Individuals and groups make thousands of decisions a day that ultimately determine the financial performance of their organizations. According to the 2010 McKinsey Global survey, “Economic Conditions Snapshot,” knowledge workers have driven more than 70 percent of the economic growth in the U.S. over the past three decades, and 85 percent of the new jobs created in the past decade required complex knowledge skills. Additionally, companies incorporating decision-making as a core competency – even a competitive differentiator – are outperforming their peers.
These companies have learned that for business intelligence to be used successfully, organizations need to overcome not only technology hurdles, but also change the organizational culture around decision-making. In order to improve results, companies need to optimize all three performance drivers: people, process and technology.
Knowledge workers are changing with respect to their type, complexity, location and sophistication. Five years ago, we would have considered people like grocery store managers or factory/plant managers as operational, but we can no longer do so. Their value is not only in what they know about retailing and production, but also in their ability to understand the financial impact of operational decisions. The new breed of knowledge worker is no longer tied to their desks, so they are not always able to pull info out of systems, crunch numbers and provide analyses. Many people who have such responsibilities and who collectively drive revenue, costs and profitability, make key decisions that have a direct effect on a company’s success.
The traditional knowledge worker is also operating in a more complex environment. They are working with systems that were built at a time when the questions often asked today would never even have been anticipated. So from a skill set perspective, they need to be resourceful and have an ability to interpret information.
Additionally, with the rapid adoption of Web 2.0 technology platforms, knowledge workers of all types have new, upgraded expectations about how to work. Hierarchies are flattening as knowledge workers grow accustomed to connecting with colleagues and having access to others’ expertise. Knowledge workers need to be good collaborators.
As the sophistication and location of the knowledge worker changes, so must the technology.
Technology has become powerful in enhancing decision-making. However, these knowledge workers are not necessarily business/technology analysts trained in business intelligence tools, so BI technology must serve them, and not vice versa. Intelligence must be delivered in a fluid manner as a part of the new knowledge workers’ ordinary routine. They are not always at desks, but they do tend to carry a variety of wireless devices, such as smartphones or tablets.
This is one reason companies need to provide BI content not only in the traditional manner of reports, dashboards and scorecards, but also to mobile devices, pushing information out to users and allowing decision-makers to get on with their day. BI should be available as widgets and alerts, providing knowledge workers with consumable information, leading to quicker decision-making. Additionally, as new types of data become available and more widely used (such as RFID and social media monitoring) companies should ensure that their BI tools integrate with any form of data.
It’s important for companies to assess the success of the technology currently in place to enrich decision-making and identify opportunities for improvement. Such improvements can come in the form of:
- Establishing and tracking key performance indicators associated with important decisions so that it’s easy for people to determine if they are on track.
- Delivering complete, timely and actionable information from any and all data sources so that people trust it to make decisions.
- Generating role-specific reports, dashboards, scorecards and alerts in context, so the data is easy to consume.
- Providing models to test and adjust intuitive predictions for the larger, more strategic decisions.
Solid BI content used by the new knowledge worker will have the best result if it is combined with sound decision-making processes.
Regardless of good data, how information workers use it and perceive it is fundamental to decision-making success. Dr. Courtney Hunt, principal of Renaissance Strategic Solutions, suggests that when making decisions, knowledge workers may use heuristics, or simplifying strategies, to expedite the process.
While heuristics can be useful structures for thinking about frequently encountered problems and can help decision-makers cope with decision-making complexities, individuals are often unaware that they are using them. More importantly, the use of heuristics can lead to biases and judgmental errors that prevent decision-makers from using good business intelligence to make high-quality decisions. The following are sample decision-making process biases that managers should be aware of to get the most out of their team members’ decisions.
The Representativeness Heuristic
This occurs when people making judgments look for traits and circumstances that correspond with previous situations and make decisions that ignore the entirety of current situational data. This can show itself through a variety of biases, where decision-makers ignore factors that make this situation different from the last one, such as sample size, the starting or base rates or other data points. A related bias is the confirmation trap, which occurs when people more actively seek information confirming their instinct on a decision than they do for information that would refute their instinct on a decision.
Judgment can be impaired by the self-serving bias – the tendency for judgments to be subconsciously and powerfully biased in a way that is proportionate to one’s self-interest. Decisions that affect the decision-maker’s personal work seem more important than they may actually be to the organization’s bottom line.
We create a tendency toward inertia in the presence of uncertainty, which can cause anchoring, where we become rooted to an initial value (and don’t sufficiently adjust when provided with new information), or we tend to prefer the status quo (repeat the same decisions) rather than take a perceived risk by making a different decision.
Escalation of Commitment
This is the degree to which an individual sticks to his or her guns despite new data, perhaps even further committing resources to a point beyond which a rational model of decision-making would prescribe. This occurs as a result of some of the aforementioned perceptual and judgmental biases, or from a desire to manage one’s image organizationally, separate from decision-making goals.
Organizations seeking the benefits of the wealth of data in their organizations should train knowledge workers to recognize these and other biases that may hinder the quality of their decisions. From there, a culture in which bias is avoided and data is shared as freely as possible can transform the bottom line.
Fuel for the Knowledge Worker
Clearly, a new trend is emerging that centers on making BI more pervasive, engaging and collaborative in order to facilitate end-user decision-making for every kind of knowledge worker and help organizations perform better. The power is moving to the end users' hands; BI providers are designing with them in mind.
End users are looking for self-service BI that accommodates their specific needs. They expect data to be available on all of their devices, from desktop PCs to smartphones. They want their most frequently needed reports accessible via widgets. They want BI that takes unstructured data into account and makes it searchable. They want to collaborate and provide feedback about reports in an attractive interface that's designed around how they work, rather than one that forces them to accommodate a vendor's approach.
With the right technology platform and the right data by people who actively work to overcome decision-making biases, companies can perform better. This combination will increase the number of optimized business decisions that are made based on facts, giving the organization the opportunity for increased flexibility, productivity and fuel for the ultimate goal – profitability.
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