Data analytics makes it easier for business users to interact, share and collaborate with data—which is the very essence of data literacy—regardless of where or how they work. Nish Patel, senior director, Qlik, discusses how data analytics can help optimise work and improve employee efficiency, in an interaction with Deepshikha Shukla.
Q. Why are data literacy skills important to render efficiency of employees?
A. Data literacy is not just about improving employee efficiency and productivity; it is also about helping them become more effective and add greater value to the organisation’s business operations. As per Qlik’s Data Literacy Index, organisations can realise the enterprise value of up to US$ 534 million with higher corporate data literacy (CDL). Organisations with higher CDL also typically witness better performance across various KPIs such as gross margin, return-on-sales, return-on-equity, return-on-assets, etc.
Data literate employees can read, work with, analyse and argue with the data available to them. This essentially means that they can ask better questions to get better answers. This helps them build a more accurate knowledge base, make better decisions and communicate their message more effectively. It is hardly surprising, then, that data literate employees are found to be more confident in their abilities, perform better and have higher credibility at the workplace.
Q. How can maximum benefits be retrieved from huge piles of data?
A. Extracting benefits from huge piles of data depends on the intent of the person asking questions of that data. Do they want to find quick answers to their data queries to make a decision? Or, do they want to dive deeper into the data to uncover more insights and strategic value?
Analysing data can help drive more revenues for the organisation just as easily as it can help in reimagining business challenges and finding unique solutions to them. Flexibility and applicability are why most digital transformation projects within the Indian business landscape are designed around optimum utilisation of data.
Q. What is the role of machine learning (ML) and artificial intelligence (AI) in data analytics?
A. Such technologies as ML and AI are playing a major role in enabling cutting-edge data analytics. By leveraging their massive computational power, these technologies can parse through huge volumes of data to find correlations and connections that human eyes might have missed—all in a fraction of the time it would take to manually analyse the same data.
This is why leading data analytics players are leveraging state-of-the-art AI and ML capabilities to power their analytics frameworks to complement human skills, knowledge and decision-making.
Q. What are the technologies behind business intelligence (BI)?
A. The conventional approach to BI is reactionary and extremely ill-suited to the dynamic, fast-changing needs of modern-day businesses, which is why it can today be considered to be an application of data analytics. Integration of AI-powered data analytics into the BI value chain allows organisations to translate their data into actionable insights at much higher speeds and with greater accuracy.
For instance, a data analytics system extracts data stored across the enterprise environment, whether on-premise or in the cloud, combines and analyses it, before presenting the most relevant and contextual insights to the end-user. These insights can then be explored by business users via conversational or visual analytics.
The best part is that the analytics are not static. Any addition or change to data can be processed in real time, with business insights updated to reflect the latest information. This constant analysis of information to generate real-time insights enhances the value of data and, in turn, the efficacy of BI deployments.
Q. What are the types of data that Qlik Insight Bot can analyse from various sectors?
A. Qlik Insight Bot extracts, combines and analyses all the enterprise data from across multiple data sources and storage environments. It allows authorised users to gain access to contextual insights by just putting in their data query. Such access to relevant information at their fingertips can help them make better decisions.
All they have to do is enter their query in the way they normally speak to gain instant insights. These insights can be accessed and explored further, and can even be shared with relevant stakeholders at the touch of a button.
The bot can learn by itself. This makes asking questions, getting insights and making data-driven decisions extremely fast and convenient. Users can also set data alerts for when certain predefined conditions are met, such as sales targets and revenues.
The bot can be accessed through such collaboration tools as Slack, Skype or Microsoft Teams, making it easier for users to access data and make better decisions, wherever they work.
