The ability to make wise decisions swiftly will place you well ahead of your competitors. With smart automation of the decision flow you will eclipse competitors.
Decisioning tools and processes range from excel spreadsheets and intuition to sophisticated enterprise solutions. But no one disputes anymore that speed and accuracy of today’s decisions determines tomorrow’s success. To help you set up a robust decisioning framework we’ve identified the top three challenges to be addressed by decision automation solutions.
Enhancing Business Decisions with Data from Non-traditional Sources
We totally confirm the necessity of embedding alternative data sources into daily operations. We’d like to draw your particular attention to the importance of social media sentiment analysis. Sourcing and analyzing data from social media channels allows you to see the real-time picture of the operational environment while overcoming headaches of merging data from multiple influencers. For instance, you can use data on social media activity to uncover suspicious actions and detect fraudsters; to enhance customer pre-screening and cross-selling, and more.
It is very important to join insights gained through non-traditional data sources with your own analytical models. For example, you can boost customer retention efforts. Using metrics on social media authority of each client you can target your “Superstar” customers during your promotional campaigns and thus maximize the outcome of your promotions.
Presenting the Information in the Best Way
Decision automation systems need to instantly identify which data insights are expected by any given business role. This radically streamlines users’ access to large quantities of data, thus empowering businesses to resolve potential problems in real time or even before they arise.
Acting on Big Data in Real Time
Decision automation systems must follow through with the results of their analysis and actually transform them into profitable decisions. Today’s solutions learn to predict the best next action. Previously they have been operating on an if-then basis, and now artificial intelligence allows implementing a sophisticated self-learning mechanism.
An example of such advances is the inclusion of champion/challenger testing approach into a decisioning strategy. In this case, a decision automation application is supplied with different strategies, and selects the next best action based on how every strategy has performed in the past.
Magic Formula: Insights-Comprehension-Action
We’d like to encourage you to use decision automation applications in a way, which can be encapsulated into the Insights-Comprehension-Action formula:
- Insights: use decision automation systems that are embracing advanced analytics on every stage of decisioning process, and that can enhance existing facts with data from non-traditional sources;
- Comprehension: ensure that all the insights are presented in a transparent way, and that user interfaces are tailored according to their business roles;
- Action: make sure that the system follows through the end of decisioning process and transforms the knowledge into actions. Take advantage of the self-learning capabilities of your system, instead of simply following rigid algorithms.
We hope that these recommendations will help you set up an efficient and robust decisioning process.