Decision Analytics for Crisis Response

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A data analyst reviews graphs at his desk, using several computers.

Benjamin Franklin once wrote that the only certainties in life are death and taxes. However, Franklin excluded another of life’s certainties: crises.

We don’t know when or where the next crisis will strike — or what form it will take. The only things we can control are how we prepare for crises and how we respond to them. In both instances, decision analytics plays a critical role.

When Harvard Business Review investigated why some companies had reached new levels of success in the years following the Great Recession of 2008 to 2010, the researchers concluded preparation was the differentiating factor.

  • Crises change markets, industries and economic processes.
  • The key to success is change management.
  • The best way to manage change is by making wise decisions about debt, workforce management and new technologies.
  • The best way to improve business decision-making is by supporting those decisions with advanced data analytics.

Decision analytics helps organizations anticipate crises, minimize their impact, and recover quickly and advantageously. Earning an Online Master of Science in Business Analytics prepares data professionals for a range of careers in decision analytics.

Use of Decision Analytics Before a Crisis

The first step in readying an organization’s response to a crisis before it strikes is to have a data analytics strategy in place for collecting and analyzing relevant information quickly and accurately. The data sources include emergency guidelines, directives and restrictions issued by government agencies, and up-to-date information about changing market conditions and industry trends.

Software vendor Dundas Data Visualization describes a three-pronged approach to data analytics devised by research firm Gartner:

  • Visual exploration quickly sorts through mountains of data to uncover the insights that inform business decisions.
  • Reporting and dashboarding help company managers measure and monitor their resources, facilities, employees and suppliers via clear and simple interfaces.
  • Advanced self-service analytics brings the power of modern analytics tools to business managers who lack a data science background.

Efficient data monitoring tracks events that affect industries so organizations are ready to respond quickly and effectively to any problem as soon as they detect one. While some events can be anticipated, such as storms and floods, others are unforeseeable. By having a decision analytics system in place, companies are ready to formulate a fast and effective response to nearly any emergency situation.

Use of Decision Analytics During a Crisis

The first steps organizations take when a crisis breaks set the tone for all of their subsequent actions. The decision analytics system must ensure that companies continue to receive critical data in the event of power outages and other disruptions. As the crisis unfolds, the focus becomes monitoring the effect of the organization’s responses on the situation and adjusting the response as new and more relevant information becomes available.

Data visualization service Kin and Carta has developed an “agile strategy” for leveraging decision analytics as a crisis unfolds. The strategy’s three components create a “minimum enterprise data set” that converts qualitative and quantitative data into business intelligence in near real time.

  • Track market, competitive and organizational dynamics.
  • Constantly measure leading and lagging indicators as well as anticipatory indicators that may foreshadow a crisis.
  • Drive operations based on anticipation, control, improvement and communication.

Several different technologies support real-time decision analytics for crisis response:

  • Cloud data platforms such as Google’s Big Query make it possible to set up and manage advanced analytics of structured data without requiring a database administrator.
  • Google’s Natural Language Processing and Document AI application programming interfaces (APIs) allow organizations to analyze unstructured data and extract intelligence from diverse data sources.
  • Automated knowledge graphs such as those created by the Elasticsearch service connect disparate data sources and generate visualizations on the fly. The visualizations identify relevant data that can be used in downstream decision-support applications.
  • Automated machine learning such as Google’s BigQuery ML and Elasticsearch’s machine learning module highlight anomalies and anticipatory patterns in time-series data.
  • Rapid dashboarding helps to visualize complex data quickly to support decision-making via platforms such as the Google Data Studio.

Use of Decision Analytics After a Crisis

As businesses adapt to a world changed by the COVID-19 pandemic, they understandably focus on survival. Yet forward-thinking companies know that times of great change are also times of great opportunity. By increasing their investment in decision analytics, these organizations are preparing to “take charge of the new normal,” as data analytics vendor Visier states.

Companies are using decision analytics to measure the damage caused by the COVID-19 crisis, assess the effectiveness of their initial responses to the crisis, and determine how they can apply the lessons learned from the experience to better anticipate and respond to future crises. For example, companies can analyze data collected on the productivity of remote workers to identify the effectiveness of data-sharing and collaboration efforts.

Analytics firm Sisense highlights three areas in which companies can gain a competitive advantage by applying decision analytics to plan their post-pandemic strategies:

  • Increase sales by determining which of the organization’s customers and business processes generate the most revenue and why.
  • Identify the departments and programs that are underperforming by applying analytics to compare operations, performance and financial management.
  • Track changes in leading indicators in real time to improve forecasts of supply, demand and other business trends.

Laying the Foundation for a Career in Decision Analytics

Decision analytics improves business decision-making by ensuring the information on which decisions are based is complete, timely and accurate. Leveraging data analytics tools to support decisions requires a solid background in the latest analytics technologies and current management practice.

Programs such as the University of Maryland’s Robert H. Smith School of Business Online Master of Science in Business Analytics (MSBA) provide students with the skills they will need to convert large amounts of data into forecasts and other business intelligence that companies rely on increasingly for their success. The degree program offers application tracks in health care, marketing, finance and business leadership.

Learn more about the Online MSBA program at the Smith School of Business and how it prepares data professionals for a range of career opportunities in data analytics.


Recommended Readings

Management Analyst Career Path: Necessary Skills, Education and Experience

6 Data Analyst Skills for the Modern Marketer to Master

Comparing Analytics Careers: Business Analyst vs. Data Analyst



Business 2 Community, “Why Data Should Come Before a Decision During a Crisis”

Dundas Data Visualization, “How Analytics Offers a Single Source of Truth for Crisis Management”

Harvard Business Review, “How to Survive a Recession and Thrive Afterward”

Kin and Carta, “The Power of Data in Times of Crisis”

Sisence, “Why Analytics Are Essential in Times of Crisis”

Visier, “How to Make Data Meaningful During a Crisis”

World Economic Forum, “How Data Can Help Fight a Health Crisis Like the Coronavirus”