In my first blog in the Active Intelligence series, I explored how enabling cycles of rapid innovation in data and analytics is required to provide the agility and resilience needed for Digital Transformation. But how is this changing the rules of decision-making?
Organizations that choose to maintain the decision-making practices supported by traditional data and analytics tools are choosing to bury their heads in the sands of the past.
Having the right decision-making processes matters more than ever when digital technologies are in the mix – with due consideration given to requirements for decision augmentation and automation.
Here is why.
The Shift to Digital Business Is Going to Push the Next Innovation Cycle in Data and Analytics on Decision-Making
In the digital age, decisions are becoming more contextual, more continuous and more complex. Traditionally, decisions have been classified as strategic, tactical or operational. And, while I do not think this classification is changing for digital businesses, what is changing are each of their characteristics and how the lines between them blur.
Strategic decisions are the major choices we make that influence the whole or a large part of a business. They set organizational principles, objectives and priorities.
In the digital age, these decisions require more real-time insights, which are traditionally used for operational decisions. Real-time decision-making is being elevated from operational to strategic decision-making. Why? There is a need to provision information in context and integrate it in near real-time. A great example of this shift is the way data informs leadership discussions at Forbidden Planet. By leveraging the Qlik platform, its management team is now able to digest and assess its most meaningful business metrics and data sets within 15 minutes, rather than spending meetings sifting through stacks of paper or navigating Excel sheets. This leaves them more time to look forward, not backwards. They can focus on what’s important and “move fast enough to take advantage of opportunities” in the moment.
Tactical decisions relate to the implementation of strategic decisions. They are meant to be applied multiple times, and involve processes and policies.
These decisions now require more governance, collaboration and augmentation. Managers who are tasked with making these decisions are dealing with more data and greater complexity. Where humans are still the main actors making the final decision, they need to access a governed data fabric and use augmented analytics solutions that leverage augmented descriptive and diagnostic analytics to offer several decision alternatives.
Such solutions should also create a synergy between humans, enabling them to share knowledge, collaborate and apply common-sense contributions in decision-making. I love how Enolytics created a collaborative data ecosystem to enable better decision-making for the wine world. Marrying the romanticized elements of the wine industry and data may not have appeared a natural fit to some producers at first, but the augmented knowledge it is providing is helping the wine industry get closer to the consumer. In turn, it is helping consumers get closer to wine (something I can get on board with!).
Operational decisions relate to the day-to-day operations of the enterprise. They have a shorter shelf life as they are taken repetitively.
With digitalization, more businesses are introducing business applications to handle day-to-day operations. Today, individual departments can easily pick and implement the best-of-breed SaaS applications they want for their specific tasks. However, the critical success factor is the fluid exchange of information between these applications, so that the operational decisions are not made in silos and can be automated. Automation takes away the tedious, repetitive tasks that are needed for operational decisions. Nobody wants to go between two different applications and move data manually to be able to make a decision.
Now with technologies like Qlik Blendr.io, our customers can automate repetitive tasks and data processes, which frees people up to do the work that requires non-linear thinking and gives them time to focus more on tactical and strategic decisions. A good example of this is the Blendr.io SaaS workflow that enables one of our customers to automate the data entry and inventory order decisions between four of their cloud-based CRM, ERP, accounting and inventory systems. This Blendr.io workflow freed up data reconsolidation work and enabled their people to focus on what they are good at, making hard, non-linear, judgment decisions.
Automating and Augmenting Every Decision With Active Intelligence
The complexity of digital business is increasing, with more intense competition, more data and constant changes in business dynamics. Together, this means that decision-making must improve in terms of speed, accuracy, governance, scalability, collaboration and automation.
In this new digital age, decision-making should be facilitated by a wide range of technologies. From dashboarding to augmented data exploration, storytelling to collaboration, a real-time governed data fabric, to process automation and artificial intelligence (AI), they all have role. Digital businesses need platforms that provide the optimal combination of these elements. Once found, Active Intelligence is achieved.
In my third blog post, I will focus on the need for a real-time governed data fabric. But if you can’t wait till then, find more information on how Active Intelligence can help you optimize decision-making here!