The art of Data-Driven Storytelling – What is it and why does it matter

Be it in the form of fables that teach good morals or mythological tales that underpin the major world religions, stories have been a proven medium for teaching, explaining, and influencing. Combining these benefits of stories with the factuality of quantitative data to drive decision-making is what data-driven storytelling is all about.

Put simply, data-driven storytelling is a communication technique that involves weaving stories or narratives around data to ensure that the insights from it are well received, retained, and acted upon.

The effectiveness of stories in getting a point across stems from a psychological phenomenon called neural coupling. It is an event where, while listening to a story, the neural activation patterns of the listeners mirror those of the storytellers.

What this implies is that the listener becomes more receptive, trusting, and empathetic to the storyteller since both their brains are synchronized. And when infused with facts and data, stories or narratives can be a highly potent tool to bring about organizational change and shape business outcomes.

Why businesses should adopt data-driven storytelling

Businesses today are collecting more data than ever from various sources like social media, research firms, and their own processes in the form of analytics reports and logs. All this data can be used to analyze existing trends and make informed decisions.

However, most businesses are unable to make the most of their data. That’s because business leaders who are not data-savvy may have a hard time making sense of this data even after it is cleaned, sorted, and visualized by data analysts.

“Most organizations recognize that being a successful, data-driven company requires skilled developers and analysts,” – Daniel Waisberg, Analytics Advocate at Google (Source)

They are unable to interpret the data by considering the context, and thus cannot gain any actionable insights. Hence, even if an organization’s data scientists perform incredibly astute data analyses, they cannot drive organizational change.

By adopting data-driven storytelling, business leaders are able to understand where their organization has been, where it is, and where it is heading in the form of a fluid, easy-to-understand narrative. By reading or listening to data-driven stories, business leaders can easily grasp the most remarkable highlights from their data and can also gain clarity in terms of future steps to take.

And since stories have greater influential power than just data alone, data-driven storytelling can actually transform the way an organization functions and lead to improved business outcomes.

What businesses can do to facilitate data-driven storytelling

In order to implement and benefit from data-driven storytelling, businesses can do either of two things:

  1. They can invest in training their data analysts to be good storytellers. It may take some time, but eventually, they can have analysts capable of providing insights through easy-to-consume narratives. Or,
  2. businesses can use natural language technology to turn their analytics reports into stories written in an engaging tone. These stories can build a convincing and impactful narrative around analytics data to tell business leaders what’s happening in their organization, making the analysts’ job easier.

Regardless of the means used by businesses to implement it, data-driven storytelling can undoubtedly help them make the most of their data. Thus, by combining the objectivity of data with the fascination of stories, businesses can accelerate their journey towards becoming more data-driven, and hence more efficient and effective.

How big data analytics aids Media & Entertainment

Digitization in the media and entertainment industry has empowered businesses to have unprecedented access to data on their customers. By using big data analytics, entertainment companies have been able to gain detailed insights regarding not only their customers but also their systems and processes.

The most pre-eminent players in the media and entertainment industry, such as Netflix, Amazon, Hulu, and Disney, have already been leveraging big data as part of their operations to enhance the customer experience. Hulu, for instance, has been known for using analytics for purposes like content acquisition and recommendation.

Similarly, many other entertainment companies have been using big data and AI analytics to enhance their offerings and streamline their business processes. As a result, the media and entertainment industry as a whole is gaining massive advantages.

How big data analytics is making media companies competitive

Attracting and keeping customers engaged are the biggest challenges faced by media and entertainment companies across the world. By using big data analytics, businesses have been able to overcome their challenges and achieve higher business outcomes such as:

1. Increased customer retention

Media companies like Viacom18 have been using big data analytics to ensure viewer retention during break slots between program segments by identifying the right times to place commercial breaks. As a result, they have been able to retain viewership even during commercial breaks to drive significant revenue for themselves as well as advertisers.

Similarly, other media companies are using big data analytics to drive similar initiatives aimed at improving customer engagement. A well-known example of this is YouTube, which uses big data and machine learning to predict the kind of video recommendations that would keep users on the site for extended periods.

2. Effective media investment decisions

Media streaming platforms need to ensure that the content (movies, TV series, music) they are investing in will be successful among their audiences and deliver an adequate ROI. To assess the potential success of different media assets and projects, media companies are using big data analytics. The most popular example of this is Netflix’s investment in an American version of the British Show, House of Cards using insights gained from big data analytics.

By ascertaining the potential success of the show, Netflix made a considerable investment that paid off huge dividends. Similarly, leading media company Warner Brothers. has invested in predictive analytics technology to predict the success of movies. The company is using this technology to guide its decisions when investing in new movie ideas.

3. Precise ad targeting

Advertising revenue is a major source of income for media streaming and entertainment companies. To ensure that they show the most relevant ads to different users based on their demographic information, media streaming companies like NBC use big data analytics for ad targeting and audience segmentation.

As a result, these companies are delivering greater ROI to advertisers by helping them reach their ideal target audience. By combining big data analytics with machine learning, online streaming platforms like YouTube improve the relevance of the ads displayed to their users.

4. Detailed performance analysis

Network media companies are using analytics information in the form of language-based reports to understand the performance of their different channels and media assets. With the help of this analytics information provided by research bodies like the Broadcast Audience Research Council of India (BARC), businesses get real-time insights into the performance of their shows with respect to their competitors and make improved strategic decisions.

To refine the insights delivered through analytics, these media companies are using reporting automation powered by natural language generation. As a result, they are able to convert insights into action quickly, achieving better channel performance.

On the whole, the incorporation of big data analytics by media and entertainment companies is enabling them to provide high-quality entertainment to clients while ensuring better business outcomes for themselves.

Applications of AI in the media & entertainment industry

The global media and entertainment industry is witnessing a rapid transformation in the way content is distributed. The growing ubiquity of content creation tools like high-resolution cameras, content creation software, and smartphones is allowing pretty much anyone to create, publish, and distribute written, audio, and video content.

This trend is further accelerated by the proliferation of the internet, which has led to the replacement of traditional media channels like cable and radio with on-demand streaming platforms like Netflix and YouTube. As a result, consumers have potentially limitless options to choose from, in terms of media consumption.

Thus, media companies are facing the need to raise the quantity as well as the quality of content they create to attract as many consumers as they can to drive higher value. To help them achieve this objective, media companies are adopting advanced technologies like AI.

The use of AI in the media and entertainment industry is helping the media companies to improve their services and enhance the customer experience. Here are a few AI use cases in media and entertainment that are transforming the industry:

1. Metadata tagging:

With countless pieces of content being created every minute, classifying these items and making them easy to search for viewers becomes a herculean task for media company employees. That’s because this process requires watching videos and identifying objects, scenes, or locations in the video to classify and add tags.

To perform this task on a large scale, media creators and distributors like CBS interactive are using AI-based video intelligence tools to analyze the contents of videos frame by frame and identify objects to add appropriate tags.

Metadata tagging
AI-based video intelligence tools identify the objects and scenes in images that are specific to your business needs. Source

This technology is being used by content creators or media publishing, hosting, and broadcasting platforms like NFL Media to organize their media assets in a highly structured and precise manner. As a result, regardless of its volume, all the content owned by media companies becomes easily discoverable.

2. Content personalization:

Leading Music and video streaming platforms like Spotify and Netflix are successful because they offer content to people belonging to all demographics, having different tastes and preferences.

Content Personalization
Netflix uses AI & ML to share personalized recommendations

Such companies are using AI and machine learning algorithms to study individual user behavior and demographics to recommend what they may be most interested in watching or listening to next keeping them constantly engaged. As a result, these AI-based platforms are providing customers with the content that cater to their specific likings, thus offering them a highly personalized experience.

3. Reporting automation:

In addition to automating day-to-day or minute-by-minute operations, AI is also helping media companies to make strategic decisions. For instance, leading media and broadcasting companies are using machine learning and natural language generation to create channel performance reports from raw analytics data shared by BARC.

The weekly data that is usually received from the Broadcast Audience Research Council of India (BARC) is generally in the form of voluminous Excel sheets. Analyzing these sheets on a weekly basis to derive and implement meaningful learnings proves to be quite daunting for the analytics team.

Channel Performance Report
Media network companies use NLG-based reporting automation to enhance their channel performance reports.

By using AI-enabled data analysis and natural language generation-based reporting automation tools, business leaders can create performance reports with easy-to-understand language commentaries, providing them accurate insights to make informed data-driven decisions.

4. Subtitle generation:

International media publishing companies need to make their content fit for consumption by audiences belonging to multiple regions. To do so, they need to provide accurate multilingual subtitles for their video content. Manually writing subtitles for multiple shows and movies in dozens of languages may take hundreds or even thousands of hours for human translators.

Youtube Subtitle generation
Youtube’s artificial intelligence allows publishers to add automatic transcription to their videos.

Besides, it may also be difficult to find the right human resources to translate content for certain languages. Additionally, human translation can also be prone to errors. To overcome these challenges, media companies are leveraging AI-based technologies like natural language processing and natural language generation. For example, YouTube’s AI allows its publishers to automatically generate closed captions for videos uploaded on the platform, making their content easily accessible.

To sum it up…

As competition and the need for efficiency continue to rise in the industry, the role of AI in entertainment is only expected to grow in the coming years. By exploring and experimenting with the above and other AI use cases, media and entertainment companies are maximizing their business performance by enhancing the user experience and entertainment value delivered by them with greater efficiency.

How Natural Language Generation Is Helping Democratize Business Intelligence

Regardless of how large an enterprise’s operations may be in size and scale, it is made up of hundreds or even thousands of employees at different levels and in different functions doing small tasks. And each of these tasks performed by the employees contributes to the overall performance of the enterprise.

Delivering business intelligence data to these employees in the company leads to improved decision-making at every level. Due to these improved decisions, all the employees can work using the most optimal methods and be more productive in their functions. The contributions of these employees add up and compound to massively enhance the overall business performance.

Thus, achieving a quantum leap in operational performance begins with the democratization of business intelligence. And a key enabler of BI democratization is natural language generation. Here’s how natural language generation-enabled business intelligence reporting can make data accessible to all employees of an organization:

Real-time business intelligence reporting

Traditionally, analytics and business intelligence have mostly been used by top business leaders for strategic decision-making. These business leaders get their business intelligence reports periodically prepared and curated by data analysts.

The reports — accompanied by visualizations and analysts’ comments and recommendations — make it easy for business leaders to gain crucial insights and make decisions. However, manually creating such detailed yet simple reports for every other employee in the organizations, such as those working in operations, is not practical.

Unlike the top leadership at enterprises, operations personnel have to rely on raw data, tables, and charts to make sense of real-time analytics and make decisions. They need to be aware of data that changes on a daily or even hourly basis.

And generating reports or explaining dashboards so frequently proves to be exhausting for both analysts and executives. And the organization as a whole misses out on achieving operational excellence.

With the help of natural language generation, the information in business intelligence dashboards and reports can be translated into easy to understand written narratives. These dashboards contain the most vital real-time data pertaining to the enterprise’s operations.

For example, a logistics supervisor can use a real-time dashboard to know details about the location and performance of delivery vehicles. Given the right information, they can identify deliveries that are running late and optimize the routes to speed up the process.

Using such dashboards and reports, other operations personnel can make decisions in response to real-time changes. Since every employee is updated with real-time information in a simple format, the enterprise as a whole gains agility in its operations.

Function-based customization of business intelligence reporting

Using AI and NLG, data-driven narratives can be created for and tailored to different functions. These reports help employees performing different functions to understand business intelligence within the context of their function. This leads to an increase in the adoption of BI tools throughout the organization, leading to an improvement in the overall business performance.

For instance, while the sales head of an enterprise can find enough actionable insights from an overview of the enterprise-wide sales performance report, regional sales managers may not find value from this data. They may require reports that focus on the sales performance in their region, with a detailed breakdown of the performance of individual sales employees working in that region.

And most large enterprises have hundreds or even thousands of sales personnel working in different regions. Generating customized performance reports for all these people is impractical for a team of human analysts.

In such a case, natural language generation-based business intelligence reporting can help to create customized performance reports or dashboards. Similarly, managers of different bank branches can receive performance reports customized to their branches, giving them insights that are relevant to them.

Businesses are beginning to realize the value of democratizing business intelligence and the role of natural language generation in the process. As a result, NLG adoption is on the rise.

Gartner has predicted that NLG will become a standard feature of 90% BI and analytics platforms by 2020. Thus, to stay competitive and relevant in an increasingly data-driven business landscape, enterprises will have little choice but to adopt business intelligence reporting powered by natural language generation.

It’s time to upgrade your BI with natural language generation

The increasing availability of data to businesses has helped in making everything quantifiable. From the efficiency of small tasks to the potential outcomes of strategic decisions, everything can be measured in numbers.

And modern-day business intelligence tools help businesses visualize these numbers to make fact-based decisions — but only to a limited extent. That’s because, while BI tools offer data visualization using interactive dashboards and reports filled with graphs, charts, and tables, decoding these visualizations remains a challenge.

Only data scientists, analysts, and a select few data-literate personnel are able to make sense of this data and derive actionable insights from it. And while these data scientists and analysts can help senior leadership make better decisions, the maximum value from data can be only realized if it is used by the entire organization.

However, expecting all the employees to be data-literate and interpret dashboards and reports accurately by themselves to make data-driven decisions is inviable. So, how can organizations empower their entire workforce with data? The answer is, by using natural language generation.

What is natural language generation

Natural language generation is an advanced subset of artificial intelligence that can convert structured data from tables into natural language text. This technology has many applications in areas where there is a need to generate human-consumable content at a rapid rate, on a large scale, and with high accuracy. For instance, NLG is being used by e-commerce companies to write product descriptions in human-like language.

NLG technology can convert basic product features, specifications, and other product-related data from spreadsheets and databases into highly engaging product descriptions. The potential of NLG has also been explored in the field of healthcare, where it can be used to automate the generation of medical reports to help physicians save time on paperwork.

Similarly, natural language generation can be used to translate analytics data into concise reports written in everyday language. These reports can highlight the most critical facts that can help businesses make the best decisions. Anyone reading these reports can easily grasp the most vital facts without any possibility of misinterpretation.

Why BI needs natural language generation

Business intelligence tools are incredibly adept at processing large volumes of data and finding patterns and trends in them. They can also represent these trends in the form of captivating graphs, charts, and colored tables. However, business users may have a hard time interpreting these trends.  Though the users may get some information from BI dashboards, they may end up missing out on the most noteworthy insights for solving their biggest problems.

On the other hand,  adding the capability of text with natural language generation, to the BI Dashboards, will present the data in the form of easily understandable narratives written in a human-like tone. As a result:

  • businesses can easily spot hidden patterns and trends and understand the context surrounding each data-point,
  • and every member of the organization can utilize business intelligence and hence leverage the power of data to enhance their performance.

How BI tools can leverage natural language generation

Enterprises can use natural language generation technology to enhance their business intelligence dashboards with written narratives instead of just charts and graphs filled with numbers. As a result, employees will no longer need to perform analysis from BI dashboards and reports as ready-to-use insights will already be available in plain words.

Using advanced natural language generation-based business intelligence reporting, employees from all functions can be motivated to make data-driven decisions by providing them with relevant insights, written in any language they are most comfortable with.

This can lead to improved work methods in all departments, increased operational efficiency, and ultimately, increased profitability. And it all starts with leveraging natural language generation for business intelligence.

How AI can add value to Human Resource Management

As more and more functions are automated with technologies like artificial intelligence, the jobs that still require human involvement are becoming more valuable and harder to fill. This is making the work of enterprise HR teams riddled with challenges. A recent survey of HR professionals revealed that the biggest of their challenges included finding high-quality candidates, retaining their best talent, and increasing employee engagement and retention.

Overcoming these challenges means becoming more effective at finding, retaining, and developing employees. To do so, enterprises can use intelligent automation for HR operations. With the help of AI-based HR automation, enterprises can monitor not only their internal workforce to ensure they work to the best of their capabilities but also the job market to find talent to meet their evolving needs.

By doing so, they’ll be able to constantly track multiple variables pertaining to their recruitment process, the job market, their existing employees, and the enterprise’s overall business strategy. And by using all this data, HR personnel can make the best possible decisions to manage their human resources to aid their overall organizational strategy. Following are a few ways that HR teams can use artificial intelligence:

1. Recruitment Automation

Enterprises receive huge volumes of job applications on a daily basis. It is the job of the HR team to pore through the applications to find and sort the best candidates that can meet the enterprise’s needs. AI tools can be used to filter the most irrelevant and unqualified candidates by looking for key datapoints like education, experience, and willingness to relocate.

This will save a lot of time for the HR who can get a shortlist of the most suitable candidates to test and interview. Artificial intelligence and machine can also be used to assess candidates before inviting the best ones for face-to-face interviews. As a result, enterprises can significantly cut down on recruitment costs.

2. Performance Analysis

With the help of artificial intelligence-enabled HR automation, enterprises can assess the performance of individual employees in a highly detailed and personalized manner. AI can be used to objectively assess employees’ performance across multiple data points and help them to identify ways for improving their contribution to the organization.

Artificial intelligence-based performance evaluation can also help HR teams to offer appraisals to employees more fairly. As a result, employees will be more motivated to improve and contribute to the organization.

3. Reporting Automation in HR

With the help of reporting automation, HR teams can generate standardized reports using raw information from spreadsheets and other analytics tools. Thus, the use of reporting automation in HR can not only save the time spent by HR personnel in manual data entry but also eliminate clerical errors, leading to fewer compliance risks. Thus, NLG-based reporting automation in HR can help by ensuring compliance with both internal processes and external regulations.

Artificial intelligence can also be used by HR teams to generate highly interactive reports and dashboards for various purposes, like analyzing their recruitment performance to monitoring individual employees’ performance.

With the help of AI-driven natural language generation technology, businesses can not only save time on generating these reports but also make the information contained within them easier to understand. Thus, HR leaders as well employees can use the reports to gain the insights they need to make performance-enhancing decisions.

4. Employee Development

Employees stay longer with enterprises and are more engaged when provided with opportunities to grow and develop. AI can be used to identify the potential growth areas for individual employees and plan their training and development based on their function, strengths, weaknesses, and the enterprise’s future skill requirements.

AI can also help to organize training sessions for all employees based on their individual work schedules to minimize disruptions. As a result, every employee will be recommended a training curriculum and schedule tailored to them. Thus, HR teams can spend less time on repetitive tasks like creating training schedules. Instead, they can work on strategizing and further improving the existing employee development programs.

Conclusion

With the job market evolving at a rapid pace, the work of HR managers will only get harder as time passes. Moreover, the competition among employers to acquire the best talent available will only get tougher.

Enterprises that follow a reactive approach towards HR management will continue to fall behind those that take a more proactive approach. And the first step towards taking a proactive approach is adopting technologies like artificial intelligence for HR automation.

How AI is Transforming HR Management

People often misunderstand the role of artificial intelligence in the workforce of the future. They view AI as a potential replacement for — and hence a threat to — human employees. After all, AI systems can perform most tasks that involve human-like cognition, analysis, and decision-making with greater speed and accuracy. And they can perform these tasks at an unprecedentedly greater scale.

However, despite these advantages, the role of AI in enterprises is not to replace humans, but to help them perform better and deliver greater value to enterprises. And the biggest way in which AI is adding value to human resource management is by enhancing the HR functions.

By helping enterprise HR teams to manage the entire life cycle of employees from recruitment to retirement with greater ease and effectiveness, artificially intelligent automation for HR management is offering them benefits like:

  • improved employee experience and engagement,
  • higher job acceptance rates by new candidates,
  • increased employee retention,
  • reduced employee turnover, 
  • effective training, development, and appraisal of employees, and
  • higher productivity for HR teams.

Ultimately, AI-driven HR automation is enabling HR professionals to focus less on routine tasks, and more on supporting employees and improving their performance. As a result, more and more enterprises are adopting AI technology to aid their HR teams.

How enterprises are leveraging AI for HR automation

Traditionally, a majority of the work done by human resources personnel has involved routine tasks like crafting internal communications for employees, resolving employee queries around company policies, constantly keeping track of their companies’ HR needs, analyzing recruitment performance, hiring employees based on requirements, and managing the payroll, among others.

By using AI to automate these tasks, businesses are now able to conserve their HR teams’ time and effort. As a result, the HR teams are able to direct most of their time and energy towards more strategic and creative ends by entrusting the repetitive tasks, as follows, to AI:

1. Automating employee communications

Using AI-enabled chatbots, HR teams can ensure that all internal communications are automatically and efficiently handled. These chatbots can ensure that every bit of information that needs to be conveyed to employees, such as

  • policy changes, 
  • announcements, 
  • special instructions, 
  • SOP changes, and 
  • leave approval/rejection messages,

are delivered to the concerned people promptly. This not only saves a lot of time for the HR personnel but also minimizes communication lags and errors.

2. Measuring employee engagement

Employee engagement is a key driver of organizational performance and profitability. In fact, it has been proven in the past that companies that report the highest employee engagement levels also report a significant edge in terms of profitability.

To achieve high employee engagement, it is first essential to measure the engagement levels of existing employees. And to do so, enterprises are using AI-enabled tools that can gauge the engagement levels of individual employees. By using such HR automation tools, employers can identify employees who are one there verge of leaving. As a result, they can minimize employee turnover.

3. Generating recruitment reports

A key aspect of an HR team’s KPI is its recruitment performance. The amount of time and money spent on hires, the number of candidates attracted, and the quality of new hires together determine the effectiveness of recruitment teams. However, measuring these performance indicators for large enterprises can be difficult, as they have to go through large volumes of data for the same.

And the result of these analyses needs to be communicated to senior HR leaders so they can make improved hiring decisions. By using AI-enabled analytics and natural language generation technology, these enterprises can generate insightful recruitment performance reports.

These NLG-enabled reports written in easy to comprehend human-like language, minimize the chances of misinterpretation. And also enable standardization of information, minimize the risk of clerical errors and ensure compliance. Using such reporting automation in HR management, HR leaders can have the necessary insights at their fingertips to make optimal decisions using real-time information to stay ahead of the curve in terms of hiring the best talent.

The debate on whether artificial intelligence can completely replace humans in the workforce still rages on. However, what is certain for now is that by using intelligent automation for HR operations, leading enterprises can ensure that they hire the right people, keep them for longer, and help them contribute more to organizational success.

Why healthcare needs big data analytics

Healthcare is among the few industries — alongside aviation, defense, space research, and law — where precision in decision-making and certainty in action are of the highest importance. That’s because every major decision and action in this field can directly impact human life and health. To gain certainty and precision, healthcare organizations and professionals need accurate and reliable data.

And they need a lot of such data. Hence, it is no surprise that healthcare has taken well to big data analytics as a key enabler in recent years. After all, the “3 Vs” that characterize big data also apply to the data generated in the healthcare industry, as follows:

  • Volume: the healthcare industry generates massive volumes of data through endless sources ranging from electronic health records to sensor-embedded wearables;
  • Variety: every operation in healthcare and medicine involves different types of data such as health parameters, drug proportions, behavioral data, among others;
  • Velocity: a lot of healthcare data, such as that from intensive care units and wearable devices, is constantly generated in real-time, leading to data generation at an unprecedented velocity.

By leveraging all this data using analytics, healthcare institutions are able to provide better outcomes for patients as well as healthcare providers, such as:

  • quicker, more accurate diagnosis of medical conditions,
  • timely treatment to prevent terminal diseases,
  • high success rates of surgeries and medications,
  • increased productivity of physicians, and
  • improved health in the general population.

And how exactly is big data leading to these outcomes?

How big data analytics and data science are revolutionizing healthcare

Healthcare institutions are utilizing the evergrowing cornucopia of data at their disposal to find trends and patterns that can help them identify problems in patients with increasing accuracy. In addition to helping them solve existing problems, big data along with other techniques like data mining, statistics, modeling, machine learning, and artificial intelligence is also enabling healthcare professionals to perform predictive analysis.

Predictive analysis allows the professionals to identify emerging issues in patients and preemptively take actions that lead to the best patient outcomes. Following are a few ways in which big data is aiding the healthcare sector:

1. Aiding diagnosis to predict and prevent illnesses

Big data analytics is being used to track a large number of indicative and causative factors of diseases and report the findings to doctors. The doctors can then use this information to perform tests that can confirm the presence of a medical condition and initiate appropriate treatment if needed. An example of this is the use of big data to identify patients with a high likelihood of developing cancer

By analyzing the large volumes of data from Electronic Health Records (EHR), scan reports like MRI and CT, as well as genetic data, analytics can highlight data points and patterns that characterize a typical cancer patient.

The doctors can then investigate the information further to identify high-risk patients and monitor them for early signs of cancer. As a result, doctors can enable patients to avoid the worst effects of cancer, since it is easier to treat the earlier it is detected.

2. Expediting clinical research to help develop new treatments

Clinical trials help in the development and testing of new treatments such as medicines and procedures. These procedures require the researchers to monitor a large number of test subjects, their individual EHR information, medical histories, their habits, allergies, genetic details, and other data that may be relevant to the study being performed.

Keeping track of all this data manually is virtually impossible, let alone analyzing it to find hidden correlations. Big data analytics can be used to analyze such data and find patterns and correlations that can help in assessing the effectiveness of different medicines with greater accuracy.

Researchers and analysts can explore the data from the analytics reports to verify and test their hypotheses before confirming the effectiveness of a drug or a medical procedure. As a result, new treatment methods can be commercialized quickly, benefiting both the patients and healthcare institutions.

3. Expediting clinical research to help develop new treatments

Using wearables and other biosensing devices, doctors can monitor patient health after surgeries and other treatments. These wearables can constantly collect data on key health parameters of patients after they’ve been discharged from hospitals and record the same in the patients’ personal health records.

Any anomalies in the health parameters that indicate a potential health complication can be reported to both the patients and relevant doctors in real-time. By receiving health alerts in real-time, patients and doctors can quickly schedule consultations to investigate and spot signs of relapse or complications. As a result, any potential negative side-effects of treatments and surgeries can be mitigated in time.

To sum up…

In addition to the precision and certainty offered by big data analytics, the healthcare profession also has another critical factor — time. Interpreting analytics reports, which are often in the form of large quantitative tables, take time and effort to understand and interpret. In the absence of data scientists and analysts, converting these numerical tables into medical inferences may not be a straightforward process for doctors.

Thus, it is essential to deliver the analytics reports to doctors in a language they can understand. To overcome this hurdle, healthcare organizations can leverage automated report writing with Natural Language Generation (NLG), an advanced AI technology to convert this big data into simplified text summaries.

Using these plain text reports, doctors can easily make sense of the analytics reports and make decisions quickly. As a result, doctors will be able to treat more patients and minimize the average waiting time for patients. They can also further expedite treatments and potentially save more lives.

The Role of NLG-based Reporting Automation in the Pharma Industry

The healthcare and pharma industries have always been among the leading adopters of cutting-edge technologies like AI. AI applications in pharma, such as the use of deep learning for developing new drugs and machine learning for clinical trial design, are well documented. However, AI-driven automation in pharma and healthcare is not only limited to these long-term R&D activities. AI is also adding value to the day-to-day operations of pharma and healthcare organizations.

One of the leading ways in which AI is helping healthcare organizations is by interpreting the results of CT, X-ray, and MRI scans. These scans can be interpreted with the help of Natural Language Generation (NLG), an advanced AI technology. First, computer vision and deep learning are used to analyze the images and other patient-related data to detect disorders.

These findings, which are in the form of structured tables and images, are automatically converted to a natural text report that outlines the patient’s health using natural language technology. These reports highlight the exact points that need attention from doctors, leading to diagnosis within minutes.

Similar reporting automation applications are providing healthcare and pharma companies with innumerable benefits, such as:

  • increasing efficiency,
  • minimizing errors in reports,
  • eliminating the misinterpretation of data,
  • allowing non-analyst employees to focus on and improve their core functions,
  • improving data analysts’ productivity, and
  • enabling pharma companies to understand and keep up with market trends.

To achieve these benefits, the following are the biggest ways in which healthcare and pharma companies are leveraging NLG-driven reporting automation:

Monitoring medical representative performance to increase sales

The volume of sales achieved by pharmaceutical companies is closely tied to the performance of their medical representatives. These medical representatives travel from place to place, meeting doctors, drug stockists, and distributors to expand the company’s distribution channels. Their performance is based on metrics like:

  • the number calls they make on average to doctors,
  • the number of meetings attended with doctors and stockists,
  • the number of meetings missed, and
  • the number of incentive-driven doctors visited.

Helping medical reps to monitor these metrics can bring out insights into improving their performance. Businesses are using ERPs and advanced analytics tools to record and measure these representatives’ performance. However, these insights aren’t often delivered to the representatives themselves in a language they can understand.

Medical Representative Performance Report
Medical Representative Performance Report

Instead, this data is often provided to them in the form of complicated spreadsheets. These spreadsheets not only take time to interpret but can also lead to misinterpretation due to a lack of data literacy skills. And spending hours trying to decipher their performance-related data is not the most productive use of the reps’ time.

That’s why leading pharma companies are using AI to automate medical representative (MR) performance reporting. Advanced reporting automation tools use natural language generation technology to convert the MR performance data from spreadsheets into concise reports written in simple, natural language.

These reports can be read by medical reps to quickly understand how they are performing. And when combined with predictive analytics, reporting automation can even suggest measures to improve their performance through the reports. By using these reports, medical reps can keep a close tab on making enough sales calls and visits to meet their targets.

Exploring IMS data to aid strategy and decision-making

In addition to analyzing individual medical reps’ performance, pharma companies also use market data gathered from the industry to make informed decisions driving overall performance. While there is no shortage of accurate market data due to sources like IMS Health, extracting the necessary insights from the massive IMS database and providing it to different people in the pharma supply chain can be challenging. Especially when you have to customize data for hundreds of different people based on their designation and region.

IMS Health Data Analysis

The use of reporting automation in pharma companies is helping them make the most of their market data. With the help of NLG-based pharma reporting software, the IMS health data can be translated from tables and graphs into easy-to-understand trends written in simple understandable language.

IMS health data analysis can highlight the trends and patterns that would allow these enterprises to make performance-enhancing decisions. For instance, marketing leaders can use AI-driven reporting automation to:

  • identify their best and worst-performing brands, 
  • measure their market share in different drug categories and
  • analyze their region-by-region sales statistics.

Using this information, they can make quick decisions in response to market trends that can lead to improved business results.

Conclusion

Many leading pharma companies are already utilizing NLG-enabled reporting automation to improve their ability to make sense of data. Using NLG, these companies are augmenting their data analysis capabilities and extending the same to all of their employees, regardless of their function and data literacy level. As a result, all members of the pharma supply chain — from C-suite executives to on-field medical representatives — are reaping the benefits of data-driven decision making, thanks to reporting automation.

Develop a data-literate enterprise with NLG

Data doesn’t lie. But it doesn’t give you the whole truth either — that is if you don’t know how to question it. That’s why you need analysts who are trained to ask your data the right questions to help you infer solid, objective facts. Facts that can help you make sound decisions and improve your business performance.

However, it becomes impractical for your small team of analysts to go through the large volumes of data generated every day to provide your employees with actionable insights. It is also not feasible to train all your employees to become certified data analysts who can make sense of all the dashboards, spreadsheets, and reports for making fact-based decisions.

What you can do, however, is cultivate data literacy among all your employees to make them better explorers of data. But the road to achieving data literacy in your workforce can be long and hard, riddled with multiple challenges.

Understanding the roadblocks to data literacy

Gaining awareness of the biggest obstacles of data literacy and identifying the appropriate methods to overcome them are the foremost steps towards building a data-literate organization. Following are the most common obstacles to achieving data literacy that enterprises face:

1. Misinterpretation of data

Generally, the process of interpreting data requires the analyst to clean, query, analyze, and present the data in the form of simplified dashboards, visualizations, and reports. These reports, although highly detailed, cannot be objectively interpreted by the non-analyst employees.

Even data visualization in the form of graphs and charts can be interpreted differently by different people. As a result, depending on who is viewing the report, the inferences made from it can widely vary. This can potentially lead to suboptimal decisions that may lead to undesirable outcomes.

2. Data comprehension at different levels of hierarchy

Another challenge in achieving data literacy is providing data that is relevant to employees at different levels of the organization. To make the most out of data, enterprises must encourage data-based decision-making at every level of the enterprise. This means that not just strategic decisions but even those pertaining to day-to-day operations must be informed by data.

However, most employees in non-leadership roles are not equipped with the tools to understand insights. They can be intimidated by data-based problem-solving. Result? Employees are unable to improve their work methods and solve problems despite the availability of the requisite data.

3. Depleting analyst resources

If given enough time, the enterprise’s analysts can create custom reports for different functions. However, creating multiple reports on a daily basis is not the most productive use of an analyst’s valuable time. Every hour an analyst spends on creating reports and populating dashboards is an hour not spent on innovation that can add greater value to the business.

To overcome these problems, businesses can leverage Natural Language Generation (NLG), enabled by machine learning and artificial intelligence.

Overcoming the challenges with Natural Language Generation

An effective way to overcome these challenges of data literacy is making data tell a story that everyone can follow. This means building a simple narrative in plain English or any language that your employees can easily understand. And Natural Language Generation (NLG) technology, an emerging frontier in AI research, can help to achieve exactly that.

NLG gives machines the ability to convert structured statistical data from spreadsheets and other databases into plain text summaries. Using NLG-powered tools can enable enterprises to publish reports written in natural, day-to-day language instead of confusing tables and graphs. These reports and dashboards give employees the exact information and insights they need to make quick decisions.

Such reports can also be used in conjunction with statistical tables and visualizations to tell employees what each component of a table or visualization means. By using such reports, not only can employees take immediate action by gaining the necessary insights, but also develop data literacy skills in the long term.

The use of natural language generation technology with AI-based data analytics capabilities can ensure that each employee gets data in a form that is relevant to their function, designation, and data expertise. NLG-generated reports can talk to each employee in a language that they best understand. By using NLG to decode data, employees can become more capable and confident in utilizing data for day-to-day decision-making. In other words, they will have the tools that can help them become data literate.

Using NLG-based report generation tools to improve data literacy can unburden your analysts and spare them from having to repetitively create and update reports for different employees. Analysts can, thus, spend more time on solving big-picture problems and add greater value to the organization. In the long run, NLG can help enterprises to empower all their employees with data, ultimately resulting in continuous growth and improvement.