Organizations used the promise of ‘better decisions’ to adopt data, analytics, and AI. In theory, if a company invests in data and analytics, it is bound to make better decisions. But the reality is different. The data-centric idea is fading away as 60% of the data investments remain wasted. Only 22% of decision-makers leverage the data-driven insights they receive from BI, analytics, and AI tools.
The emerging strategies for decision intelligence seem different from the data-first strategy. As Gartner highlights, decision-making is now emerging as a measurable business process. Decision intelligence is the new enterprise transformation strategy from the tech-centric BI, AI, and data solutions.
Why do you need to measure decisions?
Businesses have a greater need to make faster and better decisions. Yet, they lack context and remain in a fuzzy and immeasurable realm. Decision intelligence, a strategy and not technology, suggests that decisions should be data objects. Organizations need to model, train, track, and establish a feedback loop for decisions like any other business process to improve business performance.
For years, finance teams have tracked budgets, expenses, and revenues. Sales executives constantly update the lead status, conversions, nurturing, and pipeline through the CRM systems. Marketing folks now track their every step in the process for results. Managerial decision-making is more crucial than the processes mentioned above. Why should we resist systematization and learn from the outcomes of past decisions? No, we should not. In short, decisions need to be transparent workflows than unpredictable black boxes. Google’s first Chief Decision Officer, Cassie Kozyrkov, established a new Decision Intelligence Engineering discipline to augment data science with behavioral science, economics, and managerial science.
Many businesses might be concerned that when they adopt data, analytics, and AI for business intelligence, how will the decisions’ strategies add value? What is the underlying reward of decision intelligence? A deep understanding of the acceptable margin of error for each decision while analyzing the risk and reward and the ability to build trust and confidence in the automated decision-making processes.
Let us discuss more strategies for decision intelligence kick-off in our further sections. But let me start with the value of decision intelligence
Catalysts for Decision Intelligence – Data, Analytics, Automation, and AI
Organizations moved ahead with data, analytics, and AI to understand the processes. Decision intelligence builds on these technologies as the foundation, but only to bring more confidence and reliability to the decision-making process.
When most companies now leverage data, analytics, and AI, bringing in more transparency, tracking, and optimizing the decision flows became crucial.
The goal of decision intelligence is simple – apply frameworks to harness process data, generate insights and then apply it to the decision model. It relies on four components – data, analytics, automation, and AI.
Efficacy in monitoring decision models
A study by Bain found that business performance seems 95% correlated to the effectiveness of decisions. Supporting this statement, another research by Cleverpop found that 98% of managers overlook applying best practices in decision-making. As a result, the decisions fall short of expected results 70% of the time.
Often, decision-makers make complex decisions without enough information, time, or experience. Decision intelligence systems improve efficacy by explaining and justifying the decisions, learning from past decisions’ feedback, and comparing the impact to improve decision effectiveness.
Strategies for Decision Intelligence set up
Decision intelligence provides a way forward for organizations to automate the decisions and constant learning about the effectiveness of the decision models. Let us discuss the strategies for decision intelligence that will help any organization initiate it.
Initiate change with a well-defined process
Decision intelligence is never a once-and-done process. Organizations need to refine the approach constantly based on the feedback and learning from decision engines and models. Consider a risk analysis decision – you can improve it by adding more factors like location and time of the day based on the learning.
Considering these, organizations must choose a well-defined, low-risk process with excellent examples to initiate decision intelligence. Though the process may be automated earlier, implementing decision intelligence increases accuracy.
Take cues from new data
As organizations repeat the same processes, gather data, and have precise results about the decisions, they have a chance to improve them.
For example, LexisNexis makes 300 million fraud-related decisions every day. They use AI and ML to sort the behavioral patterns related to every transaction to predict fraud. Do you think they are 100% accurate? No.
In case a current transaction is reported as fraudulent after a few days, they learn from the behavior and incorporate the data accordingly for future decisions to flag fraud.
Tweaking algorithms
As a business, you might have leveraged linear regression when you initiated AI and ML to detect correlations from historical data to predict the future. But as you scale up, the approach may not be efficient as it does not consider any non-linear relationships. What do you do? To consider minor data points and relationships, you may need to upgrade to the latest algorithms like gradient machine learning.
Apart from considering new data points, organizations must also constantly rethink underlying algorithms to improve the decision quality.
Augment complex processes
What do you do when decision steps are unclear, outcomes are vague, and you may face a bigger risk if the decision goes wrong? You can augment the decision-making with human intervention.
The decision intelligence component may not come only at the end. Instead, you can automate the data collection process too. You can use it not only for conclusions but also to generate trends and reports and identify correlations.
Identify the decisions where outcomes are not clear
Often, it is difficult to find the efficacy of a decision with smaller data sets. It might go wrong or right, but it is more based on luck than on data. When it comes to complicated and less frequent decisions, businesses may not be able to define an approach to measure the decisions. The boundaries of technology may not provide solutions in such cases.
Organizations need to formalize such decision-making processes and can only leverage technology.
Learn to manage the bias
Decisions are primarily dependent on the underlying data. They are good only if the data is unbiased, and if the history is problematic, the decision intelligence system will inherit the same.
For example, if a company only hired males above 35 years, the recommendation system based on decision models will also recommend the same.
We all know that people are inherently biased. We will find supporting data based on our decision if we think of specific outcomes. Hence, decision intelligence systems may be able to learn from the past while also managing the inherent bias.
Establish trust with AI
Though businesses generate recommendations from analytics and AI, often, stakeholders fail to understand the technology. Many companies struggle to accept the accuracy of the insights provided on the fly. For example, a German store leveraged AI for its ordering seven years ago. But they gave it up within three years. Why?
Most stakeholders did not understand AI. Managers were always convinced that they were right as they did that way every time. During peak seasons like Christmas, they usually ordered 50% more excess supplies than that predicted by AI. Though the technology leverages historical patterns to predict the demand, they fear they do not have enough. It resulted in excess costs of more than a million euros without revenue.
So, organizations may need data analysts’ assistance to guide the AI decisions until stakeholders find the true meaning of insights.
Leverage synthetic data
Organizations can compensate with synthetic data if they lack training data. This artificial information, synthetic data, is accurately modeled to use instead of actual historical data to enable automated intelligence for many use cases.
Gartner predicted that 60% of the data leveraged for AI and advanced analytics solutions might be synthetic data by 2024.
As the analysts pointed out, decision intelligence is not a technology. It is a discipline established by leveraging different technologies. Organizations must constantly redefine their approach to evaluate outcomes and improve decisions with feedback. Are you interested in initiating decision intelligence? Contact us for more information.