This article was written by Elif Tutuk and originally appeared on the Qlik Blog here: https://www.qlik.com/blog/unlocking-the-success-of-digital-transformation-with-active-intelligence
Data and analytics are the key accelerant of an organization’s digitization efforts, and traditional Business Intelligence technologies can’t offer the transformative speed to insight and actions needed in this digital age. At Qlik, we believe digital transformations needs Active Intelligence – where real-time, up-to-date data triggers immediate action to accelerate business value across the entire data and analytics supply chain.
In this blog post, which is the first one of a series of six, I will explain the digital, data and insights needs of analytics consumers, highlight the main issues that many of the current generation of Business Intelligence tools have in not delivering against these needs, and explain in-depth how Active Intelligence serves to close these gaps.
Analytics consumers need real-time insights that drive actions from hyper-contextual data with the scale of cloud computing to support decision-making at the speed of business.
Real-time agility: Digital business transformation will lead to improved outcomes and the development of new business capabilities. But real-time information and analysis is needed to inform data-driven decisions and optimize every business moment. This requires a shift away from batch uploads towards a data analytics pipeline that uses automation in the context of Change Data Capture (CDC). At Experian, for example, the implementation of Qlik Data Integration has been critical in ensuring that the billions of rows of constantly changing data are accurately captured, so that credit reports are based on the latest information.
Hyper-contextual data is at the core of every modern digital business: As businesses are digitally transforming, and the lines between business processes and technology blur, data is the one constant denominator. The synergy of multiple digital technology innovations, like event stream processing, real-time data analytics, artificial intelligence (AI) and the Internet of Things (IoT) requires data technologies that can enable analysis of data in motion and data at rest. Analytics consumers need assistance uncovering insights about complex relationships within all these hyper-contextual data, which includes more than just operational and transactional data, encompassing situational and real time context. It would be nigh impossible for Vancouver International Airport, for example, to manually optimize its choice of gates for a flight based on the diverse parameters, from wind, size of aircraft, or where most incoming passengers are transferring onto. However, intelligent analytics can understand the indirect relationships between distantly connected datapoints.
Intelligence-driven decision making, automation and actions: In digital business, decisions are becoming more connected, more contextual and more continuous. To boost organizations’ operational speed and efficiency, some decisions should be entirely automated, through the application of process automation, IPaaS and machine learning algorithms. More complex, strategic decisions still should be made by humans, and can be informed by real-time insights that support collaborative capabilities. The power here is the combination of human and technology working together.
The current generation of Business Intelligence tools can’t deliver against the demands of the digital economy.
Most Business Intelligence technologies use preconfigured data and mostly support batch analysis: They’re designed to answer ‘known’ questions with batch reloads. Because they use ‘constraints’ to define the relationships between entries in different data tables and sources, those constraints also define what queries can be used to answer business questions. This creates a significant bottleneck to surfacing hidden insights, and limits the agility needed to answer constantly changing business questions in real-time that today’s market conditions require.
They do not deliver a governed end to end analytics data pipeline: Most current technologies comprise a series of solutions for data ingestion, integration, delivery, analytics, collaboration and storytelling that haven’t been brought together into a unified pipeline. However, when bringing these capabilities together, for instance at IAS through the implementation of a Qlik Data Integration toolset and Qlik Sense data analytics platform, information can be integrated faster, with insights gleaned in record time. Indeed, for IAS this drove significant productivity gains: analysts flipped from spending 90% of their time on reporting to solving business problems, while developer productivity increased five-fold.
Finally, they are designed to inform, but not to compel actions: Traditional Business Intelligence was designed to inform humans, and by extension it could be used to inform action. It was not designed to compel action, and certainly not to trigger it autonomously in real-time.
Active Intelligence should be one of the main considerations on the path to digital transformation.
Digital transformations need a much more dynamic relationship with information. One where data has high business value because it reflects the current moment. And one where information flows continuously into everyday processes, empowering users to engage with it in intuitive ways at any time, creating in-the-moment awareness about every aspect of the business and the market.
This requires Active Intelligence - a state of continuous intelligence where technology and processes support the triggering of immediate actions from real-time, up-to-date data. Active Intelligence closes the gap between what’s happening in the business right now and the insights and actions available. As a result, it introduces tremendous opportunity for boosting innovation and accelerating value for digital business.