Unstructured data holds the building blocks for better CX
Now more than ever, data is invaluable in organizations, whether it’s rising market trends or customer insights.
Some regard data as the oil of the 21st century. Others say that’s a poor comparison. Regardless, few can argue just how important data is in driving business decisions today, but managing this rising surge of information is proving to be the challenge.
Most enterprises require clean and organized data for business analytics, yet unstructured data (whether it’s social media data, audio files, emails, chat transcripts, and images) is increasing in volume.
In a survey by Deloitte, just 2 out of 10 companies are taking advantage of unstructured data, even though this type of data makes up 80 to 90 percent of all data collected by enterprises. Organizations just don’t have the resources, tools, and expertise to filter and analyze it.
If the vast amount of data is processed manually, it may take a portion of time to complete and the data may no longer be valuable at the end. However, artificial intelligence (AI) and machine learning (ML) algorithms could be the answer for organizations to extract intelligence from the bulk of unexamined data as it continues to bloom.
Presently, enterprises are looking at storing unstructured data in data lakes for further process and analysis. Once stored in data lakes, the next step is to prepare and process unstructured data through technology such as cloud-based solutions. Various cloud architecture services are available for small, medium, and larger corporations with a range of affordable prices.
An example of employing a cloud infrastructure to parse structured and unstructured data is the Department of the Treasury. The US government body uses Workplace.gov Community Cloud (WC2), a cloud service that collects, transcribe collected audio files and provides text sentiment analysis.
In this case, the technology not only stores unstructured data but also uses it as an input to generate informative input, which later can be used for evaluating business plans.
Since a large portion of unstructured data is made up of files and documents containing text, many organizations turn to text analysis to make sense of the vast data. Together with natural language processing, chatbot transcripts of customer queries or refund and exchange messages are assessed more accurately and in context.
Customer service teams can grasp a better understanding of the problems faced by customers and act accordingly to deliver better quality services. Moreover, enterprises can gather insights from disparate data to embed personalization into their services. Based on initial questions or messages from customers, from example, machine learning algorithms can direct customers to the right customer representative for assistance.
Enterprises might miss out on improving customer services if data is left untapped, and not integrated into the system for decision making. CEO of Coseer, a San-Franciso based cognitive computing consultancy, Praful Krishna, explained the value of extracting unstructured data with AI technology; “Recent advances in artificial intelligence have enabled a highly granular search over any kind of unstructured information.
“It is possible to answer very specific questions and, using this capability, auto-populate structured templates or tables.”
So, while the glut of unstructured data that keeps building may seem overwhelming, businesses should consider the value of the insights to their business if they enlist the help of AI to help sift through it. Of course, human analysis and execution will always remain a crucial ingredient.