Tag: Data Science

Throughout 2024, data analytics continued to play a crucial role in driving data-based decision-making for B2B companies, while investment in advanced data technologies surged. These new tools offer deeper insights, predictive capabilities, and more sophisticated analytics to improve governance. The tools listed below are equipped with powerful features across BI, predictive modeling, natural language processing, and governance, reshaping the landscape of B2B data analytics.

Here are our expert reviews on the top five data analytics tools that defined 2024 and how each one can help businesses leverage data more effectively.

Key Trends Shaping Data Analytics in 2024

Before diving into the tools, let’s look at some major trends in data analytics for 2024. AI and machine learning have moved from innovative add-ons to essential components that support real-time insights and forecasting. Data quality and governance have become even more significant, with more companies prioritizing structured data environments for better compliance and integrity.

Natural language processing (NLP) has unlocked new capabilities in handling structured and unstructured data, such as analyzing customer sentiment and predicting market trends. The tools in our list excel in seamless integration, AI capabilities, and enhanced governance, addressing these trends.

Tool #1: Power BI — Leading in Visualization

Overview:

Microsoft’s Power BI retains its top position in data visualization and reporting due to its strong integration and user-friendly interface. It makes complex data accessible, empowering B2B teams to make informed decisions.

Key Features:

Seamless Integration with Microsoft Suite: Easy data extraction, manipulation, and analysis.

AI-Infused Predictive Analytics: Predictive models based on past data trends for future insights.

Enhanced Governance and Compliance: Features such as role-based access and data governance, essential for regulated industries.

Best Use Cases:

Highly effective for sales and operations teams looking for real-time analytics. B2B companies can use Power BI’s KPI and data modeling features to optimize customer service metrics and sales funnels.

The best data analytics tool depends on a company’s specific needs, such as scalability, integration ease, and advanced analytics like NLP or machine learning. Tools like Power BI and Tableau lead in visualization, while Looker Studio offers a seamless cloud-native experience. SAS shines in compliance-focused environments, and ML tools offer robust predictive capabilities.

As we look to 2025, AI-driven analytics, real-time decision-making, and embedded data tools are set to continue their rapid growth. Each of these five tools laid the groundwork in 2024 and will continue shaping the data analytics landscape in the years ahead.

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

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With data-driven decision-making now the best competitive advantage a company can have, business leaders will increasingly demand to get the information they need at a faster, more consumable clip. Because of this, we’ll continue to see calls for AI to become a business-consumer-friendly product rather than one that only technically savvy data scientists and engineers can wield. It’s this vision for the future that’s driving the five trends in AI-driven analysis that we see right now:

Users demand an explainable approach to data analysis

As AI technology advances, understanding the processes behind its results can be challenging. This “black box” nature can lead to distrust and hinder AI adoption among non-technical business users. However, explainable AI (XAI) aims to democratize the use of AI tools and make it more accessible to business users.

XAI generates explanations for its analysis and leverages conversational language, coupled with compelling visualizations, so non-data experts can easily interpret its meaning. XAI will be crucial in the future of AI-driven data analysis by bridging the gap between the complex nature of advanced models and the human need for clear, understandable, and trustworthy outcomes.

Multimodal AI emerges

Multimodal AI is the ultimate tool for effective storytelling in today’s data-driven world. While Generative AI focuses on creating new content, Multimodal AI can be seen as an advanced extension of Generative AI with its ability to understand and tie together information coming from different media simultaneously. For example, a multimodal generative model could process text to create a story and enhance it with pertinent images and sounds.

As data sets become more complex and robust, it’s become difficult to comprehensively analyze that data using traditional methods. Multimodal AI gives analytics teams the ability to consume and analyze heterogeneous input so they can uncover critical information that leads to better strategic decision-making.

Across all AI-driven analytics trends, it is crucial to emphasize AI safety and ethical practices as fundamental aspects in all areas of the business. For instance, Ethical AI is essential to help ensure that AI technologies are beneficial, fair, and safe to use. That is because AI models can inadvertently perpetuate biases present in the training data. As AI becomes increasingly personalized, incorporating a wider variety of data inputs and innovations, it is crucial that responsible AI governance and training are implemented across all levels of the organization. When everyone understands both the advantages and limits of AI, the future truly becomes brighter for all.

To Know More, Read Full Article @ https://ai-techpark.com/five-key-trends-in-ai-driven-analysis/ 

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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/ 

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As the trajectory of computing power continues its exponential ascent, quantum computing stands at the forefront, poised to tackle challenges that have long confounded traditional computational methods. In the ever-evolving landscape of the 21st century, quantum computing emerges as a dynamic field brimming with promise, offering a plethora of solutions across diverse domains such as climate modeling, energy optimization, drug discovery, and healthcare.

The allure of quantum computing lies in its ability to conduct simulations and optimizations on a scale previously unimaginable, presenting a paradigm shift that beckons computer engineers, scientists, and developers to delve into the realms of quantum physics. Indeed, the fusion of quantum principles with computational prowess heralds a digital revolution, paving the way for transformative innovations and novel approaches to age-old problems.

To facilitate the journey into this exciting frontier, a curated selection of quantum computing certification courses stands ready to guide aspiring learners:

The Complete Quantum Computing Course by StationX:

Tailored for STEM professionals embarking on their quantum odyssey, this foundational course unravels the mysteries of quantum regulations and their pivotal role in bestowing unparalleled computational supremacy. From quantum computing basics to error correction techniques, quantum algorithms, and states manipulation, participants gain insights into applications spanning cybersecurity, pharmaceuticals, and engineering.

Quantum Computing: The Big Picture by Pluralsight:

Delving into the nuances of quantum mechanics, this professional course offers a panoramic view of key concepts such as superposition, entanglement, and the crafting of quantum algorithms. Designed to empower IT engineers, developers, and computer scientists, it sheds light on the transformative potential of quantum computing across diverse domains including IoT, wireless security, network engineering, and augmented reality.

Applied Quantum Computing III: Algorithm and Software by EdX: 
Catering to the discerning palate of IT engineers and computer scientists, this advanced-level offering delves deep into the intricacies of quantum Fourier transform, search algorithms, and their myriad applications. With a focus on optimization, simulation, quantum chemistry, machine learning, and data science, participants are immersed in live sessions and personalized learning experiences, honing their skills in programming, data science, and algorithmic design.

In conclusion, the imperative of familiarizing oneself with quantum computing in the digital age cannot be overstated. These meticulously curated certification courses offer not merely a gateway, but a pathway to mastery, equipping computer scientists, engineers, and programmers with the requisite knowledge and skills to harness the transformative potential of quantum computing and chart a course towards innovation and excellence.

To Know More, Read Full Article @ https://ai-techpark.com/top-5-quantum-computing-certification-in-2024/ 

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As we have stepped into the digital world, data science is one of the most emerging technologies in the IT industry, as it aids in creating models that are trained on past data and are used to make data-driven decisions for the business.

With time, IT companies can understand the importance of data literacy and security and are eager to hire data professionals who can help them develop strategies for data collection, analysis, and segregation. So learning the appropriate data science skills is equally important for budding and seasoned data scientists to earn a handsome salary and also stay on top of the competition.

In this article, we will explore the top 10 data science certifications that are essential for budding or seasoned data scientists to build a strong foundation in this field.

Data Science Council of America (DASCA) Senior Data Scientist (SDS)

The Data Science Council of America’s (DASCA) Senior Data Scientist (SDS) certification program is designed for data scientists with five or more years of professional experience in data research and analytics. The program focuses on qualified knowledge of databases, spreadsheets, statistical analytics, SPSS/SAS, R, quantitative methods, and the fundamentals of object-oriented programming and RDBMS. This data science program has five trackers that will rank the candidates and track their requirements in terms of their educational and professional degree levels.

IBM Data Science Professional Certificate

The IBM Data Science Professional Certificate is an ideal program for data scientists who started their careers in the data science field. This certification consists of a series of nine courses that will help you acquire skills such as data science, open source tools, data science methodology, Python, databases and SQL, data analysis, data visualization, and machine learning (ML). By the end of the program, the candidates will have numerous assignments and projects to showcase their skills and enhance their resumes.

Open Certified Data Scientist (Open CDS)

The Open Group Professional Certification Program for the Data Scientist Professional (Open CDS) is an experienced certification program for candidates who are looking for an upgrade in their data science skills. The programs have three main levels: level one is to become a Certified Data Scientist; level two is to acquire a Master’s Certified Data Scientist; and the third level is to become a Distinguished Certified. This course will allow data scientists to earn their certificates and stay updated about new data trends.

Earning a certification in data science courses and programs is an excellent way to kickstart your career in data science and stand out from the competition. However, before selecting the correct course, it is best to consider which certification type is appropriate according to your education and job goals.

To Know More, Read Full Article @ https://ai-techpark.com/top-5-data-science-certifications-to-boost-your-skills/ 

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Can you tell us about your journey and what motivated you to co-found ForwardLane, particularly focusing on AI’s role in financial services?

My journey into fintech came to me when I worked at the multi-asset alternative asset manager, CQS. There, we could find insights and act on them far ahead of financial institutions. When I saw how difficult it was for advisors to get to insights, I came up with the vision of an AI co-pilot for every financial advisor. With EMERGE, that vision is now a reality. EMERGE analyzes all your data to uncover opportunities and deliver insights tailored to each user and client.

ForwardLane is known for its proactive and personalized advisory platform. How does AI play a pivotal role in achieving this level of personalization and what sets it apart from traditional approaches?

EMERGE combines AI, data aggregation of portfolio, market data, marketing, behavioral, demographic and psychographic and natural language generation to provide hyper-personalized guidance. Imagine having a data scientist, a personal communications coach, and a strategist dedicated to each client – that’s the power of EMERGE. It detects signals and recommends next actions unique to the individual.

You’ve been a noted commentator on AI’s application in financial services. Could you share some specific examples of how AI has benefited asset managers and insurance distribution?

EMERGE digests and learns from your data enterprise-wide to reveal new distribution opportunities. It informs your sales teams which clients to focus on and what to talk about. It can create advisor profile briefs on the fly, and then recommend an engagement plan. The ROI can be game-changing.

ForwardLane’s AI platform combines NLP with enterprise data aggregation. Could you elaborate on how this combination enhances client engagement and provides personalized content?

EMERGE’s hybrid AI extracts insights you never knew existed from both structured and unstructured data. This gives a 360-degree view of each client by connecting the dots across siloed datasets. EMERGE GPT has all of these insights to seed accurate answers and provide advice on how to engage clients effectively.

In the context of ForwardLane’s offerings, could you explain how the API framework seamlessly integrates insights into existing workflows and CRM systems?

EMERGE seamlessly integrates guidance into your existing platforms. Imagine having your CRM proactively guide your next best action for each client interaction.

You have expertise in a wide range of areas including global capital markets, derivatives, and high-performance applications. How have these areas of expertise contributed to the development of ForwardLane’s technology and solutions?

My background in hedge fund quant finance, high-performance computing and high-frequency trading technology allows EMERGE to leverage institutional-grade analytics in a turnkey platform. The inefficiencies in large organizations, led us to create new tools to make life easier for enterprise users. EMERGE democratizes insight creation, data science, effective communications and client engagement for all users

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

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Many businesses have learned the hard way that not every AI project leads to glory and success. In fact, a 2023 CIO.com survey found that more than half of AI projects fail to produce actionable results at all. There are many reasons for this, but one of the biggest causes we frequently see is a disconnect between the data scientists who are actually building the models and the end users who would consume or use the models.

 Most data scientists would agree that deep data exploration of all the relevant data is crucial to any analytics project. Unfortunately, these same data scientists are regularly faced with tight deadlines and often have no clear way to quantify the ROI for data exploration. As a result, data scientists frequently do not spend as much time as they would like when framing and scoping new projects and exploring the corresponding data. Additionally, the onus of data exploration typically falls to the data scientist who may be fairly removed from the end users within the organization. This means that when data exploration happens, it happens apart from the business analysts closest to decision-making. As a result, organizations miss out on domain expertise that could guide bigger data-based projects such as AI.

The New AI: Analyst-powered Intelligence

There’s an enormous opportunity for companies to upskill their analysts. With AI-powered analytics, they can accomplish data exploration without getting blocked by too much data, too many correlations between the attributes, or an inability to find signal in a dataset.

Say a financial services company wants to boost its business in lines of credit for SMBs. Maximizing this opportunity requires the company to understand who their ideal customer is and how best to reach them. Using AI-powered analysis, the analyst can find groups of businesses that would be strong candidates for credit extensions and understand why they were recommended.

Armed with this insight, the analyst then collaborates with the marketing and environmental-social-governance (ESG) teams to identify the ideal customer persona to target, then prioritize the appropriate business development projects, such as chatbots that can alert the sales team when these customers interact with the website.

From start to finish, the analyst partners with their business team to get the best results out of the right AI projects. Moreover, the same AI-driven analytics platform can be used by the data science team to solve more complex problems that an analyst may not have the specific skillset for yet. It’s a win all around for the organization.

Surface Hidden Opportunities

When analysts have the power of advanced analytics in their hands they can discover business advantages buried within mountains of data. Decision-makers can have confidence that any AI project proposal that emerges as a result of deep analyses has emerged organically from data and was put together in full collaboration with those on the business side—ensuring there’s value in pursuing it.

To Know More, Read Full Article @ https://ai-techpark.com/why-ai-use-cases-fail-and-what-to-do-about-it/

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Data science is a rapidly evolving field. How does Tredence stay ahead of the curve and ensure its solutions incorporate the latest advancements and best practices in the industry?

At Tredence, we constantly innovate to stay ahead in the rapidly evolving data science field. We have established an AI Center of Excellence, fueling our innovation flywheel with cutting-edge advancements.

We’ve built a Knowledge Management System that processes varied enterprise documents and includes a domain-specific Q&A system, akin to ChatGPT. We’ve developed a co-pilot integrated data science workbench, powered by GenAI algorithms and Composite AI, significantly improving our analysts’ productivity.

We’re also democratizing data insights for business users through our GenAI solution that converts Natural Language Queries into SQL queries, providing easy-to-understand insights. These are being implemented across our client environments, significantly adding value to their businesses.

How does Tredence leverage data science to address specific challenges faced by businesses and industries?

Tredence, as a specialized AI and technology firm, delivers bespoke solutions tailored to businesses’ unique needs, leveraging cutting-edge data science concepts and methodologies. Our accelerator-led approach significantly enhances time to value, surpassing traditional consulting and technology companies by more than 50%. Tredence offers a comprehensive suite of services that cover the entire AI/ML value chain, supporting businesses at every stage of their data science journey.

Our Data Science services empower clients to seamlessly progress from ideation to actionable insights, enabling ML-driven data analytics and automation at scale and velocity. Tredence’s solutioning services span critical domains such as Pricing & Promotion, Supply Chain Management, Marketing Science, People Science, Product Innovation, Digital Analytics, Fraud Mitigation, Loyalty Science, and Customer Lifecycle Management.

Focusing on advanced data science frameworks, Tredence excels in developing sophisticated Forecasting, NLP models, Optimization Engines, Recommender systems, Image and video processing algorithms, Generative AI Systems, Data drift detection, and Model explainability techniques. This comprehensive approach enables businesses to harness the full potential of data science, facilitating well-informed decision-making and driving operational efficiency and growth across various business functions. By incorporating these data science concepts into their solutions, Tredence empowers businesses to gain a competitive advantage and capitalize on data-driven insights.

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

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