Big Data and AI in corporate communications

The use of Big Data and Analytics changes strategic communication in companies, as well as the role of communicators. On one hand, communication processes have to be redesigned and controlled. On the other hand, communicators become management partners by real-time evaluation of information when experiencing uncertainties in business-critical situations.

Companies increasingly drive their business decisions based on data. Digital technologies are constantly creating new sensors that provide information and can be used for digital processes. For the first time, this enables an informal elicitation of the entire process, from product development to sales, marketing and corporate communication. The self-optimization of business processes makes it easier to adapt the management of value creation to changing market, competitive, and regulatory conditions.

Additionally, there are social trends and changes driven by a wide variety of stakeholder groups that companies can no longer ignore if they do not want to jeopardize their business model. That is why today’s businesses and organizations are keeping up-to-date with news and debates that take place 24/7 worldwide. Conventional analytics and reporting systems help corporate communication stay organized and distinguish between relevant and irrelevant information. At the same time, digital technologies are changing traditional tools of corporate communication and increase the requirements for professional corporate communication. In order to continue to engage in dialogue with stakeholders in the future, corporate communication must consistently deal with Big Data and technologies related to Artificial Intelligence as well as new communication platforms. As a result, future corporate communication will inevitably become more data-driven and automated.

This was the assessment of the 2016 European Communication Monitor. For the first time that year, the knowledge and perceived significance and use of large amounts of data in corporate communication were researched. More than 2,700 experts in 43 European countries were interviewed for this initial evaluation. The results of the survey were used to initiate fields of action and implications for corporate communication when dealing with real-time monitoring and data analysis as a basis for developing strategies to safeguard the reputation of a company. Even though three-out-of-four communication professionals in Europe (73 percent) believed that Big Data would change their line of work, the study shows that, so far, little use has been made of Big Data in practice. Out of eleven possible reasons, lack of analytical skills (49 percent), lack of time (45 percent), lack of technical skills (37 percent), budget reasons (24 percent) and organizational barriers (23 percent) were seen as the biggest hurdles when using Big Data in corporate communication. Then, there are the possibilities of using Artificial Intelligence in analysis, based on Big Data.

Basically, Big Data and Analytics is about the structured acquisition, analysis and interpretation of large amounts of data. Data available for analysis grows and changes with breathtaking speed. Therefore, corporate communication is challenged to capture and interpret large amounts of data in real time, and in such a way that reliable decision-making bases are created that support management in a target-oriented and efficient way in the evaluation and qualification of business decisions. To this end, corporate communication must decide how Big Data can be used for strategic communication, which requirements must be met in order to successfully implement Big Data, and in which areas Big Data applications are to be used. This also changes the process of planning and controlling communication on the basis of Big Data. The question is, how are Big Data and Analytics applications used and orchestrated? Current process models outline an ideal process comprising seven steps: 1. Target Definition, 2. Data Generation, 3. Data Clearing, 4. Data Transformation, 5. Data Analysis, 6. Evaluation, and 7. Result Processing. All corporate communication must adapt this process to its own conditions and implement them. This is a demanding and complex task that involves testing and feedback loops until the overall process is effective in obtaining reliable results. Each of these steps in the process model is associated with specific challenges. Since the supposed added value of Big Data lies in the evaluation of large amounts of data, data analysis plays a specific role in the overall process.

Big Data Analytics can be divided into four levels of complexity: Descriptive Analysis provides information on any variable, such as the number of ‘likes’ or ‘page views’. The information collected can be evaluated in real time or viewed retrospectively. Diagnostic Analysis goes one step further and enables a deeper analysis of reasons, contexts and patterns. On the basis of historical data and by using algorithms, for example, the virality of a topic or the future of a message can be presented together with the reasons for its development. Predictive Data Analysis goes one step further and enables predictive statements. This makes it possible to make a prediction of the probabilities of future events. Several predictive data are combined into a predictive model to analyze future probabilities with an acceptable level of reliability. Predictive analyses collect and convert data into a statistical model to make derivations and predictions. If this process takes place in real time, the model is continuously validated and adapted when additional data is available to further enhance the model. In practice, predictive data analysis is increasingly being used in research or in industrial production environments à la Industrie 4.0. However, this requires an amount of data that too often is not available for use in making predictive analyses. Therefore, predictive analyses in corporate communication are still very rare, currently, even if in the near future, new applications for specific areas of application will come onto the market. Experts see areas of application above all in online marketing (cumulative sales data for predicting sales trends more precisely), in issue and stakeholder management (derivation of attitudes and conduct in specific situations or in crises) or in reputation management (dissemination and perception of topics, product and brand messages).

The pinnacle of them all is Prescriptive Analysis. It not only provides descriptions or predictions, but also concrete decision support and recommendations for action derived from it. The prerequisite for this is linking different data silos along a communication process, such as between a customer-relationship-management system, marketing-automation system or for the distribution of content and sentiment or network analyses. Prescriptive analyses are related to descriptive, diagnostic and predictive analyses. While descriptive and diagnostic analyses provide insight into what has happened and predictive analyses assist in modeling and predicting possible future events, prescriptive analyses seek to identify ideal solutions or the best result among various options based on known parameters. Prescriptive analysis may also suggest options for decision-making processes regarding how to take advantage of a future opportunity or mitigate a future risk and illustrate the consequences of options for decision-making processes. In practice, prescriptive analyses can continuously and automatically process new data to increase the accuracy of predictions and provide better options for decision-making processes. Advances in processing speed and the development of complex mathematical algorithms applied to the data sets have made prescriptive analysis possible. Specific techniques used in prescriptive analysis include optimization, simulation, game theory, and decision analysis methods. Today, prescriptive analysis can be found more in Business Analytics (BA) than corporate communication. However, in the coming years, it is highly probable that prescriptive analyses will also be used in corporate communication to find ideal procedures for specific situations, and to answer questions. The rapid development of data-based and automated Big Data and AI applications, which are currently used primarily for the optimization of industrial manufacturing processes, points the way to the opportunities corporate communication will have in the near future to sustainably improve value creation within companies.

 

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