The Expense of Nearshore IT Services

Amidst the great resignation, nearshoring IT projects is rapidly proving itself to be the solution for businesses affected by the worker shortage. We recently published an article on the Future of Sourcing with four key considerations to reflect on when considering a partnership with a nearshore provider.

In the Wall Street Journal article, Tech Talent Shortage Is Helping Drive M&A Deals, Angus Loten cites an analysis by COMPTIA that states that in January 2022 employers posted close to 340,000 unfilled IT job openings, which was 11% higher than the 12-month average. These numbers could be the result of a rise in demand for software and AI expertise due to the pandemic, but also as a consequence of the tech talent shortage that has been going on for a while. 

Tech professionals are now in control when it comes to increased compensation. Still, it is difficult and expensive to find qualified candidates for internal IT positions. Factors, such as the pandemic, have helped companies change how they do their work and they’ve learned to distribute work and manage off-site teams. As a result of this new work model, companies are now redefining what constitutes a team. 

Productivity is still flowing as usual, regardless of where the team members are located. Enter nearshoring- companies are seeing that nearshore providers and partners already have the advantage of being able to work remotely and being part of a distributed team long before COVID-19 hit.

The Ideal Partnership

Even with other models of outsourcing available, nearshore is rapidly becoming the preferred model because it provides the best of both worlds. It offers a balance between being cost-effective and easier collaboration due to closer proximity.

What can companies expect to pay when collaborating with a nearshore partner? A general digital transformation development sprint, conducted over three to four weeks, could range from $25,000 to $30,000. The development of a basic chatbot to be used to automate customer service functions could average $15,000.

That is but one type of model. There are a variety of short-term and long–term partnership alternatives, partnerships where IT teams are built by on-site and nearshore teammates for more extensive digital transformation initiatives.

According to recent research, hourly rates for nearshore services vary widely depending on the role, but for a lead developer in Latin America, a company can expect to spend anywhere from $56 to $105 per hour on average. 

The Current Cost of a Nearshore Partnership

Companies should consider the costs associated with their needs, but to only focus on the cost would be a disadvantage to their projects and could even affect their reputation. When partnering with highly regulated industries, such as healthcare and government, privacy and compliance with regulations are a priority and the mistake of partnering with a less than stellar partner could mean the loss of brand reputation. 

Clients are now looking for a strategic partner with qualified team members to expand their team. Given this shift in the relationship, here are some things to consider when thinking about partnering with a nearshore IT services provider.

  • Experience in your specific project. When choosing a nearshore partner, you must take into account their extensive technology expertise and their experience in projects similar to yours. 
  • It’s not only expertise but compatibility that’s important. Experience and expertise are important attributes to consider, but it is equally important to consider their work ethic, work style and corporate culture in comparison to your own. 
  • Seamless integration with an existing team. Sometimes, a digital transformation project starts with an internal team and you find that you are lacking some required resources. By outsourcing, you are quick to resolve this issue, but you must consider how each new team member can blend into your internal team with minimal obstacles.
  • Quality digital transformation projects from design through development. The right nearshore partner should be able to provide a full lifecycle of services. This ranges from designing a solution to the deployment of the solution. This will save your company from having to switch from different firms for each stage, which would only lengthen the project duration. 

In the future, remote and internal staff and outsourcing partners will become a unified team working toward the same goal, to successfully meet business goals. Through this new team model and careful planning, businesses will reach success for their future digital transformation projects.

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AI and the Science Lab of the Future

AI and the Science Lab of the Future

According to a Reports and Data report, the global computer vision market is forecast to reach USD 25.69 Billion by 2028. Computer vision(CV), a form of artificial intelligence that uses an algorithm to identify, process, and analyze vast amounts of information, such as texts, images and videos, is increasingly becoming a high-demand requirement to achieve digital transformation. 

Recently, we published an article, Computer Vision is Providing a New Lens Into Biopharma Innovation, where I discuss computer vision’s role in biopharma.

AI  in Life Sciences

Computer vision is pushing for new standards for intelligent medical devices, diagnostics and treatments that will establish new norms for the people affected by them. The future for scientific labs will entail human intelligence being amplified by AI, specifically computer vision.

 Digital transformation via computer vision will become the strategy of choice in life sciences, due to the need for faster diagnoses and quicker commercialization of medical devices and pharmaceuticals. Here are applications to consider:

  • Vaccine delivery. Computer vision has the ability to sort through thousands of biological images and data to identify those that are most likely to trigger an immune response. By depending on vast amounts of data, computer vision is able to detect the proteins that make up a virus, as well as those that are mutating. Additionally, it is tracing toxicity markers, disease triggers and aiding in the discovery of molecular combinations.
  • Rapid at-home testing. By applying computer vision to art-home tests, it is able to identify the presence of COVID-19 and other viruses. In the near future, a mobile app will be available for a rapid antigen single-use, self-test that will provide a speedy diagnosis.
  • Medical diagnostics. Computer vision models are now being used to read scans with extreme accuracy, by using X-ray and MRI images. This is excellent for medical professionals because it is helping them quickly identify the presence of diseases, recognize anomalies and assess the severity of a disease case faster.
  • Clinical trials. Using computer vision for tracking and identifying vast amounts of datasets collected during clinical trial processes helps assess the efficacy of drugs and their impact on the patients participating. This would make the researcher’s process quicker.
  • Assessing biomedical data. Computer vision can help analyze images and data and identify biologically relevant information and correlation, overcoming the potentially unstructured patterns of the medical data available.

AI Regulation

The emergence of the necessity of AI in life sciences,  such as computer vision, is guiding the evolution of the regulatory environment for AI-driven pharmaceutical methods and medical devices.

The constant need for training once new data arrives, means the inspection guidelines for AI are evolving. These may require new FDA approval each time the algorithm is modified in a significant way. Still, the goal continues to be that AI-assisted discoveries, diagnostics and treatments are providing safe and effective outcomes.

The Future of Computer Vision

Computer vision has the potential to improve outcomes outside of the traditional labs, clinics and hospitals. Other areas to consider include patients in war and natural disasters. Equipping hand-held MRI machines with AI algorithms could help detect conditions on the spot. This could also benefit patients that require urgent care.

Challenges to Overcome

The current cost to adopt computer vision is functioning as an obstacle. Companies depend on a transformative shift to be able to integrate computer vision. They also require investments by other companies to build a better tech infrastructure.  CPU power is also needed for the processing of huge datasets. There is a lack of affordable CPU power. The cloud and AI innovation will proliferate the market, while major players, such as Apple and Intel, continue to work to deliver affordable CPU power.

Computer vision is proving to be essential for clinicians and life science professionals looking for support during trying times. It will continue to help them receive highly accurate data-driven results and help them focus on giving their best effort and delivering optimal patient care.

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Value Stream Management

What is Value Stream Management

Value Stream Management (VSM), also known as Value Stream Mapping (VSM) is a methodology used in many industries, but most often in relation to software development. It helps to identify waste, improve processes and their cycle times and subsequently reduce development times while streamlining workflows and boosting quality.

For anyone familiar with lean methodologies, this may sound familiar, since it’s considered a key part of the lean toolkit. Yet, VSM is gaining traction beyond lean practitioners. Software developers are realizing that the goal of software delivery is not simply accelerating timelines. According to Forrester, “shifting their focus from outputs to outcomes is providing greater insight than velocity alone.” In the same report mentioned above, using value stream management (VSM) tools, Forrester customers noted that “once armed with better data, insight, and business connectivity, they were finally able to make meaningful improvements to their AD&D processes.”

The roots of VSA can be traced to Toyota, well-known for its manufacturing processes. Toyota first leveraged VSM (even before they called it that) to outline the processes required to create and deliver a vehicle. It created a process that analyzed every single step involved to optimize them and removed nonessential ones.

VSM’s Role in Software Development

Leveraging Value Stream Management, software development managers analyze all relevant activities that go into the creation and support of software – from when users request new features, designers create the functionality and engineers build the software, to when it’s shipped to the end-user and supported. They then look for areas across this journey to identify where waste can be eliminated.

When it comes to software development, waste most often refers to time or resources.  It can come from many areas, such as when extra features are created that were never expressly required by users; or from features and functionality that miss the mark because user feedback was never secured in the first place. It also can come from not properly documenting investigations when software fails or contains bugs. Likewise, when there is staff turnover or project teams change, there can be waste from a lack of collaboration or sound project management. In fact, according to MoreSteam, total cycle times consumed by non-value-added activities are in excess of 80 percent.

Why VSM Matters 

While Value Stream Management has been around for some time now in many industries to reduce costs and cut out waste. The renewed focus on VSM brings out the collaborative benefits of the approach in order to ultimately boost the user experience. 

By dissecting different steps in the journey to software delivery it removes siloes and empowers teams with an understanding of the complete software development journey – not just parts of it.  As an article in TechBeacon points out, “software developers are unintentionally harming their organization’s overall efficiency because they simply can’t see the big picture. In this case, what they don’t know can hurt them.”  Leveraging value streams, project teams can see the big picture, what is working and what is not, and where, so that they can focus their energies on rowing in the same direction to improve quality, deliver value, and better meet end-user needs. 

The Key Steps to VSM

While there is a very specific methodology to conducting a formal VSM program, there are some broad steps that are required along the journey.

  • Identify the problem that needs attention. Maybe customers are complaining because there are too many bugs in the software; or upgrades are not keeping pace with changing user needs. Leveraging user feedback, it’s important to isolate the problem and then work across all development steps to identify areas where not enough emphasis is being placed, or, where too much emphasis is being placed needlessly.
  • Secure the team, set up the training. It’s important to identify who your VSM team members will include within the software development team and provide the VSM training to effectively learn how to conduct mapping and analysis.
  • Properly document what is seen. As you study processes, activities, and workflows, it’s important to record key observations each step of the way firsthand. You shouldn’t rely on employees who might not be objective, but record your own observations, by visiting each department.  The end-goal is to accurately identify weaknesses and strengths in processes in order to improve software quality.
  • Analyze the value stream map. The actual VSM process is quite specific and collects information in very detailed data boxes and timelines. After reviewing this you can map out an ideal process that can lead to better product quality, reduced waste, and greater efficiency. Once you’ve done this you can carry out the new plan and continuously monitor KPIs and adjust accordingly.

Software development is an intricate process with many steps, that most often take place far from the customer. Utilizing a Value Stream Management approach, software development makes customers part of the process.  By understanding user needs and gathering user feedback, and then analyzing each step of the process to address any issues standing in the way of progress, the user becomes part of the delivery cycle –  and quality becomes the software differentiator. 

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Offshore Software Development Trends in 2022

In 2022, we will continue to see a high-speed evolution of industries and societies towards digitization and virtualization. In this digital-first era, which skyrocketed due to the global pandemic, software development companies lead the way toward true digital transformation.

Flexibility and innovation are the drivers responsible for IT companies and tech businesses’ quick adjustment to clients’ ever-changing requirements.

With a steady list of demands from consumers, let’s share some trends to be on the lookout for if you are planning to offshore your development projects.

  1. Hybrid teams are the new black.  COVID-19 kick-started working from home and other dynamics. It is clear that the workforce is now accustomed to the new norm- which means not returning to the office. Since working from home, apps, like Zoom and Google meet, have made for easy collaboration and better communication between teammates. It appears to be highly effective for many companies. This means newfound confidence in hybrid dynamics, which will lead to an increase in offshore development partners. 
  2. The helpful cloud. Cloud technologies require minimal effort. It sets up business applications online while maintaining low costs. Companies will continue to benefit from these services, which allows them to keep growing. Cloud services have flexibility that leads organizations to consider them as an excellent answer. Incorporating cloud services also allows for better security for data, since the technology might be recovered in the event that the servers are down. It makes for a better business flow.
  3. The rise of the machines. Artificial intelligence continues to be on the rise. Industries are wanting quick and efficient solutions and machine learning is the answer for it. With the ability to automate repetitive tasks, algorithms are responsible for shifting how we manage our day-to-day. AI possesses certain risks- it could be lead to partiality, exploitation, and other issues. By ensuring quality data, AI will continue to help companies increase their efficiency, process optimization, and more.\

Offshore software development is critical for a company looking to scale its business and be successful. By outsourcing, you will have the right resources to face every obstacle and reach digital transformation. Don’t want to be left behind? Make sure your company is staying ahead of the game by implementing these trends.

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Top AI Startups in Puerto Rico in 2022

Puerto Rico is home to beautiful beaches and vibrant culture. It is also home to a booming ecosystem consisting of startup companies. We have an array of industries that are having an impact on the economic ecosystem of the island. They are also supporting other businesses by creating more job opportunities. This means potential growth for the island as a whole.

Among those industries, are the businesses focused on integrating AI into their projects. Entrepreneurs on the island have taken advantage of the available opportunities and are helping our economy flow.

 Here are some companies that you should have on your radar:

  1. Abacrop

This veteran-owned business created a solution to help farmers maximize their efforts and growth. In their words, “by optimizing our data capture we improve our time management all while adding Business Intelligence to our farming operations”. A solution, created with the intent of increasing production and expansion capabilities, has managed to minimize high risk in quality control and improve the farming experience.

  1. TagShelf

This is a computer software startup that focuses on document organization. It created Alfred- a virtual clerk with the ability to automate document-driven processes. The algorithm has the ability to access existing documents, categorize, and validate information without the need for human interaction. 

  1. TextualMind

This startup corporation focuses on helping FDA- regulated companies realize high levels of compliance and a significant reduction in product quality issues. They are revolutionizing compliance in the life sciences industry. They achieve this by combining their machine learning technology with a key element; tell-tale signals are left in the written procedures and document trail created by the company’s business processes. Their intelligent software agents being developed are capable of detecting these signals, allowing companies to target and solve issues accordingly and effectively.  By solving these issues in a timely manner, the AI agents are preventing changes in product quality. 

AI for Digital Transformation

We are proud to be part of companies promoting a booming ecosystem and supporting a community of entrepreneurs. AI is the future and Wovenware offers services aligned with that path. In the words of our COO, Carlos Meléndez, “Our best services are delivered on the AI front, creating custom and predictive analytics algorithms, specifically in computer vision,  AI, and complex software development for enterprise projects, most often in regulated industries”. 

Wovenware, as an AI and software development consultancy, believes that we can accomplish more when we have a machine and human workforce working together. We currently have a team made up of multitalented experts: over 140+ software engineers, data scientists, and design experts- all committed to continued growth and the creation of innovative solutions.

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The Hot Duo: Design Thinking and Data

Digital transformation has been measured by the evolution and improvement of technology. This transformation wouldn’t be possible without the humans that use these tools. Humans are the key figures- in this digital-first world- that are not going away.

In our recent Forbes article, we highlight this. Without people in the heart of every digital experience, algorithms wouldn’t succeed in being truly effective. Forrester predicts that 2022 will carry “heightened expectations for digital experiences [and] pivots to human-centered tech transformation.”

This means a shift in thinking- including when it comes to creating effective AI algorithms. We’ve been hearing over and over through the years that data is the “new oil”. We even wrote recently how there never seems to be enough of it. It is true that in order to accomplish digital transformation, you must have lots of data to train an algorithm- you can have vast amounts of data, but we are now realizing that you must know what to do with it and who will benefit from it. 

A new way of thinking is the solution for developing human-centered algorithms. We need to include the human experience- or design experience- into our design processing for quality solutions.

A Human Perspective

2022 is the year where this perspective will be the focal point. Software engineers and data scientists will join forces with design experience professionals to understand the human problem and develop the right solution for it. 

Here are five best practices that focus on the design aspect of data engineering to help you better understand the heart of any issue and create solutions fit for digital transformation.

  1. Don’t develop tech for tech’s sake. Before creating something, developers must ask themselves the “why” behind it. This means an understanding of the individual users, their needs, past experiences, and more; and then using that knowledge to design better solutions. 
  1. Data goes two ways. Good quality data must be a priority. Scientific data helps uncover the patterns of behavior that may need to be addressed, but the human data is essential to identify and develop the right solutions for these patterns. To understand the symbiotic relationship between data and design, consider the challenges faced during the pandemic. Data-driven science led to life-saving vaccines. 
  1. Remove siloes. Software engineers, design, and development must work together to create innovative solutions based on relevant technologies. They need to balance the business and user needs for a possible technology solution that achieves the desired outcome. 
  1. Engage all stakeholders. It is important to engage external and internal stakeholders to prevent biased program algorithms from happening. By having the perspective and data from different departments in the organization and from those out in the world- the people impacted by the technology- we ensure that this does not happen. 
  1. Continually test and improve. Once a solution is created, it is not the last solution to be developed. This solution must be continuously tested to ensure that it is accurately addressing the business problem and effectively meeting human needs. Being human means constant change in our experiences, beliefs, and more. It is important to constantly check that the algorithm works for any new data that is applied to it. 

Humans are the driving force behind technology. This is why we must start with the human experience and work toward a solution for the tools that improve that user experience. This is the line of thinking that will lead to better innovative solutions.

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