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The number of software applications varies according to the size of the organization. According to a research small businesses use an average of 10 to 22 applications, and in large enterprises, this number rises to an amazing 700 to 1000 software applications!
All these disparate software applications often need to work together, and this is where software integration comes in. I see various motivations for software integration when I talk to business owners and IT managers. They usually want to achieve one of the following:
In the early stages of software integration, one of the main issues would be that everything was proprietary and closed. You would buy an application, and all the information you put in it was accessible only from within that application. Not to mention that often it would be available on a single machine or on a limited set of machines. If you wanted to, for example, access that information from another software application, you were into trouble.
But when there is a will, there is a way, and so software integration started. The software integration challenges were initially addressed by implementing in-house integration solutions that used ad hoc and proprietary protocols themselves.
Eventually, with the addition of more and more software systems and the wider spread of internal and external networks, home-grown solutions were no longer adequate. Software integration evolution had reached a new level. The motto "no system is an island" became common, but there was still no standard solution to the problem.
Over several decades, enterprise integration technologies leveraged one or more integration styles. The earliest integration style was the Remote Procedure Call (RPC) style, used in single-language environments and very popular in the 80s and early 90s. Later, multi-language systems such as CORBA appeared. In these, an explicit Interface Definition Language (IDL) was used, so that the caller and callee could be implemented in different programming languages and even different platforms.
In the end, the use of Application Programming Interfaces, best known as APIs, became the rule. APIs emerged to expose business functionalities provided by one application to other applications. APIs exist so you can reuse them – the concept has been, from the beginning, that multiple applications could consume the same API. The idea was that developers could rely solely on the API to access an application's functionality without worrying about their implementation details.
When developers start using an API, they hope that the API is a contract that will not change. However, APIs are susceptible to the same environmental pressures to change that all software systems face. They are often upgraded, reworked, and sometimes refactored. When this happens, finding actual points of change in the API and making things work again is painstaking work for the developer. This is why API lifecycle management appeared.
Developers also hope they will have to work with as few APIs as possible. This is because each new API represents a new learning curve and involves time and effort. Moreover, when you come across the upgrade problems we mentioned, the developer knows it will help to have few APIs and few inter-dependencies - not to fall into spag
The thing is that this is not always up to the developer, as the need to integrate different software systems grows.
This is what API management is: API lifecycle management for multiple APIs.
As a result of sheer demand, API management using middleware has emerged as a way of using APIs and getting their advantages, while avoiding their known problems. Please note we are looking at API management from the API consumer perspective. If you look at it as an API producer, then the focus will be different.
The middleware acts as a translator that speaks all the API variants the developer needs, translating them into ANSI standard SQL syntax that the developer knows well and can use together with his favorite programming language, such as Python, Java, or C#, just to name a few.
By using such translating middleware, the developer no longer needs to learn a new programming language or gain expertise in the target system API. This makes all the difference, dramatically reducing the time and effort necessary to integrate software.
Using SQL Connector, the developer has two options:
In both cases, completing the integration will require few lines of code and be quite straightforward.
Using such middleware also eliminates the need to redo your code when you upgrade the target system or its APIs. The middleware company itself will handle all the maintenance efforts. It is now their job to guarantee forward compatibility (and sometimes backward compatibility too).
Ultimately, API management gives enterprises greater flexibility by enabling them to go for the software integrations they need while shielding them from the negative aspects and not compromising on security, e.g. maintaining GDPR compliance.
Software integration has long been a pain point for businesses, often leading companies to either maintain their legacy systems for longer than they should or fork over large sums of money on developers to migrate to the latest and greatest.
Fortunately, with software integration evolution, you can easily solve current integration challenges and prepare companies for the future by using today's technology of API management middleware. Whether to simply share data between systems, to modernize legacy systems, or to meet complex requirements, endless integration possibilities are at your fingertips once you start using API middleware.
DevOps implementation strategy is key focus of most organizations as they embark on Digital Transformation journey. Though it sounds like a quite a straight forward initiative to automate the software delivery process it has many challenges as I have discussed in the past posts.
DevOps Market size exceeded $7 billion in 2021 and is expected to grow at a CAGR of over 20% from 2022 to 2028 to a value of over $30 billion. In 2021, 65% of the DevOps market’s value in the USA was made up of DevOps solutions (tools) with 37% accounted for by services. By 2028, around 55% of the market’s value is forecast to be accounted for by DevOps services and the remaining 45% by tools.
In 2001 when I implemented my 1st development process automation it was more about automating redundant manual processes to save time and avoid manual errors in the build and release process. We were a small team delivering a small project for a US client , we used to face failure during every release and it was very embarrassing for the entire team to attend the post release meeting. All we wanted to do was a smooth bug free release without spending all night at the server machines. We automated our build and release process and unknowingly we started working closely together to ensure all the issues we faced in the past did not occur again. We collobarated across teams, we stopped blaming other teams, we learned every step of the code/build/test/release/configure/deploy process ,we automated manual tasks and we monitored every step of the release process. Soon we started doing perfect code drops for every release and we started leaving office together to enjoy post release drinks. We were not doing DevOps but experienced a cultural change and we were working as one team.
Over the last few years we are recommending DevOps to our clients as the right way to do the release thing for their business transformation or the digital transformation journey. What we have observed is that in spite of a large number of new tools, dashboards and on demand infrastructure it is still a big challenge to implement a successful DevOps process in an organization. Lets take a quick look at some of things that can help implement a successful DevOps process.
To implement technology strategically, businesses need to start by creating a cultural shift toward the principles of DevOps. It starts with people and processes, and then products. You cannot simply throw a tool or even multiple tools at the problem and hope that it will be solved. To transform your business, you need to embrace velocity: making incremental changes, delivering small iterations and going faster. This often means disrupting your own business and cannibalizing your existing offerings before disrupting the market. There are a few key elements of DevOps culture that must be adopted before you begin thinking about your product toolkit.
Encouraging collaboration is one crucial way to empower employees. By
keeping all stakeholders involved in the process, employees can
communicate impact in real time, keeping the execution process moving
along. Collaboration enables product manager,
development, QA, security and operations teams to work together
directly instead of waiting for handoffs. The values of diversity, inclusion and belonging are fundamental to
creating a culture of collaboration within your organization. Collaboration across teams, across levels brings in multiple perspectives, and by ensuring that each perspective has a say we invite innovative ideas, empowered teams
and smarter, more informed decision-making. The culture of collaboration has to be driven down the hierarchy by the top leadership leading by example and rewarding collaborators. If collaboration is not one of the KPI for management leaders so far it is time to embrace it now.
You can go faster by breaking things down into smaller pieces. The smaller we split things up, the smaller the steps we take, the faster we can go.' Smaller iteration are better because they take less time, get delivered faster and there is lower risk and have quick turnaround time encouraging people to be more creative. I remember my mother telling me to take small bites, chew well and the food will show on you, it worked there and it works everywhere. Encouraging iterations is also a step towards moving away from the stagnant waterfall mentality to developing an agile calculated risk taking attitude.
Employees should be acknowledged for what they accomplish and complete, not how long it took them or where they worked. . Create a culture where team members feel trusted to structure their own days and do what it takes to get the results that customers require. Start by finding simple solutions to the problem instead of flashy complicated ones.
It is impossible to transform a business without setting the mood with collaborative culture.Start by finding ways for collaboration in areas where currently you have silos, iterations where there is stagnancy and efficiency where there are lags
Gartner predicted that through 2022 75% of DevOps initiatives will fail to meet expectations due to issues around organizational learning and change and in 2021 Tech Radar Survey indicated 80% of the DevOps initiatives failed to achieve desired goals - mind you project this is the percent of projects that failed to meet the desired goals and expectations. In other words, people-related factors tend to be the bigger challenge while implementing DevOps as compared to implementation technology/tools challenges.
It has been observed that organizations often launch DevOps efforts with insufficient consideration of business outcomes and without clarity of goals. I&O leaders need to ensure that staff and customers connect with the term "DevOps," and the value it will bring, prior to introducing the initiative.
Organizations should use marketing to identify, anticipate and deliver the value of DevOps in a manner that makes business sense. "Leaders must seek to refine their understanding of customer value on an ongoing business to evolve capabilities and further enable organizational change,"
In another Gartner 2017 Enterprise DevOps Survey, 88% of respondents said team culture was among the top three people-related attributes with the greatest impact on their organization's ability to scale DevOps. In 2020 TechRadar did a similar survey and over 90% CIOs responded that their priority was to build DevOps culture However, organizations overlook the importance of getting the right mix of staff on board with the upcoming DevOps change and instead focus efforts on DevOps tools.
It sounds repetitive but I still need to reiterate that "Tools are not the solution to a cultural problem," Organization have to Identify candidates with the right attitude for adopting DevOps practices. Individuals who demonstrate the core values of teamwork, accountability and lifelong learning will be strong DevOps players.
Successful DevOps efforts require collaboration with all stakeholders. More often than not, DevOps efforts are instead limited to I&O. Organizations cannot improve their time to value through uncoordinated groups or those focusing on I&O exclusively.
Break down barriers and forge a team-like atmosphere. Varying teams must work together, rather than in uncoordinated silos, to optimize work. "This might start with seeking out an executive who can carry the teams and champion the effort,"
It is important to realize that a big-bang approach — in other words, launching DevOps in a single step — comes with a huge risk of failure. DevOps involves too many variables for this method to be successful in a large IT organization.
Is is recommended to use an incremental, iterative approach to implement DevOps to enable the organization to focus on continual improvements and ensure that all groups are collaborating. Is is recommended starting with a politically friendly group to socialize the value of DevOps and reinforce the credibility of the initiative.
Similar to struggling with grounding DevOps initiatives in customer value, a disconnect exists in many organizations between expectations for DevOps and what it can actually deliver.
Manage expectations by agreeing on objectives and metrics. Use marketing to identify, anticipate and satisfy customer value in an ongoing manner. Expectation management and marketing are continuous efforts, not a one-time affair.The bottom line is unless entire organization understand develops and appreciates the benefit of DevOps and take efforts to collaborate and work to bring a cultural change across Development, Testing and Operations teams, DevOps will not be successful.
There was a time in pre-digital era when a New business idea would disrupt the business landscape. Take example of the restaurant industry that we all are familiar with. Let's assume that couple of decades back the 1st restaurant, with a seating capacity of 10 people decided that it could grow its business if it started taking home delivery orders on phone. The restaurant increased its business from 10 orders/hour to 30 orders/hour - mind you, there is no software used, just a great business idea implemented using the telephone. Recently another disruptive idea that leverages software changed the food business landscape. I am talking about Cloud Kitchen that has disrupted the food business over the last 4 years. The same restaurants that had seating capacity of 10 people could now outsource its kitchen to 10 cloud kitchens across the city and they started servicing 300 orders every hour. If you do not know what is Cloud Kitchen do read about it it most interesting software driven business disrution that many people don't know about. Today businesses continue to be disrupted not just by a new business idea but also by software agility and innovation. Software defined disruption like Mobile apps or Cloud Kitchens have changed the food business landscape and continues to drive tremendous business value like never before. Is it important to understand that as software is disrupting the industry, every enterprise is turning into a software company. As a software company all these enterprises need a seamless SDLC (software delivery lifecycle) process integrated with testing tools,deployment tools and application and business monitoring tools along with team that can promptly support any issue in the software delivery with a short turnaround time.
The main driver to adopt devops comes from the need of enterprise to innovate and accelerate the business. Unless the new ideas are implemented in the software and shipped fast by a unified capable team the business does not get the desired business outcome.
Key points to note about business and need for devops are as follows-
Businesses develop software for a reason: to get business value
Today every Businesses is either Digital Businesses or in process of Digital Transformation
Businesses will continue to be disrupted by software innovations & agility for example Mobile Apps
Disruption like Cloud Kitchens are changing landscape & deliver tremendous business value
Software disruption also means that enterprises are becoming software companies.
So the main driver for DevOps is the need for enterprise to innovate and accelerate the business
Auto Machine Learning or AutoML enables developers with limited machine learning expertise to train high-quality models specific to their business needs. Build your own custom machine learning model in minutes. Automated Machine Learning (AutoML) is tied in with producing Machine Learning solutions for the data scientist without doing unlimited inquiries on data preparation, model selection, model hyper-parameters, and model compression parameters.
Auto Machine Learning is typically a platform or open source library that simplifies each step in the machine learning process, from handling a raw data-set to deploying a practical machine learning model. In traditional machine learning, models are developed by hand, and each step in the process must be handled separately.
AutoML automatically maps the optimal type of machine learning algorithm for a given task. It does this with two concepts:
Users with minimal machine learning and deep learning knowledge can then interface with the models through a relatively simple coding language like Python or R. These are some standard steps of the machine learning process that AutoML can automate, in the order they occur in the process:
I think AutoML is game changer because it represents a milestone in the fields of machine learning and artificial intelligence (AI). AI and machine learning have been subject to the "black box" criticism -- meaning that machine learning algorithms can be difficult to reverse engineer. Although they improve efficiency and processing power to produce results, it can be difficult to track how the algorithm delivered that output. Consequently, this also makes it difficult to choose the correct model for a given problem, because it can be difficult to predict a result if a model is a black box.
AutoML makes machine learning less of a black box by making it more accessible. This process automates parts of the machine learning process that apply the algorithm to real-world scenarios. A human performing this task would need an understanding of the algorithm's internal logic and how it relates to the real-world scenarios. It learns about learning and makes choices that would be too time-consuming or resource-intensive for humans to do with efficiency at scale.
Fine-tuning the end-to-end machine learning process -- or machine learning pipeline -- through meta learning has been made possible by AutoML. We can say AutoML represents a step towards general AI and making AI accessible for non techy domain experts.
You can get started by trying some popular AutoML platforms like :
Like most automation, AutoML is designed to perform rote tasks efficiently with accuracy and precision, freeing up employees to focus on more complex or novel tasks. Things that AutoML automates, like monitoring, analysis and problem detection, are tasks that are faster if automated. A human should still be involved to assess and supervise the model, but no longer needs to participate in the machine learning process step-by-step. AutoML should help improve data scientist and employee efficiency, not replace them. Will AutoML reduce dependency of business on data scientist? Yes to a limit it will reduce the dependency on data scientist to do menial machine learning tasks but it also helps in enabling domain experts to use machine learning and applying across less complex tasks.
As of 2021 Auto Machine Learning is a relatively developing area and even the most popular tools are not yet fully developed. If you look back at history of software it is inevitable for automated tools to evolve and automate the mundane tasks reducing dependency on developers. Those who have entered the field of data science without programming background should be wary that one day Machine Learning will become a packaged software and the demand for ML developers my prediction is the demand for ML developers would reduce in another 3 to 4 years maybe around 2025. For every developer/data scientist entering the data science or machine learning space my advice would be to build a strong programming base along with machine learning and AI which will enable them to adapt to the inevitable change in demand of the IT industry.
Oxford dictionary defines healthcare as ' the organized provision of medical care to individuals or a community'. The crisp definition does not quite explain the purpose and goal of a good healthcare system.
According to me the complete definition of 'Healthcare' should be - an integrated system that proactively delivers care to individuals. A healthcare system should store and uses patient data and clinical data to provide better insights to patients health which in turn could helps the medical profession to give better service to the patient, at a lower cost.
From a technology providers perspective a good healthcare system uses continuous advances in technology to connect and organize the disparate entities of healthcare landscape to deliver a seamless experience to individuals and entities. Every entity in heath care landscape benefits and profits from a good healthcare system but the ultimate beneficiary has to be individual seeking healthcare services.
What I am trying to say is that most of the healthcare systems that exist today are focused on delivering medical services rather than health care to individuals. There is a need to build health care systems that keep the individual care at core of system design and that means life long care of every individual who approaches the system. Once a individual requires medical services he becomes part of the healthcare system and the system should proactively monitor, manage & deliver health care to individual patients. We are talking big, we are talking about system that is built around individual health care, we are talking about building a system that reaches out to individual rather than waiting for individuals to seek medical services because the purpose of a responsible society and medical community in a vibrant democracy is to ensure good health for every individual.
1) Keep a record of all individuals from birth or from the time they register
2) Own the responsibility of maintaining medical records of every registered individual
3) Use medical records and clinical data to proactively reach out to individual for health checkups
4) Post treatment of various chronic diseases proactively monitor health of registered individuals
5) Proactively deliver medical advises to all registered individuals
6) Share and connect individual's medicals history across health care network
Let me take example of a cancer patient who becomes part of health care system at a age of 60yrs. Let's assume that after taking treatment the patient gets well and goes home and does not feel the need to approach the health care hospital. Health care providers know that cancer is a chronic disease and needs life long monitoring. The health care systems should device a health care plan for the cancer patient and proactively connect with the individual to check the individuals health and recommend timely checkups to check 'recurrence' of cancer. Recurrence is common in some types of cancer and as healthcare expert the system has data to predict possibility of recurrence and can save lives by doing periodic checkup.
Another example is of an individual who becomes part of the healthcare system when he gets treated for a coronary blockage. Medical professionals and healthcare system have data to show that even after removing the coronary blockage their is high probability that the patient 'with a heart condition' may face similar medical conditions over a period of time and requires periodic checkups. The point I am trying to put across is Health Care is not just providing Medical Services, health care is about providing care for health of individuals. We as experts of IT and medicine know we can provide the Health Care in true sense by designing smart system that use the individual and clinical data and save individual's lives. Individuals who often neglect medical conditions because of lack of knowledge and ignorance can be kept in the healthcare network by proactive followups.
There is a cost associated with building such smart systems , maintaining data and proactively connect with every individual registered in the healthcare system. This cost is very small when we compare it to the medical expenses and suffering that individual has to bear if the diseases is not detected early. Insurance companies would love to have such smart health care systems that do proactive checkups and detect a medical condition which will help them save billions in treatment of the insured individuals. The challenge is we do not have such Smart Health Care systems that have built in Care Module that benefits individuals, insurance companies as well as health care providers because everybody wants affordable health care.
#Covid is a latest use-case that proves that a Smart Health Care system would have simplified management of Covid cases, it would have helped us give better treatment to all registered individuals and it would have given us real time clinical data to find effective treatment procedure for epidemic like Covid. After months of treatment scientist found that certain medicine was not effective for treatment of Covid because we do not have a unified system to collect data of individuals. If we had every individual registered with one or more healthcare systems we could have analyzed data in real time and within weeks we could have identified the most effective treatment procedure and saved millions of lives. In 2021 everybody understand the value of data, unfortunately we do not have a system to collect, store and derive insights from the data.
I hope you have followed my thought behind this post. In the next post I plan to share a high level design of a smart health care system that is beneficial as well as profitable to every entity in healthcare system.. A system that delivers benefit to individuals, to hospitals, to insurance companies as well as the scientist and pharma companies. I am talking about changing the way we look at health care 'as a service for those who want it' and make healthcare 'an essential service that takes care of people in an inclusive manner'. The time has come to move from Health Care to Human Care and guarantee proactive monitoring of health and timely and affordable treatement to every individual, to woman, men as well as new born children by plugging them to the healthcare network.
In a connected world no human should be disconnected from Health Care network. When our public as well as private healthcare providers unite to build a seamless heatlcare network we can really truly deliver Human Care aka healthcare with human touch and not just medical treatmen to those who reach a hospital for treatment and those who can afford the hospital expenses.
1. Costs and transparency. Implementing strategies and tactics to address growth of medical and pharmaceutical costs and impacts to access and quality of care.
2. Consumer experience. Understanding, addressing, and assuring that all consumer interactions and outcomes are easy, convenient, timely, streamlined, and cohesive so that health fits naturally into the “life flow” of every individual’s, family’s and community’s daily activities.
3. Delivery system transformation. Operationalizing and scaling coordination and delivery system transformation of medical and non-medical services via partnerships and collaborations between healthcare and community-based organizations to overcome barriers including social determinants of health to effect better outcomes.
4. Data and analytics. Leveraging advanced analytics and new sources of disparate, non-standard, unstructured, highly variable data (history, labs, Rx, sensors, mHealth, IoT, Socioeconomic, geographic, genomic, demographic, lifestyle behaviors) to improve health outcomes, reduce administrative burdens, and support transition from volume to value and facilitate individual/provider/payer effectiveness.
5. Interoperability/consumer data access. Integrating and improving the exchange of member, payer, patient, provider data, and workflows to bring value of aggregated data and systems (EHR’s, HIE’s, financial, admin, and clinical data, etc.) on a near real-time and cost-effective basis to all stakeholders equitably.
6. Holistic individual health. Identifying, addressing, and improving the member/patient’s overall medical, lifestyle/behavioral, socioeconomic, cultural, financial, educational, geographic, and environmental well-being for a frictionless and connected healthcare experience.
7. Next-generation payment models. Developing and integrating technical and operational infrastructure and programs for a more collaborative and equitable approach to manage costs, sharing risk and enhanced quality outcomes in the transition from volume to value (bundled payment, episodes of care, shared savings, risk-sharing, etc.).
8. Accessible points of care. Telehealth, mHealth, wearables, digital devices, retail clinics, home-based care, micro-hospitals; and acceptance of these and other initiatives moving care closer to home and office.
9. Healthcare policy. Dealing with repeal/replace/modification of current healthcare policy, regulations, political uncertainty/antagonism and lack of a disciplined regulatory process. Medicare-for-All, single payer, Medicare/Medicaid buy-in, block grants, surprise billing, provider directories, association health plans, and short-term policies, FHIR standards, and other mandates.
10. Privacy/security. Staying ahead of cybersecurity threats on the privacy of consumer and other healthcare information to enhance consumer trust in sharing data. Staying current with changing landscape of federal and state privacy laws.
“We are seeing more change in the 2020 HCEG Top 10 than we have seen in recent years and for good reason. HCEG member organizations express that the demand for, and pace of change and innovation is accelerating as healthcare has moved to center stage in the national debate. It shouldn’t be surprising that costs and transparency are at the top of the list along with the consumer experience and delivery system transformation,” says Ferris W. Taylor, Executive Director of HCEG. “Data, analytics, technology, and interoperability are still ongoing challenges and opportunities. At the same time, executives need to be cautious, as individual health, consumer access, privacy, and security are on-going challenges that also need to remain as priorities.”
Turning challenges into opportunities
Reducing costs means lower revenue for providers and almost all of the players in healthcare––except for consumers and payers, says Mark Nathan, CEO and founder of Zipari, a health insurtech company. So while there are many incentives to keep healthcare costs high, if consumers are provided with the information they need to improve their health and drive down their personal costs, then we could see consumers en mass making decisions that drive down costs across the industry, he adds.
“Predicting cost in the traditional health insurance environment is shockingly complex,” Nathan says. “The most advanced payers can simulate claims and predict the cost of procedures. However, as you layer in full episodes of care, such as knee surgery, it becomes much harder to accurately predict the patient's total out-of-pocket cost. Bundled value-based payments start to make cost transparency a little easier to predict, but most plans still have a way to go to get to that type of offering.”
The greatest opportunity to drive down health costs––for payers, consumers, and system-wide––is with the payer-consumer relationship, he says. “Payers have the information consumers need to make better decisions about their health and finances––if plans can build positive and trusted relationships with their members. Once a payer proves it can make valuable and trusted recommendations, the consumer can make the decisions that will not only lead to better health outcomes but also to reduced cost of care.”
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