When documenting and communicating the findings from an analytics initiative, analysts let the data drive the conclusions. Any conclusion reached should be based on the data collected; let the data speak for itself. Document and Communicate Findings from Completed Analysis includes identifying how to best package and communicate the data analysis results, making decisions about the level of summarization required, and grouping information for optimal understanding.

Analysts highlight the main themes, synthesizing results to build a narrative that can be understood by the intended recipients. Depending on the communication needs of the stakeholders, they may also produce reports and analytics dashboards.

Some questions to consider when reporting results are:

• What are the most important aspects of the conclusions for each stakeholder?
• Is there a graph or other form of visual representation that can communicate the information more effectively?
• What method of communication is going to be most effective to display the results in a meaningful way?
• Is there a way to make the communication more engaging (for example, a video or dynamic visualization rather than a pure text report)?

The findings include the results as well as an explanation of the methods used in the analysis, the process followed to derive the results, and any limitations or weaknesses in the data or methods used. When building the narrative, questions such as “Where are the data gaps?”, “What does this mean for the business?”, and “How can the business improve?” are addressed.

Data visualization uses visual models to communicate data relationships and results. The objective is to visually communicate information that is too complex to convey effectively in textual form. Through data visualization, tools, static graphs, and charts can be turned into dynamic models that decision-makers can use to view resulting analytics information from different perspectives and level of granularity.

Data storytelling involves the development of a narrative around the results of data analysis using patterns, trends, and behaviours observed. Stories are intended to create engagement so that stakeholders feel invested in the insights that are discovered. Data stories provide context to the situation being investigated through analytics with the objective of providing supporting information for organizational decision-making. Depending on the setting and mode of presentation, multiple techniques such as storyboards, elevator pitch, 3-minute story, the big idea, data journeys, and orchestration can be used to create the data stories.

It is here that the fundamental value proposition for business data analytics is demonstrated as the organization replaces its decision-making process based on instinct with one that is built on evidence-based decision-making. Data storytelling and data visualization work together to enable clear, concise, and visually appealing communication. These techniques are best performed by those who are visual thinkers and have effective communication skills.