What are data analytics to a communicator?

14 August 2020

I have always been wary of numbers and data. But as I started engaging with stakeholders, as part of my role as a communicator, I realised that numbers always add value and play an important role when disseminating information. So, I began to seek, learn and understand what data is and how it can be used efficiently by a communicator. I believe that the COVID-19 pandemic makes this even more pertinent.

I began by examining various documents and reports where data analytics and data science were central to the policymaking or programme design process. I realised that ‘data analysis’ simply means understanding and looking for meaning through numbers and discerning patterns in information relationships.

Based on my learning/observations, I’ve identified four main types of data analytics. Here I outline what they are and what they can mean for a communicator.

1. Descriptive analytics

Descriptive analytics answer the what question of any content. It is often considered as the preliminary step in a process where one is referring to historical data to understand what has happened. Descriptive analysis becomes important in helping to track trends and identify patterns of behaviour of certain phenomena.

From a communicator’s perspective, information from all sources is important if you want to dig deep and draw insights based on the evidence. However, this data merely provides answers to the question what happened and when; it does not provide answers to the question of why it happened in the way it did.

For a communicator of evidence, descriptive analysis becomes useful when dealing with case studies. A case study is an important output that is often used to tell a story, including a detailed description and analyses of people or events. Descriptive analysis helps in identifying different options to the narrative one wants to build.

In addition to the above, descriptive analysis is useful for a communication team/communication professional in not only monitoring but also learning what was useful and what could have been better. For example, descriptive analysis can help unpack information from social media platforms: to better understand information trends, their transmission, how information/a communication is performing, who the audiences are and their preferences to make better sense of the information. The analytics interface helps a communicator to view the number of followers, their demographic profile, their reactions (tweets, likes, clicks etc.), which help in improving the quality of the communication and/or narrative analysis you wish to undertake.

2. Diagnostic analytics

Diagnostic analytics identify why something happened and seeks to establish co-relationships, or better still, causal relationships between events and their underlying causes. Typically, it helps situate your communication in the temporal, spatial and contextual frame that enables your audience to gain a more nuanced understanding of the historical data against other types of data.

Simply put, diagnostic analytics seeks to answer questions like why and how something happens. Broadly speaking, it involves trend analysis and statistical analysis.

On the one hand, from a communicator’s perspective, trend analysis helps in predicting the future using past events/behaviour. For example, if an event is being organised by a communicator/communications team then trend analysis becomes an important tool to measure and learn. Care can be taken to plan better, based on events and instances that may have occurred in previous (similar) situations. This may include what worked well, what are the areas and scope for improvement, did the event meet the expectations set forth? Were there any hindrances? As a statistical technique, trend analysis helps to decide future actions.

On the other hand, statistical analysis involves collecting, studying and presenting data to understand underlying patterns and trends.  For a communicator this technique becomes useful when one seeks to optimise and improve events, situations or actions. For example, pie charts can be used to represent the number and types of staff in an Annual Report.

In short, diagnostic analysis builds on the facts from descriptive analysis and helps to draw correlations.

3. Predictive analytics

Predictive analytics help explain what may happen. It acts as a tool for forecasting. It deduces data and helps in identifying the ‘best option’ by combining experiences from past performance.

The algorithms help generate insights to predict future outcomes. In other words, predictive analysis uses information gathered from descriptive analysis and predicts results based on this.

4. Prescriptive analytics

Prescriptive analytics explains what action is needed to be taken, especially to avoid problems in the future. It helps in optimising the future course of action, based on the availability of different options.

Prescriptive analytics includes descriptive, diagnostic and predictive analytics but has a different purpose: it seeks to determine the best course of action among various choices, taking into consideration the known alternatives and variables that could affect the desired strategic outcome. In short, prescriptive analytics unearths historical data to provide a foundation for understanding past performance and what is currently happening.

For example, in a training programme, prescriptive analysis technique can be used to identify the required skill sets and experiences required to undergo a particular training. If a trainee is not eligible for the programme, they can be advised to undertake some additional programmes to qualify for said training programme. This trend may have to be customised for different situations/individuals.

Often both predictive and prescriptive analytics are denoted as proactive analytics data and can be used to move forward and identify probable trends/problems so that they can be addressed, when identified.


Communicators can use data effectively by:

  • Becoming comfortable with numbers.
  • Using and understanding descriptive analysis by interpreting historical data through statistical analysis.
  • Using automated information for quick answers.
  • Using data analytics as inputs to understand trends for increased effectiveness.

Of course, as a communicator never forget that tactical planning and innovative thinking also plays an important role.