June 7, 2022Implementing a Hub-and-Spoke Federated Approach to Scaling Data Analytics: Key Insights from Loblaw, Vodafone, and Enbridge
In every organization, thousands of decisions are being made every single day. These are orchestrated across enterprise functions, geographies, and at the strategic and tactical levels. Surprisingly, research results reveal that the vast majority of these decisions turn out to be poor, ill-informed choices.
For example, a survey found that a typical Fortune 500 company wastes about 530,000 days of managers’ time every year due to poor decision-making. For enterprises, this translates to $250 million wasted in annual wages. In other words, an average employee loses 47 days annually due to ineffective decisions leading to rework, poor time to market, and lost business opportunities.
To improve the quality and impact of decision-making, business leaders are looking to build repeatable capabilities and institutionalize them across the organization. The discipline of decision intelligence (DI) is the solution they need.
Fundamentally, Decision Intelligence is the use of actionable data insights to deliver effective, impactful, and timely decisions across the organization at scale. DI is rapidly emerging as a key enabler of business performance by bringing together modern data and analytics platforms, a scalable model for decision-making, and a culture that fosters the actioning of data-driven decisions.
Gartner rates DI as a Top Strategic Technology Trend for 2022 and predicts that this discipline will be a game changer for the data and analytics industry in this decade.
What makes a decision good?
First, let’s define the three elements of every good decision. It is effective and addresses the underlying challenge. It is made on a timely basis. Finally, it is pertinent and factors in the relevant context and stakeholders.
Decision-making may seem complex and even random. However, when you look at the anatomy of decisions in an organization, there are five factors that influence the effectiveness of every decision. We call them the decision-making elements.
1. Decision maker:
Every individual has a unique set of capabilities, preferences, and biases. Their understanding of the business, their ability to leverage data to make choices, and the level of understanding of their stakeholders significantly influence how they approach a given problem.
2. Target stakeholder:
Once decisions are made, they may be acted upon by other individuals in an organization. And each decision impacts several target stakeholders. A strong understanding of who would act on the decisions and how it impacts the audience is critical to make the right decision.
3. Nature of problem:
Problems could be strategic with an influence on the entire organization or tactical by impacting a subset of the business. The level of complexity, the extent of data available, and how readily one has access to actionable insights determine the quality of decisions made.
4. Organizational context:
Every organization has a unique set of processes that govern when and how decisions are made. The organizational norms could incentivize holistic, data-driven decisions, or inhibit them due to dysfunctional, counter-productive practices.
5. Feedback loop:
Our decisions are continuously shaped by our prior decisioning experience – how an earlier decision played out and whether it achieved the targeted outcomes. Consciously and subconsciously this impacts every decision we make in the future.
How can Decision Intelligence help?
Decision intelligence is the discipline that enables the translation of insights from data into actionable decisions to solve business problems and achieve the targeted outcomes.
DI brings together principles, tools, and best practices from three areas:
1. Cognitive Science
The path to any good decision begins with a strong grasp of the target audience and the decision-maker (self). Cognitive science helps us understand how the human mind behaves. This entails collecting information about the audience needs, priorities, and pain points.
2. Data Analytics
With a good understanding of the key stakeholders, the next step is to internalize the business problem and understand the questions to be answered. Data analytics helps ask the right questions of the data and translate them into actionable insights. This calls for building the right ecosystem of tools, technology, and processes.
3. Organizational Intelligence
Decisions must be adapted to the organizational context to ensure they are relevant, can be adopted, and lead to the desired outcome. Organizational intelligence helps us understand the context and relationships that drive business behavior. This calls for equipping people with the right skills to work with data and incentivizing behavior that promotes data-driven decisions.
In summary, Decision Intelligence makes data-driven decisions a reality by ensuring a strong understanding of the decision-maker and audience, enabling easy access to data and actionable insights, and cultivating an organizational culture that fosters decisions using data.
The three drivers to scale Decision Intelligence in your enterprise
We’ve seen how Decision Intelligence is both a science and an art. However, it can be orchestrated at scale and in a repeatable manner in your organization by leveraging three key drivers:
i) Embracing modern data platforms
Easy access to sufficient volumes of good quality data is a prerequisite for data-driven decisions. Modern data platforms go beyond storing and enabling access to data. They make data discoverable across user classes, scalable across use cases, and flexible by adapting to organizational growth.
ii) Organizing around a scalable operating model
Gone are the days of organizing data and analytics teams around either a centralized (top-down) or a decentralized (grassroots) approach. Hub-and-spoke models enable true data democratization. A central core or hub lays out the data strategy, direction, and architectural blueprint for the organization. The spokes are closer to the business or the point of consumption to enable integration, flexibility, and business alignment.
iii) Adopting a data-driven Culture
The best tools and technology must be complemented with a strong people focus to ensure successful outcomes. Foster a culture of data-driven decisions by securing executive sponsorship. Ensure data literacy by enabling every individual in the organization to read, interpret, and leverage data for decision-making using the institutionalized tools and processes.
In summary, Decision Intelligence is the key to enabling data-driven decision-making in your organization. The principles and practices of DI help unlock business value and secure ROI from your organization’s data and analytics investments.
Note: This is the first post in a series of blogs on Decision Intelligence. Stay tuned for the next post that will reveal how to implement DI using a semantic layer in your organization.