In the current digital world, there is no industry that won't benefit from actionable insights. With time, every industry will adapt to it. For example, in financial technology, a problem that requires actionable insights is credit risk assessment. A well-structured fintech algorithm can easily use machine learning to suggest to employees whether they can approve and reject a loan application. That advice is actionable insight.
The algorithm assesses the applicant's probability of load success by training on heaps of historical data and market conditions. The result of the algorithm is the insightful judgment of the applicant's acceptable or unacceptable risk. Action, in this case, is the human loan agent's decision to grant the loan or deny it. The majority of businesses in today's day and age depend on such insights. They require actionable insights incorporated in the workflows to drive better business outcomes without people having to leave their primary tasks to sieve through data for answers.
In every industry, actionable insights that arise from artificial intelligence and analytics is a necessity for achieving the desired competitive edge and not a luxury. E-commerce platforms need high-level advanced fraud detection systems to advise the employees what the correct course of action should be. Actionable insights are a core part of business operations.
An algorithm evaluates vast heaps of data and provides users with effective advice. Machine learning technology will allow the algorithm to identify patterns and outcomes in time-series data, something that humans can not do in practical time.
Some of the many opportunities for turning data into actionable insights in industries are:
Industries are competing to use actionable insights to their advantage. When one organization in the industry uses data for actionable insights, other competitors follow to keep up the pace. By using innovative technologies like artificial intelligence and machine learning methods, organizations will get more powerful actionable insights to mark their place in the industry.
According to Franck Scandolera from webAnalyste, "As an expert on data implementation, my first strategy to turn data into actionable insights, is to collect actionable data, like the most useful attributes of the traffic source, of the content, of the product, of the customer, of the visitor, of the functionality to analyze the factors and the segments that affect the conversation. Foremost, the actionable data should be valid and reliable, the measurement of the phenomena must be correct, and that one time or thousand times in the context given.
As an expert on data analysis, my first strategy to turn data into actionable information is to get a good understanding of the business answers researched. Once you are at the end of the story, it's easier to determine the best data set, the appropriate techniques for analysis, and the best visualization to tell the story of what is important and why you should care. In summary, actionable insight = good understanding of the business questions + good, valid, and reliable data set + good data visualization + good story".
