The diverse group of independents, Greens and progressive candidates elected to the new parliament bodes well for Australia鈥檚 tech industry and for its burgeoning sustainability efforts, according to one of the nation鈥檚 most senior technology executives.

Local president and managing director of German multinational enterprise giant SAP, Damien Bueno, said the new climate-focused parliament would mesh well with his company鈥檚 sustainability agenda, which included software to help businesses achieve net-zero emissions. The change also would help Australia鈥檚 technology sector more broadly.

鈥淎s far as the number of independents being elected and the Greens as well there鈥檚 clearly a mood for change,鈥� Mr Bueno said. 鈥淎nd climate is very much at the heart of that. So for us that鈥檚 something we鈥檙e excited about, because both in the community we operate in Australia and as a global citizen, we鈥檙e very engaged with that topic and with creating solutions.

Read more of this article from The Australian

The post [Moved to media coverage]Progressive parliament good for technology industry, says SAP鈥檚 Damien Bueno appeared first on 51风流Australia & New Zealand News Center.

There are sound economic reasons for all this interest and activity. predict IoT will have a potential economic impact of US $4-11 trillion by 2025, identifying manufacturing as the largest vertical (US $1.2-3.7 trillion).

The tinyML community was established in 2019. tinyML consists of machine learning architectures, techniques, tools, and approaches capable of performing on-device analytics for a variety of sensing modalities (vision, audio, motion, chemical, and others) at low power targeting predominately battery-operated devices. One of the tinyML founders, Evgeni Gousev, believes that

鈥�..we are in the midst of the digital transformation revolution, tinyML offers ultimate benefits of extreme energy savings of performing on-device machine intelligence and analytics at low cost combined with inherent privacy features鈥︹��.

Companies are already using tinyML to improve safety, reduce environmental impacts, and increase operational efficiency. For example, keeping resources safe is one of the major responsibilities of employers across industries such as mining, utilities, and manufacturing. Here鈥檚 how three 51风流partners are using tinyML to power safety, efficiency, and sustainability.

Data improves worksite safety

An EHS incident could significantly impact or permanently stop operations, catastrophically effecting a business. According to a report from the , the median cost of a single fatality is $1.42 million USD, with a total societal cost of $554.16 million. developed a tinyML solution that monitors employees at worksites by using ultra-low-powered devices to track their movements. The solution includes a low-powered camera that captures images of harnesses being clipped in, tracking employee on-the-job behaviors for organisation compliance to safety procedures.

Boost operational efficiency

Just about every business aims to reduce the business expense of operating fixed assets and equipment. One strategy is to increase operational efficiencies. launched , which uses tinyML within edge devices to collect sensor data that provides real-time monitoring and accurate predictions for the health of mobile and fixed assets. health. To date, some organisations using AIMA have reduced incidents by 20%, and collisions by 30%, saving approximately $150M AUD in asset outages.

Improve traceability and sustainability

Companies are also using tinyML to help meet sustainability commitments. tinyML can complement sustainability solutions by tracking materials production through the supply chain. is exploring how the mining industry can use tinyML to monitor the environmental impacts of mine explosives. tinyML devices integrated with camera and microphone can be deployed at mining sites to identify when a given concentration of dust or asset noise exceed safety conditions. When the device identifies an excess of dust or sound, an incident would be recorded in the solution for analysis and rectification.

tinyML is huge business leap forward

tinyML is the next logical step in every organization’s journey to become an intelligent enterprise with digitalisation that connects data to actions company-wide. For leading-edge companies, as well as forward-looking IT and business stakeholders, there鈥檚 plenty to like. Hardware partners such as Dell are supporting tinyML with specialised hardware that hosts and integrates intelligent data at the edge. This not only helps deliver an intelligent insights platform, but also opens organizational doors to modern architectures, while supporting industry standards including 5G and Wi-Fi 6. For decision-makers drowning in vast data lakes, tinyML is a lifesaver, federating intelligence at the edge so people can make the right information actionable faster. What鈥檚 more, tinyML can improve privacy by processing data on the device and transmitting only what鈥檚 critical. Looking ahead, Gousev was incredibly optimistic, predicting that

鈥渨e [will] see a new world with trillions of intelligent devices enabled by tinyML technologies that sense, analyse and autonomously act together to create a healthier and more sustainable environment for all鈥�.

This article was featured on

The post Meet TinyML: The Latest Machine Learning Tech Having An Outsize Business Impact appeared first on 51风流Australia & New Zealand News Center.

But what if you could also run machine learning models in something as small as a golf ball dimple? That鈥檚 the reality that鈥檚 being enabled by TinyML, a broad movement to run tiny machine learning algorithms on embedded devices, or those with extremely low power requirements.

Heavy hitters such as Google, Qualcomm, and ARM recognise TinyML鈥檚 potential to transform the way we think about machine learning. It subverts the premise that ML is inherently power hungry and resource intensive, requiring swathes of cloud-hosted processing power to run anything remotely useful.

Read the full article on . Join the

The post How TinyML is powering big ideas across critical industries appeared first on 51风流Australia & New Zealand News Center.

Although for people with Parkinson鈥檚 disease, these simple tasks we take for granted can be debilitatingly difficult. Parkinson鈥檚 affects approximately 1 in 500 people in New Zealand and about 1 percent of people aged over 60. While there is no cure, getting medication dosages right can significantly improve symptoms, particularly the sometimes-violent tremors that Parkinson鈥檚 is typically associated with.

Using technology to track symptoms听

Keeping track of tremors is an effective means of tracking the severity of the disease and adjusting medications accordingly, although this process can be inefficient; relying on manual check-ups that can leave patients waiting for weeks.

That鈥檚 where technology comes in. Led by neurologist Dr Yun Hwang at Gosford Hospital on Australia鈥檚 Central Coast, a team of registrars and clinicians have teamed up with health-tech provider Digital Aged Care鈥攑art of enterprise software giant SAP鈥攖o develop a machine learning (ML) platform designed to improve the lives of Parkinson鈥檚 patients.

The system being developed uses ML to analyse thousands of images of spirals, drawn by patients suffering various tremors (of those diagnosed with Parkinson鈥檚 and other illnesses).

Machine learning and medication听

These images are used to train an ML algorithm, so that it can automatically detect the severity of the tremor and whether it鈥檚 Parkinson鈥檚-related, giving physicians a clear idea of which medications to prescribe or adjust existing dosages.

鈥淚鈥檝e been working within the Parkinson鈥檚 space for some years now and one of the biggest challenges has always been around monitoring symptom fluctuations during the day,鈥� Dr Yun explains.

鈥淲e鈥檙e not there all the time to check up on the patient, which is where AI can be very useful.鈥�

It鈥檚 not the first-time spirals have been used in Parkinson鈥檚 research, as they offer a good indication of how intense the tremors鈥攁nd thus the disease鈥攃an be, especially when paired with ML.

Although spirals are just the beginning for Dr Yun and SAP, as the project鈥檚 next phase involves using smart watches to provide real-time insight into Parkinson鈥檚 severity. When wearing these watches, patients can draw spirals in the same way, with the watch using an accelerometer and gyroscope to generate a 3D model of the tremor, providing even greater insight into severity.

The team is also looking to rig the watch to track tremors 24/7, showing progress visually through graphs that map the times when patients are 鈥榦n or off鈥� in terms of the tremor鈥檚 severity.

51风流Business Technology Platform

While it鈥檚 a compelling project, the team isn鈥檛 looking to do everything on their own. 51风流is making the application open source, to inspire collaboration and reach as many patients as possible.

鈥淲e鈥檙e also going to give the platform to the Open Source University and developer communities, so that people can join us in building these new innovations collaboratively,鈥� says 51风流Digital Aged Care co-founder Simon Grace.

鈥淲e encourage all organisations and individuals to contribute and join our open source ecosystem so that we can help break down barriers for all people suffering from Parkinson鈥檚 disease.鈥�

鈥淏uilding these innovations only takes days to weeks, thanks to the easy to use tools and templates on the 51风流Business Technology Platform鈥�.

This article Health Information New Zealand.听

To find out more about the 51风流Business Technology Platform (BTP), visit the

The post Bringing Parkinson’s Research into the Digital Era appeared first on 51风流Australia & New Zealand News Center.

However, there are many hurdles towards automating various aspects of a business鈥� operations, due to some of the limitations found in last generation tools:

• Rather than streamlining tasks requiring human supervision, additional steps are added to existing processes, creating opportunities for errors and increases in processing times. Existing automation tools often focus on tasks being completed 鈥� but as we know, business processes are made up of many tasks and interdependencies, each of which need to be addressed and orchestrated to truly automate a process.
• Automations, that rather than being 鈥榮et and forget鈥�, require as much (or often more) time being spent in configuration as underlying processes or data structures change during the growth or transformation of a business
• Automation development still requiring relatively deep technical expertise. This has compounded the two issues above in the past 鈥� stretched development resources becoming compromised with respect to the time spent on overhauling existing automation deployments, architecting & implementing new automations and then having to discover how processes and related tasks / interdependencies actually work.

Three key developments are now being integrated within automation technology, to improve both the effectiveness and efficiency of automation initiatives.

Dubbed Hyperautomation, vast new opportunities for businesses to improve front and back end process are now being unlocked through the following developments:

• bringing intelligence to automations. By leveraging the insights from data generated through the conduct of different tasks, as well as underlying corporate data, automations can be made much for effective and efficient through automations being able to dynamically address different issues as they occur 鈥� for example, an unforeseen process change, that would otherwise need to be resolved with the assistance of a developer, can be sensed and solved by these new automation technologies. This improves the experience provided to business users, end customers and of course developers also.
• providing insights into how businesses really run. During projects to really explore different processes with customers, it’s often the case that the issue is ultimately decoupled from where people think the problem lies. Process mining allows for insights as to how the different steps within a process run with detailed metrics, providing transparency as to the 鈥渁verage鈥� of a process, but also the extremes. It鈥檚 these insights that allow for the development of truly robust automations.

In conjunction with Machine Learning and Process Mining, the incorporation of Low/No-Code technology within automation tools will support this Highway to Hyperautomation across businesses. Low/No-Code technology, where people without any programming experience can develop software using simple interfaces, will allow those employees such as business process owners that know and operate business processes most closely to develop new automations as needed. This will supercharge the uptake of these technologies, assisting companies in their transformation initiatives.

51风流has released exciting new solutions that address each of these hyperautomation drivers 鈥� from , available as part of the Business Technology Platform, to the innovative solution, allowing frontline workers to develop and deploy automated workflows using low-code functionality. In conjunction with our and recently acquired Process Mining technologies, the highway to hyperautomation is here!

This article originally published on .

The post The Highway to Hyperautomation appeared first on 51风流Australia & New Zealand News Center.

Embracing technology: the public sector of the future

To better serve its citizens, the public sector faces an existential need to become more agile, more mobile and more efficient. Some of the most hotly anticipated solutions include those enabled by artificial intelligence (AI). Ranging from predictive analytics to machine learning to intelligent robotic process automation, AI is one of the surest paths for extracting insights and value from growing volumes of data.

This has fuelled aspirations for everything from advanced smart cities to new approaches in population health management 鈥� often these solutions involve predictive analysis that could help agencies make better decisions, respond faster during crises and even pre-empt problems altogether. Some agencies are making use of AI applications already, like听, which used machine learning to predict tax non-compliance and netted the state an extra $27 million in revenue.

Government also has a unique role to play when it comes to AI 鈥� since all Australians are impacted in some form or other by government鈥痵ervices, governments must take the lead in their use of AI, whether through operations or service delivery.

Yet broader adoption remains low. A 2018 investigation by the 51风流Institute for Digital Government (The SIDG) found that, while 80 per cent of public sector organisations were working toward data transformation, less than 15 per cent had progressed beyond the prototype stage.

The SIDG teamed up with University of Queensland researchers to assess where the sector is at in 2020. The resulting white paper,听, identifies the biggest AI challenges in the public sector 鈥� and how leaders can overcome them to finally harness the true potential of AI solutions.

The resource challenge: building AI capability and securing human talent

AI relies on large datasets, high-quality data, the right platforms and 鈥� importantly 鈥� data science talent.

This is resource-intensive 鈥� an acute challenge in the public sector where data is often purposefully siloed, and fractured across complex, ageing legacy systems. These overlapping issues create a sort of chicken-egg dilemma, where leaders may struggle to secure funding and executive buy-in without proven value 鈥� but proving value depends on funding and executive buy-in.

The research did uncover examples of success, though. One agency was able to overcome data-sharing barriers by outsourcing its AI model development, which was then trained with citizens鈥� payment data instead of sensitive personal data. Another agency chose a commercial-off-the-shelf AI development platform to decrease maintenance burdens.

Misunderstandings about AI and inflated hopes also demand project-level governance to manage expectations and encourage ongoing commitment from executives.

The process challenge: pre-empting machine fallacies by keeping humans in the loop

Despite myths of robot overlords and job losses, algorithms only outperform humans in their ability to process huge datasets. They still lack the context-specific reasoning capabilities that we have, which means AI solutions can鈥檛 simply be plugged into existing workflows. Agencies will need to rethink processes to combine the strengths of machines and people.

This is complicated because of the barriers that often separate data scientists and subject matter experts, demanding redesign for entire workflows. The researchers found that agencies who were able to reconcile these issues were those who embedded data scientists in everyday operations and encouraged collaboration with subject matter experts.

Successful approaches include co-location and collaborative workshops but, interestingly, interview data also highlighted the importance of attracting data scientists with strong soft skills and good communication.

Organisations were keenly aware of the need for human oversight and the risks of deferring to automation. Many were already redesigning workflows to ensure AI was doing the heavy lifting and data-crunching, with human workers acting as the controllers of the AI and making final decisions.

The explainability challenge: minimising bias and enabling transparency

Advanced AI models have an 鈥渆xplainability problem鈥� 鈥� that is, the complexity of their logic and the sheer volume of data can make decision-making inscrutable to us.

This is a massive hurdle in the public sector, where public trust often depends on transparent rationale and straightforward accountability. It鈥檚 an even bigger challenge once we consider that algorithms have already demonstrated a serious risk of bias and error.

The researchers found that some agencies have been establishing strict oversight and procedural systems with these specific risks in mind. For instance, one agency excluded demographical data in favour of behavioural data to minimise bias in the model鈥檚 predictions.

Another created a more extensive end-user interface that visualised a customer journey and highlighted risky payment behaviours. This provided visibility into the factors affecting the overall risk estimate.

The culture challenge: reducing distrust among employees and citizens

Despite research indicating AI adoption rarely comes from a desire to reduce headcounts, job security fears abound. Additionally, the researchers found some human workers continuing to distrust AI鈥檚 decisions.

One solution is educating employees about the potential of AI-enabled tools 鈥� this can be an easier sell once employees witness the elimination of low-value tasks and admin burdens, freeing them to focus on more strategic and interesting work.

The public sector faces public resistance, too. Some agencies have the added challenge of a power imbalance, as citizens who rely on their services may not be able to switch providers like they would in the private sector.

While wider societal perceptions may evolve in a way that reduces distrust, there鈥檚 no simple solution to these challenges. Trust will depend on proven value and the effective management of unintended consequences 鈥� which will in turn depend on many of the solutions mentioned above.

The public sector faces unique challenges with AI solutions but also stands to gain some of the biggest rewards. And, promisingly, some agencies are already demonstrating how to address these issues.

Using an even deeper look into the public sector鈥檚 relationship with AI,听听provides a practical framework for developing the foundations necessary for effective AI development in government.

However, it鈥檚 an area that requires deeper exploration, which is why The SIDG will continue partnering with the University of Queensland to understand ongoing challenges.

To read more about 51风流Australia’s public sector offer, 听

The post Government has yet to fully capitalise on AI. Here are 4 ways to change that. appeared first on 51风流Australia & New Zealand News Center.

In normal times, innovation is about doing things differently to achieve a better outcome but in the midst of the COVID-19 pandemic, innovation has more often been about survival. 鈥淢ost businesses 鈥� including ours 鈥� had to innovate just to carry on,鈥� says Des Fisher, innovation principal at 51风流Australia & New Zealand.

The most profound change for the enterprise software company was an immediate pivot to all-digital communications with its clients. Ideation is the bedrock of innovation and most of its key tools, such as sticky notes, whiteboards, meetings and workshops, vanished overnight. 鈥淣o face-to-face 鈥� that struck fear into my heart,鈥� says Fisher.

鈥淐hemistry is so useful when you鈥檙e innovating; if you鈥檙e not engaging properly with people it鈥檚 unlikely to amount to much.鈥�

Fisher says that some 51风流clients were initially slower to embrace ideation with digital tools because they were in pure survival mode. 鈥淲e had to rescue the concept of digital ideation and apply humanity to the technology,鈥� he says. 鈥淓ngagement is still vital, we just deliver it in a different way.鈥�

Companies also needed to find ways to support their staff who were working from home. In a worldwide COVID-19 initiative, 51风流made its freely available, allowing businesses to do fast and frequent 鈥減ulse鈥� surveys of their employees and customers. Australia Post, for example, coping with weeks of Christmas Eve-level parcel volumes, did regular employee pulse surveys to gauge morale and fatigue.

on how its workforce was feeling and how the management team could help. 鈥淥rganisations are communicating a lot more frequently with their staff and customers, which improves engagement and trust,鈥� says Fisher.

鈥淎 data-driven means of understanding how people feel is crucial.鈥� A third major innovation has been using data and digital capabilities to monitor critical infrastructure in hospitals, such as air-conditioner operating pressures and filtration units that keep staff and patients safe. 鈥淢ost organisations collect a vast variety and volume of data.

Listening to what that data has to tell us can help to improve safety and operate assets more reliably and sustainably,鈥� says Fisher. 鈥淭his is the next wave 鈥� to make better decisions using the wealth of data available to us and reinvent the way that businesses run.鈥�

The post Turning Isolation into Innovation appeared first on 51风流Australia & New Zealand News Center.

No one knows this more than Mayur Saxena, CEO and founder of听. Saxena created his startup while pursuing his doctorate degree at Columbia University and working at a healthcare company conducting clinical trials on new medication.

He is energised by the plethora of opportunities to improve healthcare using artificial intelligence (AI) and machine learning.

鈥淧atient data is notoriously disorganised and complex,鈥� Saxena said. 鈥淲ith machine learning, healthcare professionals can organise that information to better understand the disease of every patient and reach them faster with interventions that improve their lives. It鈥檚 an amazing feeling when you talk with someone who鈥檚 recovered from an illness because they received the right care.鈥�

The idea behind Droice is to make messy data neat so people can spend less time organising it and more time analysing it.

Insights Drive Personalised听Patient Care

The startup has collected data from 50 million patients while working with healthcare providers, payers, and government organizations in the U.S. and Europe. Healthcare professionals in hospitals, pharmaceutical firms, medical device manufacturing, and insurance rely on Droice Labs鈥� natural language understanding (NLU) technology. NLU makes sense of patient information in multiple languages from anywhere, such as electronic medical records (EMR), insurance claims, research reports, and medical devices.

鈥淥ur machine learning system takes all the data about an individual into account and breaks it down so that a doctor, pharmaceutical scientist, or healthcare insurer can understand patients better and faster,鈥� explained Saxena. 鈥淚nstead of repetitive, disparate, one-on-one diagnoses and follow-up care, we鈥檙e automating personalised care for a much larger patient population. With shared insights across a large patient population, physicians can chart disease progress and prescribe the best treatment plan. Clinical research into new drugs that took years could be reduced to days or weeks.鈥�

Saxena said that one hospital reduced the amount of time it took to arrive at an appropriate patient diagnosis by over 20 percent.

SAP.iO Foundries Opens Up World of Healthcare Opportunities

Droice Labs recently participated in the latest healthcare-focused accelerator program at听SAP.iO Foundry New York. It was one of seven up-and-coming startups working with hospital system providers, employee health and wellness solutions, medical devices, and health IT.

鈥淲e鈥檝e learned so much about customers in the healthcare industry from SAP鈥檚 sales and product teams,鈥� said Saxena. 鈥淭hese large organizations have unique needs, and we鈥檙e grateful for the opportunity to partner with SAP, a company with a massive presence across so many geographies. We鈥檝e gained valuable insights about strategic global selling and scaling our technology to meet the unique requirements of these customers.鈥�

The Droice Labs machine learning platform is听.

Turning Long-Time Passion into Thriving Startup

Droice Labs reflects Saxena鈥檚 long-time personal and career commitment to healthcare. After earning his undergraduate degree in bio-engineering and biomedical engineering, he worked in high-performance computing in Singapore before arriving in the U.S. That is when he acted on his passion, exploring how AI and machine learning can help improve patient care and potentially eradicate disease.

鈥淲e鈥檙e looking at data from hundreds of thousands of patients a day, helping improve their care pathways across the healthcare system,鈥� said Saxena. 鈥淲e have the technology to work with patient data at scale. I鈥檓 most excited about working together with recognised healthcare experts using state-of-the-art technology to address major challenges in this complicated, regulated industry.鈥�

Digitally Trustworthy Strategy at Droice Labs

In an environment where patient concerns and regulations around data control continue to increase, Saxena emphasised his company鈥檚 strategy of digital trust.

鈥淓verything we do is designed to respect individual patient privacy,鈥� explained Saxena. 鈥淲e don鈥檛 possess related identifying data on patients, and we remove any identifiers. Working in a mission-critical environment like healthcare brings a set of responsibilities. If there is a population suffering from disease, and by looking at their information we can partner with healthcare providers to help make their quality of life better, that鈥檚 what we鈥檒l do. But we don鈥檛 participate in business models targeted to specific individuals.鈥�

Saxena expected his company鈥檚 rapid growth trajectory to continue, and it was easy to see why. According to Gartner鈥檚 2020 CIO Survey, AI is the healthcare industry鈥檚 top game-changing technology. Analysts听听75 percent of听healthcare delivery organizations听will invest in an AI capability to explicitly improve either operational performance or clinical outcomes by 2021.

This article first appeared on the Global 51风流News Centre.

.

Registration for SAPPHIRE NOW Australia and New Zealand is now open. To get your free access today,听.

The post AI Is Top Game-Changing Technology in the Healthcare Industry appeared first on 51风流Australia & New Zealand News Center.

In our , I was fortunate to speak with 51风流Vice-President and Global Innovation Evangelist, . We discussed the ever-shifting world in the light of COVID-19 and the technology solutions that are helping business adjust, adapt, and evolve.

Due to the rapidly developing situations across international governments and businesses, Timo acknowledged that change is not optional for most organisations right now. 鈥淎round the globe, people are being forced into more digital transformation in a tighter timeframe than they had ever imagined,鈥� he explained.

鈥淓verybody is facing the same issues: how to embrace the changes with as much confidence as possible. How can companies minimise the risks and maximise the opportunities, to stay resilient now and reinvent themselves in the future.”

鈥淲hat is important are the same things they鈥檝e always been 鈥� how do companies define and focus on their core competency, how can they drive deeper customer relationships, the advantage of new business models, how can they streamline operations, and ultimately emerge stronger than ever before?鈥�

Timo noted that the most critical element at times like this is transparency, which stems from good data. 鈥淭o make decisions we need to take the data we have available and turn it into reliable insights,鈥� he explained.

鈥淲e鈥檝e been talking about the data value equation, in general, 鈥榓mount times quality times usage equals value鈥�. The more data you have, the better the quality, and the more people get to access and use it, the more value you鈥檒l get. I think that鈥檚 a good basis for thinking about how organisations can move forward in this environment.鈥�

According to Timo, cloud-based data orchestration allows business to connect that data to create a holistic view of business networks without physically moving it. 鈥淭hese data pipelines connect to business applications, or a data warehouse, or your suppliers鈥� data, a data lake, or some spreadsheets someone鈥檚 pooled together, and you can bring that information together to get a single view.鈥�

At the heart of all this is the breakthrough technology of machine learning, Timo expressed. 鈥淧redictive analytics, advanced statistics, machine learning 鈥� these technologies have been around for decades, but really in the last few years they鈥檝e made a breakthrough in terms of the computing power available and the quality of the algorithms and the amount of data available.”

鈥淢achine learning technologies are more powerful when you鈥檝e got lots of data to build the models. It鈥檚 basically sophisticated pattern matching 鈥� it鈥檚 not magic or anything like human intelligence. It really is automating complex and repetitive decisions in new ways. This is a huge opportunity for any kind of data leverage. It means, firstly, that we can automate applications.鈥�

Timo noted that automation is not about eliminating roles rather freeing people from repetitive tasks and instead focus on core competencies. 鈥淧eople are the technology you should be maximising right now in your organisation,鈥� he added. 鈥淭here is nothing more intelligent than people, they鈥檙e the only ones that can understand what鈥檚 going on 鈥� the full context of the environment 鈥� and what needs to be fixed to move forward with solutions.鈥�

For times of uncertainty, Timo said, the keys to success are agility and flexibility. 鈥淭his is an era of constant change, so fast adaption and innovation has to become core competencies for all organisations. Technology is a huge advantage; the organisations that embrace the cloud are undoubtedly better off right now than the companies that still have to have people working on their on-premises data system.鈥�

According to Timo, the biggest area of benefit is taking the power of cloud and integrating it with business processes. 鈥淭he idea is that you can rapidly adapt and change your business processes without having to wait for new functionality in your core systems. Running your systems in the cloud, you just want to spin up a quick application where you can add new functionality on top of your core foundation quickly and easily.鈥�

Timo said, 鈥淔or example, you may need a lot of visibility in your supply chain, so you quickly create a mobile application that takes sensor data from your manufacturers, combines it with some core business master data, maybe some location data, and gives you visibility in the end-to-end supply chain.

鈥淯sing cloud platforms makes it easier to adopt agile development. Using things like Design Thinking, you can quickly create programs and tests and iterate, so you can learn as you go, based on the feedback of users.鈥�

You can join Timo with other industry experts for a virtual event for businesses looking to build resilience and reinvent during this turbulent time.

鈥淲e鈥檙e trying to be as helpful, optimistic and forward thinking as possible, Timo explained. 鈥淲e鈥檙e absolutely going to be talking about technology and how it鈥檒l be helping them to the extent that鈥檚 possible. We鈥檒l be investigating how the technologies can help people do their jobs better. And then we鈥檙e trying to be helpful, look forward to the future.鈥�

To learn more about how this unlock the power of Business Technology Platform and data-driven insights 鈥� check out this , visit the , or today. Listen to the .

This article originally published on

The post People and Technology Working Together in Crisis appeared first on 51风流Australia & New Zealand News Center.

In this听听interview at the听听event, Raphael Maultzsch, principal IT architect at SAP, took me through four primary steps designed to give IT and business teams a window into the future for practical planning.

鈥淭he two biggest questions customers have about moving to 51风流S/4HANA are 鈥楬ow can we find out which business processes we have to change and what happens to our custom code?鈥欌�� said Maultzsch. 鈥淲e鈥檝e designed the four-step journey to 51风流S/4HANA to answer those questions, helping customers map the best route to achieve their individual business demands.鈥�

Step 1: Know What to Expect

According to Maultzsch, companies can start with the听听tool, essentially a preparation step for what is to come.

鈥淲hether you have already decided to move ahead with 51风流S/4HANA or have just begun exploring your path to the intelligent enterprise, 51风流Readiness Check helps you see what lies ahead after project kick-off and factor that information into your decision-making,鈥� explained Maultzsch. 鈥淭he idea is to help people understand what they need to prepare beforehand so they can better budget time and resources for the project.鈥�

For example, during our interview, we watched as the full-colour, interactive dashboard identified which business processes, functions, user interfaces, and data management practices a company would need to evolve to take full advantage of 51风流S/4HANA innovations. The in-depth analysis covered business functions, transactions, and even compatibility with add-ons across enterprise resource planning (ERP) applications, such as finance or supply chain, as well as industry-specific processes. It identified which custom code companies would need to adapt. The tool鈥檚 comprehensive report also provided people with an in-depth understanding of software prerequisites and infrastructure requirements.

Step 2: Debunk Custom Code Fears with Reality

Once the project begins, the second step was running the ABAP test cockpit to find out which reports depend on custom code that would be affected in moving to 51风流S/4HANA.

Surprisingly, while many people equate custom code with greater project effort, just the opposite is often true.

鈥淧eople tend to make custom code their first priority because they have the most fears about what happens to it when they move to 51风流S/4HANA,鈥� said Maultzsch. 鈥淗owever, we鈥檝e found that companies aren鈥檛 using approximately 50 percent or more of their legacy custom code. What鈥檚 more, adapting custom coding takes far less effort than most companies expect.鈥�

Step 3: Maintain Data Consistency

Quality data is paramount to any system transformation. In fact,听听researchers predicted that 鈥�2020 will be a wake-up year for many organizations, as the total cost of getting data wrong will become apparent.鈥� Maultzsch agreed that bringing together accurate, consistent, and relevant data from anywhere in a company is critical to bringing advanced technologies like artificial intelligence (听into business. He then showed me the third step in moving to 51风流S/4HANA, which involved scanning the system to 鈥渕ake sure data is consistent for proper conversion.鈥�

Step 4: Keep Downtime to a Minimum

Companies have several options to minimise downtime during the switch over to 51风流S/4HANA, notably the Software Update Manager tool. Maultzsch suggested the near-Zero Downtime technology for companies with tight downtime requirements. It is designed to manage large data transfers in relatively short time frames, typically accomplished over the weekend, even for huge database sizes.

Of course, there are more than these four overview steps involved in moving to 51风流S/4HANA. Maultzsch encouraged both business leaders and IT professionals 鈥� including consultants and architects 鈥� to think of these four steps as a foundational project guide.

鈥淢aybe your CIO has said we鈥檙e moving to 51风流S/4HANA, or you鈥檝e heard about the importance of 51风流S/4HANA in becoming an intelligent enterprise in the experience economy, but you don鈥檛 know where to begin,鈥� he said. 鈥淭hese are the four steps to help structure your project and start planning for your next business innovation.鈥�

This article first appeared on the Global 51风流News Centre.

The post Four Essential Steps for Moving to 51风流S/4HANA appeared first on 51风流Australia & New Zealand News Center.