Blockchain applications: Bringing in the next wave of new technology jobs

This article was first published in Business Today on November 6, 2019. Co-Author: Sanjay Fuloria; https://www.businesstoday.in/opinion/columns/blockchain-applications-next-wave-of-new-technology-jobs-forget-analytics/story/388743.html

Blockchain applications are suitable across industries due to their security, immutability and decentralised properties. This means the next wave of new technology jobs would come from blockchain.

Blockchain is hot news these days. There was a time, in the not so distant past, when working-age people were going after analytics courses. People's inboxes were flooded by emails from sundry institutes and organisations offering analytics courses, degrees and diplomas.

Everybody wanted to get into analytics. While the availability of data is huge and the requirement to analyse and make sense of it is still there, analytics doesn't seem to be so popular now. Blockchain seems to be the next analytics.

As per Yli-Huumo etal, the idea of Blockchain started in 2008 (Yli-Huumo, Ko, Choi etal, 2016). Blockchain is defined, as the name suggests, as a chain of blocks of information. This is stored in a database. Merriam Webster defines blockchain as "an open, distributed ledger that can record transactions between two parties efficiently and in a verifiable and permanent way."

Blockchain is also defined as "a digital ledger of economic transactions that is fully public, continually updated by countless users, and considered by many impossible to corrupt." (Carlozo, 2017). The use of cryptography makes the blockchain transactions trustworthy and secure (Holbl, Kompara etal). All cryptocurrencies including bitcoin have the blockchain technology at its base.

The above properties of blockchain make it applicable across industries. As usual, banking was the first industry to make use of blockchain technology. The banking industry has always been the first mover when it comes to the latest technology. Barclays and UBS are trying the blockchain technology to speed up settlement and their other back-office functions.

This could lead to an annual saving of $ 20 billion in costs. Middlemen could be eliminated just like that. Payment collection and automation of digital invoices are other applications in the banking industry. Crowdz is a B2B startup that is blockchain-based.

Barclays bank has invested in Crowdz in May 2019. JPM Coin is being launched by JP Morgan to enable transactions between one institute and the other. JPM Coin is based on blockchain technology.

Blockchain technology is slated to revolutionise messaging. It is going to be used to build an improved and secure communication infrastructure. The security expectations would be uniform across platforms. Currently, different platforms have different protocols which might compromise security.

SuchApp is working at creating a "5G ecosphere" using blockchain. Commercial transactions would also be possible on SuchApp. Then there is BlockMesh. This will work outside the range of cellular towers. It will work on the concept of peer to peer networking.

Telegram Open Network (TON) is being developed by the popular social networking app Telegram. They are planning to get into censor less browsing, payments and file storage. Other chat platforms like Kik are also raising money via Initial Coin Offering (ICO).

Kik is into in-app currency. There are other nuances which some companies utilise to make themselves unique. There's an app called Echo that uses a different protocol named Interplanetary File System (IPFS) which leads to quicker messaging. Echo is unique because other apps require the interacting parties to access the blockchain directly whereas Echo bypasses this by using the IPFS client.

Ride-sharing services have started using blockchain technology in a big way. Although we hear a lot about ride-sharing, according to a U.S. report, only 1% of the Vehicle Miles Travelled (VMT) (standard terminology in the ride-sharing industry) are accounted for by the ridesharing services.

There is a huge opportunity in rural markets. Blockchain technology can help by removing intermediaries between the driver and the rider. Driver vetting is another advantage of blockchain technology. Smart contracts make the rules and regulations transparent. These can be viewed by any stakeholder of the platform.

Any variations would be accomplished by enforcing smart contracts. The drivers' traffic record could be added to the blockchain to be used later for feedback and corrective measures. An Israeli company is working on a community-owned transportation platform that utilises any unused capacity for the benefit of the rider.

They are using the blockchain technology to device a ''fair share" reward system for all the stakeholders. There's another example of Arcade City that uses blockchain technology for all transactions. They permit drivers to set their own rates, build their own clientele of riders, and provide other services like delivery.

Education Industry has a lot of potential for the use of blockchain technology. Academic credentials could be added to the blockchain. This would make the verification process easy. Any fraudulent claims could be nipped in the bud.

There is a U.S. based startup, Learning Machine, that has created a toolset called Blockcerts that can be used to prepare, provide, view, and verify blockchain-based certificates. Student records can be shared and secured using blockchain. There are a lot of education apps and services available nowadays. Identity management for these services is a major problem. There are blockchain-based platforms available that help users carry their identity around the internet.

Internet of Things (IoT) could use blockchain technology for its advantage. A new concept christened as Autonomous Decentralised Peer-to-Peer Telemetry (ADEPT) uses a technology similar to blockchain to let devices (things) to communicate with each other directly without the presence of any mediator.

Data security is a major challenge with IoT as multiple devices get connected in real-time. If the data gets leaked or it is in some way not secure, it could be detrimental for all the concerned parties. The security aspect of blockchain could be utilised to its full potential when dealing with IoT.

Real Estate industry is another where blockchain technology could play a pivotal role. There are software as a service (SaaS) platforms where property information could be put in and documents could be recorded.

Blockchain applications are suitable across industries due to their security, immutability and decentralised properties. This means the next wave of new technology jobs would come from blockchain. Get ready to be swarmed by promotions from organisations/institutions offering blockchain courses. Working knowledge of blockchain could be the next great differentiator.

Social Media Analytics and Its Place in Management Education

This article was first published in GARP, Risk Intelligence on August 30, 2019. Co-author: Sanjay Fuloria; https://www.garp.org/#!/risk-intelligence/technology/data/a1Z1W000003mAbvUAE

Business schools can teach the power of the technology and stress its ethical application

There is a surfeit of social media data available for anyone who cares to generate insights and use it for legitimate (or illegitimate) purposes. However, to capture the data in the best possible manner and to get the desired outcome, one must know what to look for and where.

Space, time, content and network are the four key dimensions of data collected or information disseminated through social media. But how does one capture and analyze these? Are the management graduates and post-graduates of today equipped to make the most of this data? The point we will try to make is that social media analytics can be used for making positive impact on business outcomes and hence must be introduced in B-schools as an elective.

Calculating the impact of company marketing campaigns is one such use. In order to do this, questions about the brand could be asked on any of the social media platforms such as Twitter. These questions could generate a lot of discussion about the brand. Then, the company that has launched the product can measure sentiments through the discussions. Twitter metrics like engagement rate, potential impressions, geographical locations, tweet frequency, hashtag usage, top tweets, and followers' activities can be measured. All this would give a fair idea about the success or failure of the marketing campaign.

Social media analytics can help organizations learn from their competitors. By analyzing the social media activity of competitors, organizations can understand what new product launches are happening, how the customers are reacting, what are the good/bad product features, the kinds of complaints customers have, etc. This analysis could lead to prevention of similar mistakes by the company that is analyzing the data.

The use of social media in trading and investing is well documented. In financial markets, information and the speed of information is the key. Short-run movements in the Dow Jones average can be quite accurately predicted through the sentiments expressed in tweets, thereby giving an edge to traders able to make such predictions.

Soft and Hard Skills

On the jobs front, analysis of social media sites like LinkedIn could help users comprehend the types of jobs that are aplenty. They could also help indicate supply and demand for various skills in the jobs market. This kind of social media analytics could be most useful to MBA students who are about to get into a full-time career.

A quick search on the internet for most sought-after soft skills that companies are looking for in 2019 are creativity, persuasion, collaboration, adaptability, and time management. The most in-demand hard skills are cloud computing, artificial intelligence, analytical reasoning, and user interface design.

Another important aspect of business that could be strengthened by the right use of social media analytics is problem resolution. If a customer complains about a product or service on social media, the company should try to resolve the issue in a timely manner, in real time if practically possible. If the social media analytics reveals a sizeable number of complaints about the same service or the same feature, then the company can take stronger action to rectify the problem: changing/correcting the feature, replacing the person handling the issue, or maybe even re-launching the product/service with improved performance.

Management Initiative

In all this, the management professionals in any organization would play a key role, as they are the decision-makers. If they understand how to use social media analytics, then the job for any organization would become easier.

Any analytics starts with defining objectives clearly, asking the right questions, collecting the right data, analyzing the data and, finally, gathering insights from the analysis. The two most important links in the analytics value chain are clear objectives and asking the right questions. If these two aspects can be somehow hard-wired into the brains of management professionals, right from their MBA days, the outcomes would be better.

MBA curriculums have many analytical subjects these days. Introducing social media analytics into the curriculum would be an added advantage. The topics to be covered should include open-source programming languages like R or Python.

However, it needs to be realized that there are two sides to every coin. Social media analytics can also be used to influence outcomes illegitimately. Cambridge Analytica, a London-based election consulting firm, was in the news for analyzing data from an estimated 50 million Facebook profiles for insights that were used to influence election results in the U.S. and other countries. Online materials favoring candidates were delivered to individuals based on their psychographic profiles. This was a wrong and sinister use of social media analytics that compromised personal information and wrongly influenced election outcomes. Hence, the study of social media analytics must have an ethics component as well.

The Power of Democratized Data

This article was first published by Global Association of Risk Professionals, Risk Intelligence, on September 21, 2018. Coauthor: Sanjay Fuloria

https://www.garp.org/#!/risk-intelligence/technology/data/a1Z1W000004B9QmUAK

How readily available data sets and crowdsourcing can promote problem-solving and policy solutions

While reading a post on the Reddit social news aggregation site, we were amazed by a link to download government data. The data pertained to parking tickets issued by the Chicago Police Department over a decade. The data was anonymized but had all the details regarding the reasons for the tickets and demographic details of the violators.

The local government put this data up in a comma separated value (csv) format to invite inputs from researchers and intellectuals for help in policy formulation. This would improve the performance of the police department. They could answer questions, for example, about gender or racial bias in issuing parking tickets.

This approach should be used by the Indian government at the Center, and by state governments and local civic bodies. Anonymity is important to maintain, but other than that, there is no reason why this data should not be made available to the public at large for analysis. While the government has started data.gov.in, the amount of data available there is pretty limited.

Apart from that there is this need for accuracy. We compared the actual seasonal rainfall data for the state of Telangana on two government websites, www.imd.gov.in and http://www.tsdps.telangana.gov.in/. 

They were not the same. As per IMD, the actual rainfall to date was 665.8 millimeters as of September 5, 2018, which is 6% in excess of the 50-year Long Period Average (LPA). The other website showed rainfall of 584.8 millimeters as of September 7, 2018, which is a 7% deficit. 

IMD publishes data weekly, whereas the Telangana data is daily. Which to believe? It looks like the two agencies don’t talk to each other. No wonder the forecasting models used by these government agencies are far from accurate.

The data availability would enhance the scope for better inputs to shape public policy. The areas could be as diverse as traffic management, crime control, queuing in hospitals, school admissions, etc. The government could in fact have contests, with prizes given for best policy recommendation or for the best machine learning algorithm to solve a particular problem.

Downloadability

The government could take a cue from the likes of Kaggle, where such contests are the norm. On Kaggle, a lot of companies provide their data free of charge to solve their problems. The only requirement is the availability of data in an easy to download format.

The census data available through www.censusindia.gov.in is very difficult to download. The navigation of the website is itself a bit challenging. The data sets are distributed into different files. The best alternative would be to make it available in one file and at the village level. Such granularity is needed for analysis and to make sense of data.

Census data, if easily downloadable, could lead to a lot of analysis. Much of it would be superficial, but some would definitely be meaningful and could be used by the government to inform its policy choices. 

As James Surowiecki says in The Wisdom of Crowds, “A diverse collection of independently deciding individuals make better predictions than individuals or even experts.” The wisdom of crowds can be leveraged.

While financial data is made available by the government, data on other socially relevant fields can be hard to come by. With so many open source tools available for handling the data, it has become relatively easier to make sense of data. Moreover, there are many MOOCs (Massive Open Online Courses) available to whoever is interested in learning data handling.

With the democratization of education, it is high time the government thinks of democratizing data. We are not saying data privacy is not important, but as long as the details don’t identify an individual, it should not be an issue. Let a hundred flowers blossom.

General Data Protection Regulation: What will happen to all the micro level analytics?

This article was first published in Analytics India Magazine on May 19, 2018; Co-author: Sanjay Fuloria

https://analyticsindiamag.com/general-data-protection-regulation-what-will-happen-to-all-the-micro-level-analytics/

The recent episode involving Facebook and Cambridge Analytica raised data privacy concerns once again. This resulted in the closure of Cambridge Analytica, once a high-flying consulting company. They, allegedly, stole user data from Facebook to micro analyze the profiles of individuals.

Based on this micro analysis, Cambridge Analytica advised political parties on campaigning in elections. Facebook CEO Mark Zuckerberg also had to face some very tough questions at the Congressional hearing as a fall out of the above episode. On the face of it, data theft notwithstanding, this is a practice quite rampant in today’s digital world. 

Companies have terabytes of data which they analyze to promote their products and services by targeting the right audience, whether the consumer likes it or not.

With this background, the General Data Protection Regulation (GDPR) formulated by the European Union (EU), is coming into full effect from May 25, 2018, with the objective to protect data at the individual/consumer level. According to this regulation, the control of the data resides with the individuals. If they decide not to share their data, companies can’t use that data. Non-compliance or breach would result in huge fines. Some of the important points covered under GDPR are as follows:

1. There is a penalty of €20 million or 4% of worldwide revenue for non-compliance.
2. Consumers should opt-in for consent.
3. GPS locations are also included in the definition of personal data.

Now, considering most of the global companies have huge analytics departments filled with data scientists, many of whom are PhDs, consumer data is their stock in trade. All their algorithms need copious amounts of data to function well. A lot of marketing programs use the individual level data to design marketing campaigns, both online and offline. 

When someone searches for a book online, for example, she gets a list of similar books she could buy. Now, if the individual doesn’t want to share this data with the company, the company will have to delete this information about the individual. 

The individual has a right to be forgotten as per the regulation. If a majority of individuals decide not to share their data, the marketing campaigns and the analytics engines would all go for a toss.

Some other global companies have outsourced their analytics work to niche vendors. They would also face the same data problem. This regulation applies to any company providing services to EU citizens irrespective of whether the company has a physical presence in the EU or not.

The companies will have to find an alternative approach to deal with consumers. They may not have sufficient time though. As far as GDPR compliance goes, only 7% companies are fully compliant as the May 25th deadline approaches.

As per a Crowd Research report, very few companies (only 40%) are hoping to be compliant by the deadline (https://www.zdnet.com/article/gdpr-compliance-for-many-companies-it-might-be-time-to-panic/). This becomes a tricky situation for the companies even before the analytics experts ask for individual data.

If the individuals raise a complaint after the deadline that their data was misused, companies are bound to pay a hefty fine. What about the individual level data that is already stored in company servers and on the cloud? It will take a huge effort and investment on the part of the companies to make that data safe and reach compliance.

This applies to machine learning algorithms as well. The whole premise of machine learning is that new data should be continuously fed to the algorithms to make adjustments and provide better predictions. Autonomous cars, for example, use image processing and they do need human images. 

Imagine a scenario where human beings refuse to share their images. Of course, the data scientists will have images of animals, trees and other objects to feed into their algorithms but this won’t prevent the autonomous vehicles from bumping into human beings leading to dire consequences.

We have already had a case in the U.S. where an autonomous car hit a human being resulting in death.

With the latest voice recognition technology and the advent of devices like Amazon’s Echo and Google’s Home, individuals are supposed to feel more comfortable with less friction in their lives. 

For this to happen, the speaker has to be ‘ON’ all the time and it will record whatever is spoken at home leading to dire consequences. This data will also be stored somewhere on the cloud. Sooner rather than later, individuals would not want their data to be shared between devices, going back to the earlier era of zero connectivity. It is still to be seen how all this plays out.

Companies are concerned that GDPR compliance will make the jobs of their employees more cumbersome and would make it very difficult for them to do business. They are hoping fines won’t be levied so soon after all and the deadlines might get extended. 

Whatever be the situation on May 25th, 2018, data privacy concerns are going to get shriller. Companies need to be ready with a ‘Plan B’, as far as their analytics strategies are concerned.

News: Speed and technology

This article was first published by the Analytics India Magazine on February 15, 2016; Co-author: Sanjay Fuloria (Cognizant Research Centre)

http://analyticsindiamag.com/news-speed-and-technology/

The advent of social media has meant that information travels fast. Everyone participating is judged, liked, trolled, and slammed, every moment. A decade after the advent of Facebook and Twitter, no one can afford to ignore the impact that they have. Businesses, politicians, actors, academicians, writers, have all used social media to advance their cause.

In fact, taking a step forward are companies like Banjo. Banjo released a consumer app in 2011. This was a news app and the sources of information were social media feeds. This might not sound new as there are hundreds of such apps available for download. What’s new is the enterprise software they have developed recently that can detect events. The events are organized by location. The events are shown in great detail using pictures, texts and location. The posts are sourced from the mobile devices closest to the location of the event.

There are implications of such technology for different industries. A tweet posted by an expert about the possible price of a stock could make or break billions of dollars for investors. In the field of finance, especially in high frequency trading, time lag is the key. Research has shown that effective use of Twitter sentiment about a company can result in superior returns.

In another example, if there is a group of people witness to a crime and they are able to post pictures and messages via their mobile devices, the police can be alerted as they would get to know the exact location of the happening. Similarly, in the case of a disaster, the government can be alerted into action to manage the disaster.

Google can predict the onset of epidemics based on individual’s searching for particular medicines. They can even predict hurricanes and storms based on searches for emergency rations, blankets, torches and other items. Now, compare and contrast this to the ways news was gathered and disseminated historically.

A popular method was to appoint messengers to either collect or send news. There were people who were designated as ‘criers’ to shout out the news loud and clear. People were asked to gather at cross roads of their respective localities to spread the news. The great travelers were a good source of information and news from distant lands. They were accorded grand welcome and one of the reasons was their news carrying capability.

Then there are examples of daily handwritten news sheets started by Julius Caesar. The present day marathon can also be traced back to the phenomenon of spreading news through a messenger. Marathon celebrates the courage of Pheidippides who ran 26 miles from Marathon to Athens to carry the good news of Athenian victory over the Persians. He sadly collapsed and died shortly after providing the news due to exhaustion.

As a method of spreading news, pigeons were used by Persians. They trained these pigeons. The Mughals and Romans used pigeons. In fact they were used to aid the military. Surprisingly pigeons were used by financiers before the advent of the telegraph. Just imagine how much time all these modes of communication would have taken and sometimes the message would simply be lost owing to the mortality of pigeons.

From pigeons who would take days to deliver news we are at a stage where we can get the news almost instantly. The use of pigeons to deliver news now seems a very distant past. But the truth is that they were used for transmission of news and information till just about a century ago. With the advent of the telegraph in the late 19th Century, the use of pigeons started to decline.

The world has really evolved. However, there are some privacy issues involved. Not every individual who is privy to an event would like that information to be seen by everyone with an internet connection. The privacy settings in the respective social media sites should do the job. If some individual doesn’t want his/her location information to be shared, they can simply turn that off. There could be some deleterious effects of news spreading fast. During riots, quick news could incite more people pretty fast and soon the mob would become difficult to handle. On the other hand, the law keepers would get the information quickly and they could arrive on the scene pretty quickly before much damage has been done.

There are both positives and negatives like the proverbial two sides of a coin, to this lightning transfer of news. Used judiciously, this technology could redefine the world we live in forever.

The case for Mental Health Insurance

This article was first published in the IIB Bulletin, Vol 2, Issue 2, pp17-18

https://iib.gov.in/IIB/Articles/IIB%20Bulletin%20Q2%202015-16.pdf

As per the World Health Organization (WHO), Mental health refers to a broad array of activities directly or indirectly related to the mental well-being components included in the WHO's definition of health: "A state of complete physical, mental and social well-being, and not merely the absence of disease". It is related to the promotion of well-being, the prevention of mental disorders, and the treatment and rehabilitation of people affected by mental disorders.

Common forms of mental illnesses include Depression, Anxiety/ Phobias, Eating Disorder and Stress, among others. Some of the severe forms of Mental Illness are Schizophrenia, Bipolar disorder (Manic depression), Clinical depression, Suicidal tendency, and Personality disorder.

According to National Institute of Mental Health and National Alliance on Mental Illnesses, in the US, 1 in every 4 persons suffers from some form of Mental Illness or the other, while this statistic is 1 in 6 persons in India. The impact is that people with mental illness die 25 years earlier than other Americans and more than 90 percent of suicide cases are found to have one or more mental disorders.

In a study done by BeyondCore, Inc. on people insured between the ages of 18-35, in the USA, it was found that Mental Illness has a compounding effect on claims (cost of treatment). For example, the annual cost for young adults with heart failure was $42,000, for people taking antidepressants was $7,700, but people who had both heart failure and were taking antidepressants had an annual cost of $70,000 (see Figure 1). 

Figure 1: Compounding effect of Mental Illness

To the economy, the loss of earnings due to mental illness amounts to US$193 billion per annum. Globally, depression alone affects 400 million persons and was estimated to cost at least US$800 billion in 2010 in lost economic output, by WHO, a sum expected to more than double by 2030. While such statistics are not available for India, it will be reasonable to assume that the impact would be significant.

In fact, the situation in India may be worse as acknowledging suffering from some form of Mental Illness is culturally a taboo in India. On top of that, the availability of help in terms of psychiatrists, psychiatric beds, clinical psychologists, etc. is much below the required numbers. For example, there are approximately 3000 psychiatrists in India vis-à-vis a requirement of 150000.  

Health Insurance policies also exclude Mental Illness specifically. Extracts from the policy documents of a few health insurance products read as follows:

• “the following fall under permanent exclusions: Any expense incurred on treatment of mental Illness, stress, psychiatric or psychological disorders”
•  “this policy excludes: Psychiatric, mental disorders (including mental health treatments)”

Insurance plays a key role in Healthcare financing. Insurance is based on law of large numbers and there is no denying the large number of people suffering from mental illness. The trouble of course is that Insurance contracts are based on utmost faith and the policyholder must disclose complete known information about his physical and mental health at the time of buying the policy. The fear of inadequate disclosure by the customer may deter the Insurers from offering policies on Mental Health insurance.

Assessing the risks may remain a challenge for the underwriters till adequate data becomes available. Collating the data from various institutions like National Institute of Mental Health and Neurosciences and the Institute of Mental Health and Hospital, Agra may help the Insurance companies design appropriate products.

Use of innovative techniques may come in handy to some extent. For example, social media analytics of an individual may reveal suicidal tendencies or enquiries about specific problems like depression, anxiety, etc. Sentiment analysis can help find people at risk. These can then be verified with the customer and specific undertaking may be taken from the customer if he does not agree with the findings.

Mental illness is also a major cause for the high number of suicides in India. Intervention at the right time, access to healthcare, along with health financing will play a major role in talking the problem of suicides related to mental illness as well as prevention of the illness getting aggravated. It is a serious issue and the Insurers can play a major role to make a difference!

Analytics: Missing the woods for the trees

This article was first published in the Analytics India Magazine on June 23rd, 2015; Co-author: Sanjay Fuloria (Cognizant Research Center, Hyderabad)

http://analyticsindiamag.com/analytics-missing-the-woods-for-the-trees/

Big Data Talent Gap is a serious problem. Recognizing it, Universities are introducing courses on Analytics and Big Data. This has resulted in a larger supply of people who are well versed with data manipulation, handling and running codes. But, it has created a gap of another type. 

In an article on “Three Problems All Data Scientists Experience”, Drew Farris of Booz Allen Hamilton Inc. writes that “…problems go beyond technology and machine learning and are broadly encountered regardless of the task at hand: interpreting the problem, sourcing the data, and describing the outcomes”.

A lot of new joiners in analytics teams across companies face a serious problem especially while describing the outcomes. They fail to understand what their effort will lead to? This effort could be the software code they are working on or the project module that is assigned to them. Why only new joiners? Even employees with 6 to 7 years of experience find it difficult to look at the big picture. The institutes where they learn these analytics’ techniques are partially to be blamed. They are taught to play with the software. It could be coding or working on the dime a dozen graphic user interfaces that are available. They understand how to handle data, get the results and interpret the data. How this interpretation would lead to business gains or efficiency gains is not clear to them!

A simple example could be a segmentation exercise where the collected data is used to segment customers into various groups. These groups could be divided demographically or by using the customers’ choices and preferences. Once this segmentation is done, each segment can be profiled both on the basis of demographics and choices. Up to this point, all analytics greenhorns would do a perfect job. The next step is where complications arise. When they present this to the client, the client enquires about the usage of this exercise. They do not have an answer to this. If they can tell the client how each segment can be uniquely targeted using specific marketing campaigns and what amount of efficiency gains they would achieve, the client would be delighted. If this is done correctly, apart from the short term gain of client appreciation, they can expect long term career growth opportunities.

With so much of data available through various sources like smartphones, internet and social media sites, the requirement for experienced analytics professionals is bound to grow. The beauty of the situation is that this data availability is only going to increase with the advent of internet of things. In internet of things, devices will talk to each other with an app on your smartphone helping you to switch on your television and air conditioning just before you enter your home. A stage will come when the data of your home arrival times can be analyzed and the app will trigger the switching on of your devices   automatically without you even tapping it.

We also keep hearing of big data silos across data stores within the same organization. This happens because people with skills in data analytics do not understand which problem can be solved using the unified data. If they can be exposed to such problems and solutions, a lot of data can be unearthed from data warehouses and used productively.

There is an urgent need for institutions teaching analytics courses to equip their students with the ability to look at the larger business problem and then use their data skills to solve that. Instead of starting with the data, they should start with the business problem and while working on it they should not miss the woods for the trees. This can be done easily when the focus is on the business problem and not on the data.

Health Insurance Hospital Registry

This article was first published in the IIB Bulletin, Vol 1, Issue 4: Co-Author: Varsha, GS1 India
https://iib.gov.in/IIB/Articles/IIB%20Bulletin%20Q4%202014-15.pdf

Poor data impacts many areas in the healthcare system. One of the areas that has an impact on Healthcare Analytics is the way hospitals are identified and stored in the various databases. In the case of the Insurance Industry, each Insurer has their own naming convention for Hospitals. For example, Table 1 shows that five different Insurers can name the same hospital in 5 different ways in their databases.

Table 1

Database A	Database B	Database C	Database D	Database E
ABC Hospital	The ABC Hospital & Emergency Services	ABC Hospitals Pvt. Ltd	ABC Hospital Group	ABC Group of Hospitals

In the above illustration one cannot be certain if all the names are referring to the same entity or if they are all different entities, without painstaking manual intervention. Using the list as it is would not give a clear picture of the number of claims, average claims, top diseases in a period in a particular Hospital, total insurance claims paid per Insurer to the hospital, and many more such statistics.

To overcome this issue it is recommended to identify each entity (hospital) with a standard and unique number. Think of it as a mailing address: an identifier for a single location in the world that is globally unique to that location. No other organization, agency, or affiliate can use it to identify their locations, but all parties can and should use it to identify that location.

The Standard adopted globally to identify a location using a unique and unambiguous number is a GS1 Global Location Numbers (GLNs) based on the GS1 System of Standards. Utilizing a GLN can help improve data integrity. In turn, it will help reduce cost and time spent on data cleaning and making it more reliable.

Such a system enables global and unique identification of products and locations, as well as the continuous, automatic update (i.e., synchronizing) of standardized information across all stakeholders. Unique identification provide the necessary foundation for achieving the best results when using complementary applications like automatic data capture, e-commerce, electronic record management, etc.

Insurance Information Bureau of India has undertaken a project to identify each Hospital in the Health Insurance Providers Network. GS1 India would allocate a GLN to each hospital, which is a unique, 13-digit number for a specific location. Implementing GLNs simplifies the exchange of information and provides the opportunity to manage accurate and authenticated data more effectively.

The GLN, or the globally unique ID would not only identify a specific location, but also provide the link to the information pertaining to it (i.e., a database holding the GLN attributes such as postal address and GPS co-ordinates of the location, services offered at that location, key contact person at that location etc.). This is a key advantage of using a globally unique identifier because all information can be held and maintained centrally in a database or registry reducing the effort required to maintain and communicate information between multiple parties on a national or global basis.

This enables various stakeholders to simply reference a GLN in communications, as opposed to manually entering all of the necessary party/location information. Using a GLN to reference party/location information promotes efficiency, precision and accuracy in communicating and sharing location information.

Figure 1

Several countries like UK, Australia, Austria, North America etc. use GLN’s in their procurement processes to enable efficiency and transparency to deliver better patient care.

The use of GLNs provides a method of identifying locations that are:

·         Unique: with a simple structure, facilitating processing and transmission of data;

·         Multi sectoral: the non-significant characteristic of the GLN allows any location to be identified - regardless of its activity

·         International: location numbers are unique worldwide.

By identifying hospitals with GLNs enables interoperability with other GS1 Healthcare Registries in the world, building global visibility of Indian healthcare facilities, services and capabilities for international patients

However, the most immediate impact of the Unique Identification would be on the quality of Analytics. Only when hospitals are properly identified, logged and data generated on health aspects from them are reliable, can any meaningful analysis be carried out. A list of unique hospitals will be beneficial to Hospitals, Insurers, Govt. Agencies and also the Public.

·         Claims payment can be accelerated

·         Fast, reliable and relevant Analytics

·         Geography based trends, patterns of disease occurrence, cost patterns, etc.

·         Footprint is visible

·         Will aid in the Fraud Analytics efforts of IRDAI

Ministry of Health and Family Welfare is working on standardizing treatment procedures and costing templates. Efforts are being made by IRDAI-FICCI to categorize hospitals. Unique Hospitals would complement all of these projects as well.

A simple illustration may be seen in Table 2 where the outlier analysis throws out more meaningful results when the hospital is correctly identified.

Table 2

	Cost of treatment for Disease type Cholera
	Database A	Database B	Database C	Database D	Database E
	ABC Hospital	The ABC Hospital & Emergency Services	ABC Hospitals Pvt. Ltd	ABC Hospital Group	ABC Group of Hospitals
Claim Paid 1	16,016	2,093	33,115	24,299	39,113
Claim Paid 2	16,577	27,929	22,919	19,366	26,343
Claim Paid 3	12,122	23,767	30,916	29,279	26,000
Claim Paid 4	16,134	25,958	31,108	21,147	15,500
Claim Paid 5	10,280	15,981	1,99,400	26,828	25,000
Average claim paid per hospital	14,226	19,146	63,492	24,184	26,391
Overall Average claim paid	29,488
Highlight Outliers where Claim paid or amount claimed is above/below +/- 50% of the average for the hospital
	Database A	Database B	Database C	Database D	Database E
	ABC Hospital	The ABC Hospital & Emergency Services	ABC Hospitals Pvt. Ltd	ABC Hospital Group	ABC Group of Hospitals
Claim Paid 1	-	Outlier	Outlier	-	-
Claim Paid 2	-	-	Outlier	-	-
Claim Paid 3	-	-	Outlier	-	-
Claim Paid 4	-	-	Outlier	-	-
Claim Paid 5	-	-	Outlier	-	-
If the Hospital is identified as the same hospital in all databases, the average claim paid will be Rs 29,488/- across all 25 claims.
	Database A	Database B	Database C	Database D	Database E
	ABC Hospital	ABC Hospital	ABC Hospital	ABC Hospital	ABC Hospital
Claim Paid 1	-	Outlier	-	-	-
Claim Paid 2	-	-	-	-	-
Claim Paid 3	Outlier	-	-	-	-
Claim Paid 4	-	-	-	-	-
Claim Paid 5	Outlier		Outlier	-	-