Advances In Informatics Driven Decision Making

Advances In Informatics Driven Decision Making
Informatics driven decision support is changing the way leaders make decisions.

Business intelligence driven by complex information and analytics is changing the way leaders make decisions.  The more effective the engineering of information, the better insight that is provided leadership.  The more confidence leadership has in the information, the more confidence they have in acting.

Measures and metrics are used across the organization, from planning to operational performance, from marketing to HR, and from finance to customer satisfaction.  A number of formal paradigms use measures and metrics to track performance against expectation, including Balanced Scorecard (BCS), individual performance measures for HR, Operations & Financial Key Performance Indicators (KPI’s), Strategic Objectives – Objective Key Results (OKR’s), and the Baldridge Framework.

No matter the planning paradigm or phase of the implementation cycle, having the ability to track and even predict performance as well as expected outcomes is essential to making timely decisions.  There are a number of keys to defining quality metrics and measures, but two of the most important are:

  • Focus on elements of information which indicate when targets are met, success is insured
  • Organizational effort can utilize that information to direct activity

The development of business intelligence is a knowledge engineering task.  Identifying key organizational challenges and understanding where progress needs to be made to overcome them, provides a good starting point. Next, working with leadership to understand their processes and what information they rely on to make decisions can determine metrics which are most valuable.  Finally, determining what data is available and what information can be created to support decision making is essential.  All the while, implementing a governance process that goes beyond bureaucracy, to create cross functional vision, data, and analytic innovation will ensure knowledge integrity and trust.

Organizations are creating metrics across organizational boundaries that support planning, execution, and operational performance. Including:

  • Strategic outcomes
  • Financial performance
  • Operational assessment and best practices
  • HR – employee engagement and retention
  • Marketing and business development
  • Customer satisfaction, requirements, and expectations
  • Competitive assessment

A progressive view of metrics and measures include deeper analysis of performance beyond individual data points and provide data drill down and interactive assessment.  Additionally, as greater amounts of information become available it is a necessity to utilize AI driven analytics.  Machine learning and expert systems can monitor early indicators, provide detailed guidance to executive decision makers, and generate health and status assessments of complex measure sets.

Measures and metrics need to not only measure expected end results, but also provide early and intermediate indicators that show progress ahead of any expected outcome.  Early indicators, signal issues early enough to allow for leaders to mitigate risk of a given impact of a missed target. Leading and lagging indicators help organizations create momentum and measure final outcomes.  Using industry benchmarks as key metrics, helps organizations stay abreast of competition and where their industry is headed.  

It is also important to avoid metrics that mislead the organization, causing focus on the wrong things.  Vanity metrics tend to measure things that are easy for an organization to achieve, but do not deliver outcomes that have an impact on organizational success.  An example would be number of visitors to a web site.  While that measure might make the organization feel good, but what really matters is the number of visitors that convert to customers.

Analyzing The Components of Customer Satisfaction

For an organization to be effective, they must understand the elements of satisfaction, capture metrics that gauge customer interaction, and be able to predict behavior based upon previous behavior.


Measurement drill down into customer satisfaction begins with the individual aspects of customer interaction that may impact satisfaction.  Individual measures and metrics include service access, ease of scheduling, quality of service delivery, call support wait time, and marketing outreach. 

An organization might align each measure with a given set of initiatives intending to improve the measure. Since overall customer satisfaction lags behind the more direct measures, drilling down on individual components of customer satisfaction can show how well each component measure is performing giving leaders the ability to identify the root cause of poor customer satisfaction performance. By maintaining the relationships of metrics to initiatives, leaders will be able to drill down to those initiatives, to look at the status of each individual effort, identifying where improvement is at risk.

An example of predictive analytics is employee retention.  By assessing various factors such as length of employment, salary, performance ratings, level of education, level of employee engagement, relative competitive benefits, and peer reviews machine learning can be used to anticipate potential probability of an employee leaving, generating alerts and making recommendations for mitigating the risk.


Business intelligence requires integration with a number of organizational disciplines including full collaboration with the IT organization.  Building next generation business intelligence solutions requires a significant level commitment to requirements assessment, experimentation, and governance. But, as trust develops, business process will begin to take full advantage of data driven decision support, making the organization more responsive and adaptive to the dynamics of the environment.

Leave a Reply