening at an ever-quickening pace. A survey by McKinsey & Company found that “companies with the greatest overall growth in revenue and earnings receive a significant proportion of that boost from data and analytics.” But many others respond to these technology shifts through ad hoc or one-off initiatives, rather than long-term adjustments.

Wherever you are in your digital transformation journey, the following strategies may provide a road map to success.

1. Transformation of organizational capabilities: There is often a disconnect between organizations’ existing technology capabilities and current digital culture. To deploy advanced analytics successfully, your digital approaches need to align with how your company actually makes decisions, and your digital tools need to be designed for the people who actually use them. (The bottom line is that it is important to adopt modern change management techniques within the organization and work with knowledgeable vendors and consultants.)

2. Selection of the right data: As the volume of your company’s data grows, the imperative to gain deeper insights into this information will only accelerate. You need to identify your most useable data to improve your operations, customer and partner experiences, and overarching business strategy. (Remember, though, advanced analytics warrant the adoption of the most reliable, AI-driven software.)

3. Building of models that predict and improve business outcomes: Company data is crucial, but improving business performance and gaining competitive advantage come from analytics models that allow you to anticipate and optimize outcomes. To build such a model, you need to determine how to improve your business performance and identify any and all business opportunities in the first place.

Becoming A More Intelligent Enterprise

According to Gartner, legal departments will increase their spending on legal technology threefold by 2025 to improve their productivity and find new sources of growth. Other teams, meanwhile, will transform themselves into truly intelligent enterprises in which machines do the heavy lifting—in the cognitive sense—and all business decisions are data-driven. As the Harvard Business Review acknowledged in its 2019 book Artificial Intelligence, “given the combination of short-term incremental value and long-term opportunity, many companies are tempering expectations about AI while still providing motivation to move forward aggressively with the technology." In other words, senior leaders should look at the application of AI analytics as a series of small evolutions that one day could amount to an analytical revolution.


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