Did you know, according to a report by Accenture and BI-organization Qlik, India is ranked as the highest among nine countries with respect to data literacy? The report showcased that 46% of respondents1 from India have expressed confidence in their data literacy skills! But (there is always a but), the study also showed that 85% of Indian respondents were unhappy and overwhelmed when working with data. Nobody enjoys perusing through dry data to derive insights because it can get tiring, overwhelming, and even frustrating sometimes. Why does this happen? What leads to people finding data analysis a difficult task?
Data is currently visualized with charts and graphs but can these be accurately interpreted by a layman? According to the same survey by Accenture and Qlik, 53%2 of the surveyed also stated that they derive accurate and ideal insights from data. However, it can get quite tedious and redundant to work on data to get the best results. For instance, a person without a data science background will find it challenging to read and interpret data as opposed to a data scientist or someone familiar with the concepts of data. Due to a lack of fundamental data skills, not many are equipped with the ability to explore, understand and communicate with data. Another complication that sprouts from this approach are the abundance of technical jargon in the narratives drawn by industry experts that might be difficult for employees at various management levels to comprehend. One needs to be data-literate in order to read, analyze, communicate with data and gain meaningful information in the form of natural language. Communication with data can only be effective when the person is able to understand the language of data.
Data can explain and narrow down problems and even guide people to specific questions that need to be answered. However, the complication crops up when the person is unable to effectively communicate with the data. Often visuals such as charts and graphs are not enough to work with. They lack the vital component of narrative that would help facilitate effective communication of information and essential insights. Business users or analysts are expected to manually correlate the data points into meaningful inferences. This approach could lead to an increased risk of data misinterpretation as it would translate to different meanings for individuals in disparate management positions. In other words, every person in the office chain who has to deal with the data would have a different interpretation that might not align with the vision of the enterprises.
In simpler words, a good story answering the 5 Ws and 1 H3 questions (what, why, who, when, where and how) is easier to understand as opposed to a pictorial representation of data in the forms of pie charts and graphs. These questions constitute a formula to get a holistic story on a specific subject. The same approach is also used in project management and decision-making. As and when businesses are able to answer these questions, understanding data becomes a piece of cake.
Here is where data storytelling plays an essential role. To be able to leverage the expansive volume of data available and open new avenues of business opportunities for enterprises, language is important to help understand, analyze and question the data. Through data storytelling, users can communicate with data to receive a tailored response for a specific audience. It is recognized as the fastest and most effective way to empower a team to actually understand data and act on focused decision-making. Much like how we speak to each other to gain insights and find solutions, handling data is the same. By asking the right questions and conversing with the data, you can get all the necessary answers to your questions. The 5Ws and 1H questions are all answered by the data accumulated. But the key to getting those accurate answers to propel productivity and business insights is by learning the language of data.
However, is everyone well-equipped to handle data? Can every person in an organization learn the nuances of data and build data literacy? The answer is no. Indulging a staff to be data-literate is not a day’s job. There are many concerns that do come up, such as lack of training, upskilling, utilizing appropriate artificial intelligence reporting tools ,and other such challenges. Expecting the entire workforce to be data-savvy and data-literate would be quite unfair since there is a grave inconsistency between what the company wants and the reality of the employees. Every employee in an organization comes with a different set of roles and responsibilities. Luckily, to enable employees to be more data-savvy and take advantage of the data in hand, many organizations are coming up with products to bridge the gaps. In fact, enterprises are increasingly investing in powerful data analytics and business intelligence software to help employees deduce real and relevant linsights from the data. Augmented analytics tools such as Phrazor help make the process a whole lot easier and facilitate productivity for enterprise.
How can Phrazor help with business insights and relevant data reporting? By using advanced Artificial Intelligence technologies, the BI tool summarizes data into key insights in the form of narratives that help understand the data better. As a reporting automation platform, Phrazor uses natural language generation (NLG) to help enterprises generate personalised reports. These comprehensive reports with narratives are customized depending on the user, making it easier for employees at every management level to gain key insights and expedite decision-making. Tools like Phrazor enable you to deal with data in a more nuanced manner to propel enhanced productivity and make the workforce data literate. You can ask questions to the data in front of you and you will get all the answers you seek in real-time.
For the same, NLQ (Natural Language Querying) which falls under the NLU(Natural Language Understanding) domain of AI for linguistics is utilized. NLQ is the ideal solution for users as it allows knowledge workers such as analysts, managers, data scientists, etc., to ask ad hoc questions to the data from rich conversational interfaces like a chatbot. Through NLQ, it is easier to gain real-time insights. The output of the queries is in the form of charts, insights in texts or audio files, depending on what is requested. They make data interactive and simpler to analyze current trends, market behavior, augment marketing and sales intelligence, save time – a lot of time, help facilitate productivity and bring forth an overall improvement in return of investment. Through comparative analysis of a plethora of BI tools, such as Phrazor, the best key insights can be squeezed out that would be cost and time effective with lower risk factors. Basically, a data understanding job that would take humans at least a week to comprehend and gain insights, BI tools like Phrazor can do it in a jiffy!
With the current rat race that businesses are prevailing in and trying to stay relevant, opting for a well-equipped data solution is the way forward. And what better way to address data than by conversing with it through the means of technological tools? In conclusion, understanding data is not a challenge anymore, with AI-driven tools enabling a solution-based approach!
2 Same as above
3 Rule of reporting
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