In the first part of this blog I shared how by definition analytics is what transforms data into insights, thus unlocking the value of the data, and how these insights inform decisions and actions that ultimately transform business. This is important because while few, if any, would dispute that data is a strategic asset, it’s clear that there is a huge gap that exists between that data and the potential value it holds. The opportunity cost of not closing this gap is frankly too big for us to ignore.
Qlik’s approach to deliver on the promise of the 3rd generation of business intelligence and analytics is built around three key shifts in the way we approach the problem - the democratization of data, the application of augmented intelligence and drive for embedded analytics everywhere. All are built around the unique enterprise-class foundation of the Qlik associative engine and multi-cloud platform. If Qlik delivers on this, and you build a holistic data and analytics strategy on Qlik, together we close the value gap.
Democratization of Data
The more we look at a problem from multiple viewpoints, the more perspective and understanding we get. The same is true in solving complex business problems by examining multiple data sets, from multiple sources, and how they are related. For knowledge workers to dive deep into complex problems, they must have free, yet governed, access to all potentially relevant data sources. The typical approach other vendors take is to require data to be moved into a central repository such as a data warehouse or data lake. Yet anecdotal evidence shows that most enterprises manage to only get about a fifth of their structured data into enterprise data warehouses and data marts. This is contrary to the overall proliferation of new data sources, which is only gathering pace. And of course, there are additional unstructured data stores and even streaming data at the edge. How do you see the whole story if you are missing chapters?
Qlik’s Point of View: All data, and any combination of data, must be readily accessible via governed, analytics-ready, Enterprise-Wide Information Catalogs.
Associative Indexing * Augmented Intelligence = AI2
While there are niche applications for Artificial Intelligence that completely rely on machine automation, most complex business problems require human interaction and perspective. This is why Qlik refers to AI as Augmented Intelligence, where human intelligence and perspective is augmented with cognitive machine learning. This in effect raises the data literacy competency of every user no matter their level of expertise. But if you don’t offer a rich analytics experience, how can you bring the two together and break the ask-wait-answer cycle that SQL-based solutions result in?
Qlik’s Point of View: Building on Qlik’s Associative technology, human-machine interfaces must be harmonized and cognitive machine learning applied to holistic data views to accelerate and deepen insights. This increases data literacy and trust in the conclusions across all users.
Embedded Analytics from the Edge to the C-Suite
Previous generation BI approaches typically create reports, dashboards or analytical applications that are separated from systems of engagement that make insights actionable. While we’ve seen examples of prescriptive and actionable analytics for years in areas like next-best-offer optimization in customer-facing (B2C) environments, this has typically not been the case with employee-facing (B2E) BI applications. If employees want to be more data-driven, they must know what report or dashboard to pull up, what steps to take to dig deeper into the data, and then determine what actions to take based on their own personal interpretation of the data. As you strive to drive analytics beyond the internal user population, this becomes even more challenging. It’s this notion of analytics as a destination, and the sparse use of analytics in decision-making processes, that must be addressed.
Qlik’s Point of View: Analytics must move away from being its own destination and become a necessary part of any and all decision-making journeys via Embedded Analytics. This applies to both human decision-making, as well as machine decision-making in areas like IoT and automation. Leading analytics platforms will enable real-time data and insights to be embedded in apps, processes, devices, and anywhere data can be leveraged for an advantage.
In an era of over-utilized buzzwords, Artificial Intelligence has become increasingly commonplace in vendor speak. Our perspective is that this alone is not enough. Human intelligence and machine learning must be combined, applied to analytics-ready data sets that represent the enterprise, and embedded at the point-of-decision. If you want to learn more about the 3 waves of analytics and how our point of view is being brought to life in our roadmap and product strategy check out our new whitepaper or hurry along to one of our remaining Data Revolution Tour Events.