The alarming levels of global warming (and subsequent climate change) are a direct indicator of the widened gap between the reality of CO2 emission and the goals. A global climate-conscious effort is now the need of the hour and an essentially sustainable lifestyle that also facilitates the survival of our very own nature.

History has witnessed impressive human capabilities to bring significant changes- be it the discovery and use of fossil fuels (that led to the first industrial revolution), or the search for alternative sources of energy (to cater to the growing demands across the globe while the conventional sources of energy are at the brink of exhaustion).

While companies like Tesla are disrupting the transport industry with their introduction of electric cars and quad bikes, these initiatives fail to be enough given the rate at which the world is still dependent on fossil fuels to meet its global energy demands.

According to the latest IPCC reports, the average annual GHG emissions were at their highest levels during the past decade. While the growth rate of emissions has slowed, limiting global warming to the targeted 1.50C levels is unlikely, owing to a lack of immediate and deep emissions reduction across all sectors.

“It’s now or never if we want to limit global warming to 1.50C. Without immediate and deep emissions reductions across all sectors, it will be impossible”, IPCC Working Group III Co-Chair Jim Skea.

Innovative solutions and accelerated technological advancement, in sync with sincere human efforts and grit, are a way to sail humanity out of the climate mess (that the world is experiencing currently).

Green Technology to combat climate change

Some integral solutions for efficient and transparent carbon management are classified under the umbrella term – Green Technology. This strategy is gaining momentum in the wake of rising concerns to address global warming and climate change. AIoT, Big Data, and blockchain are powerful technologies that hold the promise to beat climate change.

AIoT (Artificial Intelligence of Things)

A myriad of databases and backend systems are involved to categorise and organise huge chunks of data from multiple business units. This requires the employment of an equal volume of manual labour. Integrating AIoT to this segment would enable seamless sourcing of data depicting real-time activity levels. It also provides an asset inventory from a plethora of systems.

This technology leverages the structuring, collection, and transformation capabilities, thereby facilitating accurate monitoring and measurement of the emission levels. Additionally, it reduces overall efforts for data collection and enhances data quality (and report resolution).

AIoT also helps in creating insights based on real-time data. Thus, accurate process emissions can be effectively predicted by analysing this data. This can efficiently refine the evaluation of the performance of abatement measures and reduce the overall marginal abatement costs.

Big Data

Historical and real-time big data solutions can help in identifying pressure points by locating sources of harmful emissions and their respective concentrations. The technology ensures transformative change by tapping the potentials within available data- advent of the Fourth Industrial Revolution.

Blockchain

The decentralised network of blockchain offers smart contract features that enable automate the enforcement of climate policies and regulations. In addition, it also enables the programming of incentives for businesses, individuals, and governments to comply with sustainable practices. Thus, smart contracts act as an incredible tool for mass participation in green practices globally.

Being in its nascent stage, green smart contracts have not enjoyed popularity in the past. With rapid growth over the past decade and intersection with other upcoming technologies, conscious efforts are giving it the required boost.

Tech alone can’t fix it

Technology offers sustainable solutions to manage global CO2 levels, enabling the world to meet net-zero pledges. However, technology has never been the cause of this climate mess. It is, therefore, logical not to put too much faith in it to be able to solve it entirely. Strong societal, political, and economic agendas are the driving factors for the application (and integration) of these advancements for realising the net-zero targets.

Technology is, certainly, a part of the solution. Nevertheless, it is only effective with collective human will and action.

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post Technology That Can Fix The Climate Mess appeared first on OpenBusinessCouncil Directory.

Sundeep Reddy Mallu pursued Electrical Engineering at the undergraduate level and began his journey by selling seeds to the farmers in his native place. Grasping the basics from his early years, he postgraduate in management and business administration with Marketing and IT as his majors. He gives credit to this stellar journey that made him adept with Data Science skills and being able to deliver business values to Fortune 100 clients globally.

Sundeep is currently serving as SVP Analytics at Gramener to advise executives on aligning their data science strategy with business vision. He is also responsible for building teams to apply analytics and data visualisation, while also helping businesses adopt a data-driven culture. His keen interest in the application of Data Science and Machine Learning in ESG makes him believe that the action of humanity in the next decade would be critical.

“It’s no longer within 1.5ºC of temperature rise. It’s the case of how far are we overshooting it. This means ESG, as an overall ecosystem is gaining traction. This is evident in the number of companies who are making public commitments to carbon-neutral initiatives. This means that the climate effects are slowly starting to show up in the company top lines and bottom lines.”, he indicates.

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Hilton asked Sundeep about the drivers that helped him apply his skills to agriculture in India. He fondly replies that though India is a country with its own share of challenges, he has been able to provide leverage by introducing simple yet effective technological concepts to farming.

While citing the exemplary work he has done in the country, Sundeep adds, “The kind of solutions that we could bring into the Indian context was way different from the ones in the US, particularly due to the huge difference in the percentage of population dependent on farming as their primary source of income. Therefore, while the AI and ML applications have been in practice for a very long time in the US, India is still experimenting with tailor-made solutions. With startups budding up in the country, there seems to be a whole lot of optimal progress being made here”. 

On this note, Hilton was interested to know what prompted Sundeep to join Gramener. In the words of Sundeep, “The vision of Gramener is about the public impact. While the organisation is a commercial entity, the underlying goal that the founder’s support is the impact it makes on society. The intent is to take the help of data to narrate inside stories to create action so that impossibilities can come to life. Gramener focuses on custom solution-building in pharmaceutical and life sciences, logistics, and ESG.”

He further adds, “Given that our ESG solutions span not just across climate or agritech alone, it’s the fusion of these multiple problems that interlap with each other. And we, being at a position, where we can understand the science part of it the climate standpoint, understanding the solution point of it from a technology standpoint, we are fortunate that we are able to play a small role in this big picture of assisting in the ESG space to our clients.”

On being asked by Hilton about how data is managed, Sundeep tells him that to reduce the noise and create relevant data, technology helps them connect to various data sources. They are also able to aggregate the data, and standardise the data cleaning access and data quality checks, thus reducing the manual effort.

Sundeep told Hilton that three basic ways to maintain data ethics at Gramener involve the collection of only the required data, maintaining standards even while procuring the data from the third party, and aligning the policies while complying with industry regulations.

Explaining the challenges while providing solutions that meet ESG goals, Sundeep says, “The popular culture, folklore, is for companies like big companies consuming every data that they can collate and use it. They have the means and resources to spend on these. When it comes to the ESG space, you already have a limited monetary means that you have. That is a space where we invest a lot of time to optimize that stretch the dollar to the longest possible impact. And getting the data right is a big step in how much you invest. Because each of these solutions has to be maintained in a longer haul. It’s n0t that you built that and it’s done. These are the systems that have to survive and fight any of the windows. Unlike other engagements, ESG engagements don’t give you results in a quarter, they have their long tail where they play and out and they generate value, which means every system that you build has to withstand the test of the time. And that’s where creating a long pull- the total cost of ownership of a solution goes a long way. And hence, starting with data is key”. 

Concluding the interview that promises to enlighten the audiences with its deeper impact and awareness about the challenges in creating a sphere of intersection between technological advancements (AI, Data Sciences, and ML) with ESG, Hilton asks Sundeep to give his views on the future that lies here. “The hunger for data is constant. What we will see increasingly happen in my view is that the access to data would become uniform. The differentiation would not be a lot more different. How data gets put to use for digital making is what will go into a drastic change”, he shares.

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Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post citiesabc Podcast: Hilton Supra With Sundeep Reddy Mallu On Sustainable Application Of Data For Business appeared first on OpenBusinessCouncil Directory.

As the technology evolved, the world transitioned smoothly from wired phones to pagers, mobile phones, and smartphones. Similarly, the paradigm has shifted in almost every sector- fashion, retail, gaming, events, and whatnot. The growing interest in technological advancements is gauging society for a phenomenon called the Metaverse that promises to boost every life experience in unimagined ways.

A vision that defines the next chapter in the evolution of the internet, the Metaverse is an immersive digital world that allows its participants to interact and engage in many different ways. The term has been in flux for the past few years. Still, in its infancy, the concept is evolving with the passing of time.

Hype or the reality of the future, the interest in the metaverse continues to grow over time. From gaming to NFTs, real estate, tourism, and shopping, the metaverse buzzword keeps repeating itself with newer and fancier versions everytime.

With ample business opportunity involved with Metaverse, prevalent names in the market like Qualcomm, Nvidia, Epic, HTC, and Apple are entering the space innovatively. As a motive to expand the ingression of Metaverse in our lives, Microsoft planned the acquisition of ActivisionBlizzard for $69 billion. World-building games like Minecraft and Roblox, events like the Fortnite concerts, and platforms like Enjin and Sandbox- all these are efforts to gear up society for a ‘Metaversive’ future.

The role of technology in empowering the Metaverse business

A clear understanding of driving technologies enriches the quest for the metaverse to be ethically uniform and fair for all. In other words, creating an interoperable space that is accessible equally to everyone could prevent the limitation of the conventional internet space- the dominance of biggies.  Now, this quest directs the businesses in an impactful way, aiming for a booming digital economy.

Further, understanding the technology behind the Metaverse also governs the economy of adoption of that technology for the business. For instance, currently, the average cost of an app design in the USA is $48,000, which definitely would increase with its demand. This, therefore, is an imperative parameter to factor in.

What started as a gaming space on the internet has now grown into a massive opportunity for almost every vertical of commerce. The nature of the content, thus, has changed altogether in the vast space. Creating quality content that is immersive for the users would entirely depend on the progress of the technology here.

Here are the five major technologies that are empowering the Metaverse towards a brighter future:

Artificial Intelligence

As of late, AI and automation have been broadly applied to our lives. From arranging business procedures to giving out driving instructions, facial recognition, and so on, AI applications include managing operations like anomaly detection, event correlation, and predictive alerting. This can be made even more accurate with the combined use of Big Data and Machine Learning.

Implementation of AI has spread its influence to the formation of vivid metaverses, enhancing user experiences by creating Metaverse assets like buildings, landscapes, avatars, and character routines. AI could be employed to accelerate the software development process. This could be beneficial to creating complex Metaverse assets even when the resources are limited.

AI could also be used for improving efficiency by employing its capabilities to communicate and comprehend the users. Warning, for example, the user to stay alert while performing difficult tasks. This bridges the real-time users closer to the metaverse environments.

Blockchain

A technology built on strong foundations of security and transparency, blockchain provides a decentralised platform that ensures digital collectability, value transfer, governance, interoperability, and convenient accessibility.

Cryptocurrency, the fungible feature of the blockchain, is the primary way to trade for goods and services inside the Metaverse, ensuring no regulations and interventions in transactions. For instance, Decentraland allows the purchase of virtual property using crypto, MANA.

It is also a way to incentivise the users, especially the ones working within the Metaverse. Additionally, Metaverse supports (and enhances) job and business opportunities. This favours the increasing remote working culture across the globe.

AR and VR

Even though Metaverse does not necessarily depend on these two technologies, they offer an enhanced Metaverse experience to the user. While AR utilises digital visual components and characters to integrate the virtual world with the real one, VR provides more immersive features using 3D modelling techniques. Components like optical projection systems, headsets, sensors, gloves, and display systems.

By creating virtual environments, these technologies mimic real-life situations. Meta (formerly Facebook) invested in Oculus quest (VR headsets) to enter their Metaverse. The app can be accessed using smartphones and does not require a computer system.

“Augmented reality will change the world more than a lot of other technologies. Travelling around to meet people will be much less important when you can stand in a room and chat with a virtual representation of a person that’s so close to reality – it’ll be a whole new level.” –Tim Sweeney, CEO and Co-Founder Epic Games.

The harmonious relationship between AR and VR has successfully created an early model for the Metaverse. With the expansion of technology, a more interactive and experiential Metaverse environment will evolve.

Internet of Things (IoT)

IoT enabled devices can help solve some of the real-world problems effectively in the metaverse. This gives an opportunity to accurately integrate the complex systems over the existing framework of technology. For instance, enhancing the user experience by using the 3D user interface over mobile phones and computers, customised as per users’ interests and preferences.

Thus, IoT can bridge the real world with that of the internet, using sensors and devices as the connecting link. These devices have the capacity to send or collect data naturally. Indoor regulators, clinical devices, facial recognition, voice-actuated speakers, thermostats, and many others provide a wider scope of information. The data, thus, collected would enhance the precision for an advanced representation, for example, altering the function of Metaverse collectibles based on current weather conditions. This ensures a better immersive nature of the space.

Big Data and Data Analytics

Even when data science technologies do not contribute to the development of the Metaverse, the data science process definitely will. Data processes like analytics could be helpful in managing Metaverse-based events, like building a 3D avatar, or a virtual stage. Similarly, data collected (like current trends and events) could be used to forecast using predictive analysis tools and also for decision making.

The “Metaversive” future

Rapid enhancement in technology over the past few decades has manifested the notion that nothing is impossible, even the 3D virtual Universe popularly known as the Metaverse.

“The technology will be so good it will be very hard for people to watch or consume something that has not in some sense been tailored for them.” -Eric Schmidt, Google Chairman.

It is not hard to imagine a highly interactive and interoperable space where the users have immense freedom to get immersed in it. Still, in the very early stages, it is hard to predict the development materialises or the impact it will have on society as a whole. As of now, the audience is yet to witness how this realm unfolds itself for its users.

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post Top 5 Technologies Powering The Metaverse appeared first on OpenBusinessCouncil Directory.

Imagine that data could be shared seamlessly with partners, governments, and other organisations, without breaking any data protection law, to facilitate innovation. How will it be possible to use closely guarded customer data while still maintaining the highest privacy and safety standards? Is it possible to monetise data without compromising the sensitivity of the information (or data)? The following write-up spills it all.

Data is the fuel for the rapidly progressing Artificial Intelligence (AI) industry -as it is for almost all other industries. Digitisation, interconnection of the network channels, and IoT generate mountainous volumes of data at an unimagined and unprecedented scale. IDC predicts that more than 175 zettabytes of data will be available by 2025, growing at an exponential rate. Thus, unbelievable data would be available, and accessible to all. However, there are still a huge number of AI innovations and projects that do not reach the stage of viability, owing to an insufficient amount of data.

The inaccessibility of authentic data for innovation and AI

Accurate data collection and processing is extremely straining in terms of expenses and time. While certain sectors like financial services, telecom, healthcare, internet companies, retail, etc, are in direct contact with the customer data, this access is restricted only to these touchpoints.

Then there is another category of data that is fragmented and siloed. Stringent regulations like GDPR watch over the sharing of data that can be processed only with user consent, strictly for lawful purposes. Data security measures are another reason that restricts data access over a larger scale. The data related to Research and Development requires regular hypothesis testing. This is, thus, another challenge pertaining to the presence of any real-time data from the field.

While AI projects require deep learning and innovation are constantly ravenous for large volumes of data, both structured and unstructured, to train models. Scarcity and huge expenses for labelled training data make it difficult to make it available for AI.

These challenges decelerate authentic data monetisation and realisation of benefits (from innovation and business). This is a major reason for many great projects to never even see the light of the day. While alternatives like data masking, anonymisation, and obfuscation ensure data security and privacy to abundant data, synthetic data is a preferred choice when data is scarce.

Synthesising data

Data generated as a result of using computer algorithms and simulations to the real-time data is synthetic data. Digitally synthesising data reflects the real-world either statistically or mathematically. Simply put, the data is generated by reproducing the statistical properties and patterns of the existing real-time datasets. This is done by modelling the probability distribution of these datasets and sampling them out. Essentially, the algorithm creates a new dataset with the same characteristics as the original data.

Although this synthesised data lead to the same answers, is it almost impossible that the original data can be ever reconstructed from either the algorithm or this synthesised data. Thus, synthetic data is almost as potent as the original data with equal predictive power. It further carries no baggage of privacy concerns or restricted usage of any kind.

Synthetic data is being increasingly employed for a wider range of applications. For instance, Syntegra is using its synthetic data generator to create and validate an anonymous replica of NIH’s database. This database has a record of more than 2.7 million individuals that have been screened for COVID-19 and more than 413,000 patients that have tested positive. This synthetic data set duplicates the real-time data quite precisely. Due to its anonymous nature, the data can be conveniently shared and used by researchers and medical professionals worldwide. A remarkable step to accelerate the progress in research for COVID-19 treatment and vaccines.

The AI team at Amazon uses synthetic data on Alexa for training its NLU system (National Language Understanding). Consequently, new versions of Alexa in three new languages have come out: Hindi, Brazilian Portuguese, and US Spanish. This invalidates any further use of large customer interaction data. Synthetic data is also being used by Waymo, a Google company, to train its autonomous vehicles. Synthetic data technology is extremely useful for American Express to enhance its fraud detection capabilities.

Synthetic data could, therefore, fill in the gaps that are hindering the evolution of AI technology. These are proving increasingly helpful in creating inexpensive, yet accurate, AI models. According to MIT Technology Review, Synthetic data for AI is among the top 10 breakthrough technologies in 2022. Analyst firm Gartner predicts: “By 2024, 60% of the data used to develop AI and analytics projects will be synthetically generated. The fact is you won’t be able to build high-quality, high-value AI models without synthetic data.”

Potential challenges

With compelling benefits, authentic and accurate synthetic data generation requires truly advanced knowledge and specialised skill sets. Further, the required sophisticated frameworks should be put up to enable its validation and alignment with the objective.

It is critical that the synthetic data generated does not relate to or expose the original data set in any way, while it should match the important patterns in the original. Failing would result in either overlooking potentially larger opportunities or generating inaccurate insights for any subsequent efforts to model the data.

For the AI models that have been trained on synthetic data generated by simply copying the original one, there is always a risk that inherent historical biases might creep in. Complex adjustments are, therefore, necessary for a fairer and more representative synthetic data set. Hard, yet achievable.

For the synthetic data generated that has been optimised on a predetermined abstract of fairness, the resulting dataset accurately reflects the original one while still maintaining inherent fairness. So, no bias mitigation strategies are needed, with no compromise on predictive accuracy.

The certainty of synthetic data in AI’s future

“There is a risk of false, early-stage perceptions surrounding the use of synthetic data in some circles. This is most likely due to the naming of the term itself, as anything ‘synthetic’ might naturally be thought of as plasticized, non-organic, or in some way fake. But, of course, there should be nothing more natural than machine learning tuition being driven by machine intelligence. Properly generated, managed, maintained, and secured, synthetic data’s level of bias handling, safety, privacy, and cadence represent a significant accelerator and enabler for the AI capabilities of tomorrow”, Nelson Petracek, CTO, TIBCO Software.

Already being used in healthcare (for training the machines to monitor a patient’s post-op recovery), security and surveillance (for detecting a suspicious object or behavioural pattern), and delivery drones, synthetic data is progressively making an accelerated advancement in its evolution.

Synthetic data, surely, is synthetic in origin, but it has real-world DNA to it. Its validation and application are unbelievably tangible, pragmatic, and multifarious. Nevertheless, it is, in fact, a reality we all exist in.

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post The Shift In The Paradigm To Synthesise Data For A Data-Driven AI Industry appeared first on OpenBusinessCouncil Directory.

But according to ActiveOps, a leading provider of digital operations management solutions, there are three areas where workforce data can help organisations tackle burnout. These deal with the current state of play where organisations have burnt-out employees that they need to support and help protect the workforce from burnout in the future.

Richard Jeffery, Group CEO, ActiveOps, stated: “Finding meaningful solutions to employee burnout isn’t just a social responsibility for organisations –though that is incredibly important if organisations want to live up to their values and be an employer of choice for the best talent out there. Beyond that, reducing burnout is vital to an organisation’s top and bottom lines.”

Data enables better workload management

Though the causes of employee burnout are varied, the most significant cause of burnout is overwork – or too much of the wrong kind of work for a specific employee. Organisations must be aware of how much work they are expecting of their employees, what they are getting through, and the work coming down the line. Armed with this data, managers can divert workload away from burnt-out employees while they recover and gradually increase their workload as they regain their wellbeing. By getting a holistic view of the whole team, those managers can see whether they should be diverting that work within the team – perhaps to an underworked team member – or whether they should be looking at additional short-term resources to fill the gap.

Seeing where your employees are spending their time may also help you identify whether they are in the right kind of work.

Data helps managers engage employees more effectively

The company attains employee engagement by understanding and providing what employees want from their work – whether involved in decisions that affect them, measurable and actionable feedback, ongoing development, or a sense of satisfaction and purpose. Disengaged employees are at an increased risk of burnout.

Software that aggregates workforce data can identify employees who are off-task for significant periods or who spend a lot of time using training sections of programmes – both of which may be signs that they are disengaged and struggling. That data can be used as the basis for a productive conversation between employees and managers to help analyse and resolve the issues affecting those employees.

Data brings hybrid work to life

The move to hybrid working styles has presented both opportunities and challenges for employee wellbeing. Many organisations lack a transparent view of the time employees spend working, especially during their non-working hours.

Technology enabling organisations to accurately track when employees log on, showing managers how well they balance work and rest, will help managers proactively intervene to protect their employees from overwork and thus burnout.

“Organisations with a high level of burnout will soon (if they haven’t already) see their retention rates plummet as employees look for other roles where they can recover themselves. Burnout damages the company brand and incurs high costs as the organisation has to hire and train replacements for those employees they lose. Eliminating burnout, therefore, can reduce the cost of churn for organisations. 

“Key to the success of any organisation’s efforts to reduce burnout is to revisit how they measure employee productivity. It’s no longer enough to look simply at outcomes or the volume of tasks completed. As work has grown more complex, the methods of measuring success have also increased in complexity – and often, organisations find it very hard to measure performance without technological intervention,” concluded Richard Jeffery.

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post Three Ways Data Can Help Resolve Employee Burnout  appeared first on OpenBusinessCouncil Directory.

Healthtech will rise to prominence in 2019 as Big Data and Artificial Intelligence (AI) become a transformational force in healthcare, says GlobalData, a data and analytics company.

The company’s latest annual outlook report, ‘The State of the Biopharmaceutical Industry – 2019’, reveals that 38% of global industry respondents believe that Big Data will have the greatest technology impact on the pharmaceutical industry in 2019.

Increasingly high volumes of data are required for all decisions and Big Data will not only alter the regulatory process as we know it, but payers will increasingly require this evidence as a pre-requisite for reimbursement. Bonnie Bain PhD, Global Head of Pharma at GlobalData

Compared with other industries, healthcare adoption of Big Data has been slow but its use will increase dramatically in 2019 across a number of core functions, namely;

• drug discovery and design
  – to determine potential drug candidates;
• clinical trials
  – for real time monitoring;
• electronic health records (EHR)
  – to depict trends and ultimately lower treatment costs and improve quality of life;
• healthcare intelligence
  – to revolutionize the tracking of patient statistics and vitals;
• hospitals and healthcare systems
  – to predict healthcare outcomes and design treatment protocols.

Not surprisingly, AI is the second biggest trend anticipated in 2019 as confirmed by 32% of survey respondents. Its use in healthcare will continue to grow rapidly, especially when considering the amount of data that can now be mined from patient records and used to design treatment plans, develop drugs or improve clinical trial outcomes.

So what do Pharma companies need to consider in order to capitalize on the potential of AI and Big Data in 2019?

According to Dr Bain, ‘‘Companies need to understand the specific benefits that technologies such as AI can offer and how it can help their organization. Investing in AI and Big Data will not provide a cure for business problems so do not believe the hype and set realistic expectations for success. There are also a lot of AI solutions on the market and not all of them are right for every business objective. Choosing the right solution for your business need is key! Also partner for success.’’

Unlike traditional drug development where M&A is common practice to acquire novel targets, this is probably not the best approach for adopting emerging technologies.

Companies should consider partnering with an established technology provider or a start-up with a proven track record to ensure successful execution. Dr Bain 

The focus on Big Data and AI is a marked shift from the past two years where Cybersecurity was a key area of interest. This was driven by several highly publicized breaches including the 2017 network cyber-attack, which led to a temporary production shutdown for pharmaceutical giant, Merck & Co and subsequent disruption of its global operations. While Cybersecurity may be a core competency now, GlobalData anticipates that AI will lead the way in 2019.

In 2018, most pharma companies were still in the early stages of implementing their AI strategy but in 2019, AI will be one of the core areas of focus and investment in the pharmaceutical industry.

Dr Bain adds, “Organizations are investing in AI not to reduce cost but rather to drive product innovation, grow revenue, gain operational efficiency and improve the customer experience. Once fully implemented, AI will enable real-time decision-making and could become a transformational force in healthcare.”

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

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