Autore: martechcubejohn

Whether you’re choosing a restaurant or deciding where to live, data lets you make better decisions in your everyday life. If you want to buy a new TV, for example, you might spend hours looking up ratings, reading expert reviews, scouring blogs and social media, researching the warranties and return policies of different stores and brands, and learning about different types of technologies. Ultimately, the decision you make is a reflection of the data you have. And if you don’t have the data—or if your data is bad—you probably won’t make the best possible choice.

In the workplace, a lack of quality data can lead to disastrous results. The darker side of AI is filled with bias, hallucinations, and untrustworthy results—often driven by poor-quality data.

The reality is that data fuels AI, so if we want to improve AI, we need to start with data. AI doesn’t have emotion. It takes whatever data you feed it and uses it to provide results. One recent Enterprise Strategy Group research report noted, “Data is food for AI, and what’s true for humans is also true for AI: You are what you eat. Or, in this case, the better the data, the better the AI.”

But AI doesn’t know if its models are fed good or bad data— which is why it’s crucial to focus on improving the data quality to get the best results from AI for IT use cases.

Quality is the leading challenge identified by business stakeholders

When asked about the obstacles their organization has faced while implementing AI, 31% of business stakeholders involved with AI infrastructure purchases had a clear #1 answer: the lack of quality data. In fact, data quality ranked as a higher concern than costs, data privacy, and other challenges.

Why does data quality matter so much? Consider OpenAI’s GPT 4, which scored in the 92nd percentile and above on three medical exams, which failed two of the three tests. GPT 4 is trained on larger and more recent datasets, which makes a substantial difference.

An AI fueled by poor-quality data isn’t accurate or trustworthy. Garbage in, garbage out, as the saying goes. And if you can’t trust your AI, how can you expect your IT team to use it to complement and simplify their efforts?

The many downsides of using poor-quality data to train IT-related AI models

As you dig deeper into the trust issue, it’s important to understand that many employees are inherently wary of AI, as with any new technology. In this case, however, the reluctance is often justified.

Anyone who spends five minutes playing around with a generative AI tool (and asking it to explain its answers) will likely see that hallucinations and bias in AI are commonplace. This is one reason why the top challenges of implementing AI include difficulty validating results and employee hesitancy to trust recommendations.

While price isn’t typically the primary concern regarding data, there is still a significant price cost to training and fine-tuning AI on poor-quality data. The computational resources needed for modern AI aren’t cheap, as any CIO will tell you. If you’re using valuable server time to crunch low-quality data, you’re wasting your budget on building an untrustworthy AI. So starting with well-structured data is imperative.

To Know More, Read Full Article @ https://ai-techpark.com/data-quality-fuels-ai/ 

Related Articles -

Digital Technology to Drive Environmental Sustainability

Democratized Generative AI

Trending Category - Threat Intelligence & Incident Response

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/ 

Related Articles -

Deep Learning in Big Data Analytics

Generative AI Applications and Services

Trending Category - Patient Engagement/Monitoring

Marketing organizations are increasingly adopting artificial intelligence to help analyze data, uncover insights, and deliver efficiency gains, all in the pursuit of optimizing their campaigns. The era of AI augmentation to assist marketing professionals will continue to gain momentum for at least the next decade. As AI becomes more pervasive, this shift will inevitably reshape the makeup and focus for marketing teams everywhere.

Humans will retain control of the marketing strategy and vision, but the operational role of machines will increase each year. By 2025, it is projected that 70% of lower-level administrative duties will largely disappear as artificial intelligence tools become more deeply entwined in the operations of marketing departments. Similarly, many analytical positions will become redundant, with smart chatbots expected to assume up to 60% of daily responsibilities.

However, the jobs forecast is not all doom and gloom because the demand for data scientists will explode. The ability to aggregate and analyze massive amounts of data will become one of the most sought-after skillsets for the rest of this decade. By 2028, the number of data science positions is expected to grow by 30%, remaining immune to economic pressures. These roles will be less susceptible to budget cuts, highlighting the critical importance of data analysis in the evolving marketing landscape.

Effects of the AI Rollout on Marketing Functions

As generative AI design tools are increasingly adopted, one thorny issue involves copyright protection. Many new AI solutions scrape visual content without being subjected to any legal or financial consequences. In the year ahead, a lot of energy and effort will be focused on finding a solution to the copyright problem by clarifying ownership and setting out boundaries for AI image creation. This development will drive precious cost and time savings by allowing marketing teams to embrace AI design tools more confidently, without the fear of falling into legal traps.

In addition, AI will become more pivotal as marketing teams struggle to scale efforts for customer personalization. The gathered intelligence from improved segmentation will enable marketing executives to generate more customized experiences. In addition, the technology will optimize targeted advertising and marketing strategies to achieve higher engagement and conversion levels.

By the end of 2024, most customer emails will be AI-generated. Brands will increasingly use generative AI engines to produce first drafts of copy for humans to review and approve. However, marketing teams will have to train large language models (LLMs) to fully automate customer content as a way of differentiating their brands. By 2026, this practice will be commonplace, enabling teams to shift their focus to campaign management and optimization.

To Know More, Read Full Article @ https://ai-techpark.com/future-of-marketing-with-ai-augmentation/ 

Related Articles -

Future of QA Engineering

Mental Healthcare with Artificial Intelligence

Trending Category - IOT Smart Cloud

Platform engineering is considered a practice built up on DevOps guides that assist in improving each development team’s compliance, costs, security, and business processes, eventually helping to improve developer experiences and self-service within a secure, governed framework.

Lately, there has been quite a buzz about the permanent implementation of platform engineering in the IT industry. According to a recent report by Gartner, it is estimated that more than 80% of engineering organizations will have a crew dedicated to platform engineering by 2026, where these teams will focus on building an internal developer platform. This also implies that regardless of the business domain, these platforms by nature will help in achieving high business scale and reduce the time it takes to deliver business value.

In today’s exclusive AI TechPark article, we will help IT developers understand the need for platform engineering along with the top three trending tools they can use for an easy business operation.

Getting Started with Platform Engineering

Platform engineering is not for every company; for instance, in fledgling startups, where every individual does a bit of everything, this guide doesn’t come in handy. On the other hand, for companies that have two or more app teams where duplicate efforts are observed, platform engineering is the best option to tackle that toil, allowing the developers to think outside the box.

The best way to start the platform engineering journey in your organization is to have a conversation with the team of engineers, allowing them to understand and survey bottlenecks and developer frustrations, further advising the use of platform engineering that embeds and pairs programming within application teams.

During the process of building an application, developers need to question the size of the requirements, patterns, and trends needed in the app, the bottlenecks, and many more. However, it doesn’t end here, as to further comprehend the application, they require multiple testing and opinion polls by their internal customers; developers are also required to document every minute detail and change on the platform to encourage self-service and independence in the long run.

Therefore, whether it is infrastructure provisioning, code pipelines, monitoring, or container management, the self-service platform will be a guide to hiding these complexities and providing developers with the necessary tools and applications.

Platform engineering is considered the optimal suite of tools that aids in orchestrating a symphony of tools that align with developers’ unique operational needs and aspirations while also keeping cost, skillset compatibility, feature sets, and user interface design in consideration.

To Know More, Read Full Article @ https://ai-techpark.com/top-platform-engineering-tools-of-2024/ 

Related Articles -

Data Privacy With CPOs

Cloud Computing Chronicles

Trending Category - AI Identity and access management

Hardly a week goes by without news of a ‘technical issue’ or outage. Sainsbury’s, Marks & Spencer and Tesco Bank are just some of the well-known brands that have experienced tech meltdowns in recent weeks.

We’ve come to expect IT crashes as a part of life, but if handled poorly, they can snowball into a major crisis, tarnishing a company’s reputation, eroding consumer trust and resulting in lost sales.

Here are five safeguards brand owners can put in place to protect their sites and minimise fallout from an IT crash.

Be alert

Do an audit of your site at least once a year with penetration (PEN) testing, where you look for any vulnerabilities in any of your systems. It’s not a guarantee that your site is absolutely secure and glitch-free, but as a brand, at the very least, you will have tried to identify any potential issues and protect your site and stored data.  Being proactive with system security alongside testing and QA reduces the risk of outages drastically.

Alongside PEN and code testing, you need to know when systems go down. There is nothing worse than a customer notifying you that your website or platform doesn’t work.

Setting up monitors for your systems to notify you is the first step of your action to an outage. Depending on your user, you can even make these publicly accessible, like Slack (https://slack-status.com/), and other platforms so your users are aware of this issue as it happens.     

Consider your site structure

It is possible to limit outages to specific parts of a site, but it will depend on how your website or platform is built and whether different parts of it are hosted on separate services, for example.  This approach could help contain the fallout from an outage. Take X, formerly Twitter: its likes and tweets are kept separate where microservices are used for each, so if ‘likes’ were to go down, tweets would still be visible. We would advise this type of structure for brands that would benefit from such an approach. A microservices set up would benefit anyone that’s creating a platform for users need to complete things in, such as banks, ecommerce but not needed for things like marketing websites and ‘brochure’ websites.

Post-crash follow ups

The level of testing required and the number of times this is needed will depend on the size of the company, its user base and its product.  It is also essential to take tech developments into account, all of which can impact even the most robust of sites. We recommend PEN tests once a year, but above all, be vigilant, take customers seriously and respect their data.

If you’ve suffered an outage, there’s nowhere to hide. Being proactive rather than reactive shows that you care, which can make a big difference to your reputation.

To Know More, Read Full Article @ https://ai-techpark.com/when-tech-glitches-threaten-your-brand-perception/ 

Related Articles -

Democratized Generative AI

Explainable AI Is Important for IT

Trending Category - Patient Engagement/Monitoring

Cloud computing has opened new doors for business applications and programs to utilize databases to store data every day worldwide. These databases are well-known for securing data and making it accessible only to channels where the chief data officer (CDO) permits. Previously, organizations depended on database-paid suites, which were expensive and limited in options; however, now IT organizations have open-source databases for all their data, as these are affordable and flexible. However, it is often difficult to find the right cloud database service provider that will not only store the data of your company but also transfer it to the database, while data professionals can access it anywhere with an internet connection.

In this review article by AITech Park, we will explore the top five open-source cloud databases that can be used by IT professionals to build robust applications.

Apache CouchDB

CouchDB by Apache is a database duplication tool that deters data loss in the event of network failure or any other pipeline failure. The software creates a dedicated database system that can operate efficiently on ordinary hardware, not just by deploying on one server node but also as a single analytical system across numerous nodes in a cluster, which can be mounted as needed by adding more servers. For a seamless operation, the database uses JSON documents to store data and JavaScript as its query language. Further, it also supports MVCC and the ACID properties in individual documents.

MySQL

MySQL is one of the most popular and oldest open-source databases, and it is known as its best database for web-based apps such as Trello and Gmail. The database software uses the Structured Query Language (SQL), which lets data professionals store data in tables, develop indexes on the data, and query the data. MySQL supports an expansive variety of techniques and has a very low probability of getting the data corrupted as it gears for transactional uses, further supporting analytics and machine learning (ML) applications.

PostgreSQL

PostgreSQL became popular among data professionals and developers around 1995 when it started working as a SQL language interpreter, and decades later it became a popular open-source cloud database. This database software offers full RDBMS features, such as ACID compliance, SQL querying, and clearance for procedural language queries to develop stored procedures and stimuli in databases. PostgreSQL also supports enterprise applications that demand complex transactions and high levels of concurrency, and occasionally for data warehousing. It also supports multi-version concurrency control (MVCC), so data can be read and edited by various users at the same time, and it also sustains other varieties of database objects.

To Know More, Read Full Article @ https://ai-techpark.com/top-five-open-source-database-management-software/ 

Related Articles -

Generative AI Applications and Services

Digital Technology to Drive Environmental Sustainability

Trending Category - AItech machine learning

Product design is a dynamic field with an ever-evolving concept, especially with the introduction of AI and generative AI being one of the most transformative focuses in reshaping the product design landscape. The force behind creativity is the product designers who play an essential role in merging technology and design solutions to bring out advancements in products we interact with every day.

The AI tools and software help product designers test different design iterations allowing them to focus on collaborating with cross-functional teams and solving complex design challenges. These tools are built on advanced technology tool that integrate the intelligence of AI and machine learning (ML) algorithms into visual design functions. It allows product designers to construct more precise and personalized visuals quickly and more efficiently.

Therefore to ease your search for the best AI tools AI TechPark brings you a compilation of the best AI design software and tools of 2024 that will help you in designing products efficiently.

Uizard Autodesigner

Uizard Autodesigner will be a useful AI tool for  any digital product and service designer as this software allows you to generate editable UI designs from written/ text prompts. This product designing tool generates multi-screen product designers for both mobile applications and web design, allowing you to choose a visual style and themes according to business requirements.

NVIDIA Omniverse

One of the most recognized product designing software among product designers is NVIDIA Omniverse. This software has built 3D graphics and computing platforms. It integrates 3D design, spatial computing, and physics-based workflows across third-party apps and AI services. Product and industrial designers rely heavily on 3D rendering models. This is where NVIDIA Omniverse comes in handy to help them visualize the product design with ease. It delivers stunning visuals bringing ideas to life.

With numerous AI design software popping up everyday it can be a challenging task to figure out the right tools. These tools help in harnessing the power of AI to automate time-consuming tasks, understand user preferences, and make suggestions based on those preferences. Especially with these AI tools at your disposal you can create numerous creative and innovative product designs.

To Know More, Read Full Article @ https://ai-techpark.com/top-4-product-designer-software-in-2024/ 

Related Articles -

Rise of Deepfake Technology

Future of QA Engineering

Trending Category - AI Identity and access management

In the evolving landscape of data management, age-old approaches are gradually being outpaced to match the demands of modern organizations. Enter as a savior: Data Mesh, a revolutionary concept that modern organizations harness to reshape their business models and implement “data-driven decisions.” Therefore, understanding and implementing Data Mesh principles is essential for IT professionals steering this transformative journey.

At its core, data mesh is not just a technology but a strategic framework that addresses the complexities of managing data at scale, as it proposes a decentralized approach where ownership and responsibility for data are distributed across numerous domains.

This shift enables each domain or department to manage data pipelines, maintain and develop new data models, and perform analytics across all interconnected integrations to facilitate infrastructure and tools that empower domain teams to manage their data assets independently.

At the core of the data mesh architecture lies a robust domain team that is the powerhouse behind the creation, delivery, and management of data products. This team comprises professionals with domain-specific knowledge who will epitomize the decentralized nature of data mesh to foster greater ownership, accountability, and agility within the organization.

This AITech Park article will explore how to build a data mesh team by outlining roles and responsibilities to drive success in an organization.

Data Product Owner (DPO)

The DPO, or Data Product Manager, is an emerging role in the field of data science that manages the roadmap, attributes, and importance of the data products within their domain. The DPO understands the use cases in their domain to serve as per UX and gets acquainted with the unbounded nature of data use cases to create combinations with other data in numerous forms, some of which are unforeseen.

Data Governance Board

After infrastructure, the data governance board is a critical part of the data mesh as they oversee the enforcement of data governance policies and standards across data domains. The board represents data product managers, platform engineers, security, legal, and compliance experts, along with other relevant stakeholders, who will tackle data governance-related problems and make decisions across the various domains within the business.

Building and maintaining a data mesh team needs careful planning, strategies, and commitments to develop talents across all boards. Therefore, organizations must adopt a hybrid organizational structure so that they can establish roles and responsibilities that help drive innovation, agility, and value creation in the digital age.

To Know More, Read Full Article @ https://ai-techpark.com/data-mesh-team/ 

Related Articles -

Top Five Popular Cybersecurity Certifications

Top 5 Data Science Certifications

Trending Category - Patient Engagement/Monitoring

Raj, please share key insights into your role as the Founder and CEO of Techwave and your journey contributing to its rapid growth.

As the Founder and CEO of Techwave, my journey has been one of relentless pursuit of excellence and innovation. Steering Techwave from its inception to becoming a global leader in digital transformation services has been both challenging and rewarding. My role has demanded a visionary outlook to foresee industry trends, a strategic mindset to navigate market dynamics, and a people-first approach to leadership. This trifecta has been crucial in contributing to Techwave’s rapid growth. We’ve expanded our global footprint, diversified our service offerings, and nurtured a culture that champions innovation, inclusivity, empathy, and continuous learning. My leadership philosophy has always been about empowering our teams, fostering a collaborative environment, and placing our clients at the center of everything we do.

What notable accomplishments has Techwave achieved under your leadership, particularly in terms of expansion, capitalization, and employee engagement initiatives?

Under my stewardship, Techwave has achieved remarkable growth, a testament to our innovative solutions, customer-centric approach, and the dedication of our global workforce. We’ve significantly expanded our presence, now operating with a prominence presence in 11 countries across the globe and serving a diverse client base across industries. Our workforce has grown to over 3000 associates, a reflection of our robust expansion and capitalization strategies.

Our employee engagement initiatives, particularly the SPARK framework, underscore our commitment to creating a vibrant and inclusive work culture. This framework focuses on engaging employees, fostering community engagement, and promoting diversity, which has significantly contributed to our high levels of employee satisfaction and retention.

Our corporate social responsibility efforts, like supporting the Houston Food Bank and participating in Primiethon – The Hope Run, reflect our commitment to making a positive impact in the communities we serve. These initiatives, alongside our accolades such as Asia’s Best Employer Brand Award and the President’s Volunteer Service Award, highlight our achievements in fostering a culture of excellence and community service.

How has Techwave positioned itself to stay ahead in a competitive digital landscape?

In a rapidly evolving digital landscape, staying ahead requires agility, foresight, and a commitment to innovation. At Techwave, we’ve positioned ourselves at the forefront of digital transformation by continuously investing in emerging technologies and nurturing a culture that embraces change. Our R&D efforts are focused on leveraging AI, machine learning, cloud-native technologies, and blockchain to develop solutions that address our clients’ most complex challenges of today and tomorrow.

We prioritize understanding our clients’ unique needs and market dynamics, which enables us to tailor our solutions for maximum impact. Our approach to innovation is not just about adopting new technologies but integrating them in ways that redefine business processes, enhance customer experiences, and drive sustainable growth.

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

Related Articles -

Digital Technology to Drive Environmental Sustainability

Democratized Generative AI

Trending Category - AI Identity and access management

With the rapid growth in the digital world, organizations are implementing Automated Machine Learning (AutoML) that helps data scientists and MLOps teams automate the training, tuning, and deployment of machine learning (ML) models. This technology will save time and resources for the data scientists and MLOps teams, which will accelerate research on ML and solve specific problems related to ML models.

For instance, some AutoML tools focus on optimizing ML models for a given dataset, while others focus on finding the best model for specific tasks, such as picking the appropriate ML algorithm for a given situation, preprocessing the data, and optimizing the model’s hyperparameters, aiding different industries to predict customer behavior, detect fraud, and improve supply chain efficiency.

Therefore, AutoML is a powerful mechanism that makes ML models more accessible and efficient; however, to create a model, execute stratified cross-validation, and evaluate classification metrics, data scientists and MLOps teams need the right set of AutoML tools or platforms.

In today’s AI TechPark article, we will introduce you to the top four AutoML tools and platforms that simplify using ML algorithms.

Auto-SKLearn

Auto-SKLearn is an AutoML toolkit that is available as an open-source software library that can automate the process of developing and selecting the correct ML models using the Python programming language. The software package includes attributes that are used in engineering methods such as One-Hot, digital feature standardization, and PCA. It improvises the model and operates SKLearn estimators to process classification and regression problems. Furthermore, Auto-SKLearn builds a pipeline and utilizes Bayes search to optimize that channel, adding two components for hyper-parameter tuning using Bayesian reasoning: The tools also have an inbuilt meta-learning feature that is used to format optimizers using Bayes and assess the auto-collection structure of the arrangement during the optimization process.

Google AutoML Cloud

The Google Cloud AutoML suite is designed to make it easiest for data scientists and MLops teams to apply ML-specific tasks such as image and speech recognition, natural language processing, and language translation in business. The platform accelerates the process of building custom AI solutions with a variety of open-source tools and proprietary technology that Google has evolved over the last decade. AutoML supports homegrown TensorFlow and offers partially pre-trained features for designing custom solutions using smaller data sets.

To Know More, Read Full Article @ https://ai-techpark.com/automl-platforms-for-2024/ 

Related Articles -

Rise of Deepfake Technology

Transforming Business Intelligence Through AI

Trending Category - Threat Intelligence & Incident Response