Key Actions to Make Better Decisions for Technology Leaders
This article is about decision-making processes and practices for technology leaders.
Decision-makers fed up with slow or subpar results take heart. Corporate experiences can help improve decision making and convince skeptical business leaders that there is life after death by committee.
Decisions are the coin of the realm in business. No company can reach its full potential unless it makes good decisions quickly and consistently and then implements them effectively. Good companies can’t become great. Troubled companies can’t escape mediocrity. Our 10-year research program involving more than 1,000 companies shows a clear correlation (at a minimum 95% confidence level) between decision effectiveness and business performance.
In today’s fast-paced and complex business world, leaders must continuously refine their decision-making processes and practices to ensure they stay on the right path.
Four commonly recognized decision-making styles are:
Directive; The leader uses his or her knowledge and past experience to reach a decision without seeking information from others. The advantage is that decisions can be reached quickly; the disadvantage is that the leader might not consider the long-term ramifications.
Conceptual; The leader seeks ideas from team members, which encourages creativity and innovation. This style is suited for long-term projects and planning.
Analytical; The leader relies on direct observation, facts and data.
Behavioral; The leader collaborates with others on options and is highly influenced by their feelings and opinions. The downside: If a consensus can’t be reached, the leader must choose a different approach.
For most, decision-making complexity has increased, but upstream and downstream considerations are still poorly addressed. Decisions don’t take account of enough relevant variables, don’t adequately consider the future effects, and don’t happen on a cadence that positions the organization to capture opportunities and mitigate risks.
Decisions today must instead be connected, contextual and continuous, not through some academic exercise in decision theory, but by creating a truly symbiotic relationship between humans and machines to generate the optimal action.
Strategic, tactical and operational decisions are converging
Traditionally, decisions have been vertically aligned, with strategy driving tactics — which drove operations. Operational feedback would validate tactics and tactical feedback would validate the strategy. Each type of decision had its own process, its own set of stakeholders and its own dynamics. Now, those decisions are converging — and decisions often last only until the next disruption.
1. Convergence of the tactical and the strategic
• Make an acquisition to learn about digital markets.
• Create a digital sublevel for a brand to test new business models.
• Require higher-level management sign-off for initiatives and policies.
2. Convergence of the tactical and the operational
• Start an influencer service program to manage brand reputation.
• Hire scarce staff, such as data scientists.
3. Convergence of the strategic and operational
• Quickly scale up (or down) parallel supply chains, based on local needs.
• Expand into a new geography or product category on an e-commerce platform.
4. Convergence of the tactical, operational and strategic
• Implement crisis management.
Business benefits of re-engineered decision making
More inclusive
• Gather and optimize outcomes for multiple stakeholders throughout the ecosystem, bridging competing objectives and recognizing ethical dilemmas.
• Consider a wide variety of data perspectives.
• Diagnose how decisions affect each other.
More transparent
• Make decisions auditable and explainable for others.
• Establish clear accountability.
More trustworthy
• Automated and composable decisions are potentially more consistent and repeatable.
More accurate
• By including more data and more advanced modelling capabilities, decisions align closer to complex realities.
More personalized
• Particularly on the operational level, decisions require personalization, taking the specific circumstances of cases or people into account.
More scalable
• Automated decision making allows for scaling the number of decisions by extending the management span of control.
More flexible
• Re-engineered decisions keep future options open because they work out in multiple plausible futures.
• Composed decisions, strategic to operational, are designed to respond as the context changes.
The role of IT and business leaders in decision making
The world is experiencing an unprecedented crisis that is causing chaos in the global economy, disrupting supply chains and transforming society. The new reality is accelerating business model transformation at a faster pace than ever before to ensure existential survival in a crisis for which no one was prepared. The Covid-19 pandemic is having a dramatic impact on society and has forced everyone to become heavily reliant on the internet and its digital economy — what would normally have taken years has now occurred in months. The situation has highlighted the intrinsic systemic issues at the juncture of digital infrastructure, economy, geopolitics and privacy that mainly relate to the unprecedented pressure on the digital architecture and supply chain dependencies. If these are not addressed in a holistic manner, the escalating risks may have a domino effect that is likely to impact critical functions and industry ecosystems globally.
The large-scale adoption of remote-access technologies to enable work-from-home practices, with greater reliance on cloud services, enables companies to continue operations and reduce costs in conditions of social distancing and “stay-at-home” orders from government and/or employer. It is also reshaping the digital landscape and architecture while straining supply chain resiliency and cyber security operations with the escalating risk.
Business leaders seeking to accelerate their digital business aspirations will need to consider how they (and their organization) make decisions. Digital aspirations will be scaled when the organization can take advantage of the ability to make more connected decisions that are more contextualized and operate in a continuous manner.
CIOs need to set the strategy for a new style of business solution delivery and further collaboration between the CDO and CIO teams. The CIO, along with the CDO or other top D&A leaders, should advocate for this new paradigm and drive the organization to pursue it.
CDOs/D&A leaders need to ready themselves for the next evolution in D&A. The first evolution elevated D&A from a service center to an enterprise competency with the realization that D&A is central to digital business and innovation. This led to the creation of the CDO role. The next evolution reinvents how we manage and utilize data, and it redefines D&A’s fundamental purpose as part of decision making — and more broadly, redefines how an enterprise operates. D&A leaders need to internalize this major shift and work with their business peers to enable radically new forms of decision making.
Application leaders need to take a data-centric approach to implement the composable business and composable applications. The application leader and D&A leader must work together to blend composable applications and re-engineered decision-making practices.
Enterprise architects collaborate with leaders from D&A, application, risk and security, and I&O to rethink reference architectures that incorporate new concepts, such as data fabrics, decision models and composable decision components.
Risk and security leaders need to set up secure spaces in which multiple stakeholders can safely share data for decision making.
Strategically, IT leaders can inspire other business leaders to recognize the opportunities of creating and acquiring new kinds of data to infuse into decisions — positioning data and analytics as assets that can create value.
Operationally, IT leaders play a key role in prioritizing the actions around decisions.
Decisions Analytics Data
• Start identifying and assessing which decisions are insufficiently connected, contextual or continuous. This is your starter set.
• For each of these decisions, understand their connectedness; what internal and external context is important; and the need for a more continuous process.
• Model these decisions using decision intelligence technology.
Analytics
• Inventory your current analytics solutions.
• For each solution, determine the extent to which it is used, how effectively it is used and — importantly — why it is used:
– Do insights offer sufficient context to the decision?
– How do behavioral or social aspects impact decision making?
• Start improving analytics solutions, for instance by adding augmented, diagnostic or predictive analytics or by improving data literacy skills among decision-makers.
Data
• If you haven’t done so already, start initiatives to improve data quality, master data consistency and metadata management (including data catalogues, and business glossaries or ontologies).
• Apply data virtualization to improve (unified) access to data warehouses, data lakes, or other internal or external data sources.
• Complement data management with streaming data capabilities, enabling continuous intelligence.
Build decision-making skills, habits and competencies
Data isn’t the only driver of good decision making. IT leaders also need to foster organizational and analysts’ skills and competencies to improve decision making.
· Increase data literacy throughout the business. To make good decisions, all stakeholders must be able to read, write and communicate data in context.
· Create new decision-making habits. For example, systematically use logic to make rational trade-offs, channel emotions productively and build experience in extrapolating the consequences of decisions.
· Consider decentralizing decision making. One option is to establish a technology center of excellence (COE) to collaborate with multiple decentralized teams and communities and the centralized office of the CDO.
· Position some analysts as “decision engineers” tasked to diagnose and rethink decision-making processes, optimizing the roles of humans and AI. These specialists can proactively design better ways of making optimal decisions, leveraging techniques such as portfolio analysis, Monte Carlo analysis, simulation, decision modelling, systems modelling, statistics and optimization modelling.
Data and analytics is no longer a stand-alone discipline; it has become a catalyst for digital strategy and transformation.
Leaders throughout the business and across IT need to work together — each bringing their unique competencies to support the breadth and depth of the art and science of decision making. Designing an organization to deliver its strategic objectives, setting a clear mission, aligning incentives is a big topic. But if different functions and teams do not feel a connection to the bigger picture, the likelihood of executives making good decisions, whether or not they adopt the ideas discussed earlier, is significantly diminished.
