There’s no doubt that AI has taken center stage in the enterprise data and analytics world, as evidenced by the mass quantities of related headlines, conferences, and vendor marketing. But hype aside, business executives are now discovering how to leverage AI for improved decision making with augmented or assistive intelligence solutions or the competitive advantages in new products and services. AI is proving its viability in the real world, including enterprise data and analytics.
AI capabilities arrived with big data, hyper-computing power, and advances in algorithms and mathematics. AI has also become an umbrella term for everything from probabilistic predictions to pattern detections, machine learning, deep learning, and natural language processing (think Alexa, Siri, and Google). While the process of AI becoming embedded in companies is in its infancy, we can’t ignore the overwhelming potential and transformation that will come in the next decade.
Generations of advancing data and analytics are paving the way for more sophisticated uses of data within organizations. Ever since operational systems captured data from business transactions, we’ve needed to integrate them for both tactical and strategic decisions. As data warehousing gave way to data marts, the next generation that leveraged integrated data was business reports, analytics (OLAP), and dashboards that enabled companies to set, track, and achieve goals using metrics with the business intelligence wave. This was followed by another generation when Hadoop overcame big data’s volume, variety, and velocity challenges, and public cloud platforms enabled companies of virtually any size to take advantage of the same data and analytics capabilities as their big, deep-pocket competitors. And, as companies leverage the technology advances and data in new ways, AI established its relevance in business.
A Spectrum of Enterprise Analytics
While companies target specific initiatives or analytics projects, three broad forms of analytics should be taken into account. These capabilities cover a spectrum of enterprise analytics that may also correlate with the business initiative’s analytics maturity. Consider a Customer-360 initiative as an example. In addition to the data ingestion and integration from multiple operational systems (such as CRM, finance, customer support, and order management), there is also customer behavior data from the website, in social media, and perhaps product usage data. Business intelligence and reporting leverage this data initially to analyze and communicate predefined customer metrics, such as total count, engagement, calculated lifetime value, and churn rate—defined and qualified by dates, channels, products, geographies, and various customer attributes.
With all of this data, data warehousing supports business intelligence and business performance measurement to achieve business goals. As the initiative advances, business analysts and data-savvy users then need to explore data and create data analytics in a self-service manner to answer many of the questions that come up in the Customer 360 programs but exist outside the scope of the BI project. Their work will also support identifying and validating the creation of meaningful new metrics that will be tracked in BI and support decision making for improvements in achieving the initiative’s goals.
The next category of business analytics is not diagnostic but rather predictive and prescriptive in nature. This includes data science and AI to analyze data for patterns that can predict signals for when customers might leave. Machine learning models can be trained with datasets from thousands of actual encounters to provide a recommendation along with its probability. Once signals have been found that a customer is likely to leave, equally important is the analytics to predict and test the optimal retention offer to make. After this process, the BI dashboards and metrics will show the resulting progress on Customer 360 goals.
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A data analytics initiative can be categorized into one of four major business value themes: understanding customer behavior, understanding product usage, increasing operational efficiencies, and business innovation. These themes are the heart of the majority of business models that aim to efficiently and consistently deliver a product or service that customers want while improving and increasing relationships with them in innovative or disruptive ways. An initiative in any of these areas will likely rely on all three forms of analytics capabilities at some point in its course. No matter the initiative, BI, self-service data analytics, and AI capabilities will likely all play a needed role.