Effective business leaders demonstrate five critical characteristics that drive organizational success. Visionary thinking enables leaders to chart clear strategic direction, while strong communication skills support team alignment and engagement. Emotional intelligence allows leaders to understand and motivate their workforce, adaptability helps organizations navigate market changes, and strategic thinking produces informed decision-making. These traits collectively empower leaders to inspire teams and foster resilient cultures. Understanding and developing these specific competencies transforms leadership effectiveness and organizational performance.

Measuring and tracking operational performance requires establishing clear Key Performance Indicators aligned with business objectives. Organizations must implement systematic data collection processes, analyze metrics regularly, and identify improvement opportunities. Effective performance tracking enables informed decision-making, reveals process inefficiencies, and drives operational excellence. Companies that establish customized KPIs and monitor them consistently achieve better resource allocation and sustainable competitive advantages. Implementing a structured performance management framework transforms raw operational data into actionable business intelligence.

Process mapping is a visual technique that documents workflow steps, identifies bottlenecks, and reveals inefficiencies. Organizations use process maps to standardize operations, reduce costs, and improve quality. By analyzing current workflows, teams can eliminate redundant tasks and streamline handoffs. The article explores proven mapping methods and real-world implementation strategies.

Operational leadership skills serve as the foundation for organizational success. Strategic vision, adaptability, empathy, decision-making, and communication represent the core competencies required for guiding teams through complexity and change. Leaders who develop these five essential skills create resilient teams, address challenges systematically, and drive sustainable growth. Organizations benefit from implementing structured coaching programs to strengthen these capabilities across their leadership pipeline. Building a comprehensive leadership development strategy transforms individual competencies into competitive advantages that generate measurable business results. Bringing in part-time operations leadership puts an accountable owner on the execution layer without the cost of a full-time hire.

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Research is the systematic process of collecting and analyzing factual information to inform better decisions. Gathering facts reduces guesswork and reveals patterns that shape outcomes. Organizations that prioritize research make choices backed by evidence rather than assumptions, leading to stronger results and fewer costly mistakes. The following sections detail proven research strategies.

Research is often undervalued in a company as leaders rely on their own experiences, their knowledge set, and the knowledge and experiences of others. Surprisingly this can prove to be a limited amount of knowledge and insights when compared to the breadth of information that exists on nearly any subject matter.

The goal of this article is to discuss Research: Gather Your Facts for Better Decision Making. A company’s success is greatly impacted by the effectiveness of the decisions it makes. And while it is important to be efficient (aka. Expedient) in your decision making it is just as important to make sure you have done your research to consider all of the facts. And options that may be available to you.

Research versus Analytical Decision-Making

Research is very closely tied to analytical decision-making. Both are based on gathering as much information as possible. Research findings oftentimes play intoanalytical decision-making. Rather than consider them as two whole separate activities it is suggested that both be used to complement each other.

Getting Good at Research

Getting good at research requires several activities that when executed upon will result in more decisions being supported by research findings (and where necessary, analysis to understand the research).

The dictionary.com definition defines research as the diligent and systematic inquiry or investigation into a subject in order to discover or revise facts, theories, applications, etc.

When conducting research you will need to consider your research design (how you plan to answer the question or problem you are faced with). And the research method(s) you choose to execute this plan.

Depending upon your company’sbusiness modelyou may engage in the usage of different types of research methods for solving or understanding different types of problems. The research methodologies are quite varied and it is helpful to have a general understanding of….. The various types of research you may use (descriptions are the author’s interpretation of commonly used definitions):

• Basic: This is characterized when you just jump right in to research without any preconceived conclusions and are just seeking to improve your understanding of a situation.
• Qualitative: This type of research is typified by textual data, whether it is responses to questions on a survey, feedback given in a focus group, or dialogue from an interview.
• Quantitative: Any time your research involves data that is numerical in nature or is data that can be categorized and assigned numerical valuations for analytical analysis then you can conduct quantitative research.
• Observational: This is when you observe behaviors exhibited by participants in a situation to understand the unguided reactions and responses to the environment around them.
• Longitudinal: This is a term used when the research observations are measured over time. An example could be an adult’s approach to parenting subjects over time (from before they had children, during stages of when they had children, and after their children have left). To understand what changes over time.
• Cross-sectional: This is when you are working to the data you are studying represent the population or a subset that allows you to get a cross-sectional approach.
• Correlational: This is a non-experimental research method, in which a researcher measures two variables, understand and assess the statistical relationship between them with no influence from any extraneous variable
• Causal-comparative: This is when you are trying to research the relationship between independent and dependent variables differences that already exist between groups.
• Experimental: Any time you are adhering to scientific research design and starting with a hypothesis and using variables that you intend to measure, calculate and compare to prove/disprove your hypotheses you are using an experimental methodology.
• Exploratory: If you are in the early stages of looking at a problem you likely may use and exploratory approach that does not begin with any preconceived hypothesis. And is intended to conduct an initial investigation into the problem to get some general understandings which may then lead to further research using other methodologies.
• Descriptive: This is when you are describing the “what” of your research
• Explanatory: This is when your research outlines what the research is intended to study and resolve and the methods to be to resolve the problem

Preparing to Conduct Your Research

Numerous steps should be taken as you build the research muscle of your organization

To work to the quality of research is beneficial to your organization you will want to consider who should conduct the research. Whomever you choose to conduct your research should have experience in setting up the type of research you are looking to have completed.

• Senior Leadership Commitment: As noted before leaders often rely on their own knowledge or that of a small group of individuals. Getting your senior leadership to expect that deep research to be used when solving problems is a critical factor improving your research capabilities
• Hiring Research Capable Employees: It is also important to look for characteristics in new hires that would suggest they are good researchers or are interested in becoming good. Begin to look for candidates that like to research solutions on the internet, pose critical thinking questions. And measure how they respond, understand their analysis capabilities, have them explain how they solve problems.
• Training: Every manager/leader is not educated in or interested in research. However, making sure that each manager/leader is using a common language as it relates to research will help to improve your organization’s collective approach to researching. And your understanding of research findings. Partnering with your human resources department and training department will result in the development of baseline expectations as it relates to researching.
• Incorporate into Analytical Decisions: As stated earlier research and analytical decision making are closely related. It is important that any analytical decision-making consider any research methodologies that should be incorporated to improve the quality of the analysis.
• Project Prioritization: Research is also important as it relates to project prioritization. A project that is lacking adequate and fundamental research that supports the project assumptions and goals should be suspect.
• In-house Expert: Not all companies can support having Ph.D. level scientists on their payroll. However, it is beneficial to have a person who is a go-to subject matter expert when it comes to understanding in-depth the scientific repercussions of various modeling techniques. And who can guide internal leaders on matters related to research. In the absence of an in-house expert, you should consider hiring outside experts to consult on your larger projects.

Your Research Process

Each research effort may vary somewhat in the approach based on the methodology used, however, most will contain most of the following steps:

• Problem/Opportunity Identification (POI): This often comes in the form of identifying something that has occurred, wanting to understand how to capitalize on an opportunity.
• Resource Review: This can be as broad and varied as the types of POIs you develop. As you conduct a resource review consider everything from internet resources, academic journals, subject matter experts, etc.
• Update Your POI: After doing some research you may want to clarify the problem you are trying to research or the opportunity you are looking into.
• Common Terminology: Outline and define all of your research content in understandable terms.
• Who/What/Where: Define clearly the parameters of your research subject(s)
• Methodology: Clearly define how you will be conducting your research to work to the information your research and gather is as unbiased as possible.
• Data Collection: Gather as much information on your research subject(s) as possible to aid in the analysis of your data.
• Analysis: Conduct the types of analysis that are necessary to aid the research results. As spoken to early research is very closely tied to analytical decision-making.
• Interpretation: As part of your research you may be interpreting the results for a summary to your audience.
• Conclusion/Recommendation: This may or may not be a step in each research project. It is recommended that the researcher provide any conclusions or recommendations they have from their involvement. With the research as it relates to the Problem/Opportunity Identification (POI) identified at the beginning of the research process.

Building an expectation of research-based decision-making will take time and effort. Once incorporated into your culture you can expect that initiatives will be well thought out with various options considered and positioned for optimal success.

Analytical decisions involve using data and systematic reasoning to evaluate options before choosing a course of action. Organizations that prioritize data-driven decision making reduce guesswork and improve outcomes across departments. Starting with analytical approaches establishes a foundation for consistent, measurable results. The following sections explore how to implement analytical decision frameworks effectively in your operations.

Organizations live in a world overflowing with data. As a result company decisions no longer need to rely solely on the “gut” of the leaders, or opinions of the outspoken.

The goal of this article is to discuss Analytical Decisions: A Great Place to Start. Thoughts will be shared on how you can approach incorporating data-based decision making into your company culture that will actually help you to better compete based on analytics.how executive coaching accelerates leader effectivenessmarketing leadership for scaling teams

At the heart of any company wishing to get better at Analytical Decisions is the DELT2A2 framework which has its origins in the work by Tom H Davenport. The following highlights the key components that companies should address:

It begins with identifying the Data that will be used to provide insights into the areas of opportunity and where the business should be focused. In many instances, data may not exist and the company needs to find ways to gather data. This can then be turned into information to be analyzed, which can then be turned into insights.

It is critical that all departments across the Enterprise are coordinating well to support resources related to analytics (people and tools) are being properly coordinated. Most companies or divisions that choose to compete on analytics have their employees who perform analysis and reporting in a centralized support function to use talent, provide cross-training and backup, and provide for growth opportunities.

Any company choosing to compete on analytics will need senior-level Leadership commitment, without this support the proper culture will not flourish and data-supported decision making will not be adhered to.

The organization must have processes in place to Target the initiatives with the best opportunities so that resources can be focused and prioritized where companies have the highest potential. A governance process must be in place to support all initiatives (where possible) are supported by analytics.

Securing the proper Technology tools to run the analysis needed is foundational to the success of competing on analytics.

Resourcing the right Analyst (depth and breadth), and supporting their continued growth is a cornerstone to a successful analytics implementation. It is critical that a company identify the proper level of analytical skills needed to conduct the types of analysis that are needed. Not every situation requires an individual with a PhD in mathematics.

Finally, the company must assess the various types of Analysis Methods that it should be used to compete in their marketplace.

Analytical Decision-Making

Analytical decision-making is one of four styles of decision making typically used by leaders. The other styles are directive, conceptual, and behavioral. In addition, consultative and consensus may also be used.

Steps to incorporating analytical decisions into your business

Numerous steps are involved to incorporating analytical decision making into your business practices and culture:

• Senior Leader Commitment: The style of your most senior leader will set the tone for the rest of the organization. If you wish to embed analytical decision-making into your culture then make sure your most senior leader believes in the value of analytical decision-making and practices it.
• Understanding your company’s analytical maturity: It is helpful to assess where you are today with your analytical capabilities. Many models exist to assess where you are at. You will find the following common stages of analytical maturity.
  • Descriptive Analytics: This is often considered data on what happened. It is a baseline capability that includes reports
  • Diagnostic Analytics: This is when you take raw data and use various analytical methods to answer “Why did something happen”. Tableau built a business from being able to drill-down into your data, mine massive amounts of information, and identifying correlations.
  • Predictive Analytics: Prescriptive Analytics builds on diagnostics to make predictions of what may happen based on existing data factors
  • Prescriptive Analytics: Prescriptive Analytics builds upon predictive to evaluate alternatives and identify what is the best that can happen.
  • Autonomous Analytics: Machine-learning can be incorporated when large amounts of data are available and self-learning algorithms can be applied to provide more insight or direction
• Determine which analytical techniques to use: The depth of analytical techniques you use will be highly dependent on your business and the skills of your personnel. The following techniques should at least be understood and reviewed by your senior leaders to determine which you may wish to use. These are outlined and explained further in the great book “Competing on Analytics: The New Science of Winning” by Thomas H. Davenport and Jeanne G. Harris.
  • Internal Process Evaluation
    • Activity-based costing (ABC). Allocating costs amongst activities by using models that incorporate activities, materials, resources, and product-offering components and then optimization based on cost and prediction of capacity needs.
    • Bayesian inference (e.g., to predict revenues). A numerical estimate of the degree of belief in a hypothesis before and after evidence has been observed.
    • Combinatorial optimization (e.g., for optimizing a product portfolio).
    • Constraint analysis (e.g., for product configuration). The use of one or more constraint satisfaction algorithms to specify the set of feasible solutions. Constraints are programmed in rules or procedures that produce solutions to particular configuration and design problems using one or more constraint satisfaction algorithms.
    • Experimental design: For website analysis
    • Future-value analysis: The decomposition of market capitalization into current value and future value, or expectations of future growth.
    • Genetic algorithms: Used for decryption/ code-breaking or product engineering/ design such as scheduling satellite communications, optimally loading cargo containers, and optimizing delivery routes.
    • Monte Carlo simulation: Used in project valuation using a computerized technique to assess the probability of certain outcomes or risks over multiple trials and comparing the outcome with predefined probability distributions.
    • Multiple regression analysis: Used to determine how non-financial factors affect financial performance.
    • Neural network analysis: Useful in predicting needed factory maintenance in which systems are initially “trained,” or fed large amounts of data and rules about data relationships.
    • Simulation: Used in pharmaceutical “in silico” research by manipulation of parameters using mathematics and/ or rule bases to model how different values would generate a result.
    • Textual analysis: Assesses text (such as transcribed call center discussions or textual data from a survey or social media) for customer sentiment. Yield analysis: Using means, median, standard deviation, etc. to understand yield volume and quality.

• Analysis of Supply Chains
  • Capacity planning: Optimizing the capacity of a supply chain or its elements. Identifying and eliminating bottlenecks.
  • Combinatorics: A sophisticated mathematical technique optimizes components in a supply chain.
  • Demand-supply matching: Determining the intersections of demand and supply curves to optimize inventory and minimize overstocks and stockouts.
  • Location analysis: Optimization of locations for stores, distribution centers, manufacturing plants, and so on.
  • Modeling: Creating models to simulate, explore contingencies, and optimize supply chains.
  • Routing: Finding the best path for a delivery vehicle around a set of locations.
  • Scheduling: Creating detailed schedules for the flow of resources and work through a process.
  • Simulation: Supply chain simulations model variation in supply flows, resources, warehouses, and various types of constraints. They allow for both optimization and visualization of complex supply chains.
• Analysis of Marketing
  • CHAID: A statistical technique used to segment customers on the basis of multiple alternative variables.
  • Conjoint analysis: A conjoint analysis is used to determine which factors:price, quality, dealer location, and so on:are most important to customers who are purchasing a new car.
  • Econometric modeling: Modeling to gain insight into complex market trends and the variables that affect market demand, supply, and costs.
  • Lifetime value analysis: Assessing the profitability of an individual customer (or a class of customers) over a lifetime of transactions.
  • Market experiments: Using direct mail, changes in a website (known as A/ B tests), promotions. And other techniques, marketers test variables to determine what customers respond to most in a given offering.
  • Multiple regression analysis: The most common statistical technique. For predicting the value of a dependent variable (such as…. Sales) in relation to one or more independent variables (such as the number of salespeople, the temperature. Or the day of the month).
  • Price optimization: Determining the price elasticity, or the response (changes in demand) of the buyer to increases or decreases in the product price.
  • Search engine optimization (SEO): Statistical methods and activities designed to improve a website’s ranking in search engines such as Google.
  • Support vector machine (SVM): This machine learning method uses training data to classify cases into one category or another. It is often used for customer segmentation and churn analysis.
  • Time-series experiments: These experimental designs follow a particular population for successive points in time. And are used to determine whether a condition that applied at a certain point led to a change in the variables under study.
  • Uplift modeling: A predictive modeling technique that directly assesses the incremental impact of promotional activity on a customer’s behavior.

• Determining Analytical Team Structure: There are a variety of ways to structure your team. Will you have the team reporting to finance, will analysts reside within departments, will the analysis be a function within your IT department, or will analytics be a stand-alone group? Regardless of the structure, you choose it will be important to work to you communicate the role that analysts play in the organization and how their input affects decisions.
• Hiring the Analytical Team: Since analysts can be very technical in their skill sets it is encouraged that you fully identify the different types of analysis that you feel will be employed. And determine the specific skills and competencies required to fulfill these types of analysis. Depending upon the existing capabilities within your organization you may need to consult with exterior experts to work to you recruit for the proper skills and competencies.
• Securing the Tools: The types of applications and methodologies you use will ultimately be driven by the types of analytical techniques you use and the skills of the team you hire. A listing of resources and options is to exhaustive to share here. Your analytics team and outside guidance can guide the selection of the tools you will need.
• Books: Reading about analytics can often be dry and academic. However, it is helpful for senior leaders to have a baseline understanding and appreciation for concepts and usage. The following list of books offer some insightful reading:
  • Competing on Analytics: The New Science of Winning by Thomas H. Davenport and Jeanne G. Harris
  • Naked Statistics: Stripping the Dread from the Data by Charles Wheelan
  • The Next America: Boomers, Millennials, and the Looming Generational Showdown by Paul Taylor
  • Astroball: The New Way to Win It All by Ben Reitler
  • Keeping Up with the Quants: Your Guide to “Understanding and Using Analytics by Thomas H. Davenport
  • Linear Regression and Correlation: A Beginner’s Guide by Scott Hartshorn
  • Freakonomics: A Rogue Economist Explores the Hidden Side of Everything by Steve D. Levity and Stephen J. Dubner
  • Big Data: A Revolution That Will Transform How Organizations Live, Work, and Think by Viktor Mayer-Schonberger and Kenneth Cukier
  • The Triple Package: What Really Determines Success by Amy Chua and Jen Rubenfeld
  • Future Smart: Managing the Game-Changing Trends That Will Transform Your World by James Canton
  • The Connection Algorithm: Take Risks, Defy the Status Quo, and Live Your Passions by Jess Warren Tevelow
  • Generation iY: Secrets to Connecting With Today’s Teens & Young Adults in the Digital Age by Tim Elmore
  • Y-Size Your Business: How Gen Y Employees Can Save You Money and Grow Your Business by Jason Ryan Dorsey
  • Customer Data Integration: Reaching a Single version of the Truth by Jill Dyche and Evan Levey
  • The Little SAS Book by Lora D. Deheiche and Susan J. Slaughter
  • Analytics: Data, Science, Data Analysis and Predictive Analytics for Business by Daniel Covington
  • R for Everyone: Advanced Analytics and Graphics by Jared P. Lander

As computers become even more powerful, as data continues to proliferate. And as automation continues to advance it will become even more critical for companies to incorporate analytic decisions into their critical initiatives and day-to-day operations.

For hands-on support, explore business consulting tailored for mid-market operators.