We spoke recently with Kenneth Kuek, the Business Development Director of InterSystems, just before the big tech event of the APAC, the Big Data World expo, that took place in Singapore this year, where there were business intellignece platforms a-plenty on show.

“Now people are smart. They think that ‘Oh, I don’t need that amount of data; let’s choose data that [we] are able to interact, to better make use of it, and of course, use machine learning and AI to achieve better outcomes.'”

InterSystems’ specializations are in two areas where data mining for insight has been more advanced to date: the financial and medical sectors, with a strong client roster, especially in the former: JP Morgan, Credit Suisse, and HSBC. But InterSystems’ IRIS platform capability also fits well with the medical sector, where patient and pathological information is rapidly becoming digitized. “In healthcare, we are actually able to produce analytics, not only in the application layer but also in the data layer. We’re able to wrangle the data, for example, for the researchers’ analytics, [and] for the healthcare worker to understand, to digest data, and produce detailed reports,” Ken said.

Smart Business Intelligence Platforms

The mention of AI or machine learning often goes unchallenged by some data platforms’ potential audiences, but Kenneth was quick to point out that the technology is not a magic wand that can be waved over wildly different data sets to produce ground-breaking results. “So, a lot of people are trying to sell AI [or] machine learning services. But it is not that straightforward. Yeah, it’s not, ‘I have two terabytes of data and I’m just going to throw [that] into your analytic system, and I’m going to get the result that I want.’ To understand the output, or the needed outcome, is most important.”

A company like InterSystems that offers AI-powered analysis as-a-service naturally values data science and the rigorous processes required to extract meaningful results from very different data sources. And the value placed on data professionals is reflected onto InterSystems’ customers.

“We still need data scientists to come in to provide the parameters, depending on what data sources are wanted. […] We render the data in order to make [it] cleaner and very easy for the data scientists to apply the parameters and output to the reports the user expects. So it’s not that you engage [directly] in the system; you subscribe to our IRIS data platform. And, we still need professionals like data scientists to draw the perimeters: this is something very important.”

The Need for Data Science Jobs

Kenneth told us that boardroom decisions to engage in data-based projects are increasingly common, but often, the wrong choice of solution type may follow. In the short term, the use of self-built machine learning data systems will increase, assembled from the various fragmented open-source libraries and methodologies available. But in the longer term, for faster and generally more valuable results, “I think a mature data platform like the IRIS system will still have a very, very strong foothold.”

In the same way that many organizations prefer to pay others to maintain and run compute, storage, and networking in the form of cloud provisions, Ken sees InterSystems as the AI in the cloud – more scalable, more reliable, less resource-intensive, and producing better bang-for-the-buck. Ken is realistic in that AIaaS isn’t (yet) as simple as spinning up an S3 storage bucket. That’s refreshing amid the hyperbole, where seemingly every product, however mundane, is badged with ‘machine learning for better results.’ IRIS is keeping its feet on the ground (although it’s located in the clouds).

The post Business Intelligence thought leaders: Ken Kuek of InterSystems appeared first on TechWire Asia.

Platforms that help organizations turn their data resources into business gold are latter-day alchemists, and the success of BI platforms is analogous, largely, to the effectiveness with which they ingest, process, and present data. Some solutions specialize in ingestion, or presentation of information, to take two examples. Others offer the full gamut of operations in the data/BI arena, while some are establishing new paradigms like data abstraction layers that might be regarded as a natural evolution from data lakes that duplicate discrete silos’ contents.

At Tech Wire Asia, and its sister site, Tech HQ, we’re gathering thought leadership from this dynamic market space of business analytics, a scene that changes as quickly as the nature and breadth of available information changes. Alyssa Blackburn, Director of Information Management at AvePoint spoke to us recently about how the concept of business intelligence has changed over the last few years and how there are new challenges for data professionals, even as older issues (such as incomplete or unavailable data sets) may be receding.

“[All of us] have really made an effort to change the way that we approach data, how we create our data, how we store our data […] sometimes more successfully than others. But even more than that, what I would say is that we’ve also figured out that we have this new, incredible repository of stuff, which is electronic content, be it data, be it information. […] And there’s opportunity there, to monetize that, not, ‘I can sell this,’ but for other purposes; monetize it for my own internal benefit, […] for better insights, better analysis. And I think that organizations are really starting to take that seriously.”

Business intelligence to business analytics

Many organizations are well aware that they have data resources accrued for the last few years – at least, for as long as there’s been a digital element to the business. But receiving value from the resource is the challenge, beginning with realizing where data might be hiding away. Alyssa explained. “How do we actually find the stuff that’s valuable? Just hold on to that, and then be able to do something really sensible and wonderful and transformational, versus, ‘I’ve just mined all of this stuff up from the ground,, […] but I can’t do anything with it, because I don’t know what’s actually good.'”

When overwhelmed with possibilities (think about whole hours spent just choosing what to watch on Netflix as an example of the effects of too much choice), sometimes the key decision is what to throw away (“in an appropriate and defensible way,” Alyssa added). But what happens then is up to the individual company? In some cases, the questions being asked may be wrong or, at least, misdirected.

“The most important thing is to understand what you want the outcome to be. [An organization] might come and say to me, ‘I really need this button to be blue. The button’s got to be blue!’ […] But what do you actually need at the end? And they might say, ‘I need particular reporting of how many things were created over a particular period of time.’ Well, it doesn’t really matter if the button’s blue or red then does it, if it does the same thing?”

Once the desired outcomes are defined, business analytics becomes a target that’s easily focused on, and the clever use of platforms like AvePoint comes into play. “Technology should be there to make things easier, to make things more efficient, but it shouldn’t necessarily drive the process.”

For the pages of a website primarily focused on technology, it’s an approach that’s much more heavily weighted on the side of the business rather than the technologists’ fascination with how data is assayed, parsed, sanitized, run through models, and answers presented. AvePoint’s platform does all those things, but it’s not those things that necessarily create her unbounded enthusiasm for the possibilities. For Alyssa, the tech does the heavy lifting and does it well:

“We should get the technology to do that, and that’s amazing. But if we don’t know what [the outcome should be], none of the technology is ever going to be any good. I’ve got great software! But if you don’t know what you want it to do, it’s never quite going to meet those needs.”

Keep coming back to the Data & BI Spotlight area on this site to get more Thought Leaders’ insights and opinions.

The post Business Intelligence thought leaders: Alyssa Blackburn of AvePoint appeared first on TechWire Asia.

Show Notes for Series 03 Episode 01

This podcast is produced in conjunction with Prospection

Medical data on patients and their treatments forms highly complex data sets that need specialist software to decode and examine. Among the few organizations that can address the challenges of medical information is Prospection, an Aussie business that works with clinicians, pharmaceutical companies and researchers to decode the hidden patterns of cause, effect, and correlation that are hidden in the gigabytes of information that follow patients throughout their lives.

In addition to holding the key to better treatments, more effective drugs, and shorter clinical trials, medical data also represents some of the most sensitive information about individuals that’s stored digitally. Anonymization and cybersecurity practices help protect a patient’s identity, but the level of detail required for meaningful outcomes creates the need for a fine balance between data integrity and human privacy.

In this episode of the Tech Means Business podcast, we talk to Eric Chung and Peter Cronin, the co-founders of Prospection about these issues. Plus, we talk about how pharmaceutical companies benefit from longitudinal patient data – information that joins up a patient’s interactions with different parts of healthcare over the course of their lives.

Peter is a clinician, while Eric is the IT specialist, and together they’ve formed a company that’s at the cutting edge of what digital information can do to improve all our lives.

You can read more about Prospection here:
https://www.prospection.com/

Peter Cronin is here:
https://www.linkedin.com/in/peter-cronin-620a19b/

Co-Founder Eric Chung is here:
https://www.linkedin.com/in/eric-chung-13912926/

And Joe Green is here:
https://www.linkedin.com/in/josephedwardgreen/

The post Podcast: Better Health from Healthy Medical Data appeared first on TechWire Asia.

As such, self-service analytics are now becoming highly sought after. With the shortage of professional data scientists and skilled IT employees, bundled together with the effects of remote working, there is no denying that more organizations are keen to explore this option.

That said, convoluted data architectures, inefficient processes, and a lack of data governance (to ensure that companies are even storing the right information in the first place) continue to hinder such systems from becoming a reality.

In fact, many companies – including some of the largest and most tech-savvy businesses in the world – struggle to make their analytics work across distributed computing environments, or to meaningfully leverage ever-increasing volumes of data coming from AI, machine learning, 5G, and IoT.

According to Keith Budge, Executive Vice President, Asia Pacific and Japan at Teradata, the quality of the data requires a lot of care under data governance and security, especially when industries like banks and government agencies are serving customers remotely.

When it comes to data for self-service analytics, organizations need to ensure their employees have accurate data that is up to date and highly secured, especially in a remote work environment.

“In regulated industries like banking, data governance and data security have to be assured for self-service analytics at much higher levels. During the pandemic, data governance and data security became paramount, especially with employees now working remotely and using their own devices for work as well,” said Budge.

For example, Budge explained when a bank implements a new self-service analytic tool, a lot of testing is done to validate the security and veracity of the data. Depending on local law and regulations, some banks and companies even have to demonstrate to regulators the measures taken to ensure data that is used for self-service analytics is not compromised.

Budge added that this is where companies like Teradata can bring together data from very diverse sources and with very different levels of complexity, and help customers be compliant with both their internal and regulator security requirements. This is crucial in regulated industries like banking, especially with risk management issues being fundamental to the ways banks operate.

Teradata Vantage is the connected multi-cloud data platform for enterprise analytics. It enables ecosystem simplification by unifying analytics, data lakes, and data warehouses. With Vantage, enterprise-scale companies can eliminate silos and cost-effectively query all their data, all the time, regardless of where the data resides – in the cloud using low-cost object stores, on multiple clouds, on-premises, or any combination thereof – to get a complete view of their business.

While self-service analytics can help businesses rely less on IT teams for analytics, managing these tools still requires some training, be it for large enterprises or small and medium enterprises (SME).

With AI also being a key component in analytics, Budge believes that AI models in self-service analytics will be able to self-learn predictably at scale and be deployed over user environments.

Self-service analytic tools may not just help organizations rely less on their IT teams but also ensure that businesses remain competitive in the data-driven market. With the right system and training in place, employees will be able to make the most out of self-service analytical tools to maximize efficiency.

The post Simplifying business intelligence through self-service analytics appeared first on TechWire Asia.

In today’s world, data is everything and for everyone – regardless of how big or small the organization is. Data collection is now a necessary component of running a business in order to analyze an organization and its clients’ potentials. Business leaders now place such a premium on being data-driven that there is already a risk that there will be so much data generated that it will be challenging for many to know what to analyze and what to extract that is valuable.

With so much data, however, firms will simply have more opportunity to outperform their competitors. That was just demonstrated in Splunk’s Economic Impact of Data Innovation 2023, a global study that measures the financial advantages of established data practices.

The report reveals that businesses that are most adept at using data see an increase in earnings of 9.5%, are 2.9 times more likely to launch products ahead of schedule, and are twice as likely to exceed financial projections.

A solid data foundation is necessary for staying ahead of the curve, according to REI’s CISO Mike Hughes, and the company is proud of its proactive approach to generating creative value from its data. “Our data visibility bolsters efficiency, competitiveness and resilience against an advanced threat landscape and macro challenges. Splunk is a critical element of our overall strategy to ensure we can scale and deliver the best experience for our customers,” he said.

Data-driven business gains more than others

According to the report, leaders are defined as companies that have excelled in the six crucial areas of data classification, aggregation, quality, analytical expertise, analytical tools, and monitoring. To put that into perspective, research demonstrates that data leaders experience higher earnings and innovation.

Data leaders report they have been able to introduce nine new products each year that wouldn’t be have been conceivable without their data innovation capabilities, as opposed to beginners’ average of three new products per year, with an average gain of 9.5% in gross revenues. They are also more likely to report (49% versus 30% of beginners) that using data innovation in sales, marketing, and customer service/support has helped to increase customer lifetime value.

Additionally, data leaders are 11% more robust, being able to detect and address security breaches more quickly. Apart from the increased profits and innovation, data leaders also outperform their rivals.

Data leaders are 5.7 times more likely to claim that their company consistently makes better decisions than their rivals. They are 4.5 times more likely to think their company is well-positioned to compete with other businesses in its market during the next years.

Furthermore, data leaders are deliberately operationalizing and generating revenue from their data. Additionally, leaders are more likely to claim that their data monetization sources are complementary and expand more quickly. They have 2.3 times as much money coming from data monetization, while also operationalizing 38% more of their data assets.

According to Ammar Maraqa, Chief Strategy Officer at Splunk, data-driven innovation provides a significant advantage. “Organizations that prioritize investments in collecting and using their data have full visibility into their digital systems and business performance, which makes it easier to adapt and respond to disruptions, security threats, and changing market conditions.”

Innovation across industries

Data offers a chance to boost revenue and boost performance across all industries, with particular challenges and results. Key findings include:

• Public sector: Compared to the industry average of 8%, 20% spend significantly more than a quarter of their IT budgets on employees and products that look into, track, analyze, and act on data.
• Financial services: For data innovation, 79% have implemented AI/ML, compared to 67% overall.
• Healthcare: While 11% of healthcare and life sciences firms attained leader status, just 9% of the surveyed organizations were among the most advanced data innovators.
• Manufacturing: Compared to enterprises across all industries, 54% employ data innovation for supply chain and operational use cases.
• Retail: Retail firms are more likely to report higher brand loyalty, customer lifetime value, revenue from data monetization, product innovation, and a variety of other data innovation-fueled results because an above-average 10% of them are data innovation leaders.

Today’s rapidly evolving technological environment has made data usage to manage a business the new norm. Therefore, to gain a competitive edge over your sector rivals, you need to use your collected data effectively.

The post Here’s why data-driven leaders are profiting more from their business than others appeared first on TechWire Asia.

The case that good data is essential to making informed, prudent, and judicious business decisions has been made crystal clear, especially over the past two years. Hence why it is apparent that since the pandemic, companies are increasingly recognizing and appreciating that data is a business asset that flows through an organization.

However, what we do with that data is key to the transformation. Even how companies put data in the hands of every employee to help catalyze transformation and solve the most important and impactful opportunities in their industries, is at the core.  

Business Intelligence (BI) strategies and technologies have been around for years now; enterprises are no strangers to them, leveraging their power in interpreting big data to uncover insights, and to present business and market forecasts, with the end goal of driving a competitive advantage and long-term success. 

Put simply, these tools – and their many other offshoots – are enabling businesses and individuals within them to act on the data they have in front of them, every day, across a mass of different uses and applications. 

Those are strong advantages in ‘peacetime’, but in the midst of a crisis, and looming economic uncertainty, the plus points of these tools are now being magnified further. The fact is, most markets are in decline – and will be for an extended, and unknown, length of time. Customers have reduced buying power, and that means businesses must optimize what they have, pushing their now limited resources into where the real opportunities can be seized against equally hungry competitors.

Instincts may work to a degree in turbulent times, but utilizing the information available is a much more effective strategy. That seems to explain the findings from Sisense’s State of BI & Analytics Report 2020: Special COVID-19 Edition, a survey of 500 companies in the US by research firm Meidata, which found that nearly half (49 percent) of companies are using data analytics more than before the COVID-19 crisis. 

As the report goes on to say, it demonstrates the increased importance businesses are placing on examining data to understand the changes and new opportunities around them – and that rise in use is despite more than two-thirds (67 percent) sustaining lost revenues or customers. 

“If we’ve learned anything during COVID-19, no matter how tumultuous the economic environment, businesses of all sizes indicate an interest and optimism in their ability to leverage technologies like data analytics to help guide them through critical decision making,” said Howard Dresner, chief research officer of Dresner Advisory Services, told Analytics Insight. 

The report continued that BI and analytics are proving as important as ever, but offering “increased clarity in a time of tremendous uncertainty.” Companies are using BI and data in new ways, turning usage to address the most pressing challenges right now. For example, 55 percent of businesses have started utilizing data to improve efficiency, and claim to rely on it to predict changes and outcomes. 

Interestingly, smaller businesses (those between 51 and 200 employees) are leading the charge and adopting new use cases for their data. They are outpacing larger enterprises in the use of analytics across every department, according to the report.  “Robust use of data analytics is no longer simply the mark of an industry leader but a crucial quality for the success and even survival of most companies,” said Amir Orad, CEO of Sisense. 

“Companies who adopt data-driven decision making separate themselves from the pack. With analytics they find those vital pivot points and thrive versus facing existential danger.” Orad said he was aware of a Fortune 500 company whose revenue had dropped more than 95 percent due to the current crisis, while their analytics usage had shot up by 40 percent – “And this is not a single or unique anecdote.”

While businesses are quickly turning to these tools to gain a clearer market strategy, it may also be that they are better utilizing the resources at their disposal as budgets run tighter, and a review of the tools they need will have likely taken place. 

A commonly-cited challenge with BI and data analytics tools is that, despite being heavy investments, businesses don’t make use of them to their full capacity. Now, businesses are re-evaluating what they have and how they should use it, and they are squeezing more out of their business intelligence tools. 

And whether that’s for the end goal of improving business efficiency, reducing expenses or better supporting customers, BI tools can be the key to unlocking the data organizations needed to weather this storm, and to keep them thriving long term. Overall, various research have proved that organizations with successful analytics programs have weathered the economic crisis caused by the pandemic far better than those who have not prioritized BI. 

The post Growing out of the pandemic with business intel and analytics appeared first on TechWire Asia.

Malls are becoming a go to place again as retail outlets showcase their latest products and offerings. In fact, offline retail still plays a role as some shoppers still prefer the physical experience. According to Market Research Reports, offline retail represents 91% of the total retail market in ASEAN and will be the dominant channel of retail sales in the foreseeable future..

However, there was still a problem. Retail operators, especially those in malls are unable to track daily sales. For malls, they are unable to provide retail rebates and even tenant support due to lack of data. For this to happen, malls need to have retail analytics that provides valid real-time data and important insights to make business decisions daily.

Making sense of retail analytics 

Compared to e-Commerce, whereby various types of data are made available, offline retailers like malls do not have access to such data which is key to their transformation. To help mall operators to leverage technology to capture, structure, and sort real-time retail transactions, Aimazing, a Singaporean retail tech company, enables malls to access significant amounts of mall management data. This includes consumer purchasing behavior, sales, and performance can track how customers shop and why.

Aimazing’s retail analytics platform allows mall management to make data-driven decisions with complete transactional data visibility. Their proprietary technology allows mall management to seamlessly and accurately capture all transactional data in their malls without expensive integration, while their data platform provides performance and benchmarking reports, as well as the ability to customize complex recommendation engines for multiple use cases.

“We aim to enable shopping malls to support their retail tenants in making decisions by understanding data such as peak shopping periods in the malls, average basket size, best selling items, and how various categories of merchants from F&B to clothing and retail are performing in real-time,” said Jun Ting, Chief Executive Officer of Aimazing.

Jun added that this data has already been widely available to e-Commerce marketplaces but not to physical retailers and mall operators. Aimazing aims to bring physical marketplaces to a level playing field with this data visibility, as “before this, they were batting blind”.

The tech behind analytics

Malls will be able to have access to real-time insights as Aimazing uses a patented solution that captures and analyses offline transaction sale data within the mall to better equip mall owners and retail businesses with insights into making better-informed business decisions.

Malls will also need not worry about high upfront investments for hardware and setup as the solution is based on a subscription model.

Using technology similar to Google Translate’s image recognition, Aimazing’s hardware solution in the form of a tiny black box can be plugged into the Point of Sale (POS) systems of retail businesses.

The data from the receipts are then analyzed and organized through a machine learning engine and directly uploaded to the cloud where mall operators have direct and real-time access.

With projects live in malls in Singapore, Malaysia, and the Philippines, Aimazing processes three million data points a month from 1,000+ devices in the region and there is a pipeline of 10,000 devices to be implemented in the future.

As such, retail analytics may just be the best solution for them, especially in ensuring they can keep both customers and tenants satisfied as well.

The post Retail analytics a game changer for offline commerce appeared first on TechWire Asia.

The need for interoperable, cross-platform data that can be gathered in real-time is becoming more urgent for information-reliant organizations, such as healthcare providers and financial services operators. The availability of real-time and historical data is integral in those sectors. Having that information to hand at any point, with full visibility and accessibility, can make all-important contributions to delivering excellent customer service and fulfilling the needs of the business.

Digital trends

A cohesive data architecture that is interwoven like fabric is a must-have for organizations that need to make use of high-performance application ecosystems. Systems must be compatible with next-generation technologies to drive operational efficiencies, while at the same time reliably service heavy data use and established analytics to support better outcomes for all stakeholders.

And, as a greater number of organisations accelerate their adoption of digital tools – ranging from cloud migration to automation to predictive tools powered by artificial intelligence – the need for a highly interconnected information system with functionality much like data fabric will only grow in importance.

Why data fabric? As the name implies, just like how cloth can be stitched together, data fabric architecture can connect and align capabilities across a variety of data services throughout a host of multi-cloud environments. Despite differing integration methods, endpoints, and interoperability requirements associated with multiple applications, a system like the InterSystems IRIS data platform can bring together and standardise information management across cloud, on-premises, and edge environments, as well as at the device level.

The data fabric key definition

“The term ‘data fabric’ is often abused. Every data platform nowadays wants to claim they are providing data fabric, but it is actually about interconnected data, how can we wrangle the data,” says Kenneth Kuek, Business Development Director of leading data management solutions provider InterSystems, which specialises in unlocking complex data challenges in various sectors.

Some systems are woven together more intricately than others, of course. For financial services and healthcare, data security is high on the agenda, given the need for operators to protect sensitive personal information.

“InterSystem actually plays a part at the data security layer. So like with a bank offering omnichannel [payment] solutions sitting on top of IRIS, that will be another layer of security,” he emphasizes. The IRIS in question is InterSystem’s holistic, cloud-first data platform – supplying customers with the ability to implement an array of technologies and data sets, with fewer code headaches, less maintenance, and more efficient use of system resources. Together, this all adds up to delivering a higher and more efficient return on investment (ROI).

Access management

IRIS delivers better platform security too. “So we have application layer security and our data platform security. Logging into the application, the data is not visible to the user. But the bank as the owner can go deeper into the data layer, where we have captured the payment information, the product info, the inventory – everything,” Kuek comments.

The platforms and applications leveraging that data must be well protected, but also able to harmonise the information from multiple sources so that they are still usable in terms of delivering actionable insights.

“The financial institution, for example, is always interested to look at their merchants’ activities, their transactions. But the data comes in all forms,” says Kuek. “That’s why we’re working with a partner at managing omnichannel [environments], which is a very good example [of] where we aggregate different sources of data.”

“Take an e-commerce platform like Shopee, Lazada, or Amazon. The banks are always interested to understand how the merchant is doing on different platforms – are they selling their products well, are they facing a lot of challenges?” he continues. “So when a data fabric platform like InterSystems starts to ingest the data, among the information we can ingest [are a number of key details]: the product that they sell, is it a high value or low value item, is the product moving, is there a lot of inventory or just a little to test out? And of course, [there is the topic of] collection.”

Meaningful data

“What form of collection does the merchant have? Is it cash on delivery, is it PayPal, do they accept credit cards?” Kuek asks. “So each form of data coming in will be different, and it will be about how we are putting the data together, and making it meaningful for the client.”

The client could be a financial institution or a bank, who can use this data to decide action items, such as extending an additional line of credit if the data supports this. “The banks can easily decide whether to extend the banking facilities because they can see the sales activity all very clearly, very transparently,” asserts Kuek.

Thanks to its agility, data fabric can avoid the shortfalls of a data lake or data warehouse – the prevalent methods for storing and accessing cloud data in the past few years – which are just another siloed approach to information storage. Such methods can make integration with other data sources, including third parties, a veritable hindrance. It’s a real problem with these recent data frameworks because as data volumes spike, data silos grow too, and so do the complexities associated with integrating or making use of that information.

Kuek gives an example of a Singapore customer providing supply chain financing. In such a scenario, it’s important that the bank has sufficient oversight. “There is historical data, there is ongoing [real-time] data, giving the bank confidence that this is really a very low-risk style of loan to the merchant,” Kuek points out.

Data fabric the fashionable solution

The InterSystems IRIS data platform demonstrates why organisations the world over are implementing smart data fabric over outmoded data management systems. IRIS simplifies the provision of platform and data architecture, providing a host of data services including database management and inbuilt data analytics spanning a myriad of innovations, including natural language processing and machine learning. What’s more, solutions can harness a transactional-analytic database engine to deliver the sort of high-performance at scale that can facilitate both real-time and low latency applications and analytical use cases.

InterSystems IRIS data platform not only slashes operational complexity and maintenance, but also drives down the total cost of ownership while speeding up development times and lifting ROI. Find out how to best support your mission-critical, data-intensive applications at scale, try out InterSystems IRIS for free right now.

The post Stylish move: data fabric gives enterprises the edge over siloed alternatives appeared first on TechWire Asia.

The new law on October 1 will mandate all companies, foreign and domestic, with interests in major digital services like telecommunications, e-commerce, and online payments to store user data onshore. Covered under the data localization guidelines are information related to “users’ personal identities, financial records, digital footprints, and online connections and networks,” as per Nikkei Asia.

While Vietnamese tech startups have been developing their competencies at a rapid clip, and its manufacturing end has been establishing itself as a more cost-effective alternative with less political recriminations in contrast to traditionally dominant Chinese production that is now mired in geopolitical tension, the newer data localization rulings could possibly derail the positive sentiment and foreign direct investment (FDI) potential that the populous Southeast Asian economy has built up in recent years.

Multinational corporations active in the country will be given 12 months to comply with the new regulations, to be monitored by the Vietnam Communist Party’s Ministry of Public Security, with a compulsory requirement to establish locally registered offices in Vietnam “in the name of safeguarding national security, public order and the ‘legitimate interests’ of local individuals and organizations” points out Nikkei.

As China has faded into the background of FDI thanks to simmering political friction with the US (and the US’s powerful allies) and its controversial zero-COVID policy, and Vietnam has risen up in its place with a younger working-age population of 98 million – the workforce is less skilled and less technologically-advanced than China’s, but makes up for it with cheaper labor and land costs that is attractive to big plant builders – it is ironic that Vietnam’s cybersecurity and data localization decrees would mirror China’s in cracking down on “content that infringes national security, propagandizes against the state; incites violence; disrupts security or public order; is humiliating or slanderous” as noted by law firm Tilleke & Gibbins.

There is some ambiguity as to how these far-ranging laws would be enforced, but as pointed out by Tilleke & Gibbins, would also encompass the localization of data found in cloud storage, intermediary payment gateways, online transportation platforms, e-sports, social media, and “services of providing, managing, or operating other information in cyberspace in the form of messages, phone calls, video calls, email, or online chat.”

The broad collection and storage of personal data, including from major platforms like Facebook and Youtube which are so heavily used that they are major drivers of social discourse in Vietnam, could be interpreted as being outside the domain of business interests that major international companies might want to be associated with.

The unprecedented data localization changes have been drawing flak from US business groups since the rules became known, with a joint letter sent to Vietnam Prime Minister Pham Minh Chinh, the US Chamber of Commerce, the American Chamber of Commerce Hanoi, and the Asia Internet Coalition asking for clarification on the interpretation of much of the abstract language used in the clauses. Business and policy directors are speculating that how the rules are enforced could put companies at a competitive disadvantage, affecting their ability to attract vendors and certain types of customers.

The cost to quickly implement actions with potentially high overheads, such as recruiting local talent and building up a local office, is also worrying foreign entities who have been operating with a purely digital presence in the country for some time.

There also mounting concerns with Vietnam’s trade partners in the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP), like Canada and Japan. “Canada continues to urge Vietnam to implement its laws and regulations applicable to the transfer and storage/processing of data in a manner that is consistent with its commitments in Chapter 14: Electronic Commerce of the [CPTPP],” Global Affairs Canada Spokesperson Lama Khodr was quoted by Nikkei Asia, which also pointed out that the Japanese government had “expressed concerns” about data localization contravening CPTPP trade guidelines, and would be keeping a “close watch on the consistency between the Law on Cybersecurity and Vietnam’s obligations under relevant international agreements.”

It’s worth highlighting that the likes of Facebook and Youtube have gone along with previous requests from the Vietnamese government to take down content that could be deemed to be sensitive from the political and social spheres, but did not break any international laws.

A host of international players including Samsung, Apple, and NTT have invested heavily in Vietnamese production facilities, bringing in millions of dollars in FDI, and the emerging Southeast Asian economy risks endangering the influx that is helping turn the country into a regional powerhouse.

The post Vietnam’s data localization strategy is impacting its tech leadership appeared first on TechWire Asia.

Any advancements, no matter how simple or complicated, that enhance patient experiences and health outcomes are considered healthcare innovation. However, many business executives and healthcare experts are turning to new technology in response to the challenges they are currently encountering, including strict regulations, privacy concerns, and rising expense.

Artificial intelligence (AI) that can identify early diseases faster than medical professionals and new technologies that use virtual reality to speed up recovery in rehab are just a few of the developments that are now revolutionizing medicine.

Healthcare innovation is important to driving transformation in Asia. More than a billion people are impacted by digital health now, and projections indicate that by 2025, the value of the sector in Asia might reach $100 billion, up from $37 billion in 2020.

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While healthcare is being impacted by technology innovations, they eventually generate a lot of data. A report shows that the compound annual growth rate of healthcare data will be 36% by 2025.

To handle this ever-growing healthcare data mass, NVIDIA recently announced a partnership with the Broad Institute of MIT and Harvard to equip the Terra cloud platform and its over 25,000 users with AI and acceleration tools. These users include biomedical researchers in academia, startups, and large pharma companies.

The partnership that will bring innovation to healthcare

With a focus on three key areas, the collaboration aims to link the Broad Institute’s renowned academics, scientists, and open platforms with NVIDIA’s AI expertize and healthcare computing platforms.

• Six new Terra workflows now support Parabricks, a GPU-accelerated software suite for secondary processing of sequencing data. With Clara Parabricks, users can now analyze an entire genome in less than an hour as opposed to 24 hours in a CPU-based environment, and they can cut the compute cost in half.
• Using NVIDIA BioNeMo, an AI application framework released for large language models (LLMs) in biology, researchers will create fundamental models for DNA and RNA, the components of life, to more thoroughly comprehend human biology.
• NVIDIA is contributing a new deep learning model directly to the Broad Institute’s Genome Analysis Toolkit (GATK), the industry standard used by more than 100,000 researchers, which helps identify genetic variants that are associated with diseases. This will help drug discovery researchers develop new therapies.

“There’s a need across the healthcare ecosystem for better computational tools to enable breakthroughs in the way we understand disease, develop diagnostics and deliver treatments,” said Kimberly Powell, vice president of healthcare at NVIDIA.

Powell noted that by extending its partnership with the Broad Institute, the company can leverage the strength of large language models to eventually deliver shared solutions and close the gap between research findings and tangible benefits for patients.

By offering an open cloud platform that connects researchers as well as the datasets and tools they require to make scientific breakthroughs, the Broad Institute hopes to allow the next generation of collaborative biomedical research.

“Life sciences are in the midst of a data revolution, and researchers are in critical need of a new approach to bring machine learning into biomedicine,” said Anthony Philippakis, chief data officer of the Broad Institute. “In this collaboration, we aim to expand our mission of data sharing and collaborative processes to scale genomics research.”

How NVIDIA’s technologies study diseases

The BioNeMo framework from NVIDIA provides pretrained LLMs for proteins and chemistry that make scalability, inference, and training easier. The NVIDIA NeMo Megatron framework’s extension, BioNeMo, is domain-specific for chemistry, proteins, and DNA/RNA sequences.

By using billions of parameters, developers may efficiently train and use biology LLMs thanks to BioNeMo. Building on this work, teams from both organizations will produce new models to be added to the BioNeMo collection and made accessible through the Terra platform.

Researchers at the Broad Institute will also have access to MONAI, an open-source deep learning framework for medical imaging AI, and NVIDIA RAPIDS, a GPU-accelerated data science toolbox for quicker data preparation and genomic single-cell analysis.

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