Decision intelligence is the evolution of data, analytics, and insights that drives smarter business decisions and strategies says Brian Timperley, the CEO and co-founder of Turrito Networks
Gartner defines decision intelligence as space, wherein a variety of decision-making solutions and approaches come together, to create a framework and discipline that the business can leverage to guide decisions and strategies. This is a defining trend for 2022 because it drives growth, digitisation and efficiency by using the disciplines from different fields to create a holistic framework for long-term decision making that’s powered by reality, relevance and insights. But it needs to be approached with caution and patience, because while the technology is quickly evolving, it’s still not at the point of mass adoption.
Decision intelligence is the more advanced older sister of business intelligence (BI). It doesn’t just bring insights from data analytics, data stores and all other tasty data sources, it also brings in a level of context that’s typically missing from business intelligence. The improvements in technologies and solutions that surround BI and that report back to the business are now faster and more accurate, taking BI to a point where it shifts the capabilities of intelligence and insights as a whole. This shift is transforming decisions themselves, taking them from reflective and reactive steps towards more proactive and predictive insights that can fundamentally alter how the business does, well, business.
However, it’s important to note that getting decision intelligence right is all about context. It’s about drawing the correct interpretation of data right from the outset. Currently machine learning (ML) and artificial intelligence (AI) solutions must undertake a process of continual learning to sufficiently understand every possible interpretation. Unlike the human brain, AI and ML systems need to constantly analyse billions of variations to slowly learn specific attributes and approaches. This is the foundation of analytics technologies and is key to the success of decision intelligence. It still requires more insights, broader data sets and context to achieve its full potential.
A system without business or human context would look at the data and draw conclusions that would not necessarily align with reality. For example, a system without context might eliminate expenses from a business to save on costs, without recognising the context of those expenses, and ultimately deliver a result that is more detrimental than beneficial – this is a simple example, but it does highlight the risk of making sweeping decisions from data analytics without understanding the context. The context must be as accurate and complete as possible. So, design the system to learn so that decisions are made within the right context.
Data accuracy is also key to decision intelligence. With immense / broad datasets, it’s possible to get past standard data errors due to the sheer volume of information available, and for both company and system to learn how to approach the data more effectively. This goes beyond just ensuring the data has context; asking the right questions of the data is key. It’s about aligning the data with the business strategy to ensure that there is relevance in the insights and safeguards around the final decision-making process. To achieve this goal, test the data, the context and the outcomes and establish the right recipes for your organisation. Input the context will move you towards delivering decision intelligence outputs that are relevant and aligned.
While there are several steps that need to be taken before decision intelligence can truly become the force that it promises to be, it still has the potential to become a fundamental part of the organisation. When you begin, the insights will be rudimentary but, over time, as business leaders continue to intervene on the data and refine its relevance, it will become more capable and intelligent. Here, leaders can execute their strategic use of the data to lean the business towards AI and BI and ML – using data sources that are becoming increasingly available to provide the right contexts, and to ensure the right training to machines.
Right now, decision intelligence may not be the ultimate tool, but it’s the right time to invest in technology that allows your organisation to scale its intelligence incrementally. To create an architecture that’s capable of harnessing the data and learning in ways that will benefit your business in the future.
