Whether you’re a manager, controller, treasury director, financial planning and analysis director, CFO, CEO, board member, committee chair, or chapter leader, you direct and coordinate groups of people in order to achieve certain goals. Leaders influence others to achieve organizational objectives—and one way they can exercise their leadership effectively is by explaining clearly how data analysis informs their decision making. In the current business landscape, management accounting and finance leaders need to have a familiarity with how to leverage data analytics to inform strategic planning and decision making.




Data analytics is defined as the process of gathering and analyzing data in a way that produces meaningful information to aid in decision making. For a leader’s decisions to be sound, they must be based on sufficient, reliable, relevant, and useful information. This makes data analytics important for leaders to embrace.


Data analytics is classified into four types: descriptive, diagnostic, predictive, and prescriptive. Descriptive analytics answers the question “What happened?” It describes past performance or how a situation transpired. For example, descriptive analytics is used to quantify a decrease in a company’s profits.


Diagnostic analytics is used together with descriptive analytics to answer the question “Why did it happen?” It provides reasons for, and causes of, past performance or a situation. Analysts look at historical data to learn and understand past performance and look for the reasons behind success or failure. Diagnostic analytics can be used to find possible reasons for a profit decline by analyzing a profit-and-loss statement and figuring out what factors led to a decrease in sales or an increase in expenses.


Predictive analytics looks to the future and answers the question “What is likely to happen?” Historical data is combined with newer data and analyzed to identify patterns and relationships between known variables or among data sets, which are used to make predictions about what’s likely to occur in the future. A net income forecast using past profit-and-loss trends is a form of predictive analytics. Predictive analytics can be used to determine whether a profit decline is likely to continue.


Prescriptive analytics answers the question “What needs to happen?” by offering the best course of action among potential solutions and adding value based on an objective interpretation of the data. Prescriptive analytics might generate a projected income statement and then use that to determine what’s needed to address the anticipated situation. Prescriptive analytics could help leadership develop plans to reverse a decline in profits. Prescriptive analytics can utilize what-if scenarios and incorporate new data variables to predict various likely effects until company leadership reaches an optimal solution or course of action.


Leaders should be able to recognize the elements that contributed to a particular past situation and analyze how these elements led to a positive or negative performance. In short, leaders should describe what happened, explain why it happened, and identify the reasons for the failure or success. Leaders also should be able to use data analytics to predict what’s likely to happen in the future and determine what needs to be done by prescribing solutions to problems and a course of action to achieve strategic objectives. 




To be an effective leader, you need to be knowledgeable and skilled—preferably an expert—in data analytics and data visualization. In fact, the IMA Management Accounting Competency Framework includes data analytics and data visualization under the Technology & Analytics domain as competencies that management accountants and finance professionals need to learn to be strong business leaders.


A leader needs to have at least basic knowledge of data analytics and data visualization such as spreadsheet and data manipulation (sorting and filtering data, as well as knowing functions and formulas), descriptive statistics and basic calculation (including ratios and indicators), and data mining to be able to use data to make decisions and further business intelligence. It’s important to be able to create and present digestible charts and graphs using visualization tools such as Excel and Tableau to communicate results and findings.


A leader also needs to have applied knowledge of data analytics and data visualization so that they can provide explanations of data by using query language, evaluate the efficiency and effectiveness of initiatives and proposals by using descriptive analysis, and extract and transform large volumes of data and make queries. It’s also important to be able to predict outcomes and explain results by using linear regression analysis, diagnose and determine the causes and impacts of data trends, and utilize table and graph design to communicate complex information as simply as possible.


Additional data analytics and data visualization skills that leaders need include:


  • Using data mining tools to identify patterns and provide insights about the data.
  • Designing standard templates.
  • Organizing and cleaning raw and unstructured data and transforming it into an appropriate form that’s suitable for analysis.
  • Utilizing specialized reporting tools for cataloging, interpreting, and presenting results of complex data analyses in an understandable manner.
  • Using multiple regression tools for predicting events, providing solutions, and recommending the best course of action.
  • Using business and statistical software to draw conclusions from large data sets.
  • Demonstrating an understanding of communicating results with advanced visualizations such as bubble charts and network diagrams.
  • Facilitating rapid decision making by using visualization tools to create multimedia dashboards combining relevant charts, graphs, tables, and images.
  • Using predictive and correlative analytics techniques to provide solutions.


A leader can add value to the organization by reaching an expert level in using data analytics and data visualization to build financial and operational prescriptive models that help to optimize performance and make progress toward strategic objectives. Other key skills include using specialized query or interpreted language such as Structured Query Language (SQL) and Python to provide solutions, using tools such as JavaScript for creating customized visualizations, and using advanced statistical programs for data analysis to identify patterns and provide insights to achieve goals.


Demonstrating expertise in data visualization and an ability to interpret and communicate complex data to a lay audience increases leaders’ persuasiveness. Leaders can utilize data analytics to extract, transform, and analyze data to gain insights, improve predictions, and support decision making. They can also utilize data visualization to present data clearly to better explain key patterns, trends, correlations, decisions, and strategic initiatives.


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