5 technological breakthroughs that will benefit your business right away

With the world getting more technologically advanced and connected, each day presents a plethora of business opportunities to take on and more resources to exploit. At the same time, there are more challenges to tackle and a greater competition to face. This is where technology and innovation come to the rescue.

Following are the ways in which present-day technology can revamp the way your business functions:

1. Virtual Assistance

Doing monotonous, repetitive tasks each day not only depletes one’s mental energy but is also a productivity killer. Companies are deploying chat-bots for tasks that are less cognitively challenging so that humans can focus on more complex matters.

Vodafone UK’s artificial intelligence chat-bot TOBi for instance, has been providing prompt and efficient solutions to customers’ queries ranging from device troubleshooting to usage.

Natural language processing bot Luvo, deployed by The Royal Bank of Scotland is another example of virtual assistants playing a major role in enhancing customer service.       

2. Data Analysis

Although we are literally drowning in data, human capability to derive insights from it is limited, resulting in opportunities being lost. Having a machine over to understand the nitty-gritty of your business and do the analysis for you certainly gives you an edge.

Leading web-based cloud development platform Wix.com uses machine learning based algorithms to analyze the way customers interact with their product and for early anomaly detection.  vPhrase‘s AI-enabled business intelligence software PHRAZOR can automate data analysis to give out statistically summarized narratives.

3. Unstructured data to structured data conversion

80% of all the data available is unstructured- it isn’t stored in a pre-determined format, and may have irregularities and ambiguities. As much as it is crucial to make sense of this kind of data, it is also complex and time consuming thus better left to automation.

Financial data giant Bloomberg has been deploying machine learning to keep users hooked to its ‘Terminals’. The input can now be given in natural language instead of having the user to type in specialized commands. The technology in place is termed ‘Natural Language Understanding’, a subset of artificial intelligence, also used in applications like Siri and Cortana.

4. Report Writing

Organizations spend almost 40% of their time on documentation. That’s a huge number of hours annually that could have been used constructively towards developing a new version of the product or following along a new marketing campaign! Large media houses have been employing robo-writers to automate the process of writing documents. What’s more, these robo-writers are well equipped with the necessary technical expertise and domain knowledge, and do not even ask for sick leaves. Also, the documents can be customized to suit the reader-the team lead may be concerned with the technicalities whereas the manager may want to focus on the business aspect. By using natural language generation platforms like PHRAZOR by vPhrase that churn out data-driven reports within seconds, organizations are stepping up their game.

5. Recommendation systems

Suppose someone just ordered a brand new laptop from Amazon, they might have power banks and accessories showing up as “recommendations”, or at times, there are combo offers and discounts offered to encourage buyers. This is taken care of by algorithms working in the background that analyze massive volumes of data, records of billions of customers and market trends to come up with the most suitable recommendations for each customer.  Also, the amount of time a person spends checking a certain product out, their browsing pattern, etc is used to retarget potential buyers. This is useful in firms that are looking out for better ways to close more deals, have a stronger online presence and build customer loyalty.

With sophisticated applications and tools out there, organizations are taking a huge step forward towards adding greater value to their services, increasing productivity and generating more revenue.

Watch this space for more!       

How can Fintech marketplaces use data stories to derive insights from millions of customers?

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The market for financial products, online loans in particular, has evolved over the years, with metropolitan cities such as Delhi/NCR, Mumbai, Chennai, Pune, and Bangalore driving growth in the market. Most of you would have heard and used portals like policybazaar.com,bankbazaar.com, paisabazaar.com etc for evaluating financial products online. For the uninitiated, these portals act as discovery platforms and generate instant customized rate quotes on loans, credit cards and insurance products. As per the statistics mentioned on some of these portals, most of them cater to millions of customers spread over thousands of cities across India.

So, let’s say, if I were a Product Manager at an online loan aggregator. As part of my responsibilities, I would be definitely tracking metrics like

  •    the total volume of loans applied through the platform
  •    the weighted average of interest rate across those loans
  •    the total number of loans applied per city/state

Usually, these metrics would be provided to me in charts and graphs by an analytics solution on a daily basis and I would be running various algorithmic rules to slice and dice the data further. Usually, the charts and graphs may not be the most efficient way for me to understand the nuances lying in the data.

I would end up missing a lot of interesting insights due to the limitations of my intellectual capabilities to make sense of correlations between large sets of data. What if, while analyzing the charts and graphs, I also had the option of being dynamically presented with narratives at the side.

For e.g. Let’s say I am viewing charts which are depicting the total variance, distribution ranges, and standard deviation of the volume of loans applied per state/city. Chances are quite high that I might miss out on insights that aren’t as obvious and direct as per the information on the screen.

However, if I had a narrative extension which dynamically delivered me insights in natural language for the same chart, it would be a lot more efficient and effective a way to understand information in real time. I could also use the narrative to publish the findings in an internal newsletter to the management team and relevant stakeholders.

So, in a short time, I would be able to derive meaningful insights from thousands of loan applications than breaking my head over seemingly disjointed sets of charts and graphs.

In isolation, data visualization through charts and graphs doesn’t resonate emotionally with the audience. This is where real-time narrative solutions like vPhrase bridge the gap. Phrazor, the natural language solution from vPhrase can generate narratives to complement data visualization to convert meaningful data into unforgettable stories

How Natural Language Generation helps improve sales communication for Asset Management Companies?

Ravindra Jadeja is a Relationship Manager (RM) of a leading Asset Management Company in Mumbai. Every month, he dreads receiving the monthly consolidated sales report in his inbox. As you might have already guessed, the sales excel report is nothing but an indecipherable matrix of numbers staring at him in the void. Here is the sample

Irrespective of the fact that he has had a good performance this month and is on course to have a rocking quarter, he sincerely feels that the report needs to be much more humane. He’s never been able to feel a sense of pride looking at the soulless numbers shouting at him to derive the pleasure instead of explicitly sharing his glory.
If only Ravindra was presented with a report like the one below
Wouldn’t it be a lot more appreciable?

Large Asset Management companies usually have thousands of employees in their sales staff. Every month, these poor souls are subjected to an unnecessary test of their intellectual capabilities while browsing through the sales report.

While assessing sales performance, the importance of numbers is unquestionable. Many data analytics software solutions can produce sexed up charts and graphs from excel data, but they still lack a narrative juice. What good would be an attractive looking email of colored charts and graphs, when the very employee it’s meant to inspire is perspiring while trying to make sense of it.

The poor employee would end up spending hours to analyze his own performance instead of using his time to generate more sales. In all probability, the asset management firms might be aware of this challenge facing their sales employees. However, due to the paucity of resources at their end, a custom story based approach to sales performance communication can be considered wishful thinking.

Especially if the company has multiple offices in different parts of the country, making a monthly personalized sales performance communication through incumbent human resources can be an ambitiously cost and time intensive a project.

Enter Natural Language Generation

In case you were wondering, the above snapshot of the narrative form of sales communication, wasn’t written by a human. It’s a fine example of Robo-journalism at its best.

Wouldn’t you agree that the above report is much more expressive and insightful than the former snapshot of the excel file? Also, it has a lot more explicit actionable information and advice to improve the performance.

Companies like vPhrase are in the space of revolutionizing corporate journalism as we know it. They are doing it effectively and efficiently through the use of Natural Language generation.

When indecipherable numerical tables turned into personalized investment stories

Do you understand mutual funds?

I am assuming that you already know a bit about this investment product. However, if you are caught unawares, here is the Investopedia definition:

“A mutual fund is an investment vehicle that is made up of a pool of funds collected from many investors for the purpose of investing in securities such as stocks, bonds, money market instruments and similar assets. Mutual funds are operated by money managers, who invest the fund’s capital and attempt to produce capital gains and income for the fund’s investors. A mutual fund’s portfolio is structured and maintained to match the investment objectives stated in its prospectus.”

The above definition is sure to overwhelm people who are uninitiated in the financial world. Hence there exist specialist investment advisory companies which understand this asset class and can manage and grow your investments in this category. The irony is – usually they will send your investment information in a manner which will challenge your intellectual capabilities in arriving at the financial insights. At the end of each quarter, they would send you a performance report which will look something like this:

tablesThe above is just a small snapshot of a lengthy tabular report coming every quarter. If you weren’t already overwhelmed by the definition of the mutual fund, you are sure to get bogged down by the presentation of this information.

Now take a look at the below snapshot:


It would be a no brainer if I assume that you found this information a lot more readable than the above. If you are still wondering, the above snapshot is presenting the same information as in the first one, albeit in a much more digestible manner.

Information in the above snapshot is possible through the wonders of Natural Language Generation (NLG). NLG is a branch of artificial intelligence (AI) which generates language from data. NLG products can help investment advisory companies to automate the analysis and language for all funds in their client’s portfolio—every quarter, month or as and when required. Natural Language solutions from companies like vPhrase integrate seamlessly into the existing processes, thus giving portfolio managers and analysts almost spontaneous access to pre-analyzed commentary on existing portfolios. Such solutions free up time for the company’s human capital to focus more on strategic initiatives and can reduce time to market by nearly 15-20 days.

PHRAZOR, the NLG platform of vPhrase, analyses complex data, identifies key points and converts them into readable information using simple words. This makes data so much more human to understand and increases focus on actionable information. For e.g., Personalized performance reports to employees, Analysis of investment portfolio in natural language and data driven journalism (in sports, climate and weather reports etc.) can be efficiently and effectively carried out by solutions like PHRAZOR.

Why do large companies need automated content?

They say a picture is worth a thousand words. However, the same picture means different thousand words to different people. This would be the primary reason why info graphics or dashboards don’t work for an organization as they heavily depend on graphics to explain complex data. Sample this; a zone manager of a bank required his zone’s sales report and he was provided with a report filled with charts and graphs of individual performance parameters. How much sense do you think he or she would be able to make of the report? More importantly, how much time would he take to make sense of that report?

Despite pictures being compelling tools for conveying emotion and tone, they do not efficiently translate to narratives and explanations. Hence a balance of pictures and text is required to understand complex information and carry out even complicated analyses. Top financial firms often find it difficult to efficiently convey their performance statistics simply through dashboards. They have multiple products and multiple clients for these products, very often; they also have different clients for the same product. How would a generic homogenous dashboard work for all?

Communicating with such a myriad clientele presents new obstacles, both in resources in and outside the organization. This obstacle presents us an opportunity to give automated narratives a shot at the conventional organizational reporting.

Automated narratives enable you to tailor your report as per the end audience requirement in terms of tone, length, technical jargon and in-depth analyses. For example, a manager of department may not need as in-depth performance statistics as a team supervisor. Automated content lets you maintain that differentiation to improve the productivity.

Apart from customized report, automating content would give very little scope to human error or inconsistent human tone. What might be drastic for one person may be regular for another. Some would say “This year’s returns were exceptional” while there would be others who’d comment the same report as “The returns were good this year, however, cannot be attributed only to the schemes executed”. A machine cannot show such bias while reporting. In addition, automating content would let you produce multiple reports to multiple clients at once, contrary to human generated reports, thereby drastically improving the organization’s productivity while containing time.

A very important factor that contributes to adoption of automated content is its ability to provide dedicated insights. Two people can look at the same dashboard and obtain not just different but also digressing insights.  An automated narrative generated for that dashboard would direct the analysts to think in the direction required and come to collective understanding of data.

As opposed to what it is perceived to be, automated content, would collect and analyze the data only to align the analyzing prowess of analysts to obtain better insights and come to better conclusions by effectively using the data.

vPhrase is one such automated content creator and we at vPhrase, collect, analyze your raw data and present it to you in a way that would assure you of understanding it correctly and using it efficiently and exhaustively!

Big Data and AI – A collaboration made in heaven

Amidst the plethora of incoming data through social network, online web portals, e-care engines, sports live feed, we are assembling big data but are far from utilizing it to the best capacity.  According to IDC, 44 zettabytes of data could be created by 2020. There is a dire shortage of data analysts in the market, and as big data becomes even bigger, can data analysts, that companies heavily rely on today to analyze and interpret raw data, simply keep pace with its exponential growth?

Probably not. And that’s why we need AI.

Also known as cognitive science or machine learning, AI adds an intelligence coating to big data to address complex analytical tasks much faster than humans ever could. It will eventually more than requite for the dearth of analytical resources we are facing today. Quintillion bytes of data is being produced every day therefore knowledge of how AI systems collect, utilize, infer and generate data is essential. By leveraging the technology of these systems to handle the issues of Big Data, we can transform numbers to insights. Intelligent systems are built on a foundation of simple and understandable processes.

Unlike what it’s perceived to be, AI is not magic, but the interdisciplinary application of a set of algorithms ruled by data, magnitude, and processing power. AI systems today use three primary components of human reasoning: assessing, inferring, and predicting. Assessing with AI can be done by matching against comparable portfolios and profiles and then creating a preemptive prediction bank with dynamic data, for e.g. Amazon. Inferring with AI is usually done by checking similitude, classification and assembling evidence. Predicting with AI is basically translating the assessment into a prediction.

The cycle of assessing, inferring, and predicting form the foundation of many intelligent systems especially the ones with which humans interact. For any intelligent or a smart system, even humans, the ability to perceive current events, draw an inference about it, and then predict upcoming events  based on the inference drawn, is crucial to the ability to predict and plan for the future.

For example, Tesco, the famous retail store could see that individual consumers were buying wine and bread on weekends, but it could not discern that customers were not buying cheese. It could see consumers buying toothbrushes, but was unable to see that they were not buying toothpaste.

Evidently corresponding purchases were being made elsewhere, and narrow AI could have been utilized to investigate this episode and provide some answers. Tesco could then have resolved to coupon based promotional activities to bridge the purchasing gaps.

Data is only as precious as the insight you can draw from it. Successively, the insights are only precious if they are easily understood on time; mechanically converting thousands of metrics into a fewer thousand graphs doesn’t achieve this. Hence, today businesses like ours use AI not only to draw insight from data, but successfully transcribe big data into value for both the organization and the customer by providing it in an easy to use format – in human language.

What is Natural Language Generation (NLG)?

While there are many abilities that contribute to our intelligence, it’s our ability to communicate thoughts and ideas by means of a language that succeeds them all. Language is our user interface as we build language to convey our thoughts, and send them across using our voices, keyboards or gestures if we are using sign language. Whichever the means, it is important that the recipient understands our thoughts just the way we would want them.

What if we could build systems that not just spoke like humans but also answered human questions in human language?

Natural Language Generation is a way to achieve just that.

Natural Language Generation (NLG), a branch of artificial intelligence (AI) which generates language as output on the basis of data as input. There has been significant rise in adoption of NLG into business, in recent times. With NLG driven Robo-journalism and Robo-advisory taking the manual reporting arena by a storm, you can be sure that you must have read machine written articles, only without realizing it. Yes, they are as good!

As humans, we always tend to communicate ideas from data. But with the recent magnanimous influx of data that needs to be analyzed and interpreted, that too with ever increasing pressures to contain costs and meet dynamic customer demands, the business must find innovative ways to keep up.  As it turns out, a machine can articulately communicate ideas from data at remarkable scale and accuracy. When a machine automates the regular mundane routine analysis and communication tasks, productivity increases and employees can focus on decision making and end actions.

The goal of NLG systems should be to understand how to best communicate what it knows. For that it needs to have an unbiased and clear picture of the world rather than random strings of text. Simple NLG systems are capable of taking in ideas in form of data and transforming them into language. Apple’s Siri uses this concept of linking ideas to sentences to in turn produce limited yet succinct response.

Based on its extent, Natural Language Generation can be classified in three types – Basic NLG, Template driven NLG and Advanced NLG. The simplest level or basic level of NLG would identify and gather few data points and transcribe them into sentences. For e.g. a simple weather report like this: “the humidity today is 78%.” The next level of NLG, also known as template driven NLG, as the name suggests, uses template heavy paragraph to generate language as per the dynamic data. Sports score chart, basic business reports can be made using this type of NLG. Here, language is generated by the virtue preliminary business rules guided by looping statements like if/else statements. Advanced NLG is the artificial intelligence that can convert data into a narrative with distinct introduction, elaboration and conclusion. Deepest analytics are applied to execute this form of NLG. vPhrase’s PHRAZOR is one such advanced Natural Language Generation platform that generates elaborate narratives be it in sports or finance, as per the end user’s requirement.

Coming to business, you’d now wonder how exactly it would help an organization.

When you use Natural Language Generation, you can assemble more big data and by assembling more big data, you gather even more number of critical data points resulting in more insightful information to sell and pass across; thereby working toward increase in your revenue. With NLG, you can communicate insights at faster and larger scale as compared to manual efforts, increasing the overall analytic productivity of the organization. NLG, if not eliminate, can significantly reduce time-consuming and exhaustive data analysis, and manual reporting, resulting in increased operational proficiency. Additionally NLG would enable you to deliver customized, updated, data driven and simple information to all the customers as per their needs.

Big Data is here to stay and so it’s up to us to keep up with technology to harness it, and Natural Language Generation is one such tool that empowers us with utilizing this massive data while not letting our creative energies dry out in the mundane data transformation processes. Keep your intelligence reserved for decision making and action planning; and make way for Robo-writers!

Artificial Intelligence Decoded

Today’s world is so heavily driven by Siri, Google Now and Cortana, that it would have been impossible to imagine that they could have been around in the 80s let alone the 50s. But is it a concept that new and nascent?

Let’s go back to the Dartmouth conference in 1955, where the term ‘Artificial Intelligence’ was coined for the first time. Here, J. McCarthy, M. L. Minsky, N. Rochester, and C.E. Shannon in August 31, 1955 stated “We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”

Gauging from the definition quoted in the sidebar “The Dartmouth Conference,” ‘any program or an algorithm is an AI system simply because it does something that would normally be considered as an act in humans’.

Artificial intelligence (AI) is a field of computer science focused at the development of computers capable of doing things that are normally done by people, things that would generally be considered done by people behaving intelligently.

What could have been the reasons that spurred its re-emergence?

Ironically, the foundational concepts of AI have not changed substantially and today’s AI engines are, in many ways, similar to past ones. The techniques of yesteryear had a shortcoming, not due to inadequate design, but because the needful premise and environment weren’t built yet. In short, the biggest difference between AI then and now is that, there is exponential growth of raw data, focus on specific applications and increased computational and simulating resources, which can contribute to the success of any AI system.

As the name would suggest, an AI system would typically be expected to replicate human intelligence and efficiency. However, depending on the target function and application, AI can be classified in terms of the extent of AI- strong AI, weak AI and practical AI. Strong AI would be an AI system simulating actual human reasoning to think and explain human tendencies. These systems are yet to be built. AI systems that behave like humans in a certain way and execute specified functions of intelligent acts of human beings can be termed as Weak AI.  A balance between strong and weak AI, Practical AI, are the systems guided by human intelligence but are not enslaved to them. In brief for a system to be AI it does not require to be as intelligent as humans, it just needs to be intelligent.

Machine learning, cognitive computing, productive analysis, deep learning, recommendation systems are all different facets of AI.

Amazon and Netflix recommendations, Text prompt in mobile phones, Apple’s SIRI and Microsoft’s CORTANA, Thermostat, Google’s NOW are excellent examples of AI systems.

For example, IBM Watson uses the concept of facts being expressed in various forms and that each match against each possible form equals proof of the response.  The technology first investigates the language input to pick the elements and relationships needed to tell you what you could be looking for and then, uses arrays of patterns consisting of the words in the original request to find matches in colossal collection of text.  Each match would then provide a singular sample of proof and each sample of proof is summed to provide Watson with a number allocated with an answer. Watson is a good example of excellently executed classic AI.

Another excellent use case for AI is in data analysis and interpretation. As new data comes in, many of us spend our time reviewing it and making decisions based on the insights we gain from the data. While we may still want to do the decision-making, many of us would barely want to spend our time and resources digging through raw incoming data. What if we could use AI to do just that, while we just use our actual intelligence in the end? vPhrase’s AI enabled platform ‘PHRAZOR’ not just digs through data but also makes sense of it by turning it into effective narratives. With such a technology that allows for automated scenario assessment, businesses need not sift through inexhaustible data to gain insights or make decisions. In the future, we believe, our technology will be able to analyze even more extensive and enormous data sets to not just make better decisions but also derive a conclusion, as humans would do.

As you would observe, the allure is in the alliance between AI and human expertise. The machine is doing what it does best: reviewing enormous data sets and finding patterns to differentiate various activities and situations, while, as humans, we are doing what we do best: examining the situation, fitting it into a larger picture and then deriving solutions of it suitably.

What is Big Data and how to make sense of it?

‘Today is all about Big Data’; ‘let’s try to use Big Data’; ‘Big Data is the next big thing’; We hear about Big Data everywhere, so much so that it is the most trending information science buzz word right now. But do we really know what ‘Big Data’ means?

Big Data can be described as data which is extremely large for conventional databases to process it. The parameters to gauge data as big data would be its size, speed and the range. Big data can be comprised of both structured and unstructured data.

As the name suggests structured data would be the relatively simple data perhaps in numbers and characters that can be classified, recorded and read easily by a machine and hence can be stored by databases without difficulty. However, this kind of data, as most data analysts would agree, is only about 20 percent of the data that’s out there.

While unstructured data is the data that is extremely large, incomprehensible and cannot be stored in conventional databases. It’s basically data, heavy in text and human generated. Text from social media, Facebook posts, tweets essentially are sources of unstructured data. It cannot be easily recorded and classified in database systems.

Now that we know what big data is, let’s understand why companies today are investing in harnessing this indispensible Big Data. Companies are investing a great amount in elaborate dashboards and real time data streams, but none of these data representing mechanisms are able to help companies or customers gain insights from data. While the data influx has risen exponentially, there has been negligible progress in efficient data interpretation for the last two decades. Sample this, a data analyst analyzes a dashboard and then uses his or her proficiency to derive conclusion and suggest actions. That would be just one dashboard and companies receive inexhaustible data; analyzing every dashboard with same proficiency would not just be time consuming but would also require enormous manpower. This delay in effective data interpretation can cost a company time and resources.

This brings us to what I believe is, a fundamental flaw in various companies’ approach to invest in big data. The investment should not be in big data but in efforts to transform big data to simple and easy to understand reports. The investment should be in the interpretation rather than raw data. We need systems that not only tell us what the data means but also give us insights to derive conclusions.

While there are various software tools like Hadoop, Business Intelligence Software, Data Integration tools, Information Management Solutions etc., which are currently used to address the management of unstructured or raw data, there aren’t many tools to interpret that data. Our artificial intelligence platform, ‘PHRAZOR’, does exactly that. Phrazor analyses big data, derives insights and then communicates those insights, in words, like a human being. Interested? Write to us.

Why data is better explained via narratives than via charts or dashboards?

“If history were taught in the form of stories, it would never be forgotten.” – Rudyard Kipling

The same applies to data too. Companies, clients and even individual enthusiasts will remember data only if it is represented in the right way. Let us understand why it’s imperative for data to be explained in a narrative.

  • Have you ever wondered why you might not remember a movie, scene by scene but can easily summarize its story? Human mind, despite its pictorial affinity, comprehends and stores a story better than any graphical representation.
  • Imagine if while reading a movie review we were only presented with how cinematography was or how the screenplay went, would you be able to gain a proper feedback? Similarly, data charts by themselves aren’t sufficient to gain a complete insight of the relevant parameters in the bigger context. However, when data analysts and data scientists present us a narrative, a finished product is given to the client like a summarized movie review, making it easy for the end user to understand data efficiently. A captivating data narrative would not only present facts but also build connections between them; thereby being impartial and offering some uniformity to the end user. Data when explained in narrative even if taken out of context will be comprehensible as the reader would still understand what a chart is trying to explain as visualization has now turned into a story.
  • Data charts and dashboards are not only meticulous to analyze each and every constituent in order to derive at a conclusion but also take up a lot of time and resources. Veteran analysts have often stated that it takes half their time thinking about how to narrate a good story with data. But what if there were software, which could collate, analyze and store reports only to present them, in moments, in simple narratives, customized to a specific audience, based on the effective analysis (work that would otherwise take valuable long human hours)? You would not only save time but also deliver better analytical outcomes while containing the rising costs and resources.
  • A data narrative can be drafted keeping the target audience in mind and hence can be tailor made to suit the end user’s requirements. Data presented in a narrative has optimum technical details sans overwhelming buzz words for any kind of reader, facilitating easy interpretation and understanding. For e.g., a novice analyst does not necessarily require oversimplification while the managerial clientele would seek thorough, exhaustive and actionable understanding of the narrative. As data is extremely precious to an organization and not just the fellow analysts and scientists, it is important to communicate business value of data that helps viewer gain accurate and deep understanding.
  • Generally, readers rely on ideology than actual facts hence a data narrative would not only establish end viewer involvement but also give rise to considerable interest and loyalty.
  • Content marketing through data analysis using story telling is the upcoming big trend i.e. data narratives are used for efficient reviews and comparisons of organizations for the end viewers.

In brief, Tom Davenport, the distinguished professor, author and co-founder of the International Institute of Analytics, succinctly explains the effectiveness of data storytelling:

“Stories have always been effective tools to transmit human experience; those that involve data and analysis are just relatively recent versions of them.  Automated narratives is the way we simplify and make sense of a complex world. It supplies context, insight, interpretation—all the things that make data meaningful and analytics more relevant and interesting”.

We can now be reaffirmed that data through storytelling is the next inevitable form of presenting information and statistics.

In conclusion, dashboard and infographics will tell you the ‘what’ in your data, while narratives can tell you the ‘why, as they give you a story rather than just dropping a lot of data and expecting you to just sail through it !