Let’s face it, we are in the age of technology. Maybe not quite floating cars or self-tying shoes, but our world has changed significantly from even 10 years ago. Though we do not have robots amongst us just yet, Artificial Intelligence (AI) is beginning to shape various aspects of our lives. These aspects spread from the devices in our homes to the work processes in our offices. AI has made the performance of tasks quicker and easier, whilst allowing for completion of even more difficult tasks. With AI’s ability for greater responsibility, one area of wonderment is the interpretation and analysis of data. We are slowly entering the age of AI’s understanding behind the meaning of data, providing us with interpretations and accompanying solutions that we may have never noticed.
Traditional data analysis takes the form of a computer sifting through data sets which contain answers to discrete questions. These responses aid in answering questions related to:
- The frequency of “positive” words compared to “negative” words
- The presence of a particular phrase
- A selection made from five options
Answers to these basic questions can be sought after by basic computing actions. However, we are curious beings and like to understand why an event happened or how a result should be interpreted. Responding to these queries involves an added element of human logical interaction so as to notify the computer on the linearity of the interpretation. With this being said, AI’s ability to perform data analysis needs to be in balance with both the technological and the human; with the technology side answering the discrete questions and comparing these results to a benchmark created by human reasoning. The discrepancy between the human and the technological interpretation demonstrates the disconnect between validity and reporting. Your basic computer does not have the ability to understand the nuance and interpretation of a phrase. Where logical reasoning illustrates meaning and understanding (the validity side of the coin), computers report answers derived from data sets provided.
Data analytics in the era of AI will hopefully merge computing abilities with human understanding. Currently, the problems faced with the merging of these two entities takes the form of the continued access to the data set required to accurately provide both a report and interpretation of information. However, with this problem follows strides in creating a solution, two in fact. The first is Natural Language Processing, which allows for easier use of internet searching by allowing parties to speak to their device in a language deemed to be comfortable, thus affording these parties with the utmost accuracy for their inquiries. The second solution are Bots, which ask proactive questions to extract data and create a foundation to develop insights that are comparable to those created via human reasoning.
At Consentia, we take pride in our unique ability to both create and analyze data sets. Specifically, our Survey Department of Business Process Outsourcing utilizes both Consentia’s Managed and Professional Services to collect data, present findings, and analyze results. From creating and administering a survey to developing custom reporting and presentation of results (and everything in between), Consentia ensures excellence in Descriptive and Diagnostic Analytics. Descriptive Analytics answers the question “What Happened?” with Diagnostic Analytics answering the question “Why Did This Happen?” With the incredible enhancements in the field of AI, Consentia is poised to further our abilities to also include Predictive and Prescriptive Analytics, which analyzes data to predict what could happen and in turn utilizes this forecast to indicate how we can make a desired outcome occur.
The future is incredibly bright for this immensely exciting wave of analytics!