The Top Five Best Augmented Analytics Tools of 2024!

In this digital age, data is the new oil, especially with the emergence of augmented analytics as a game-changing tool that has the potential to transform how businesses harness this vast technological resource for strategic advantages. Earlier, the whole data analysis process was tedious and manual, as each project would have taken weeks or months to get executed. At the same time, other teams had to eagerly wait to get the correct information and further make decisions and actions that would benefit the business’s future.

Therefore, to pace up the business process, the data science team required a better solution to make faster decisions with deeper insights. That’s where an organization needs to depend on tools such as augmented analytics. Augmented analytics combines artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to enhance the data analytics processes, making them more accessible, faster, and less prone to human error.

Organizations using augmented analytics report up to a 40% reduction in data preparation time and a 30% increase in insight generation speed. Furthermore, augmented analytics automates data preparation, insight generation, and visualization, enabling users to gain valuable insights from data without extensive technical expertise.

Yellowfin

Yellowfin specializes in dashboards and data visualization that have inbuilt ML algorithms that provide automated answers in the form of an easy guide for all the best practices in visualizations and narratives. It has a broad spectrum of data sources, including cloud and on-premises databases such as spreadsheets, which enables easy data integration for analysis. The platform comes pre-built with a variety of dashboards for data scientists that can embed interactive content into third-party platforms, such as a web page or company website, allowing users of all expertise levels to streamline their business processes and report creation and sharing. However, when compared to other augmented analytics tools, Yellowfin had issues updating the data in their dashboard on every single update, which poses a challenge for SMEs and SMBs while managing costs and eventually impacts overall business performance.

Sisense

Sisense is one of the most user-friendly augmented analytics tools available for businesses that are dealing with complex data in any size or format. The software allows data scientists to integrate data and discover insights through a single interface without scripting or coding, allowing them to prepare and model data. Eventually allows chief data officers (CDOs) to make an AI-driven analytics decision-making process. However, the software is extremely difficult to use, with complicated data models and an average support response time. In terms of pricing, Sisense functions on a subscription pricing model and offers a one-month trial period for interested buyers; however, the exact pricing details are not disclosed.

To Know More, Read Full Article @ https://ai-techpark.com/top-5-best-augmented-analytics-tools-of-2024/ 

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AITech Interview with Askia Underwood, Chief Growth Officer at Driveline.ai

Askia, can you share more about your role as Chief Growth Officer at DriveLine.ai and the key responsibilities associated with it?

In my role as Chief Growth Officer, I wear several hats, all focused on one critical goal: driving revenue growth and expansion. Through a multi-pronged approach that leverages strategic partnerships, comprehensive growth strategies, I am responsible for propelling DriveLine to market leadership.

My key responsibilities include the development of strategic partnerships and alliances, implementing comprehensive growth strategies, identifying and leveraging category and industry trends including new market opportunities, and the productization of our audience and location intelligence.

Beyond these key responsibilities, I also contribute to other areas which support our growth including working closely with our product and business development teams, to ensure alignment and collaboration across the organization.

With 17+ years of experience in consumer strategy, how has your journey shaped your approach to driving consumer behavior for brands?

Over the past 17+ years, my approach to consumer strategy has been profoundly reshaped a few times. My journey began in 2000 at KTLA-TV, where I dove headfirst into the bustling world of advertising sales, right as the digital advertising revolution converged with television. This early exposure to the nascent digital landscape, when monetization through consumer interaction was still largely uncharted territory, instilled in me a deep appreciation for innovation and a future-focused approach has become a defining characteristic of my strategic skill set ever since.

With almost two decades of experience navigating the ever-evolving media landscape, I have not only witnessed significant changes, but actively participated in shaping them. Through triumphs and setbacks, I have acquired a deep understanding of consumer behavior and the critical role it plays in successful media campaign outcomes. This valuable knowledge informs my strategic approach, ensuring that every campaign I develop is human-centered, data-driven, results-oriented, and impactful.

Can you elaborate on your future-focused approach to campaign performance and how it is applied across various client types, whether local, regional, national, or global?

Every component of advertising is related to a time period, timing and/or seasonality, making advertising campaigns intrinsically planned for the future. By focusing on the future, I help brands achieve their marketing goals in a sustainable and scalable way. By applying my future-focused approach to campaign performance, I help brands achieve success regardless of their size or location. This means focusing on long-term trends, anticipating future consumer behavior, and proactively adapting to stay ahead of the curve.

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

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Leading Effective Data Governance: Contribution of Chief Data Officer

In a highly regulated business environment, it is a challenging task for IT organizations to manage data-related risks and compliance issues. Despite investing in the data value chain, C-suites often do not recognize the value of a robust data governance framework, eventually leading to a lack of data governance in organizations.

Therefore, a well-defined data governance framework is needed to help in risk management and ensure that the organization can fulfill the demands of compliance with regulations, along with the state and legal requirements on data management.

To create a well-designed data governance framework, an IT organization needs a governance team that includes the Chief Data Officer (CDO), the data management team, and other IT executives. Together, they work to create policies and standards for governance, implementing, and enforcing the data governance framework in their organization.

However, to keep pace with this digital transformation, this article can be an ideal one-stop shop for CDOs, as they can follow these four principles for creating a valued data governance framework and grasp the future of data governance frameworks.

The Rise of the Chief Data Officer (CDO)

Data has become an invaluable asset; therefore, organizations need a C-level executive to set the company’s wide data strategy to remain competitive.

In this regard, the responsibility and role of the chief data officers (CDOs) were established in 2002. However, it has grown remarkably in recent years, and organizations are still trying to figure out the best integration of this position into the existing structure.

A CDO is responsible for managing an organization’s data strategy by ensuring data quality and driving business processes through data analytics and governance; furthermore, they are responsible for data repositories, pipelines, and tools related to data privacy and security to make sure that the data governance framework is implemented properly.

The Four Principles of Data Governance Frameworks

The foundation of a robust data governance framework stands on four essential principles that help CDOs deeply understand the effectiveness of data management and the use of data across different departments in the organization. These principles are pillars that ensure that the data is accurate, protected, and can be used in compliance with regulations and laws.

C-suites should accept the changes and train themselves through external entities, such as academic institutions, technology vendors, and consulting firms, which will aid them in bringing new perspectives and specialized knowledge while developing a data governance framework.

To Know More, Read Full Article @ https://ai-techpark.com/chief-data-officer-in-data-governance/

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Harnessing the Power of Quantum Computing for Enhanced Machine Learning

Quantum computing (QC) and machine learning (ML) are the two most hot technologies that are being adopted in the IT field. QC has the power of quantum physics to perform computation by providing an unprecedented level of scalability and accuracy; on the other hand, ML has deep learning capabilities and intelligent automation as leverage to scale out large data sets. Thus, the combination of these two applications, i.e., QC and ML, can create new opportunities that could solve complex problems with greater accuracy and efficiency than the traditional way of computing could.

In this article, we will dive into how to implement quantum machine learning (QML) and what the best practices are for AI technologists.

Success Story- Quantum Machine Learning in Automotive Industry

The BMW Group is among the first automotive firms to take an interest in quantum computing. In 2021, BMW Group issued the Quantum Computing Challenge in association with AWS to crowdsource innovations around specific use cases, believing that quantum computing could benefit businesses by solving complex computing problems.

The objective was to determine if the image-trained machine learning system presently in use to detect fractures in produced elements might be improved. To properly train the model, high-resolution photographs of the manufactured components were required. In addition, the organization required a lot of them because those kinds of defects are quite uncommon. There is potential for improvement because obtaining and storing these photos requires time and memory.

BMW Group gave a statement that, “In light of the required human expertise to hand-tune algorithms, machine learning (ML) techniques promise a more general and scalable approach to quality control. Quantum computing may one day break through classical computational bottlenecks, providing faster and more efficient training with higher accuracy.”

After implementing the QML solution, the BMW Group has witnessed 97% accuracy by enhancing the classical algorithm by orchestrating quantum processing unit (QPU) calculations at a crucial part of the analysis. The Quantum model was trained on 40% of the whole dataset. In contrast, the Benchmark model was trained on 70%, which implies that the classical approach is more efficient and manages to provide accurate predictions without unnecessary inputs.

Future Implementation of Quantum Machine Learning

Quantum machine learning (QML) algorithms have the potential to solve maximum problems in a much faster time than the classical algorithm. According to IBM researcher Kristan Temme, there is strong evidence that QML is emerging at a significant speed in all industries. He quotes, “At this point, I’d say it’s a bit difficult to exactly pinpoint a given application that would be of value.”

There are also proven examples where QML has been an advantageous technology over classical computing.

To Know More, Read Full Article @ https://ai-techpark.com/best-practices-for-quantum-machine-learning/ 

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Intelligent Decisions With Machine Learning

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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Co- Founder and CTOof Soracom, Kenta Yasukawa – AITech Interview

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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AITech Interview with Kenta Yasukawa, Co- Founder and Chief Technology Officer at Soracom

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/ 

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