Autore: martechcubejohn

In the fast-moving business world, IT professionals and enthusiasts cannot ignore the use of machine learning (ML) in their companies. Machine learning tends to give a better insight into improving business performance, like understanding trends and patterns that human eyes generally miss out on. Thus, Machine learning (ML) and artificial intelligence (AI) aren’t just words; rather, they have the potential to change the industry positively. Through this article, we will focus on the importance of implementing machine learning and its use cases in different industries that will benefit you in the present and future.

The Usefulness of ML in Different Industries

Machine learning is a game-changer, and let’s see here how different industries have made the best use of it:

Predictive Analytics for Recommendations

Predictive analytics are generally used to identify opportunities before an event occurs. For example, identifying the customers that have spent the most time on your e-commerce website will result in profit for your company in the long run. These insights are only possible through predictive analytics, which allows your company to optimize market spending and focus on acquiring customers that will generate profit.

 Automate Decision-making

Automated and intelligent decision-making solutions and tools can be used by you to make quick decisions for efficient teamwork. For instance, some industries require strict adherence to compliance, which can only be applied by decision-management tools that help in maintaining records of legal protocols. These tools can make quick decisions if the business fails to obey any compliance rules.

 Creating a Data-Driven Culture

Creating a data-driven culture helps in getting numbers and insights that are generated through data. A data-driven organization not only empowers your teams but also improves your decision-making efficiency and effectiveness. One such example of a data-driven culture is DBS Bank, which has embraced AI and data analytics to provide customers with personalized recommendations. This is helping the customers and the bank authorities make better financial decisions and also improving customer loyalty. By embracing a data-driven culture, DBS Bank has also invested in training employees in data analytics and big data.

Machine learning is an important tool for making automated decisions in various business processes. These models help you identify errors and make unbiased and informed decisions. By analyzing data through customer interaction, preference, and behavior, ML algorithms can help identify the correct patterns and trends, which will help your company in the long run.

To Know More, Read Full Article @ https://ai-techpark.com/ml-helps-make-decisions/ 

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Digital Patient Engagement Platforms

Indeed, compliance serves as the bedrock for responsible growth, BaaS for fintech partnership and your insights have proven to be exceptionally valuable. In this episode of the MarTechCube Podcast, Alana Levine shares powerful insights on driving revenue growth and expanding business opportunities for the company. Tune in now!

Alana is a highly sought-after speaker, an author of insightful articles, and a riveting interviewee. As an industry luminary, her journey is a testament to her unwavering passion for propelling change and innovation in this domain. Complementing her professional journey are her academic accolades, including bachelor's degrees in economics and psychology and a post-graduate degree in finance from McGill University.

Digital performance marketing is a type of digital marketing that focuses on driving measurable results. Businesses that use performance marketing only pay for specific actions, such as leads, sales, or app installs. This makes it a very cost-effective way to reach target audiences and achieve marketing goals.

There are a variety of digital performance marketing channels available, including:

Search engine optimization (SEO): SEO involves optimizing websites and content so that they rank higher in search engine results pages (SERPs). This can help businesses to reach more potential customers who are already searching for the products or services they offer.

Pay-per-click (PPC) advertising: PPC advertising allows businesses to place ads at the top of SERPs or on other websites. Businesses only pay when someone clicks on their ad, making it a very targeted and cost-effective way to drive traffic to their website.

Social media marketing: Social media marketing involves using platforms like Facebook, Twitter, and LinkedIn to connect with potential customers and promote products or services. Businesses can use social media marketing to run targeted ads, share content, and engage with their audience.

Affiliate marketing: Affiliate marketing involves partnering with other businesses to promote their products or services. Businesses pay affiliates a commission for every sale or lead that they generate.

Email marketing: Email marketing involves sending promotional emails to subscribers. Businesses can use email marketing to nurture leads, promote products or services, and drive sales.

Benefits of digital performance marketing

Scalability: Digital performance marketing campaigns can be easily scaled up or down as needed. This makes it a good option for businesses of all sizes.

Listen the Full Podcast @ https://www.martechcube.com/episode-8-discussing-digital-performance-marketing-with-alana-levine/ 

Also Listen On @ https://soundcloud.com/martechcube/discussing-digital-performance-marketing-with-alana-levine 

Soracom Relay allows customers to use existing RTSP/RTP-compatible cameras for audio and video data transmission. Can you discuss how this feature enhances IoT deployments, especially in terms of computer vision and video analytics?

There are many cameras available today that claim to be “connected to the cloud,” but most of them are tightly integrated into their vendors’ vertical cloud applications and require wholesale replacement of existing hardware to take advantage of these capabilities.

RTSP is a standard protocol already widely used in various IP camera products, making them easy to integrate within a tech stack but typically only in single building on-site CCTV deployments. The ability to securely connect RTSP cameras securely to the cloud opens the door to advanced monitoring and analysis capabilities without needing to change your entire set of cameras to shift to cloud-based video processing.

Soracom Relay enables a complete new set of potential opportunities to create value from existing view/record/replay cameras over to new architectures that connect those same video streams to cloud-based processing. Single-site installations typically use disk-based recorders that implement RTSP/RTP connections so that video streams can be viewed locally, sometimes with additional proprietary cloud features for simple remote-view functionality.

With Relay connecting cameras to Amazon Kinesis Video Streams, we encapsulate the overhead of implementing the RSTP/RTP protocol into the camera’s Soracom connection and let the customer shift to an AWS cloud compute architecture to process and create valuable business-centric insights.

Security is a significant concern when dealing with data transmission, especially in IoT. How does Soracom ensure the security of audio and video data transmitted through Soracom Relay?

The security and privacy of customer data is the highest priority in the Soracom platform. We have multiple layers of security implemented in our platform and also offer services for our customers to build a secure infrastructure that supports the needs of  their particular IoT fleet.

Soracom Relay is an ideal match for the secure architecture at the heart of the Soracom IoT Platform. When we saw this use case we immediately knew that we could mitigate risks and concerns associated with RTSP/RTP connected cameras while opening up new revenue possibilities for customers  by linking cameras that traditionally only have a LAN connection directly to AWS’s video streaming services.

RTP video streams are not encrypted and can be dismissed as a source of IoT data despite the very large numbers of devices deployed in LAN environments. Similarly, the RTSP servers these cameras use have often been implemented with poor administration account credentials.

When IoT devices use Soracom connectivity they benefit from a fully encrypted link for all traffic between the devices and our connectivity platform. In the case of RTSP/RTP cameras, that means that the account login process is completely locked down to the Soracom account and moved out of reach from bad actors. The valuable video streams become tamper free, ensuring both that the stream is trustable and at the same time unavailable for others to access.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-kenta-yasukawa/ 

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Safeguarding Business Assets

Hyper-automation is a new term for technology and other industries where automation is needed. According to Gartner, hyper-automation is one of the most trending technologies that will greatly impact the next few decades. The motive of hyper-automation is to cancel out the repetitive tasks and make the whole task automatic by creating bots to perform them. These tasks will be performed with the combination of robotic process automation (RPA) and other advanced technologies like artificial intelligence (AI). In this article, we will understand the key benefits and best practices of hyper-automation. We will also get a glimpse of how it will help the manager + title achieve organizational goals.

1. The Best Practices of Hyper-automation in Business

Hyper-automation is crucial to empowering businesses through digital transformation to build fluidity in organizations that are capable of adapting rapidly to change. With the increase in competition in the business world, hyper-automation needs to be understood and analyzed properly. Here are 8 tactical points that will lay a good foundation for strategizing hyper-automation:

1.1 Creating a Strategy

When implementing hyper-automation, C-suite levels should understand what kind of strategy or which department needs this approach. Thus, a well-thought-out plan is needed to start somewhere.

1.2. Building the Right Team

To run the hyper-automation approach, a team with an accurate set is needed. The team should consist of manager-level data experts and analysts for their expertise in technical and strategic knowledge.

1.3. Everything Needs to Be Documented

Right from the start, the whole process of implementing hyper-automation, from each progress to any improvements after using the approach, needs to be documented for future reference.

1.4. Conduct an Audit

Managers need to understand the current level of digital transformation in their business and identify the processes that still need to be automated. This audit generally contains KPIs and data collection, which will help in decision-making and creating a plan for the future.

1.5. Setting a Suitable Tech Stack

To get suitable technology tools in place, C-suites from the technology profession need to focus on varieties of real-time data to provide accessibility from different sources, such as data warehouses, structured data, and data analysts.

1.6. Executing Hyper-automation Strategies

Start collecting data and creating a data quality for establishing it in the data warehouse.

Establishing automated notifications across different departments in the organization so that stakeholders can be alerted about any issues that can be addressed.

Implementing AI and ML models and training them according to continuous improvement in the business.

To Know More, Read Full Article @ https://ai-techpark.com/hyper-automation-in-business-process/ 

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Kenta, it would be greatly appreciated if you could provide us with insights into your professional trajectory and background that culminated in your position as the Co-Founder and Chief Technology Officer at Soracom.

In 2010, I was a researcher at Ericsson working on Connected Home, Connected Car and similar early-days IoT projects. I was drawing diagrams with fluffy clouds in the middle saying all the intelligent decisions would be made and things would get smart and collaborate with each other once they are connected to this new cloud. But back then the only available technologies for that purpose were rule-based engines, inference based on ontology, which did not have enough potential to be the intelligence to achieve a true Internet of Things vision.  

I felt there was potential in cloud technologies, but I didn’t fully know yet what the cloud could offer. So, I joined AWS as a solutions architect to find out. I worked with various customers to architect systems in the cloud and apply AWS best practices. That made me think, that by applying cloud technologies and best practices, any system can be made more reliable, scalable and available. It should be possible to build telecom infrastructure on top of cloud and it should enable a highly scalable connectivity platform.

I shared the idea with Ken Tamagawa, my CEO and Cofounder. He believed in the idea and we started to seek a way to execute and along the way met Dan Funato, my COO and Cofounder. We founded Soracom and I led the reinvention of telecom infrastructure on top of AWS cloud.

Leveraging the cloud-native telecom infrastructure, we have started a smart connectivity platform that can offload customers’ undifferentiated common heavy liftings in their IoT journeys and accelerate their time to market so we can achieve a truly connected world together.

The recent announcement  introduces Soracom’s new services that leverage Generative AI (GenAI) for IoT connectivity. Could you explain how Generative AI fits into the IoT ecosystem and what advantages it brings to IoT deployments?

GenAI has tremendous potential in IoT deployments. Besides adding natural language interfaces to IoT applications, GenAI applications using a Large Language Model (LLM) in particular has potential to be used for data analytics and decision making.

For example, we have tested ChatGPT to analyze time series data received from IoT sensors and trackers and confirmed it can provide insight about data as if you have a data scientist dedicated to you. By providing data and asking questions such as “What does this data mean?” and “What trend or outliers do you see in the data?”, an AI can answer in a natural language that you speak. We realized the potential and integrated GenAI to our time series data storage service, Soracom Harvest. The feature is called Soracom Harvest Intelligence and available to anyone as a public beta. An AI based data analytics is just one-click away. As in the example, GenAI can be a glue between people and data, and help them understand data. This can help people look deeper into a particular time period, detect an event and take action. If it has to be done by humans, it’d be cost prohibitive and not be scalable, but with GenAI, things can be automated and scalable.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-kenta-yasukawa/ 

Diversity and Inclusivity in AI

Safeguarding Business Assets

Hyper-automation is a new term for technology and other industries where automation is needed. According to Gartner, hyper-automation is one of the most trending technologies that will greatly impact the next few decades. The motive of hyper-automation is to cancel out the repetitive tasks and make the whole task automatic by creating bots to perform them. These tasks will be performed with the combination of robotic process automation (RPA) and other advanced technologies like artificial intelligence (AI). In this article, we will understand the key benefits and best practices of hyper-automation. We will also get a glimpse of how it will help the manager + title achieve organizational goals.

1. The Best Practices of Hyper-automation in Business

Hyper-automation is crucial to empowering businesses through digital transformation to build fluidity in organizations that are capable of adapting rapidly to change. With the increase in competition in the business world, hyper-automation needs to be understood and analyzed properly. Here are 8 tactical points that will lay a good foundation for strategizing hyper-automation:

1.1 Creating a Strategy

When implementing hyper-automation, C-suite levels should understand what kind of strategy or which department needs this approach. Thus, a well-thought-out plan is needed to start somewhere.

1.2. Building the Right Team

To run the hyper-automation approach, a team with an accurate set is needed. The team should consist of manager-level data experts and analysts for their expertise in technical and strategic knowledge.

1.3. Everything Needs to Be Documented

Right from the start, the whole process of implementing hyper-automation, from each progress to any improvements after using the approach, needs to be documented for future reference.

1.4. Conduct an Audit

Managers need to understand the current level of digital transformation in their business and identify the processes that still need to be automated. This audit generally contains KPIs and data collection, which will help in decision-making and creating a plan for the future.

1.5. Setting a Suitable Tech Stack

To get suitable technology tools in place, C-suites from the technology profession need to focus on varieties of real-time data to provide accessibility from different sources, such as data warehouses, structured data, and data analysts.

1.6. Executing Hyper-automation Strategies

Start collecting data and creating a data quality for establishing it in the data warehouse.

Establishing automated notifications across different departments in the organization so that stakeholders can be alerted about any issues that can be addressed.

Implementing AI and ML models and training them according to continuous improvement in the business.

To Know More, Read Full Article @ https://ai-techpark.com/hyper-automation-in-business-process/ 

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In AI-Tech Park’s commitment to uncovering the path toward realizing enterprise AI, we recently sat down with Chris Lynch, an esteemed figure in the industry and accomplished Executive Chairman and CEO of AtScale. With a remarkable track record of raising over $150 million in capital and delivering more than $7 billion in returns to investors, Chris possesses invaluable knowledge about what it takes to achieve remarkable results in the fields of AI, data, and cybersecurity.

Please give us a brief overview of AtScale and its origin story. What makes AtScale stand apart from its competitors?

AtScale was founded in 2013 as a highly scalable alternative to traditional OLAP analytics technologies like Microsoft SSAS, Business Objects, Microstrategy, or SAP BW.  However, our true breakthrough came with the enterprise’s shifting data infrastructure to modern cloud data platforms.  AtScale uniquely lets analytics teams deliver “speed of thought” access to key business metrics while fully leveraging the power of modern, elastic cloud data platforms.  Further, what sets AtScale apart is its highly flexible semantic layer.  This layer serves as a centralized hub for governance and management, empowering organizations to maintain control while avoiding overly constraining decentralized analytics work groups.

How do AtScale’s progressive products and solutions further the growth of its clients?

AtScale offers the industry’s only universal semantic layer, allowing our clients to effectively manage all the data that is important and relevant for making critical business decisions within the enterprise. This is so they can drive mission-critical processes off of what matters the most – the data!

To achieve this, AtScale provides a suite of products that enable our end clients to harness the power of their enterprise data to fuel both business intelligence (BI) and artificial intelligence (AI) workloads. We simplify the process of building a logical view of the most significant data by seamlessly connecting to commonly used consumption tools like PowerBI, Tableau, and Excel and cloud data warehouses like Google BigQuery, Databricks, and Snowflake.  

What potential do you think AI and ML hold to transform SMEs and large enterprises? How can companies leverage these modern technologies and streamline their processes?

AI and ML are going to have a profound impact on how we live, conduct our day-to-day business, and shape the global economy. It is imperative for every organization to leverage AI to streamline their operations and processes, improve their costs, and more importantly build and sustain competitive differentiation in the market. But without proper data, AI becomes inefficient and uneventful. The power of those AI models and their predictions rests in the organizational data and needs a universal semantic layer to create AI-ready data.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-chris-lynch/ 

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In today’s rapidly evolving business landscape, the Banking, Financial Services, and Insurance (BFSI) sector is at the forefront of digital transformation. To succeed in this dynamic environment, industry leaders, executives, and decision-makers must not only recognize the challenges but also harness the opportunities presented by technology. This article is a comprehensive exploration of how Robotic Process Automation (RPA) and Artificial Intelligence (AI) provide strategic solutions to address these challenges, foster innovation, and drive growth within the BFSI sector.

Before delving into their applications, let’s establish a clear understanding of RPA and AI. RPA utilizes software robots to automate repetitive tasks, while AI leverages machine learning and data analytics to replicate human intelligence. In BFSI, these technologies have the potential to reshape the way business is conducted.

Navigating Contemporary Challenges in BFSI

Before embarking on the journey of RPA and AI implementation, it’s crucial to acknowledge the pre-implementation challenges. Data security and regulatory compliance are critical in the financial services industry. Protecting sensitive customer data while adhering to strict industry regulations presents a complex puzzle. Furthermore, upskilling the workforce to adapt to these transformative technologies is a challenge that cannot be underestimated by CFOs, COOs, and industry professionals.

Potential of RPA and AI in BFSI:

RPA holds the power to streamline BFSI operations by automating laborious tasks such as data entry, transaction processing, and report generation. This not only reduces errors but also significantly improves operational efficiency. In parallel, AI ushers in a new era of data-driven decision-making within the sector. AI can predict market trends, detect fraudulent activities in real-time, and offer highly personalized product recommendations to customers. These capabilities lead to better customer experiences and more informed strategic decisions.

Solutions for Post-Implementation Challenges:

BFSI is an industry where every decision counts, embracing technology has become synonymous with staying competitive and relevant. As seasoned COOs, CFOs, banking professionals, and industry leaders, it is important to understand that the transformative power of Robotic Process Automation (RPA) and Artificial Intelligence (AI) can’t be ignored. While the potential of RPA and AI in BFSI is clear, the path to realizing these benefits can be laden with challenges. In this context, we present a strategic roadmap, tailored to your discerning vision, to address solutions to post-implementation challenges.

To Know More, Read Full Article @ https://ai-techpark.com/bsfi-rpa-and-ai/ 

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Can you tell us about your background and how it led you to found Cyral?

Cyral is the intersection of my passions and proficiencies. I have been on a long entrepreneurial journey. I started out as an early hire at Aster Data, which was one of the first companies to talk about Big Data, where I ran most of the engineering team. Then I founded Instart, which was in the CDN space where we focused on managing infrastructure at cloud scale. Cyral presented itself as the intersection of these two experiences — managing data at cloud scale. When I saw that companies were moving their sensitive data off-premises to the cloud, I realized they need a different way to manage the security and governance of data, and the answer is Cyral.  

Can you explain the importance of data security governance and its impact on organizations?

The number one thing most security leaders are worried about is a data breach. Companies increasingly gather sensitive information about their customers that they are tasked with keeping out of the hands of hackers. When everything began migrating to the cloud, breaches became much more common since there are so many ways for a hacker to access a database. Data is everywhere, and there isn’t a structured enough system to protect it.

Data security governance is its own category like IT security or application security, and more organizations are finding a need to address it with a specialty team or service dedicated to protecting sensitive information.

How does Cyral’s solution differ from traditional security tools, and how does it address the challenges of securing modern cloud-based environments?

Modern technology solutions are an adaptation of the past. They either take the way a company functioned in a data center and move it to the cloud, commoditize technology from big, enterprise solutions for others, or have developers recreate the work that once belonged to an IT team. Cyral does something new.

Other security tools are not database aware and have no way of knowing what’s in a company’s database or whether a user should be allowed to access a specific field or record—it’s often all-or-nothing access. Cyral addresses this issue with its complete suite of discovery, authentication, authorization, and auditing controls. Several people within the same organization can input a query into their Cyral-protected database, and depending on their role or other defined factors, each would see a different result. In fact, Cyral is the first security solution to provide all the features of database activity monitoring (DAM), privileged access management (PAM), data loss prevention (DLP), and data security posture management (DSPM) for a company’s sensitive datasets from a single platform.

Can you discuss the role of generative AI in data security and the potential risks it poses to organizations?

Generative AI is a reality for technology, so I see it working in data security in two ways. As it stands, security products make a lot of noise. They send alerts and false positives often, driving security leaders to spend time across multiple dashboards and data streams just to understand what’s happening. I anticipate that generative AI will begin to be incorporated into security products to help reduce the noise and make security analysts more productive. It will more accurately pinpoint a threat and where it is then send security teams to the right place to investigate.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-manav-mital/ 

Visit AITech Interviews For Industry Updates

Artificial intelligence (AI) has improved the outcomes for hundreds of thousands of businesses by automating and speeding up their processes. Yet, it has also helped the criminals too, making it easier for them to commit fraud and steal money.

Nowhere has this been more keenly felt than in the banking and finance industry, where the technology has been successfully deployed in the fight against fraud, tackling everything from credit card fraud to money laundering. But one of the key areas where it is proving most effective is in detecting business lending fraud.

There’s no doubt that business lending fraud has been on the rise in recent years, increasing at an average of 14.5% year-over-year for small and mid-sized businesses in 2022, as per a LexisNexis report. But that’s just the tip of the iceberg, with many of these types of fraud going undetected or unreported.

The problem was exacerbated during the Covid-19 pandemic as businesses became increasingly stretched, with employees forced to work remotely. As a result, they have become obvious targets for scammers looking to exploit them.

Types of business lending fraud

As technology continues to evolve, so the criminals’ methods have too. There are four key areas where they are now focusing their efforts: application fraud, impersonating another business, providing incorrect information and hiding data.

Application fraud is fast becoming one of the most prevalent forms of deception. It involves a business or individual using their own details to apply for a financial product such as a loan, but when they complete the application they use false information or counterfeit documents, often to try and get a larger amount of money.

Another common tactic among fraudsters is impersonation. By using fake documentation to trick the lender into believing that they are another business, they can dupe them into lending them big sums of money.

Knowingly providing the wrong information is fraud too. This typically includes but is not limited to, the submission of misstated management information and fudged bank statements, which are hard to verify without the correct records.

But perhaps the hardest fraud to uncover of all is hiding data. By withholding key information that can be used to determine a lending decision, scammers can secure a bigger loan.

Given the complexity of these kinds of fraud and the fact that they can be committed by individuals and companies themselves or others who have stolen their identity by posing as them, it makes it even harder to identify and prevent them from happening in the first place. And so deceptive are they that the victim may never know they have been targeted or only find out when they are turned down for a loan after the fraud was perpetrated without them being aware of it.

To Know More, Read Full Article @ https://ai-techpark.com/the-rise-of-business-lending-fraud-and-ai/ 

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