In today’s business environment, data is key to success. With over 2.5 quintillion bytes of data created each day, data-driven insights are the main driver in every major business decision and are essential to discovering more efficient processes, reduction in risk or new sources of revenue.

However, harnessing the power of data continues to be a challenge, due to the on-going shortage of data science skills in the labour market, as demand for digital skills still far outstrips the supply. A recent UK government report found that nearly half of businesses (46%) have struggled to recruit for roles requiring hard data and analytics skills.

IDC estimates that by 2025 we’ll have created more than 175 zettabytes globally. As the world of business continues evolving, companies are moving fast and need fast solutions – they can no longer tolerate knowledge workers, delivering low strategic output from legacy tools for the enterprise. The sheer abundance of data and its growing complexity means data skilled workers able to harness it for fast and sound decisions will be at the forefront of the job market throughout the next decade.

While not every worker needs to become a data scientist, many businesses are turning to upskilling their employees to overcome this shortage. Building their own internal pool of talented data workers with the skills, desire, knowledge, and analytical expertise to be successful and thrive in an increasingly ‘data-rich’ environment.

Organisations have already started to recognize data literacy as an important skill for their workforce. A recent McKinsey study found that 84% of executive leaders – when increasing their talent pool of data specialists – experienced more success from upskilling their existing workforce, compared to just 16% who succeeded when hiring externally.  By providing analytics solutions that upskill information workers into data-literate knowledge workers, these knowledge workers – individually and collectively – can help drive organisational transformation.  Employees have the context of the business questions to solve as well as the knowledge of the data assets available that can drive answers through analytics.

Creating a culture of upskilling is by no means an easy feat. Getting employees engaged can be half the battle. It requires building a new culture where data is accessible to workers throughout the organisation, as well as significant investment in new tools and platforms that do not require users to know complex coding languages. Low code and no code solutions provide space for employees who want to upskill, learn and practice to become skilled data workers themselves.

By implementing formal upskilling programmes that focus on key skills and technologies, in addition to providing a learning curriculum that can result in valuable and credible certifications, companies can set themselves and their employees up for success. However, these programmes should not be dry and academic. In fact, the upskilling journey can be a social experience.

For instance, businesses can host “lunch and learn” activities and company-wide “data challenges” that bring people together from across the organisation, introduce staff to data science and make it appealing and accessible. Gamification strategies can also encourage staff to use online learning resources and develop their data skills by using leader boards, points scoring and creating personal challenges and achievements.

The aim is to create an open culture of learning where staff communicate and work together to solve data problems. A company’s existing data scientists should act as coaches to colleagues, encouraging them to think analytically and ask the right questions of datasets. This will help build data skills into every team, so that data analytics becomes an enterprise-wide initiative, rather than siloed into one team of analytics professionals.

The other benefit of this more social approach to data science is how it can impact diversity. Simply put, data science has a diversity problem: as few as 15% of data scientists are women. This lack of diversity is a huge concern, because with a diverse range of approaches and points of view to tackle data challenges and ensure data models and algorithms are free from biases, businesses will see improvement in results. It’s no secret that the more diverse the workforce the richer the business outcomes will be, research by McKinsey has shown that organisations with more ethnic and gender diversity are more likely to outperform.  When we value our varied experiences, they impact how we solve problems to get to better answers.

The evolving landscape of the data science and analytics market creates an inherent need for organisations to foster and grow data analytics cultures fuelled by collaboration and diversity, presenting an opportunity for all demographics traditionally underrepresented in the technology workforce, to accelerate their careers by embracing analytic roles. For business leaders, this represents an opportunity to look within for specialists with the right attitude to problem solving, not just technical aptitude, to support and upskill in both data literacy and analytics.

By investing in upskilling, people from any age, gender and background can learn vital data skills and progress their careers. It also enables companies to recruit new individuals who don’t necessarily have an academic background or specific coding skills, which may encourage a more diverse range of applicants. This was the experience of the sports and fitness apparel company Gymshark, which uses Alteryx to empower and upskill its employees.

“We’ve been able to expand faster because we are able to find these individuals easier, rather than having to find people with very specific skillsets,” says Gemma Hulbert, CDO at Gymshark. “New hires are now able to come in and hit the ground running right away with Alteryx, even though they aren’t data analysts. We are able to create apps that empower our employees to be able to learn new skills using the platform.”

Data science doesn’t have to be the preserve of the elite few. Anyone in the workforce with a passion for solving data puzzles is now able to do it, not just a handful of specialists. In the past, employees with vast expertise in their own fields were locked out of data analytics due to the technical knowledge it required.

With the right tools and investment, anyone can learn data skills, and when people are encouraged to be creative and think critically, they are able to ask the right questions and solve all sorts of problems. Thanks to self-service platforms and automation, the power of analytics is no longer restricted to a few gatekeepers, but rather it is available to all. By enabling employees to scale their passion for data science, businesses will accelerate the knowledge workers’ journey to become data-driven, be better able to unlock data-driven insights and tackle the world’s biggest problems with a successful digital transformation journey.

The post Businesses need to show data science isn’t dull, it can be fun and rewarding appeared first on Artificial Intelligence.

The Department of Homeland Security (DHS) Small Business Innovation Research (SBIR) Program recently awarded funding to two small businesses to develop non-contact, inexpensive machine learning training and classification technologies. Integrated machine learning platforms can significantly reduce time, redundancy, cost, and improve the accuracy in detecting threats such as explosives, chemical agents, and narcotics.

“S&T embraces the significant advances in artificial intelligence and machine learning capabilities and their ability to enhance threat detection,” said Kathryn Coulter Mitchell, DHS Senior Official Performing the Duties of the Under Secretary for Science and Technology. “The SBIR Program provides the opportunity for S&T to partner with innovative small businesses and develop machine learning tools critical to addressing threat detection needs. I am looking forward to seeing the technologies that will be developed by these SBIR efforts.”

Physical Sciences Inc. (PSI), based in Andover, MA, and Alakai Defense Systems, Inc. (Alakai), based in Largo, FL, each received approximately $1 million in SBIR Phase II funding to develop technologies that can rapidly and accurately identify unknown spectrometer signals as safe or threatening. The DHS SBIR Program, managed by Program Director Dusty Lang and administered at the DHS Science and Technology Directorate (S&T), selected PSI and Alakai to participate in Phase II of the program subsequent to demonstration of feasibility in Phase I, for each companies’ compact, accurate and rapid classification Machine Learning Module for Detection Technologies solutions.

Under Phase II, PSI will continue to develop their deep-learning algorithm for detection and classification of trace explosives, opioids, and narcotics on surfaces, for optical spectroscopic systems. PSI will extend the algorithm’s capabilities from infrared reflectance spectroscopy to include Raman spectroscopy, as well as a proposed operational module prototype, which will have a classification accuracy of greater than 90 percent.

During their Phase II efforts, Alakai, will continue development of the Agnostic Machine Learning Platform for Spectroscopy (AMPS) that rapidly and accurately detects trace quantities of hazardous and related chemicals from a variety of spectroscopic instruments.

“Our impetus for developing these machine-learning modules stems from the Transportation Security Administration’s operational needs for threat signature fusion, the ability to learn, detect and classify new threats without being explicitly programmed, and, ultimately, increase accuracy of detection,” said Thoi Nguyen, DHS S&T Program Manager for the Next Generation Explosive Trace Detection (NGETD) Program. “With experienced industrial partners like Alakai and PSI, and our strong collaboration with TSA, we hope these efforts will contribute to wider applications of machine learning across the Homeland Security mission space.”

At the completion of the 24-month Phase II contract, SBIR awardees will have developed a prototype to demonstrate the advancement of the technology, spearheading the potential for Phase III funding.

Under Phase III, SBIR performers will seek to secure funding from private and/or non-SBIR government sources, with the eventual goal to commercialize and bring to market the technologies from Phases I and II.

The post DHS Awards $2M for Small Businesses to Develop Machine Learning for Detection Technologies appeared first on Artificial Intelligence.

Amid the constant disruption from unlikely competitors and changes in the industry occurring in faster and shorter cycles, time to market is constantly shrinking. To run a business and navigating this complexity in the present day and age, managers need relevant information and insights that can help understand the intended target audience, and their needs and preferences.

Thanks to the availability of multiple sources of market and customer data, analytics and artificial intelligence can be used to effectively respond to market dynamics, and drive revenue, profitability, and customer satisfaction. These new technologies are no longer a privilege for tech firms. An increasing number of companies are leveraging these tools to steer through unsettled waters and enhance their performance.

A few years ago, AI technology was being mostly used by early adopters. Any new technology typically faces a “chasm” in going from early adopters to the majority. With the pandemic and the inevitability of transformation, AI technology has “flown” over this chasm, entered the mainstream, and is now getting industrialised within companies.

There are several ways in which businesses can use AI and analytics to spur growth:

Customer monetisation

Analytics can be extensively leveraged to personalise the customer experience. The most optimum products and services can be offered at the right price and the experience can be optimised to the individual customers’ liking.

The impact of this work is amplified in the digital domain. Every interaction is being recorded which generates massive amounts of data, and this can be used to personalise the experience in real-time. All this leads to higher customer satisfaction and maximisation of revenue.

Optimised marketing spends

Companies spend a lot of money on marketing through various means and channels. It has been often said by CMOs – “I waste half the money I spend on advertising, I just don’t know which half.” Analytics and machine learning models can assess the marketing spend across channels to identify the optimum mix to drive revenue and brand equity. This can be fine-tuned by various customer segments and types.

Competitive advantage

Enterprises can collate data from within their organisation and the industry to have an upper hand in understanding the competition and market trends. By combining the information generated, organisations can get constant insights into sales, potential gaps in the market, and product improvement.

These insights enable the teams to work in collaboration, provide real-time responses to competitive tactics, and achieve better outcomes.

Optimisation of the supply chain

Analytics can be used to ensure that supply keeps up with the business with optimised costs, especially with the demands of digital business models which need short-time deliveries to customers.

The future of AI

It is impossible to anticipate every use case of AI in the future. Just like it happened with the internet, AI-based innovation will throw up use cases that we cannot fathom today.

Businesses will be able to leverage AI to answer complex questions around growth opportunities like new markets and product lines, and make multifarious decisions that are scientific and rooted in data.

Some exciting use cases are where the realm of AI is going beyond structured data to understand and analyse all sorts of unstructured data like images, audio, text, and video.

Using these techniques, AI is now even being used to optimise creativity and to help marketers decide what kind of creativity will appeal to specific audiences for specific campaign objectives.

However, the one key area where AI technology will impact the most is in disrupting entire industries and creating new business models. For example, what Tesla has done to the auto industry and what Netflix has done to the entertainment industry.

AI has a huge scope of disruption and transformation in areas like healthcare and education, and many others. All this is going to lead to transformational business opportunities for existing companies and new entrepreneurs.

The post How artificial intelligence and data analytics can help businesses thrive appeared first on Artificial Intelligence.

Artificial intelligence continues to be a focus and concern for businesses, regulators, and lawmakers alike. As we recently wrote, there was much activity and focus on artificial intelligence and the impact on privacy laws. In addition to legal developments, there have been advancements in AI business technologies by major multinational technology firms, something focused on this post in our sister Intellectual Property Law Blog. There has been an arms race underway by the world’s leading economies to win the estimated $13 Trillion of GDP this field stands to award the winner.  In a recent podcast episode, partners Siraj Husain and Michael P.A. Cohen discuss these developments, risks, and solutions that businesses are experiencing.

Putting it Into Practice: Companies will continue to implement artificial intelligence into their operations. Our recent podcast and other articles can help as companies move forward thinking about integrating these tools while keeping in mind the not insignificant legal risks.

The post What to Watch in Artificial Intelligence in 2021 appeared first on Artificial Intelligence.

How to add value to businesses with the help of an AI Factory?

Like a physical factory creates physical products reliably at scale and speed, an artificial intelligence (AI) factory delivers AI solutions for businesses at scale and speed. An AI factory combines data, people, process, product, and platform to move beyond science experiments and deliver AI that drives business value. The AI factory builds on the principles of the AI Ladder, which describes the importance of creating solid information architecture for sustained AI success. It combines DataOps, ModelOps, and MLOps to stimulate AI innovations to market.

How an AI Factory Works

Quality data obtained from internal and external sources train ML algorithms to make predictions on specific tasks. In cases like diagnosis and treatment of diseases, these predictions can help human experts with their decisions. In content recommendation cases, ML algorithms can automate tasks with little or no human intervention.

The algorithm and data-driven model of the AI factory allows companies to test new hypotheses and make a change that improves their system. It could be new features added to an existing product or new products built on top of what the company already owns. In turn, these changes enable the company to obtain new data, improve AI algorithms, and again find new ways to increase performance, create new services and products, grow, and move across markets.

How AI Factories add Value to Businesses

In many ways, building a successful AI company is as much a product management challenge as an engineering one. Many successful companies have figured out building the right culture and processes on long-existing AI technology instead of fitting the latest developments in deep learning into an infrastructure that doesn’t work. Let’s see how an AI factory helps businesses to grow at scale.

The AI Factory begins with Centralised Governance

The idea is to pool and coordinate investment and steering efforts. Only a small number of companies’ highest-value projects will be examined by those sponsors most engaged in their success. The selection of these use cases must be extremely rigorous. No project specifically should see the light if it doesn’t respect the simple law of 10X (offer a 10:1 return on investment). The success and impact of each use case should be measurable as per a simple and understandable KPI. And the systematic improvement of this KPI the most crucial reason for the teams.

Lean AI

Lean AI is a methodology that reduces the uncertainty of efficiency and applicability of AI solutions. Models are never perfect and must be examined in real-world situations. The method contains a continuous improvement loop of short cycles which include the formulation of hypotheses, the identification of pertinent data, the construction and testing of one or more models, followed by deployment on a test perimeter, and collection of user feedback.

The cycle is repeated with the formulation of new hypotheses, new data, etc. This technique enables testing in real situations, then the improvement of cases not explored, until reaching a level of satisfaction considered acceptable by the organization to begin production.

Important Ethical Challenge

The recent example of Alexa and the unpleasant surprise of her listening have been noticed. Regulations will always lag behind technology. It is important that those enterprises that employ AI understand the ethical challenges of these solutions. Seven guiding ethical principles that were published by the Committee of Independent Experts mandated by the European Commission, includes AI at the service of humanity, trustworthiness, which respects private data, transparent, non-discriminatory, dedicated to the improvement of the common good, and finally, with a clearly defined human responsibility.

People lead to the Success of an AI Factory

An AI factory requires a team of people with a variety of skills, roles, and responsibilities to be successful, just like a physical factory. AI development traditionally involves cross-functional or full-stack technical teams. It is essential to consider the AI factory not just as a technical shop but as a market-driven business. In designing an AI factory, all jobs of AI and IT stakeholders, data scientists, data journalists, IT support, business analysts, marketing, and sales need to be done. Assigning people with clear ownership, roles, and responsibilities will add value to a business.

The post ARTIFICIAL INTELLIGENCE FACTORIES HELP COMPANIES TO GROW AT SCALE appeared first on Artificial Intelligence.

The pandemic has changed the world, accelerating the pace of digital transformation and forcing businesses to find new efficiencies — not only in cost cutting but in decision making.

That’s why business leaders love AI, because it can help them make better and faster decisions by providing quicker and more accurate insights. It can tell them what customers really want — and what they want right now.

There is a problem though: Consumers don’t love AI. In one recent study, only 25% said they would trust an AI decision more than one made by a human, and less than 30% were comfortable with businesses using AI to interact with them.

Consumers are skeptical about AI because they don’t understand it. Many are scared by what they read and hear and not all their fears are misplaced. 

Unintentional built-in bias leads algorithms to make decisions, like credit denials, that unfairly discriminate against some minority consumers. It’s also probable that jobs will be lost to AI. What industry does to mitigate both of these problems will be key to maintaining consumer trust. 

Many business leaders attempt to impress customers merely by devising an “AI strategy” or building an “AI system” — and trumpeting it loudly. But customers are put off by this approach. 

What consumers value is the convenience, speed and choice delivered by AI working behind the scenes when they’re doing things like shopping or seeking health advice. 

They’re impressed by better products and services, greater convenience and better value for money, all of which AI delivers. The disconnect means business leaders need to think about their AI efforts differently, and spend more time educating consumers on how AI benefits them.

Not to be denied: AI is doing great things

Few shoppers realize the overnight arrival of their online purchases depends on AI algorithms getting items packed and shipped expeditiously. How many grasp that AI enables a customer service agent or chatbot to make quick-fire responses to their queries and requests? Few probably understand AI is locating the nearby Lyft driver within seconds of a tap on a smartphone.

The workings of AI are mostly unseen in the financial industry, where they very much benefit both institutions and customers. Take fraud detection. Some earlier protections were simple, blunt instruments. 

Sure, they stopped fraudulent transactions, but they also blocked many legitimate ones (“false positives”), often resulting in terrible customer experiences. Today, AI-based platforms pinpoint fraud faster and better, reducing false positives by more than half. That equals more legitimate transactions and fewer unhappy customers.

Within organizations, AI is eliminating repetitive tasks and enabling creativity. Business leaders should be asking, how much drudgery can AI push out of the system so my employees can do more creative work?

Diversity and discretion

What lessons can business leaders draw from all this? One is to tackle the bias issue — the danger that flawed algorithms make decisions unfairly, disadvantaging consumers. 

The first step is to understand that the bias isn’t in the AI but in the data, whether it’s data used initially to train the algorithm or data entered to generate insights. The best way to minimize bias is through diversity — finding and using the widest possible range of datasets. Not just quantity but variety. 

In human interaction, we fight bias by talking to people with different perspectives, by working with people that challenge and question the status quo. The same approach needs to be taken to the data we use to inform our algorithms.

Different groups of business stakeholders need different talking points when it comes to AI. Analysts and investors may be impressed with tales of tech capabilities, but customers just want to hear about better services or products, and that their data is being strongly protected and ethically used. 

We’re more likely to build trust by flat out saying, “We’re not going to compromise your data.” 

Business leaders need to understand AI’s limitations. AI is a tool, not a product, a strategy or a business model. Powerful it may be, but AI is only assisting us in doing the things we’ve always strived to be: smarter, faster, more efficient and, hopefully, more trustworthy

The post Why should anyone trust AI? appeared first on Artificial Intelligence.

Medical wearables, kitchen appliances and fitness equipment and other connected devices are regularly connecting to corporate networks, prompting technology leaders to warn that significant action should be taken to protect them from being used to hack into businesses.

That’s according to a new survey on practices for securing IoT (the Internet of Things) commissioned by Palo Alto Networks, the global cybersecurity leader. It polled 1,350 IT business decision makers in 14 countries in Asia including India, Europe, the Middle East and North America.

Overwhelmingly, respondents report a rise in the number of IoT devices connecting to their networks over the last year. Among the connected trash cans, light bulbs and hand sanitizers, one red flag emerged: More than half of those who polled said they either need to make a lot of improvements to the way they approach IoT security (43 percent), or that a complete overhaul is needed (36 percent).

In India, businesses surveyed have complete confidence (92%) that they have visibility of all the IoT devices connecting to their organisation’s network. However, two in five (40%) of the largest businesses surveyed (3,000 employees plus) reported that they have not segmented IoT devices onto separate networks — a fundamental practice for building safe smart networks. It is even more worrisome that only 27 percent reported following best practices of using micro-segmentation to contain IoT devices to their own tightly controlled security zones.

“Adoption of digital technologies is being seen on an unprecedented scale and so is the spike in security concerns. We need to bear in mind that cybercriminals are getting smarter, thanks to technological advancements and innovations. They are constantly exploring new avenues of cyberattacks – including through IoT devices – and therefore, more caution needs to be exercised by the organisations as they take a technological leap,” said Anil Bhasin, regional vice president, India & SAARC, Palo Alto Networks. He further added, “As we plan on a wider implementation of the Internet of Things (IoT), there has to be a solid strategy in place to overcome the security challenges that come along.”

“Traditional networks are ill-equipped to handle the surge in adoption of IoT devices,” said Tanner Johnson, senior cybersecurity analyst at Omdia. “Device behavior baselines need to be established to allow for new recommended policies to help stop malicious activity. For instance, it would raise a flag if a connected thermostat started transmitting gigabytes of data to an unfamiliar site.”

Palo Alto Networks released the survey as part of its ongoing effort to shed light on security threats posed by the surge in deployment of devices connected to the internet. Business Insider Intelligence forecasts there will be more than 41 billion IoT devices by 2027, up from 8 billion last year.

The post 79% businesses in India feel the need to improve their IoT security approach:Palo Alto Networks appeared first on Artificial Intelligence.

Founder of a data-mining company, Karabo Phungula has been accused of gaining access to the personal data of nearly 24 million South Africans and 793 749 businesses from credit bureau, Experian.

It is alleged that the information was handed over to Phungula after he ‘impersonated a legitimate Experian client’. However, Phungula says that he has been framed. He went on to tell MyBroadBand that he has “no idea what’s going on”.

Phungula has since been issued with an Anton Piller order which grants the Sheriff the right to search and seize ‘evidence’ without giving any warning.

“They came to my parents’ house which is the address they have as my business registered address and requested all devices, I further went with the Sherrif to where I stay to take my computer so they can search for evidence.”

Reports from iAfrikan suggest that “data containing the keywords of the Anton Piller order was found on the hardware that was seized”.

Experian says that it has now “laid a criminal charge against the suspected perpetrator and that a case number and prosecutor has been assigned to the case”.

According to Phungula, he has no dealings with Experian. However, “Experian acquired a business in 2019 (Compuscan) who had a once-off client-relationship with the Perpetrator in 2017”.

It is yet to be seen if these accusations hold any water.

The post Data Mining Company Founder is Accused of Experian Breach appeared first on Artificial Intelligence.

There is an enormous amount of data available for companies for business insights and analysis. Businesses now have tools to enable them to aggregate them into a meaningful report in order to use it for decision making. Combining big data in a meaningful way is tricky – but bigger brands are tackling it well.

A majority of organizations are aiming for a data-driven approach, and are seeing success in their efforts. According to a study by Dell EMC (back in 2014), there would be 1.7 megabytes of data produced in 2020 – for every person and in every second.

Businesses need to follow proven practices for data aggregation to reduce related data management challenges. A major issue is ensuring that they are running on the raw insights, and organizations can accomplish it by structuring and normalizing data. Thus, to begin with, the top methodologies need to be executed.

Basically, businesses need to realize their short-term and long-term analytics objectives. For instance, currently, a company could be trying to know its consumers’ buying preferences. After a while, it may want to aggregate data from different sources to identify audiences’ interests – in order to sell insightfully. Regardless of the purposes, there is likely to be an immediate and long-term focus that will alter the business’ data aggregation requirements. And the strategy should reflect it.

For organizations that purchase data from third parties, they need to ensure that their privacy standards and governance are compatible. In this case, healthcare data would be a great example. While acquiring patient data from an external source for sensitive issues for the purpose of analysis or treatment, the data needs to be in an anonymous format. This is to secure the privacy of such patients.

Furthermore, businesses need to determine how data will be accumulated and how the users will be accessing it. The aggregated data can be used by specific functional areas in a company or by different departments across the board. This is a critical factor because it indicates the best choice- whether the company has chosen to aggregate and keep data in a vast data repository with various access choices – or in a small database that is customized to the need of a specific user group.

In its essence, automating data integration will help. No matter where the data is being aggregated, organizations will require a straightforward way – to vet and integrate the data into the target data source. The necessity of having to hand-code the data integration interface needs to be avoided. Hence, the preferred tactics for data integration are generally processed via standard APIs and automated integration solutions tools – in order to perform secure data integration for business functionalities.

The post Big Data – How Businesses Can Manage Data Aggregation Successfully appeared first on Artificial Intelligence.

The use of microservices is succeeding for 92 percent of organizations according to new research from learning resources company O’Reilly.

It surveyed over 1,500 software engineers, systems and technical architects, engineers, and decision-makers from around the globe and finds that 77 percent of respondents have adopted microservices.

Microservices are clearly flavor of the month at the moment, with 29 percent of organizations reporting that they are migrating or implementing a majority of their systems using them. Additionally, the survey finds that teams who own the software lifecycle (building, testing, deployment and maintenance) succeed with microservices at a rate 18 percent higher than those who don’t. 61 percent of respondents say their organizations have been using microservices for a year or more and 28 percent have used microservices for at least three years.

“The majority of organizations have already started to migrate their monolithic systems, applications, and architectures to microservices, and many more are looking to begin that transition,” says Mary Treseler, vice president of content strategy at O’Reilly. “Breaking a monolith into microservices has clear engineering benefits including improved flexibility, simplified scaling, and easier management — all of which result in better customer experiences.”

Containerization is closely linked to microservices, with companies using containers to deploy and manage microservices significantly more likely to report success than those who don’t. Almost half (49 percent) of respondents who describe their deployments as ‘a complete success’ also have at least 75 percent of their microservices in containers. In total, 62 percent of respondents use containers to deploy at least some of their microservices.

“While container adoption in microservices contributes to microservices success, we saw a lower percent of container adoption than we did in our 2018 report,” adds Treseler. “For some adopters, technical debt from proprietary or monolithic systems might constrain them from using containers and it might be faster and less costly, at least in the short term, to deploy microservices in a database or application server.”

The post Microservices take off as businesses taste their success appeared first on Artificial Intelligence.