The benefits of artificial intelligence (AI) and machine learning (ML) in analytics have been topics of discussion among industry analysts for years. To take advantage of these promised benefits, enterprise companies set out to hire—from a scarce population—data scientists who could develop advanced analytics solutions.
But too often, the hype behind these solutions exceeds their real benefits. They don’t deliver broad benefits to multiple departments and teams. They are practical for only a small group of technical users, providing only a few other templates and features that fall short of business users’ demands.
Today, many nontechnical users are still uncomfortable using these business intelligence (BI) applications, let alone more advanced AI-driven analytics solutions. They lack an understanding of basic BI features—drill, dice, swap, filter, and sort, among others—even though they recognize a real need to include them in their own workflows.
Fortunately, business leaders can meet these needs by realigning analytics for a broader range of users with the help of real AI and ML. These solutions provide nontechnical users with self-service tools and detailed visualizations without requiring them to click buttons or perform the drag-and-drop functions common to other BI applications.
As BCG describes, “through machine learning and advanced data analytics … The most successful use cases will be those that seamlessly combine AI with human judgment and experience.” Here, we take a closer look at what this transformation means for both technical and business users and what capabilities are driving real business results.
In most analytics environments, providing meaningful insights for business users is a critical but time-consuming task. That’s because the appetite for those insights often outstrips users’ technical abilities, forcing them to rely on encumbered data scientists or turn to ad hoc resources to support their business decisions.
In a successful AL and ML analytics environment, there are “two sides to the same coin” that can remedy this problem. Specifically, there are (a) powerful, automated but simple and intuitive benefits regular business users enjoy; and (b) sophisticated flexibility provided to technical, data scientist-type users, all in the same environment.
Indeed, AI accelerates analytics itself by detecting complex, underlying patterns in vast amounts of data without human intervention. But AI and ML are equally critical in their support of user-facing functions as well.
In most analytics scenarios, providing actionable insights to business users requires highly technical analytics skills, a broad range of reliable data sources, and enormous manual effort on the part of data scientists. Complicating this further is the reality that different analytics scenarios require varying levels of detail, depending on audience and content. As a result, an enormous gap opens up between tasks that require data scientists with deep expertise and the business users’ decision-making they support.
Business leaders, therefore, need to equip data scientists with greater flexibility, control, and more streamlined end-to-end delivery of value from the start of their efforts to their engagement with business users. (As we will discuss, they must also empower business users with more self-service capabilities, freeing technical users from several cumbersome tasks in the process.)
For example, Python is now the predominant language used by data scientists for machine learning operations. Providing data scientists with the ability to use Python within the data-modeling stage allows them to merge data, analytics, and data science pipelines into a singular, interconnected technology arc, simplifying the core processes required to drive real business value with analytics.
Additionally, automated tools driven by AI and ML can close the talent gap between data scientists and regular business users. These reduce the manual efforts technical users must execute to deliver on basic analytics requirements, allowing them to focus on more value-added activities instead.
The real value of analytics emerges only through the interpretation and imagination of human beings. Historically, the most sophisticated analytics capabilities have been out of reach to business users. But now, AI-driven tools allow business users to develop data models, narratives, and presentations that once required a data scientist’s expertise.
Indeed, real success in analytics emerges when nontechnical users can build, share, and leverage insights without explicit knowledge of data structures, hierarchies, and measures that constitute the world of data scientists. To this end, business leaders can enable business users with a more “subtle” AI. They can provide business users with forecasts that rely on AI, for example, but don’t require plug-in algorithms, knowledge of algorithms, or an understanding of how AI works.
For example, chatbots enhance business-user experiences when interacting with analytics applications thanks to their intuitive, AI-driven capabilities. Chatbots dramatically reduce the time it takes to explore trusted business data as a result.
Pyramid Analytics’ own Smart Insights feature, our newest iteration of automated text-based analysis, is another example. Smart Insights delivers narrative-style analyses that are easy to understand, thanks to the support of a powerful AI engine. Business users need only the appropriate data and visualizations to benefit from its automated features.
Finally, AI and ML in analytics need to draw from all data sources to be effective. Although many BI tools claim to provide “advanced analytics,” they do not factor all data in this way because it is too time-consuming without AI. This is insufficient because business requirements evolve in real-time, and companies’ needs for different types of data change along with them.
With a holistic approach to data analysis, AI-driven analytics can produce results with several different levels of detail—such as light, categorical, or advanced visualizations—all generated in near-real-time. In this way, the breadth of your trusted data becomes the fulcrum for your full analytics capabilities, as opposed to the limitations of your analytics solution itself.
As business leaders look to their recovery from the COVID-19 crisis, AI-driven solutions are coming to the forefront because of their real-time analytical, employee, and customer support potential. But AI only succeeds when the strategies that support it include a deep human element as well.
As McKinsey described in August 2020, “To stay competitive, we believe companies will be best served by not putting all their resources into the fight for sparse technical talent, but instead focusing at least part of their attention on building up their troop of [automated machine learning] practitioners, who will become a substantial proportion of the talent pool for the next decade.”
Pyramid Analytics has “threaded” AI into its platform to support these two capacities—business and technical users—unifying the two very different groups of users as they function in the same analytics environment. Our AI and ML capacities enable these teams to take on any variety of business challenges, using a wide range of data types to meet those demands.
Want to witness Smart Analytics in action? See it for yourself. And contact us today to learn more about this balanced approach, and deliver enterprise analytics at scale in your own business.