Flytxt CEO Vinod Vasudevan says quality of a business decision is only as good as the quality of the data used to make it
Vasudevan spoke to BusinessLine on how companies are adopting data analytics to drive digital transformation. Excerpts:
Is analytics getting the attention of the top management?
Some sectors, such as telecoms and financial services, have grasped the incredible value of data analytics, but others have been slower when it comes to digital transformation.
There is a general understanding, though, of big data’s value. In a big data use cases survey, 69 percent of respondents said it them the ability to make better strategic decisions.
Some estimates have predicted that the global market for big data analytics could be worth as much as $100 billion by 2020. At Flytxt, we maximise the value of customer data and engagement for 100 businesses, across a global network of over 50 countries.
Do you tap artificial intelligence to enable clients to create autonomous and fully-automated decisions?
Yes, we help businesses engage their customers by learning their behavior and predicting what they want in real time.
Flytxt has built its own portfolio of AI, marketing automation, and customer-engagement technology. We make sure our clients have deeper and longer relationship with customers through advanced analytics, machine learning, and AI.
Recently, we launched an add-on capability in our product for interface voice platforms such as Google Assistant and Amazon’s Alexa. AI and machine-learned customer intelligence in our product can be extended to them, enabling enterprises to have the meaningful human-like conversation with customers, unlike the pre-recorded conversations in a typical chatbot. So we are focussing on harnessing possibilities of combining AI with other technologies like NLP (neuro-linguistic programming), to help enterprises elevate customer engagement to the next level.
How does machine/deep learning fit into the scheme for Flytxt?
We offer the analytical capabilities of the machine and deep learning in the form of analytics which is built and bundled as pre-packaged models.
These models are created by our R&D team in partnership with IIT-Delhi and TNO, The Netherlands (an independent research organization). The R&D team includes resources with multi-functional skills — data engineers, data scientists, decision scientists, and business analysts — who build, customise, package, publish and maintain a set of these packaged analytics models in the product. These ready-to-use analytical models can be readily plugged into the desired marketing workflows to realize incremental economic benefits such as increasing revenue, reducing churn and improving customer experience.
Typically, adoption starts with simple analytical models and one-off use cases. Gradually, it extends to solving more complex problems.
How do you ensure the quality of data being crunched?
The quality of a business decision is only as good as the quality of the data used to make it. AI and analytics solutions invariably should do data validation and data enrichment to improve the quality of data.
Data quality issues can be accommodated to a greater extent by looking out for redundancies and correlations in the source data. However, we only access data which is available to clients. So the responsibility of ensuring consistency and accuracy of data largely remains with the CSPs (Communications Services Providers).
Leading and innovative operators have realized the enormous value that can be generated from their data; they are investing to explore new data sources and improve consistency and quality of data.