The Ultimate Data Analysis Cheat Sheet: Tool for App Developers

 Cheat Sheet tool

Analytic insights have proven to be a strong driver of growth in business today, but the technologies and platforms used to develop these insights can be very complex and often require new skillsets. One of the initial steps in developing analytic insights is loading relevant data into your analytics platform. Many enterprises stand up an analytics platform, but don’t realize what it’s going to take to ingest all that data.

Choosing the correct tool to ingest data can be challenging. Anteelo has significant experience in loading data into today’s analytic platforms and we can help you make the right choices. As part of our Analytics Platform Services, anteelo offers a best of breed set of tools to run on top of your analytics platform and we have integrated them to help you get analytic insights as quickly as possible.

To get an idea of what it takes to choose the right data ingestion tool, imagine this scenario: You just had a large Hadoop-based analytics platform turned over to your organization. Eight worker nodes, 64 CPUs, 2,048 GB of RAM, and 40TB of data storage all ready to energize your business with new analytic insights. But before you can begin developing your business-changing analytics, you need to load your data into your new platform.

Keep in mind, we are not talking about just a little data here. Typically, the larger and more detailed your set of data, the more accurate your analytics are. You will need to load transaction and master data such as products, inventory, clients, vendors, transactions, web logs, and an abundance of other data types. This will often come from many different types of data sources such as text files, relational databases, log files, web service APIs, and perhaps even event streams of near real-time data.

You have a few choices here. One is to purchase an ETL (Extract, Transform, Load) software package to help simplify loading your data. Many of the ETL packages popular in Hadoop circles will simplify ingesting data from various data sources. Of course, there are usually significant licensing costs associated with purchasing the software, but for many organizations, this is the right choice.

Cheat Sheet tool for data analytics


Another option is to use the common data ingestion utilities included with today’s Hadoop distributions to load your company’s data. Understanding the various tools and their use can be confusing, so here is a little cheat sheet of the more common ones:

  • Hadoop file system shell copy command – A standard part of Hadoop, it copies simple data files from a local directory into HDFS (Hadoop Distributed File System). It is sometimes used with a file upload utility to provide users the ability to upload data.
  • Sqoop – Transfers data from relational databases to Hadoop in an efficient manner via a JDBC (Java Database Connectivity) connection.
  • Kafka – A high-throughput, low-latency platform for handling real-time data feeds, ensuring no data loss. It is often used as a queueing agent.
  • Flume – A distributed application used to collect, aggregate, and load streaming data such as log files into Hadoop. Flume is sometimes used with Kafka to improve reliability.
  • Storm – A real-time streaming system which can process data as it ingests it, providing real-time analytics, ETL, and other processing of data. (Storm is not included in all Hadoop distributions).
  • Spark Streaming – To a certain extent, this is the new kid on the block. Like Storm, Spark Streaming is a processor for real-time streams of data. It supports Java, Python and Scala programming languages, and can read data from Kafka, Flume, and user-defined data sources.
  • Custom development – Hadoop also supports development of custom data ingestion programs which are often used when connecting to a web service or other programming API to retrieve data.

As you can see, there are many choices for loading your data. Very often the right choice is a combination of different tools and, in any case, there is a high learning curve in ingesting that data and getting it into your system.

Reasons why insurers need AI to combat fraud ahead of time.

AI to combat fraud

The insurance industry consists of more than 7,000 companies that collect more than $1 trillion in premiums annually, providing fraudsters with huge opportunities to commit fraud using a growing number of schemes. Fraudsters are successful too often. According to FBI statistics, the total cost of non-health insurance fraud is estimated at more than $40 billion a year.

Fighting fraud is like aiming at a constantly moving target, since criminals constantly hone and change their strategies. As insurers offer customers additional ways to submit information, fraudsters find a way to exploit new channels, and detecting issues is increasingly challenging because threats and attacks are growing in sophistication. For example, organized crime has found a way to roboclaim insurers that set up electronic claims capabilities.

Advanced technologies such as artificial intelligence (AI) can help insurers keep one step ahead of perpetrators. IBM Watson, for instance, helps insurers fight fraud by learning from and adapting to changing business rules and emerging nefarious activities. Watson can learn on the fly, so insurers don’t have to program in changes to sufficiently protect against evolving fraud at all times.

insurers need Artificial Intelligence to combat fraud

Here are four compelling reasons insurers need to begin to address fraud with sophisticated AI systems and machine learning that can continuously monitor claims for fraud potential:

  1. The aging workforce. There are many claims folks who are aging out and will soon retire, taking years of knowledge with them. Seasoned adjusters often rely on their gut instinct to detect fraud, knowing which claims just don’t seem right, based on years of experience. However, incoming claims staff don’t have the experience to know when a claim seems suspicious. Insurers need to seize and convert that knowledge, getting it into a software program or an AI program so that the technology can capture the experience.
  2. Evolving fraud events and tactics. Even though claims people may have looked at fraud the same way for years, the environment surrounding claims is always changing, enabling new ways to commit fraud. Fraud detection tactics that may have worked 6 months ago might not be relevant today. For instance, several years ago when gas prices were through the roof, SUVs were reported stolen at an alarming rate. They weren’t really stolen however — they had just become too costly to operate. Now that gas prices have gone down, this fraud isn’t happening as often. If an insurer programs an expensive rule into the system, 6 months later economic factors may change and that problem may not be an issue anymore.
  3. Digital transformation. Insurers are all striving to go digital and electronic. As they make claims reporting easier, more people are reporting claims electronically, stressing the systems. At the same time, claims staffing levels remain constant, so the same number of workers now have to detect fraud in a much higher claims volume.
  4. Fighting fraud is not the claim handlers’ core job responsibility. The claim adjuster’s job is to adjudicate a claim, get it settled and make the customer happy. Finding fraud puts adjusters in an adversarial situation. Some are uncomfortable with looking for fraud because they don’t like conflict. A system that detects fraud enables adjusters to focus on their areas of expertise.

In the past, insurance organizations relied heavily on their experienced claims adjusters to identify potentially fraudulent claims. But since fraudsters are turning to technology to commit crimes against insurance companies, carriers need to turn to technology to help fight them. Humans will still be a critical component of any fraud detection strategy, however. Today, insurance organizations need a collaborative human-machine approach, since they can’t successfully fight fraud with just one tactic or one system. To fight fraud, humans need machines, and machines need human intervention

Here’s how regulatory intelligence aids strategic decision-making in real time

regulatory intelligence

Data is all around us. It’s created with everything we do. For the life sciences industry, this means data is being collected faster and at a greater rate than ever before. Data takes the form of structured content — from clinical trials, regulatory filings, manufacturing and marketing, drug interactions and real-world evidence — with regard to how drugs are used in healthcare settings. It also is found in unstructured content from the internet of things (IoT), such as social media forums, blogs and so on.

But having massive quantities of data is useless without the regulatory intelligence to make sense of it. Let’s define what we mean by regulatory intelligence. This is about taking multiple data sources and feeding those into a regulatory system that can look at the data, analyze it, make use of it, collect information from it, then take that information to distribute it where it needs to go. This might be to the regulatory agencies requesting updates or information about the drug portfolio to satisfy compliance mandates, it might be to partners that you’re working with, such as trading partners, or it might be consumed internally.

Although referred to as regulatory intelligence, it encompasses many other areas of the product life cycle, including clinical research and development for detailed analysis and safety and pharmacovigilance for signal detection.

Life sciences companies can leverage these different types of data for real-time decision making to protect public safety, respond to supply shortages, protect the brand, advance the brand — for example, into new indications or new markets — and for many other purposes. In this blog, I’ll explore some of these uses of regulatory intelligence in greater depth.

Know your target

Since data is consumed across the life sciences in different ways by different people and different functions, getting to the point of intelligence first requires knowing the target and objective. If there is real-world data indicating adverse events that weren’t detected in clinical trials, having that intelligence early on allows companies to act accordingly — both to protect public safety and to safeguard brand reputation. What action the company takes will depend on what the data shows, as well as what the agencies require. For example, it might simply be to reinforce a message about avoiding other medications or foods while undergoing a specific treatment or it might require a broader response.

Another way data can be leveraged for real-time strategic decision making is to advance the brand. For example, IoT data or data held by the authorities might show weakness in a competitor’s product or weakness in the market — perhaps a gap in a region the company has begun targeting. By leveraging that intelligence, companies can take advantage of those gaps or competitor weaknesses and promote their brand as a better alternative or prepare a new market launch.

Regulatory intelligence might also shine light on other potential indications for your product. These insights might be gathered from IoT sources, such as physician blogs, or from positive side effects observed in clinical trials. The most famous example is Viagra, which initially was studied as a drug to lower blood pressure. As was the case here, not all side effects are negative, and during clinical studies an unexpected side effect led to the drug’s being studied and ultimately approved for erectile dysfunction. Having that regulatory intelligence available gives you the leverage to make the case for expanding clinical studies into new indications and extending therapeutic use.

Adding Real-Time Intelligence

From data to intelligence

Now that we have explored the definition of and some purposes for regulatory intelligence, we should also look at how you get from that point of data to intelligence. An important first step is to deploy the right analytical tool to sift through that data and pull out relevant information. It’s equally important to know how to make use of that data, and that requires knowing your end goal and narrowing the scope of your data search to eliminate extraneous data.

Time and resources can also be saved by leveraging automation to collect data for analysis. Since data is continuously being created, updated and pushed out, automated robotic processes make it possible to keep up to date with the latest findings and pull relevant data into your regulatory operational environment.

Regulatory intelligence is the key to real-time strategic decision making across all areas of research and development. Its importance to the organization can’t be overstated.

Digital health, not genomics! The future of precision medicine.


What does the term precision medicine mean to you? Typically, people think of precision medicine as being about genomics, but it goes well beyond molecular biology to encompass everything that moves us away from a one-size-fits-all approach to medicine. As far back as 1969, Enid Balint, formerly in charge of the training and research course for general practitioners at the Tavistock Clinic in London, published a paper on “The possibilities of patient-centered medicine,” and described precision medicine as the field that understands the patient as a unique human being.

The question, therefore, is: How do we do that? Certainly, genomics has been widely touted. But another area at the forefront of precision medicine is digital health technology, which Steven Steinhubl, MD, of Scripps Research Translational Institute, addressed in his presentation, “Precision Medicine and the Future of Clinical Practice.” Digital technology moves us in the direction of understanding each patient and away from the current practice of defining health in ways that make little sense to many people. Further in this blog, I am expanding on key elements of Steven’s talk to present a different perspective on precision medicine. While many of the messages in this blog have been raised by Steven, I’d like to offer my perspective as well.

So, what exactly is wrong with current practice in our healthcare system? For starters, the current model is based on a model in which, when you get sick or hurt, you see a doctor and you get fixed. There is little to no incentive for doctors to keep you healthy, and the system rewards them on what is called “activity-based funding” rather than “outcome-based funding” or “value-based care”.

As for population-based benchmarks, they actually don’t work for you as an individual. Let’s take wellness recommendations, such as walking 10,000 steps a day or eating a certain amount of proteins and carbohydrates each day. We know that some people need more and some need fewer carbohydrates and that the 10,000-step benchmark is fairly meaningless at an individual level.

Time to stop the generic trials

As mentioned by Steven, precision medicine is, in fact, already here in several settings. The most prominent is optometry, where an eye exam determines your specific needs, and an optometrist prescribes a pair of glasses tailored entirely to your current condition. You can also pick a model of frame and material that fits your lifestyle (e.g., sports or work) and your taste in fashion. Without this specific focus, you would end up with a generic pair of glasses that might not suit your needs and lifestyle.

Medicine needs to adopt the same approach by moving away from a generic approach to clinical studies and towards trials that focus on individual responses to therapy. In his article titled, Personalized medicine: Time for one-person trials, Nicholas J. Schork looks at the 10 most-prescribed drugs and notes that for every person they help, they fail to improve the condition of between three and 24 people. Some drugs, such as statins, benefit as few as one in 50 people, and some are even harmful to certain ethnic groups because clinical trials have typically focused on participants of European background.

Dosage is also seldom geared towards the individual. We know it’s possible to do this because the company provides dose recommendations based on pharmacokinetic drug models, patient characteristics, medication concentrations, and genotype.

Generally, however, we don’t know who will benefit from a drug and who won’t. While genomics plays a key role, there are multiple other factors that have an impact on outcomes, including our environment (e.g., city vs. rural), having access to good produce or being limited to convenience store food (e.g., doughnuts vs. fruits and veggies), whether we live in a cold or hot climate, whether we live in an industrial area with pollution, and what our work and family environment is like. Taking all these factors and more into account is essential if we are to treat each person as unique.

With the growing realisation about these effects, more clinicians are turning to digital technology, deploying internet of things (IoT) sensors and smartphones to improve patient outcomes. A study of 2,000 Americans shows that the average person uses his or her smartphone 80 times per day, so why not leverage it as part of a care plan? The fact is that people are already using their phones for health, with one out of 20 Google searches being health-related.

precision medicine

Setting baselines with sensors

Sensors and apps are being used by many people to check their vitals and provide far more relevant information than using standard measures for what is normal with sleep patterns, heart rate, blood pressure, glucose, temperature and stress. The context in which these measures are taken varies dramatically. For example, maybe it is normal for my stress and blood pressure level to rise when I’m rock climbing, and perhaps a pregnant woman can expect her sleep pattern to change.

Expanding on Steven’s idea, wearable IoT devices are redefining the human phenotype (i.e., all of the observable physical properties of an individual) by performing unobtrusive and continuous monitoring of a wide range of characteristics unique to each of us. This will allow us to define our “normal” blood pressure when we are stressed. After all, do you really need to worry if your blood pressure rises when you’re stuck in traffic after a busy day at the office?

Sensor technology enables continuous monitoring, so you can create a baseline and compare your own readings. When something doesn’t feel right, you’ll be able to go back and compare it to a day when you did feel right the month before. This is a far better measure of your own health.

Genomics Is Evolving

For example, a study into temperature shows that although your normal temperature should be around 37 degrees C, the normal temperature of a person can vary from 33.2 degrees C up to 38.4 degrees C. This means that if your normal temperature is 33.2 degrees C and you have a 37-degree C temperature, you’re having a pretty severe fever, but most doctors won’t realise this because they don’t know your normal temperature.

Another study shows that although the average daytime heart rate is around 79, the normal heart rate of a person varies from 40 to 90. This makes a big difference when treating a patient for a heart condition. This data comes from Fitbit’s analysis of 100,000 persons’ resting heart rates. So obviously, you can’t apply a population average to your own body. This is important because with the trends in your heart rate, we’d be able to find early signs of influenza, for example.

The challenge for people who have wearables (like me, yeah, I own a Fitbit … how cool am I?), is that we’re not quite sure what to do with all that data.

Following this trend, the National Institutes of Health in the United States has created the All of Us Research Program, the largest precision medicine longitudinal study ever performed, which aims to follow 1 million people from all walks of life for decades. The program will provide a set of IoT wearable sensors to the participants and then correlate this data with their clinical data from the healthcare ecosystem — hospitals, family practitioners, specialists, etc.

This study differs from your typical research study because this program will provide insights on the data to its participants, so they can improve their health in real time.

Today, anyone has access to wearable technology; it’s relatively cheap and easy to use, and it gives you real-time insights into your own health. Don’t be afraid to build your own baseline and talk to your doctor. As more people and clinicians embrace wearables and apps, we’ll start to see a broader shift towards precision medicine supported by both genomics and digital health.

How to make your enterprise analytics platforms more data democratized

Managing Enterprise Analytics

It wasn’t that long ago that data was a necessary but costly business byproduct that many companies shelved on leftover and decommissioned hardware, and only because they were legally required to do so. That’s changed, of course. Data’s value has grown exponentially in just the last few years because we’ve found that when you combine, analyze and exploit it in the right ways, it can tell you some amazing things about your company and your customers.

A big step in that direction is the concept of “data democratization.” The idea is simple. When you make data available to anyone at any time to make decisions, without limits related to access or understanding, you’re able to realize the full value of the data you maintain. Where IT was once the gatekeeper of data, new tools and technologies help any user gain access. Insights from that data can be developed by anyone, not just a data engineer or data scientist.

Case in point: Many analytics platforms offer some level of universal access to information, but the ability to use it is inherently restricted to people who understand how to use complex analytics tools. However, self-service tools, like Zaloni, are helping to democratize those analytics platforms. By combining drag-and-drop user interfaces with a powerful data catalog used to search for data, these tools can help non-technical users identify relevant data sources and create new datasets tailored specifically for an analytics task.



Data democratization isn’t just a benefit for end users, it liberates data scientists as well. With users able to run their own queries, data scientists and engineers can spend more time identifying data sources, preparing them for ingestion, and cleaning and documenting them for use.

Implementing modern, self-service enabled tools raises new questions about security and privacy, so it’s important for companies to have governance in place that can ensure data is carefully managed. Additionally, anyone who plans to use these tools still needs to receive training—not only on how to use the tools, but how to ask questions and seek insights that are valuable to the company. Having governance in place for your self-service tools ensures data privacy and data quality, provides data lineage, and allows a company to provide role-based access control to data. Zaloni’s UI, for example, ensures self-guided access to data to easily get answers to questions and access pertinent data. In today’s highly regulated world, right-sized data governance and role-based security have become a requirement, not just a nice to have.

Many companies have been accumulating vast troves of data that contains a lot of unrealized value. Implementing tools that give everyone access to that data and help them explore new ideas and connections is likely to result in some surprising and valuable discoveries.

Dynamic Healthcare System: Blurring barriers between payer and provider

 Dynamic Healthcare System

Recent headlines have been full of news about major healthcare mergers and acquisitions, often involving newcomers to the industry, but also creating a convergence of traditional payerprovider and pharmaceutical benefit management companies.

Here are some of the latest examples in the changing healthcare scene:

CVS Health, a large pharmaceutical benefit manager, is purchasing Aetna, a large insurer, while Cigna, another large insurer, is acquiring Express Scripts, another pharmaceutical benefit manager.

Meanwhile, tech giants Amazon and Apple took some giant steps into the healthcare fray. Amazon entered into a joint venture with Berkshire Hathaway and J.P. Morgan Chase in an effort by all three to control employer costs, and Amazon also purchased PillPack, an online pharmacy company, and expects to expand services after obtaining state licenses. Apple showed its commitment to shake up the healthcare status quo by expanding its personal health record system, partnerships with hospitals and A.C. Wellness centers – all with a goal of gaining greater influence on healthcare consumption.

The convergence moves the industry away from the traditional separation of payers (health insurance companies and self-insured employers) and providers. Typically, payers are defined as the organizations that conduct actuarial analysis and manage financial risk by collecting premiums and managing payments for services delivered. Providers, meanwhile have typically been defined as healthcare practitioners and organizations that deliver and bill for services, including inpatient, outpatient, elective and emergent.

Those narrow definitions have been shaken up in the post-Affordable Care Act (ACA) world. In the past, the focus was on fee-for-service and capitated contracts under which HMOs or managed care organizations paid a fixed amount for its members to a provider. But the ACA moved the emphasis to value-based care, pushing more financial risk onto providers and away from payers. That means insurers and providers also need to consider how they manage pre-existing conditions and use risk scoring to determine the likely needs of their patients, as their approach can make the difference between profitable success and unprofitable failure.

In this new and complex environment, mergers and acquisitions are seen as a way for both providers and payers to build up their capabilities and respond to the need to enhance patient care, improve population health and reduce costs.

For traditional healthcare incumbents, we believe this also means using a “secret” weapon non-traditional players already leverage: data analytics.

Better data and analytics life cycle management can yield the insights payers and providers need to balance their priorities and deliver value-based care.

payer and provider

How to balance risk and patient outcomes

But first, what do all of these changes entail, and how do they take providers and payers beyond their narrower definitions?

In the post-ACA world, providers are looking to take more financial risk as their actuarial capabilities improve. This would allow them to negotiate more effectively with payers to achieve care outcomes objectives while balancing reimbursement and risk.

Payers, meanwhile, are acquiring doctors’ offices and other providers, or combining with retail clinics and other points-of-care to combine care delivery with financial risk management. To accomplish these goals, payers need to take a more active role in managing the healthcare professionals that they employ as well as the patients who visit those practitioners. Having access to the care delivery setting also allows for greater accuracy.

Managing these activities – by both the provider and the payer – needs to go beyond just financial management. It needs to include operational excellence, using robust data analytics to communicate with people and organizations delivering care. It also requires having performance-level agreements and bidirectional communication in place to measure and monitor reasonable objectives set by both payer and provider. Indeed, collaboration and communication will be crucial to overcome tensions that are building as providers try to deliver on value-based contracts. Finding a way to integrate insights from the back-end will help to ensure both the payer and provider perspectives are understood.

Use data to your advantage

A balance between the needs of the provider and the payer – while prioritizing the needs of the patient – will require change management and deeper insights on what works, what doesn’t and how outcomes for all stakeholders can be adjusted and improved. Those insights must be based on hard data, which will require more robust data, analytics and IT infrastructure. Organizations will need to deploy data and analytics life cycle management – including input, ingestion, management, storage and data utility. Integrated workflows make it easy to collect better, well-rounded encounter data, improving how providers work and increasing provider and patient satisfaction.

That data needs to encompass all parts of the healthcare continuum, meaning patient experience as well as provider and payer data. For this to happen, payers and providers must ensure better consumer engagement by spurring patients to take charge of their own care and using the data provided by patients to improve insights. Being able to see the end-to-end experience of the patient can affect the pieces accordingly.

Brave new healthcare environment

This brings us full circle to the changing industry dynamics and the entry of non-traditional players into the healthcare arena, since the big tech players such as Amazon, Apple and Alphabet know how to leverage data analytics to gain customer insights. As healthcare incumbents build and acquire assets, they will need to match these capabilities and build on their own strengths to ensure they aren’t left behind in this brave new healthcare environment.

Blockchain Projects Ruling the Decentralized Economy- Guide

The rising profile of blockchain in academe

Blockchain has come a long way since it was described by Stuat Haber and W Scott Stornetta back in 1991. The technology has become one of the biggest innovations of the century and has given rise to various new possibilities in different sectors and industries. Say it fintech, retail, healthcare, enterprises, real estate, or supply chain.

A clear evidence of which is that today, almost every entrepreneur, digital marketer, and even blockchain development firm is showing an interest in learning the basics of blockchain technology and looking ahead to entering the space. And eventually, getting a slice of the market which is anticipated to be valued USD 39.7 Bn by the year 2025.

They have also begun looking into the latest blockchain trends and the best business models top players are working with.

However, most of them are missing one main point.

With the growing popularity of the technology, different ways to embrace it for business transformation are coming into the limelight and so, the kinds of blockchain projects; making everyone intrigued to know which of these types is destined to aid them in leading the digital environments in 2020. Something that you will come to know by the time you reach the end of this article.

So, shall we begin?

[Just in case you want to take a recap of the role of blockchain on the industries, check our blog on ‘the impact of blockchain on the economy’.]

Explained: The 5 Categories of Blockchain Projects 

1.  ‘Fear of Missing Out’ Blockchain Solutions

4 Most Exciting Blockchain Projects to Watch in 2020 –

The first kind of blockchain solutions that are getting developed these days is FOMO (Fear of Missing Out).

As depicted from the name, this type of projects are brought into life just to ensure that companies do not remain behind in the market. They have not held any meeting and discussed its role into their traditional business model and the possible outcomes they would derive from it in a particular time span. Or even looked into whether investing in blockchain app development is beneficial for them.

Rather, they have just taken this step just to show that they are innovative and work with the latest technology trends impacting the business world.

These kinds of projects, as you might have guessed so far, do not create much value for the business and remain a marketing act for the company. It just increases the chances of your target audience giving a second look to your business products/services or competitors fear of missing out and take the same step.

What’s more, the poorly planned blockchain projects might overburden the existing business ecosystem and demand additional costs. This can make leaders conclude that they ‘tried and failed’ blockchain or doubt on its future. Whereas, the only problem is that they kept on focusing and investing on the wrong use case of the technology.

2.  Opportunistic Solutions

The 5 Kinds of Blockchain Projects (and Which to Watch Out For)

The second category in which blockchain projects fall is Opportunistic solutions.

These types of blockchain solutions are devised to solve any known problem, especially related to record-keeping. They add value to the business, even when not being operational for a longer period of time.

The only problem associated with this project type is that one might lose control over data and contracts later.

When looking into the real-life examples of Opportunistic blockchain projects, the blockchain solution developed by the Depository Trust and Clearing Corporation (DTCC) to regulate records from credit-default swaps is the right one to consider.

3.  Trojan Horse Projects

Transforming Food Supply chain with Blockchain and IoT - DreamzIoT

Trojan horse projects also landed into the list of type of blockchain business ideas gaining momentum this year.

These projects, just like trojan horses, are attractive, backed by respected brands and address the usual and wide-reaching problem in a particular industry. But, they demand users to share their sensitive information and transfer some control in such a way that results in market consolidation for the prime blockchain owner. So, it is required to invite different groups of people/ecosystems to participate in its processing.

A potential example of a trojan horse blockchain project is a food-tracking blockchain system. This system run by blockchain, unlike the traditional centralized ones, take comparatively less time and effort to determine the point at which the food items were adulterated/replaced, the people responsible for the same, and prevent it further. It enables users to access the records in real-time and prevent dozens of people from falling sick or suffer in other ways. But only when the participants are ready to share their personal information on the network.

These kinds of projects are quite effective. However, there’s a risk involved with considering these projects. They become reliant on the owner’s technology and locked in to the contract terms. But, with the passing time, they gain more control over the market because of having heaps of user data.

Also, the business currencies involved in the ecosystem where Trojan horse blockchain projects exist usually trade at a much higher risk level for the participating users.

4.  Evolutionary Blockchain Projects

UEFA Champions League |

Another type that business leaders focus upon is the evolutionary blockchain project.

As the name suggests, these projects evolve with time. They are designed to improve over time so as to employ tokens with decentralized governance.

One example of such kind of blockchain software/applications comes from UEFA – the central committee for European football. UEFA works with two Swiss technology companies, TIXnGO and SecuTix, to create an evolutionary blockchain platform that drives a more equitable and safer market for the sales of football tickets.

The blockchain-powered platform encourages ticket buyers to download the SecuTix and TIXnGO applications. Here, the tickets are tokenized so as to keep a real-time record of the ticket purchase and connect it to the ownership details.

In case someone wishes to give away their ticket to a friend or colleague, they can do it through the application, which then stores the record of the transfer in the blockchain ecosystem. And if they wish to send it to anyone in the open market, the SecuTix platform can help them by defining that markup resellers are empowered to charge. This, as a whole, ensures that no unreasonable pricing is being asked or illegal brokers come into play.

Besides, the secondary market for tokenized tickets could mature into a decentralized sales network with time, such that it brings all the second ticket sellers at the same place.

When compared to trojan horse blockchain projects, the business currencies in this type can trade at a comparatively low risk level for the participants.

5.  Blockchain-Native Solutions

Enjin | Blockchain Product Ecosystem

Last but not least, blockchain native solutions are also among the type of projects business leaders consider in 2020.

Designed by startups or extended teams of existing organizations, these projects are meant to bring forth a new market of opportunities or disrupt an existing ecosystem. They might begin with different perspectives and facilities, but are supposed to move in the direction of tokenization or decentralized governance with time.

When talking about blockchain-native project types, the two industries that come to the limelight are Education and Sports.

In the Education sector, these projects emerged as a non-profit digital education society where students and teachers from different parts of the world could come together and relish the perks of higher education without worrying about learning exchanges or payments. The best example of which is Woolf University, the one founded by a group of scholars from Cambridge and Oxford and known as the ‘decentralized Airbnb for degree courses’.

Likewise in the gaming sector, these projects enable users to create their own tokens to support their favorite games and players. A perfect example of which is Enjinn.

The Blockchain native solutions introduces new business approaches into the market but comes with major currency risks. Because of this, they are preferred only by those who wish to manage their own data and experiment with the concept of decentralization independently.

Now while the definition and approaches of the different kinds of blockchain projects might have helped you with understanding which one is the right pick for your business, you can reach our blockchain consultants to know further. Our team has years of experience in helping startups as well as established brands from different industries to determine the right way of integrating blockchain into their traditional system and reap higher benefits. And that too, without worrying about the hidden business and tech-based challenges.

The Internet of Things aiding Healthcare

Internet of things in healthcare

There’s so much talk across healthcare about electronic medical records (EMRs). For many, it seems to be the answer to every question, solving all problems of healthcare. At a recent Health Information Technology WA (Western Australia) conference in Perth, for example, three plenary speakers on the main stage were touting its benefits. Unfortunately, the reality is quite different.

Looking at global trends and the shift to value-based care, I believe there’s ample reason to question whether electronic medical records (EMRs) are actually the right approach, especially when the objective has changed from a hospital-centric approach to a patient-focused model that goes beyond the walls of the hospital. There’s also every reason to question whether it is a sound investment. For example, since 2011, the United States has spent $38.4 billion implementing 30-year-old EMR technology in hospitals, according to a 2018 Centers for Medicare & Medicaid Services report. Yet despite successfully computerising health practices, data is still largely locked into hospital systems, and sharing data across health systems remains difficult.

With the healthcare model shifting towards prevention and personalized care, providers and payers are rethinking their approach, and instead are turning to technologies such as the internet of things (IoT) to engage patients, improve outcomes and bring down the cost of care.

IoT in Healthcare: Benefits, Use-Cases and Challenges

From patient to customer

One healthcare organization that took a truly innovative approach to a customer-centric healthcare model is an academic health centre based in the United States. Renowned for its population health studies, the centre’s former chief executive officer wanted to engage patients as consumers, based on a simple objective — to keep those with chronic diseases out of hospital.

The project began with the creation of an innovation group, headed by a chief experience officer overseeing a multi-disciplinary team from customer-centric industries, such as hospitality, publishing, entertainment and automobiles. Most notably, there are no technologists from EMR/EHR (electronic health records) vendors within this group. To this progressive team, the health center added clinicians, who were given access to over 30 million patient records dating back 30 years to analyze the social determinants affecting chronic illnesses such as hypertension, diabetes, chronic obstructive pulmonary disease (COPD) and heart disease.

Based on a set of algorithms, the team was able to identify three social determinants that have the greatest impact on chronic disease:

  1. Access to transportation – Can you get to and from your job and school easily?
  2. Access to good food – Do you have access to quality produce or is the only store accessible from your house a 7/11 selling “convenience” food?
  3. Access to education – Is there a good school in your area with good teachers?

But how do you get good information from patients/consumers on these issues, given that surveys typically have low participation, with only 30 to 40 per cent of people taking part?

Mobile apps and IoT devices are part of the solution. Unfortunately, most apps are focused on a single condition or health issue, rather than factors that influence the patient’s overall health: socio-economic determinants, your environment, health behavior, as well as the quality of healthcare you receive.

Three months later, the innovation group released a mobile app as a proof of concept.

As part of the programme, patients were given a kit that included a Microsoft wristband, a Bluetooth blood pressure cuff, inhaler and weight scale, all connected to the app. In addition to health monitoring data, the app also captured data on life style, such as whether the patient smokes, exercises, etc.

Scaling outcomes

The pilot was a huge success, but the next step was to scale it to 4,000 patients, which was going to be another significant challenge, considering that the nurse-to-patient ratio is about one nurse for 20 to 40 patients. So, the centre started looking at customer relationship management (CRM) solutions.

Once the digital platform was in place, the innovation group had to redesign a new operating model that would support these 4,000 patients. After testing a few configurations, the team landed on a “pod” model that consisted of one nurse and two health navigators — non-clinical support staff focused on customer relationship management. Because the system works by exception, the care coordinators are notified by the platform when an interaction with the patient is required. The rest is automated by the platform, sending reminders and analysing patterns by using IoT monitoring devices and advanced predictive analytic models.

Success with such a large group of people requires engaging with patients where they are and in a way they can relate to. Thanks to the data gathered, the center knew a lot about these consumers. For example, they knew that most prefer to be contacted by text messages and most were fans of the show, Game of Thrones. With this knowledge, on the evening of the season finale, they reached out to hypertension patients with a simple message: “Tonight is the big night for Game of Thrones, and we know you might get excited, so don’t forget to take your blood pressure before the show, and take your meds if required. Have a good night and enjoy the show!” As trivial as this seems, it is details like this that engage people and empower them to make life style changes.

After 12 months, the new platform and engagement model has given the center huge insights, including enabling providers to predict future chronic disease patients with high levels of accuracy, and it has delivered significant outcomes. Here are a few numbers that I find very compelling: The centre has achieved 95 per cent of customer satisfaction, a 23 per cent reduction in emergency services costs, and reduced the total cost of care by 36 per cent.

Increasingly, no matter the healthcare model, the objective must be to improve health outcomes and keep patients out of hospital as much as possible, not only because it’s better for the patient but also to improve financial outcomes and allow health centers and hospitals to focus on truly innovative, cutting-edge care delivery. That’s not something that can be achieved with an EMR.

Failure of On-Demand Platforms- Reason & Solution

Forecasting the future of your on demand service platform and importance of having a plan to scale it - Odtap

The glaring success of the on demand era has given birth to a school of thought among the tech community. They have started believing that following the uber business model and entering the on demand industry will be the only move that is keeping them from reaching complete success.

While it has worked for a number of businesses like GrubHub or Airbnb, the number of businesses that have failed are also extremely huge. In fact, if you sit down to make an excel sheet comparing the on demand services fail vs success ratio, you will find that the number of businesses that struggled to survive were more than those which didn’t.

But does this mean that you should give up hope on your on demand platforms’ business success and give up? Of course not. What it implies is that when you plan to succeed in the crowded on demand market, you should also factor in the reasons behind the application’s failure.

The intent of this article is to help with just that.

Table Of Content 

  1. Understanding Uber’s Success in the on demand sector
  2. The List of on demand Brands That Failed Miserably
  3. Reasons Behind on demand Business Failure
  4. How Can on demand Businesses Save Themselves From Shutting Down Prematurely?
  5. Conclusion

Understanding Uber’s Success in the on demand Sector

Uber Loses License to Operate in London - WSJ

When you dissect the on demand economy, you will find that it is mainly built on three building blocks: delivery immediacy, consumption passivity, and a fixed cost. Uber did not just ticked all the three boxes of the on demand business model, but also aced some other factors that helped it build a seamless ride booking user experience.

Here are the two factors which added to the brand’s success, making it one of the most successful on-demand companies:

  • The company operates in populated urban cities where there’s enough liquidity for making the marketplace work.
  • The customer base were already very familiar with trusting a stranger to take them places. Thus, creating a trust in the market was never a problem.

The Uber model doesn’t care about the transaction’s intimacy aspect nor about the disintermediation challenges. Imagine once you on demand home service app users find a person they like for cleaning their house or planning their kids, how would you stop them from contacting those service providers directly, without going through your application? This disintermediation when continued can lead to greater burn, churn, and in some cases extinction of the business.

Since the Uber model didn’t include the need to care about intimacy, they could survive and grow on a much greater speed. But not every Uber-like story has a happy ending. There are a number of once top in the game on-demand platforms that have fallen owing to the on-demand challenges.

The List of on demand Brands That Failed Miserably 

1.  Happy Home Company 

The Happy Home Company | LinkedIn

Happy Home Company was a twist in the otherwise traditional home service market. The idea behind the brand was to offer users home maintenance plans which included recurring things that had to be kept in top working conditions. Inspite of bagging $7 million from investors, Happy Home’s founder wrote a shutdown letter which stated, “Ultimately we weren’t able to make the transition from a scrappy startup to self-sustaining company.”

2.  Pronto

Team Communication Software | Pronto works better

The business was set out for helping people get healthy meals faster. The UK based service had the work with the intent of connecting the users with chefs while enabling food delivery in under 20 minutes. It had every element to make it one of the most flourishing on demand delivery apps. Even though the idea sounded good to investors and adopters, the company couldn’t keep up with the promotion budgets of Uber and Deliveroo – one of the very commonly occurring on demand challenges.

3.  Workers On Call 

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AI has changed the face of work, this is something that has been established over time. The Workers On Call services used AI systems for streamlining matching of employers with freelancers who needed jobs. The application that boasted of freelancers getting matched and started to work in under 30 minutes, although backed by a powerful vision, was a little ahead of its time. The brand even after raising $30K funding, signed off with a tersely message saying, “Bye Bye. Sorry Workers On Call is closed.”

4.  Homejoy 

Homejoy shuts down amid lawsuits over worker misclassification – The Mercury News

One of the textbook examples of failed the businesses is Homejoy. Started with $20,000 seed funding in 2000, the business became a big name in 2013 when it raised $38 Millions – making it one of the most successful on demand startups. But soon the customers started failing to convert past their first booking. In fact, only 15-20% people re-booked in a month. The numbers were simply not enough for the brand to survive. Add to this the legal battles against classification of workers a s independent contractors led to the business’s death in 2015.

[Further Read: Why Did HomeJoy Failed]

The truth of the hour that still remains is that even after these on demand platforms failure instances, the fact how the internet has trained consumers to get services in real-time is not stopping budding entrepreneurs from entering the on demand economy. But how can businesses ensure that they are not destined to become yet another name in the list?

While one way to get some satisfaction would be to partner with an on demand software development company that has worked with the inception of popular on demand businesses, it would also help to know the on demand services failure reasons that can lead to their failure.

Reasons Behind on demand Business Failure

1.  High Competition

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The upsurge of hyperlocal service demand has led to a rise in demand of on demand services attending to the complete needs of their customers. One way entrepreneurs are competing in the market is by lowering their service costs. This, in addition to the high operational costs of transportation, infrastructure, and labour has been keeping on demand startups from flourishing.

2.  Reluctance among Venture Capitalists 

Building Radar: Silicon Valley investor funds Bavarian technology start-up - Invest in Bavaria

With on demand failure stories shooting off the roof, investors have started becoming wary of where to put in their money. As it is, getting funded on your application has been a difficult process and when you add in the unsureties that the sector now comes with, the probability of raising money lowers even further.

VCs are now becoming all the more cynical about the startup’s longevity. Businesses that are promising a strong long-term vision with a cash flow picture backing it have become the only answer to the types of apps investors will be backing in 2020.

3.  Product Incompetence 

Article: Overcoming the Unconscious Incompetence Hurdle at Work — People Matters

If there is one event that follows every successful startup, it would be the fact that competitors are soon to follow. The value that your business once offered starts getting challenged and bettered by the competitors. This, in turn, is leading to the product becoming incompetent in the market, irrespective of the efforts that went behind on demand app development services.

Brands that are failing to keep up with the changes with timely pivots are soon finding themselves crawling out of the on demand space.

4.  Inefficient Resource Set 

Human Resource Insights #2: 4 Signs of Inefficient HR Departments | Credait

The lack of an experienced set of people can most often than not result in the failure of on demand companies. The same applies for the on demand industry. Irrespective of which on demand domain you pick, you will find that the ones that survived were known for their skilled workforce. A lacking on this front can lead to on demand business losing their worth in the industry and thus get closer to their demise.

5.  Not Being Able to Solve Real Problems 

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A number of on demand companies that fail deal with band-aid type of problems in place of emergency room type problems that make the solution extremely non attractive to the end users. Example: For example, imagine an on demand car wash service. Just how frequently would users demand the service? But the expenses a business will have to make to keep it afloat would be huge. In short, the business neither ends up being cost-efficient nor effective.

6.  Under or Over Valuing Demand and Supply 

Demand And Supply Balance On The Scale. Business Concept Royalty Free Cliparts, Vectors, And Stock Illustration. Image 87121470.

The last in our list of reasons that tend to lead directly to business failure is under or over valuation of demand and supply that your on demand business would garner. Businesses, more often than not undervalue the demand that they would attract and thus plan low on supply. Likewise, they at times think too much of demand and end up with an underutilized supply of resources.

Now that we have enlisted the most common reasons behind an the app business failure, let us dive into the way outs – how can on demand businesses prevent this fate.

How Can On demand Businesses Save Themselves From Shutting Down Prematurely?

Market Expansion

A Complete Guide to Market Expansion Strategy – Welp Magazine

One of the biggest issues with today’s top-on-demand businesses is that they don’t expand from their existing markets. The entrepreneurs who are very new to the industry end up being limited to a pool of loyal customers and don’t think of expansion opportunities. The problem with this is that the moment a new competitor with deep pockets enters the market, the probability of them getting out of business increases.

Here’s a look into the expansion roadmap that we generally share with our clients when we assist them with on demand app development.

Make Your Customer Needs Your Bible 

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In the fight between what you can offer and what your users need, your offerings should always prevail. Although it can be a sweet attraction to invest in tomorrow’s big need, it can be extremely unwise to let go of your customers’ present day needs.

The truth is that you will get a multitude of opportunities and time to pivot your startup. But what you won’t get is the current time and your users present day needs. Getting insights into this information is what a sound on demand mobile app development company can help you with.

Think of Being Monetarily Prepared First 

The matter of the modern day fact is that it is very difficult to get funding on an application. No matter how well propositioned your application is or how green your cash flow statement looks, there is zero guarantee of you getting funded.

The solution on this front can only be to look into alternate financing options and not remain limited to VCs.


The growing cases of on demand industry failures have led to the sector getting the image of being a house of card. The only way for entrepreneurs to enter and succeed in the domain would be to do extensive user research. One way we suggest to achieve this effortlessly can be through the mode of product design and development sprint – one of the key practices that on demand app developers follow.

Business Benefits and Services of Blockchain

Will 2020 Be The Year Cryptocurrency And Blockchain Becomes Operational?

The real-world applications of blockchain are expanding by the minute. But the resources and skill sets needed for developing blockchain applications and hosting them are neither cost effective nor popularly available. Is blockchain as a service for business the answer of how to make blockchain technology accessible to an audience?

In this article, we are going to look into the BaaS solution for business works, the benefits of blockchain as a service, and how to choose the best BaaS providers operative in the market today.

Table of Content

  1. What is Blockchain as a Service?
  2. How Does Blockchain as a Service Business Model Works?
  3. How Blockchain as a Service is Shaping Businesses?
  4. The Region Wise Adoption of Blockchain as a Service?
  5. The Top Blockchain as a Service Providers
  6. A Look Into Self-hosting Blockchain Applications

What is Blockchain as a Service?

What Is Blockchain as a Service and How Does It Benefit Enterprise?

For offering the benefits of blockchain based services to a wider audience, the technology has started being offered in the cloud as a service business model. On the operational front, it is more or less similar to the SaaS, PaaS, and IaaS models which enables using cloud-based apps and storage.

It allows businesses of all types and sizes to access blockchain based technologies without investing in the in-house development. The BaaS model enables companies to access the blockchain provider’s service wherein they can develop blockchain applications at minimal cost. This benefit is what has made it a key part of the blockchain technology trends.

The only limitation of the BaaS solution for business is that it asks for a certain level of centralization since the transactions have to be funneled through the host’s blockchain services. And because the answer to how blockchain is used in business lies at the center of decentralization, companies tend to be wary of its adoption.

Key takeaways:

  • Blockchain-as-a-Service is third party cloud infrastructure and management that businesses use for developing and managing blockchain applications.
  • It operates as a web host which runs an app’s backend.
  • BaaS acts as a catalyst which leads to widespread adoption of blockchain technology.

How Does Blockchain as a Service Business Model Works?

What is Blockchain as a Service (BaaS) in the Tech Industry? - GeeksforGeeks

Blockchain as a service business model describes the process through which third parties install, host, and maintain a blockchain network on the behalf of organizations. The service provider offers setting up of blockchain infrastructure and technology in return for fees.

In many ways, the role of blockchain as a service for business is similar to that of a web hosting provider. It enables customers to make use of the cloud based solutions for developing and hosting blockchain applications and smart contracts in the ecosystem managed by the provider.

Here is a visual showcasing the working of Hyperledger Cello Blockchain-as-a-Service, which is a BaaS-like blockchain module utility system and toolkit under the Hyperledger project.

The BaaS integration in traditional business provides support around allocation of resources, bandwidth management, data security features, and hosting requirements. The biggest impact of BaaS on business is that the enterprises can concentrate on their main business without thinking of the complexities around blockchain operation.

How Blockchain as a Service is Shaping Businesses

7 Ways to Embrace Blockchain for Business Transformation

Businesses and consumers are willing to adapt blockchain technology. But the operational overhead cost related to development, configuration, operation, and maintenance of infrastructure along with the technical issues act as a barrier. The advantages of blockchain for SMEs, no matter how massive, are very resource intensive and energy consuming – thus preventing the technology’s mass adoption.

Renting a blockchain infrastructure in BaaS allows businesses to acquire the skillset needed for operating the blockchain infrastructure. Additionally, the investment needed for entering the technology segment is also lowered, since the service agreement can be easily scaled up or even terminated within short notice.

It offers a way for businesses to stay at the edge of technology without any unnecessary risks.

BaaS for startups

The opportunities of BaaS for businesses, especially small businesses, is deemed ideal for organizations which outsource the technological aspects, and are not very hands-down involved with the blockchain’s working mechanism. It enables these firms to get the understanding of the technology without having to develop their proprietary blockchain.

The integration of BaaS solutions is being used by a number of industries for things like identity management, supply chain management, and payments. Blockchain development services are emerging as the ideal solution for a number of SME challenges like elimination of middlemen, lack of transparency, etc.

Use cases of Blockchain as a Service for business

  • Document tracking – Blockchain technology offers a distributed, immutable document tracking system. By keeping the documents on blockchain, all the participants are given equal access to the information. Additionally, blockchain technology is immutable, thus ensuring that the documents are secured.
  • Data storage – With the data stored in the decentralised blockchain, the amount of data loss risk is reduced by manifold. The regulated industries like healthcare, real estate, etc benefit a lot from this immutable, secure facility of data storage on blockchain.
  • Contract execution – Under the smart contracts service of blockchain, a platform is provided for the contract execution which promises high transparency levels. Its distributed nature implies that all the parties should be equally informed.

The benefits of Blockchain as a Service lies in the unraveling of the several use cases that are yet to be emerged. It offers enterprises an opportunity to work on those use cases without making any large term commitments. All they would have to do is partner with a blockchain service company and then fully embrace Blockchain’s capabilities.

Now that we have looked into how is blockchain as a service valuable for SMEs and enterprises, let us look into its regional adoption.

The Region Wise Adoption of Blockchain as a Service?

Blockchain-as-a-Service Market | 2020-2027 | Industry Report | Covid Insights

The impact of BaaS on business has led to a huge demand for the service – a sign of which can be seen in the fact how the BaaS market growth is poised to be USD 24.94 Bn by 2027.

The worldwide market of BaaS is big around the US, Mexico and Canada. One prime reason behind this is the presence of SMEs and large businesses operating in the US location along with a willingness to combine the technology with the public utilities services.

Europe has also been seen as the leading BaaS market. One of the major drivers of blockchain and BaaS adoption has been the government support from different countries.

The Asia Pacific (APAC) region is the third-most biggest market for the BaaS integration. Driven by the BaaS integration in traditional businesses and growing investment in Japan, China, and South Korea, the technology is poised to grow in the region.

To take the adoption of blockchain as a service for business further, a number of tech companies have emerged as BaaS providers. Here are a few of them:

The Top Blockchain as a Service Providers

Top Blockchain as a Service Providers

A Look Into the Alternative – Self-hosted Blockchain

Up until this point, we have looked in the BaaS ecosystem and how Blockchain as a Service is influencing the small business, in addition to the list of top providers. While it all suggests that it is a good option to go with this approach, businesses can in fact lose out on the essence of decentralization – the foundation of blockchain fundamentals.

So what is the alternative? The answer is Self-Hosted Blockchain.

When we talk about the Self-Hosted Blockchain app cost, the ownership amount tends to be a lot higher because of the startup costs, retirement costs, and operational costs. Moreover, the amount of developing and deploying a smart contract under this model can amount to up to hundred thousand dollars or more.

In contrast, a blockchain app hosted on cloud as a BaaS offering can be around $0.29 per allocated CPU hour. This means, businesses would only have to pay as they go and only for the service units used.

The costs of the BaaS model vary on factors such as number of concurrent transactions, transaction rate, and the payload size on transactions, etc.

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