The editors at Solutions Review have compiled this list of the best Python courses on Pluralsight to consider for growing your skills.

Python is an object-oriented programming language comparable to Perl, Ruby, Scheme, and Java. It utilizes an elegant syntax that makes the programs you write easier to read, and it is ideal for prototype development and other ad-hoc tasks. Python comes with a large standard library that supports many common programming tasks as well, including connecting to web servers, searching text with expressions, and reading and modifying files. The language can be extended by adding new modules as well.

With this in mind, we’ve compiled this list of the best Python courses on Pluralsight and online training to consider if you’re looking to grow your data analytics skills for work or play. This is not an exhaustive list, but one that features the best Pluralsight courses on Udemy and training from trusted online platforms. This list of the best Python courses on Pluralsight below includes links to the modules and our take on each.

The Best Python Courses on Pluralsight

Python Fundamentals

OUR TAKE: With nearly 3,500 reviews, Python Fundamentals gets you started with web development, big data, science, and scripting. It’s suitable for an intermediate learner and touts a duration of more than 5 hours.

Description: Python’s readable style, quick edit-and-run development cycle, and “batteries included” philosophy means that you can sit down and enjoy writing code rather than fighting compilers and thorny syntax. Instructor Robert Smallshire is a founding director of Sixty North, a software product and consulting business based in Norway.

Python – Beyond the Basics

OUR TAKE: With nearly 700 reviews and better than four stars, this top-rated but intermediate Pluralsight training builds on the foundations of the introductory course and features more advanced topics.

Description: On completing this course, you’ll be familiar with the majority of Python techniques and constructs used in Python programs. Crucially, we’ll also advise you on when – and when not – to use the different tools available in Python to best effect, to maximize the quality of your code, your productivity, and the joy inherent in coding in Python.

Understanding Machine Learning with Python

OUR TAKE: This beginner-friendly course helps you gain an understanding of how to perform machine learning with Python through formatting problems, preparing data, and combining data with algorithms.

Description: By the end of this course, you will be able to use Python and the scikit-learn library to create Machine Learning solutions. And you will understand how to evaluate and improve the performance of the solutions you create. Before you begin, make sure you are already familiar with software development and basic statistics. However, your software experience does not have to be in Python, since you will learn the basics in this course.

Python Desktop Application Development

OUR TAKE: Featuring instruction from a senior software developer, this course shows you how to easily create desktop applications using Python and Qt. It’s also less than two hours long.

Description: In this course, you will learn how easy it is to write desktop applications using Python and its amazing friend, Qt. Python is famous for being simple yet powerful, and the same is true for Qt; in as little as 50 lines of code, you’ll be able to write a fully functioning application. Not only that, but your application will also run on all major operating systems.

Practical Python for Beginners

OUR TAKE: This training will teach you the basics of Python while enabling you to create applications that can solve for a wide array of problems. Updated in January 2021, this course offers nearly two hours of content.

Description: In this course, Practical Python for Beginners, you’ll learn to create Python applications to solve a wide variety of problems. First, you’ll explore data types, input, and output. Next, you’ll discover lists, loops, and dictionaries. Finally, you’ll learn how to incorporate what you’ve learned to read weather and space data from different web APIs.

Getting Started with Natural Language Processing with Python

OUR TAKE: This beginner-friendly course will show you how to process raw text data and apply machine learning algorithms. Students review this training highly, and it includes nearly two hours of content.

Description: In this course, Getting Started with Natural Language Processing with Python, you’ll first learn about using the Natural Language Toolkit to pre-process raw text. Next, you’ll learn how to scrape websites for texting using BeautifulSoup, as well as how to auto-summarize text using machine learning. You’ll wrap up the course by exploring how to classify text using machine learning.

NOW READ: The Best Python Certifications Online

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The editors at Solutions Review have compiled this list of the best Pluralsight data analytics courses (top-rated) to consider taking.

Data analytics skills are in high demand among organizations that are looking to use their collected data to generate valuable business insight. The pandemic and subsequent ‘new normal’ of remote work are furthering demands for these skills. Many are turning to online learning platforms to up their game and acquire the data analytics skills most likely to help them stand out. And whether you are looking to acquire those skills for work or for play, this collection of Pluralsight data analytics courses will help you learn the ropes so you can pilot some of the most widely used tools in no time!

With this in mind, the editors at Solutions Review have compiled this list of the best Pluralsight data analytics courses to consider taking. The platform is perfect for those looking to take multiple courses or acquire skills in multiple different areas, or for those who want the most in-depth experience possible through access to entire course libraries or learning paths. In sum, Pluralsight offers more than 7,000 expert-led video courses.

The Best Pluralsight Data Analytics Courses

TITLE: Grow Your Tableau Skills

Description: Learn the skills needed to become a Tableau master. All of Pluralsight’s training goes through a rigorous vetting process to ensure the quality, usability, and uniqueness of the content. Stay up to date on one of the most popular data visualization products out there, and learn how to create and display data in Tableau. Scroll down the page to see other Tableau paths or browse Pluralsight’s collection of top Tableau courses.

More “Top-Rated” Pluralsight paths: Tableau Desktop for Analysts

TITLE: Collecting and Preparing Data for MicroStrategy

Description: In this course, you will gain the ability to acquire data for use within MicroStrategy for analysis. First, you will learn how to work with the MicroStrategy object model. Next, you will discover the process for bringing in external unmodeled data. Finally, you will explore how to get that data in a format that supports your ability to analyze it. When you are finished with this course, you will have the skills and knowledge of MicroStrategy data acquisition needed to begin building your own reporting.

More “Top-Rated” Pluralsight paths: Sharing and Securing Content in MicroStrategy, Building Your First Dossier in MicroStrategy

TITLE: Alteryx Designer: Getting Started

Description: In this course, you will gain the ability to work efficiently within the Alteryx Designer platform. First, you will learn to move and navigate around the user interface. Next, you will discover how to read in data from virtually any data source, and filter/blend/manipulate the data in order to gain that all-important actionable knowledge. Finally, you will explore how to visualize your data in a distribution ready report, and deploy that report to any number of recipients, all in one place – Alteryx Designer.

TITLE: Getting Started with Power BI

Description: You’ll start out with seeing how you can quickly and easily gather data from a variety of sources, and then cleanse and transform that data with just a few clicks. Next, you’ll also learn how you can enhance the results by integrating disparate data sources and adding simple calculations. Then, you’ll learn how to explore your data with visualizations and simple dashboards. Finally, you’ll learn what steps are necessary to keep your data up-to-date.

More “Top-Rated” Pluralsight paths: Building Your First Power BI Report, Getting Started with DAX Formulas in Power BI, Power Pivot, and SSAS

TITLE: Building Your First ETL Pipeline Using Azure Databricks

Description: In this course, Building Your First ETL Pipeline Using Azure Databricks, you will gain the ability to use the Spark-based Databricks platform running on Microsoft Azure, and leverage its features to quickly build and orchestrate an end-to-end ETL pipeline. And all this while learning about collaboration options and optimizations that it brings, but without worrying about the infrastructure management.

TITLE: Qlik Sense for Analysts

Description: Pluralsight’s Qlik Sense for Analysts path will teach you how to build effective visualizations and dashboards, as well as deeper mechanics of working with your data, and extending it with expressions and calculated fields. Pre-requisites include data literacy, data analytics literacy, and basic PC skills. This path is made up of six courses, two of which were designed for beginners, two for intermediate level users, and two for advanced personas.

More “Top-Rated” Pluralsight paths: Loading and Preparing Data for Analysis in Qlik Sense

TITLE: Apache Spark Fundamentals

Description: In this course, you’ll learn Spark from the ground up, starting with its history before creating a Wikipedia analysis application as one of the means for learning a wide scope of its core API. That core knowledge will make it easier to look into Spark’s other libraries, such as the streaming and SQL APIs. Finally, you’ll learn how to avoid a few commonly encountered rough edges of Spark. You will leave this course with a tool belt capable of creating your own performance-maximized Spark application.

More “Top-Rated” Pluralsight paths: Beginning Data Exploration and Analysis with Apache Spark, Structured Streaming in Apache Spark 2

TITLE: Critical and Analytical Thinking Skills in Data Literacy: Executive Briefing

Description: In this course, you will learn foundational knowledge of critical and analytical thinking. First, you will explore critical thinking and different pieces of this crucial skill. Next, you will discover the systematic approach of analytical thinking. Finally, you will see the combined skills of critical and analytical thinking, which help impact data-informed decision making. When you’re finished with this course, you will have an understanding of these crucial skills, which are crucial for organizations to capitalize on their data.

TITLE: Data Analytics: Hands On

Description: This course covers everything from the basic concepts of data analysis, to data warehouse design and data visualization principles. If you’re looking for a career change or already making your way into the world of transforming data into value, this course will help you understand all of the key concepts and get some hands-on skills, while being directed to where you can dig in deeper when you find something you’re interested in. If you’re already working in the data world, you can use this course as a reference.

More “Top-Rated” Pluralsight paths: Big Data Analytics with Tableau

TITLE: Implementing Predictive Analytics with TensorFlow

Description: In this course, you will learn foundational knowledge of solving real-world data science problems. First, you will explore the basics of implementing supervised learning problems including linear regression and neural networks. Next, you will discover how recommendation systems can be implemented using TensorFlow. Finally, you will learn how to understand and implement reinforcement learning systems.

TITLE: Understanding Machine Learning

Description: Have you ever wondered what machine learning is? That’s what this course is designed to teach you. You’ll explore the open-source programming language R, learn about training and testing a model as well as using a model. By the time you’re done, you’ll have a clear understanding of exactly what machine learning is all about.

More “Top-Rated” Pluralsight paths: Understanding Machine Learning with Python, Understanding Machine Learning with R, Machine Learning: Executive Briefing, How Machine Learning Works, Deploying Machine Learning Solutions

TITLE: Data Visualization: Best Practices

Description: In this course, you will gain the ability to build visually-pleasing charts that effectively communicate your message. First, you will learn the basic concept of data visualization, why the field is growing, and how data viz can make an impact. Next, you will discover a variety of effective chart types and learn the design practices that make them effective. Finally, you will explore how to leverage preattentive attributes in your visualizations in order to enable easy data interpretation.

More “Top-Rated” Pluralsight paths: Data Visualization for Developers, Objectivity in Data Visualization, Introduction to Data Visualization with Python

TITLE: Data Science: Executive Briefing

Description: In this course, you will gain the ability to transform data into actionable insight. First, you will learn what data science is. Next, you will discover why data science is important for you and your business. Finally, you will explore how to get started becoming a data-driven organization. When you’re finished with this course, you will have the skills and knowledge of data science needed to transform data into actionable insight.

More “Top-Rated” Pluralsight paths: Data Science with R, Doing Data Science with Python, Data Science: The Big Picture

TITLE: Introduction to Business Analysis & Needs Assessment

Description: This course introduces the work of business analysis, explores who undertakes business analyst functions, and the type of skills necessary to conduct business analysis successfully. Then, attention turns to needs assessment, where problems and opportunities are identified, organizational ability to respond is assessed, recommendations for action are developed, feasibility is weighed for various options, best options are selected, and business cases for action are developed.

More “Top-Rated” Pluralsight paths: Excel: An Analytics Superhub

TITLE: Cloud Business Intelligence: The Big Picture

Description: This course covers the current state of Business Intelligence in the cloud, as well as goes into functional examples using Google Big Query and Tableau Online. This is a beginner course, but it is assumed you are familiar with the basics of databases and business intelligence concepts.

More “Top-Rated” Pluralsight paths: Introduction to Microsoft Business Intelligence End-User Tools, SQL Server Business Intelligence Overview, Introduction to Data Warehousing and Business Intelligence, Enterprise Business Intelligence with Tableau Server, Getting Started with Oracle Business Intelligence Enterprise Edition, Business Dashboard Fundamentals

TITLE: Excel: An Analytics Superhub

Description: This course goes through how to use Excel as an analytics client for BI and Big Data. With coverage applicable to several versions of Excel, but with emphasis on the new capabilities of Excel 2013, the content includes details on, and numerous demos of, the analytics capabilities in the core Excel product and its add-ins, including PowerPivot, Power View, and preview add-ins code-named “Data Explorer” and “GeoFlow.” This course provides great detail on each tool and shows how they can be used in combination, as well as, with data from HDInsight/Hadoop; Microsoft’s Big Data Platform.

More “Top-Rated” Pluralsight paths: Displaying Tables with Excel, Excel 2013: Analysis Techniques and Random Numbers, Loading Data into Excel, Exploring Data with PivotTables, Summarizing Data and Deducing Probabilities, Searching and Manipulating Data in Excel, Excel Data Lookup Function Playbook, Summarizing and Organizing Data in Excel, Pragmatic Self-Service BI with PowerPivot for Excel

TITLE: Programming with R

Description: In this course, Programming with R, you will learn how to manipulate different objects. First, you will learn the basic syntax. Next, you will explore data types and data structures available in R. Finally, you will discover how to write your own functions by implementing control flow statements. When you are finished with this course, you will have a foundational knowledge of R programming that will help you as you move forward to data science.

More “Top-Rated” Pluralsight paths: Data Science with R, Understanding Machine Learning with R

TITLE: Getting Started with DAX Formulas in Power BI, Power Pivot, and SSAS

Description: In this course, Getting Started with DAX Formulas in Power BI, Power Pivot, and SSAS, you’ll learn the basics of the DAX language. First, you’ll learn how DAX works and why it has such good performance. Next, you’ll explore how to encapsulate business logic using calculated columns and measures. Finally, you’ll discover a variety of ways to manipulate filters or analyze your data. When you’ve finished this course, you’ll have the skills and knowledge of DAX to model and analyze your data with the DAX expression language.

More “Top-Rated” Pluralsight paths: Common DAX Expressions and Scenarios Power BI Playbook

NOW READ: The Best Pluralsight Big Data Courses (Top-Rated) to Consider

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Solutions Review’s Solution Spotlight with Alteryx is an hour-long discussion and software demo focusing on how AI can unlock deep data insights and drive digital transformation.

What is a Solutions Spotlight?

Solutions Review’s Solution Spotlights are exclusive webinar events for industry professionals across enterprise technology. Since its first virtual event in June 2020, Solutions Review has expanded its multimedia capabilities in response to the overwhelming demand for these kinds of events. Solutions Review’s current menu of online offerings includes the Demo Day, Solution Spotlight, best practices or case study webinars, and panel discussions. And the best part about the “Spotlight” series? They are free to attend!

Why You Should Attend

Solutions Review is one of the largest communities of IT executives, directors, and decision-makers across enterprise technology marketplaces. Every year, over 10 million people come to Solutions Review’s collection of sites for the latest news, best practices, and insights into solving some of their most complex problems.

With the next Solutions Spotlight event, the team at Solutions Review has partnered with analytics provider Alteryx. Join Director of Product Management Sarah Welch and Manager of Product Management David Cooperberg to learn how the Alteryx AI Platform for Enterprise Analytics offers integrated generative and conversational AI, data preparation, advanced analytics, and automated reporting capabilities.

Featured Speakers:

Sarah Welch

Sarah Welch is the Director of Product Management at Alteryx, bringing over two decades of expertise as a strategic technology and product professional. Her leadership is marked by a clear vision that is aligned with technology trends, deeply rooted in user empathy, and informed by many years of experience purchasing and implementing enterprise software. She excels in driving business value and fostering technological agility. Throughout her career, Sarah has managed multi-million-dollar capital investments and provided direct support to Fortune 100 companies across various sectors, including technology, healthcare, retail, financial services, defense, and petroleum.

David Cooperberg

David Cooperberg is the Manager on Alteryx’s product management team. In his six years at Alteryx, he’s focused primarily on the data science and machine learning space. His current focus is on AiDIN and injecting AI throughout the Alteryx platform.

About Alteryx:

Alteryx, Inc. is an American computer software company based in Irvine, California, with a development center in Broomfield, Colorado, and offices worldwide. Alteryx offers a leading AI Platform for Enterprise Analytics that delivers actionable insights by automating analytics. The software is designed to make advanced analytics automation accessible to any data worker. During 2023 alone, Alteryx has achieved “Leader” positions and/or customer-driven recognitions in 13 separate third-party evaluations from firms including Ventana Research, Dresner Advisory, Constellation Research, Info-Tech Research, G2 Research, Quadrant Knowledge Solutions, Nucleus Research, and GigaOM.

FAQ

• What: Using AI to Unlock Deep Data Insights and Drive Digital Transformation
• When: Friday, March 22, 2024 at 12:00pm Eastern Time
• Where: Zoom meeting (see registration page for more details)

Register for Solutions Review’s Solution Spotlight with Alteryx FREE

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For our 5th annual Insight Jam LIVE! Solutions Review editors sourced this resource guide of analytics and data science predictions for 2024 from Insight Jam, its new community of enterprise tech experts.

Note: Analytics and data science predictions are listed in the order we received them.

Analytics and Data Science Predictions from Experts for 2024

Rahul Pradhan, Vice President of Product and Strategy at Couchbase

Real-time data will become the standard for businesses to power generative experiences with AI; Data layers should support both transactional and real-time analytics 

“The explosive growth of generative AI in 2023 will continue strong into 2024. Even more enterprises will integrate generative AI to power real-time data applications and create dynamic and adaptive AI-powered solutions. As AI becomes business critical, organizations need to ensure the data underpinning AI models is grounded in truth and reality by leveraging data that is as fresh as possible.”

“Just like food, gift cards and medicine, data also has an expiration date. For generative AI to truly be effective, accurate and provide contextually relevant results, it needs to be built on real-time, continually updated data. The growing appetite for real-time insights will drive the adoption of technologies that enable real-time data processing and analytics. In 2024 and beyond, businesses will increasingly leverage a data layer that supports both transactional and real-time analytics to make timely decisions and respond to market dynamics instantaneously.”

Expect a paradigm shift from model-centric to data-centric AI

“Data is key in modern-day machine learning, but it needs to be addressed and handled properly in AI projects. Because today’s AI takes a model-centric approach, hundreds of hours are wasted on tuning a model built on low-quality data.”

“As AI models mature, evolve and increase, the focus will shift to bringing models closer to the data rather than the other way around. Data-centric AI will enable organizations to deliver both generative and predictive experiences that are grounded in the freshest data. This will significantly improve the output of the models while reducing hallucinations.”

Multimodal LLMs and databases will enable a new frontier of AI apps across industries

“One of the most exciting trends for 2024 will be the rise of multimodal LLMs. With this emergence, the need for multimodal databases that can store, manage and allow efficient querying across diverse data types has grown. However, the size and complexity of multimodal datasets pose a challenge for traditional databases, which are typically designed to store and query a single type of data, such as text or images. “

“Multimodal databases, on the other hand, are much more versatile and powerful. They represent a natural progression in the evolution of LLMs to incorporate the different aspects of processing and understanding information using multiple modalities such as text, images, audio and video. There will be a number of use cases and industries that will benefit directly from the multimodal approach including healthcare, robotics, e-commerce, education, retail and gaming. Multimodal databases will see significant growth and investments in 2024 and beyond — so businesses can continue to drive AI-powered applications.”

Nima Negahban, CEO and Co-Founder at Kinetica

Generative AI turns its focus towards structured, enterprise data

“Businesses will embrace the use of generative AI for extracting insights from structured numeric data, enhancing generative AI’s conventional applications in producing original content from images, video, text and audio. Generative AI will persist in automating data analysis, streamlining the rapid identification of patterns, anomalies, and trends, particularly in sensor and machine data use cases. This automation will bolster predictive analytics, enabling businesses to proactively respond to changing conditions, optimizing operations, and improving customer experiences.”

English will replace SQL as the lingua-franca of business analysts

“We can anticipate a significant mainstream adoption of language-to-SQL technology, following successful efforts to address its accuracy, performance, and security concerns. Moreover, LLMs for language-to-SQL will move in-database to protect sensitive data when utilizing these LLMs, addressing one of the primary concerns surrounding data privacy and security. The maturation of language-to-SQL technology will open doors to a broader audience, democratizing access to data and database management tools, and furthering the integration of natural language processing into everyday data-related tasks.”

Vasu Sattenapalli, CEO at RightData

NLP-Powered Analytics Will Be the Next Wave of Self Service

“Analytics have been stuck in dashboards, which will no longer be the only way to consume business insights. Voice and Generative AI will enter the analytics space where you can ask questions of your data verbally and get a response back in minutes, if not seconds. Imagine even pulling out your phone with an app specific to your organization’s data and being able to access a world of insights. It’s coming!”

Shawn Rogers, CEO and Fellow at BARC

AI is driving innovation in data management, especially through automation and speed

“Having strength at this core level of your data stack is critical for AI success. NLP and conversational UI’s will open the door for the true democratization of analytics. It’s an exciting time for data and insights.”

Bernie Emsley, CTO at insightsoftware

CTO’s will need to bring even more collaboration and education to the C-suite

“Over the past few years, the CTO role has become the bridge between the tech-savvy and the business-savvy, charged with enabling the right solutions to create the best overall business outcomes. This comes with its communication challenges as the CTO needs to navigate how to translate tech into an ROI for the organization’s board and C-suite. In 2024, the ability to educate their C-level colleagues will become even more important as artificial intelligence (AI) technologies become commonplace. The CTO will not only need to be able to collaborate with the tech side of the business to ensure what is realistically possible in the realm of AI but will need to communicate on a business level its potential – both from employee productivity and product standpoint.”

Strong data engines will make financial data movement possible

“Financial organizations are just starting to realize the potential their data holds, using it for guidance in financial planning and analysis, budgetary planning, and more. However, much of this data is still siloed, and we have reached the point where these organizations have so much of this data, that they need to start thinking about how it can bring value to the company or risk losing their competitive advantage. In 2024, we will see finance organizations seek to classify and harmonize their data across repositories to enable new solutions. In response, data engines, data platforms, and data lakes will be just a few tools that will become crucial to understanding and utilizing such data effectively. As a result, we can expect to see the growth of fintech applications to enable this aggregated data analysis, reporting, and visualization to take place.”

Joy Allardyce, General Manager, Data & Analytics at insightsoftware

A continual shift to cloud resources

“The continued push to re-architect technology landscapes to a cloud/SAAS approach will prevail, and many organizations who’ve made large bets ($1B+) contracts on the cloud will find they can’t innovate fast enough to deliver on those commitments. Some, on the other hand, don’t see it as a migration for cost, but an opportunity to modernize and transform how they use data in their business.”

The rise and adoption of AI

“AI, like all reporting projects, is only as good as the data it has access to and the prompts used to make a request. With the push for AI, many are still stuck getting their data foundations established so that they can take advantage of AI. To avoid pilot purgatory, starting with the outcome (use case) in mind that shows a quick win and demonstrable value vs. a one-off project is key.”

Democratizing data

“While the notion of centralized data management is a trend, the reality is that departments still own their data AND have domain expertise. How organizations can adopt a democratized and open fabric but employ the right data governance strategies to support faster innovation and adoption will be crucial. Doing so will only further support the adoption of AI, which requires strong domain knowledge for value to be truly extracted.”

Andy Oliver, Director of Marketing at CelerData

Java will continue to be used for a great many legacy and even current systems and applications

“Java, though showing its age and looking slower in today’s environments, will continue to be used for a great many legacy and even current systems and applications, regardless of the low level of support and leadership from Oracle*

The challenge with implementing real-time data has been more about storage than anything else. I think in the past people were obsessed with real-time versus batch. Sometimes it seems like a choice between something that’s big enough but too slow vs. something that’s fast enough but too small.

However, real-time and batch will come together, to meet the requirements of user numbers, and we will see more unified analytical database technologies for functions and insights that demand real-time analysis.

Not everything will need to move over to real-time, though – there are plenty of things where there’s no good reason to do it.

I think we’re going to see most of the nonsense shake out from operational AI if it can really learn and stick to core organizational needs, and be deployed responsibly and effectively. That’s where VCs are going to focus in the future, the rest will keep falling by the wayside.”

Casey Ciniello, Product Owner and Marketing Manager at Infragistics

More Businesses Will Rely on Predictive Analytics to Make Decisions in 2024

“Making decisions based on gut instinct is a thing of the past as organizations are fully realizing the power of analytics to make data-driven decisions, evidenced by the number of software platforms incorporating embedded analytics. Analytics will be all encompassing in 2024 as we become reliant on data for everything from everyday business research such as inventory and purchasing to predictive analytics that allow businesses to see into the future. Predictive analytics will drive businesses forward by helping them make informed, data-driven decisions, improve productivity, and increase sales/revenue — rather than merely reacting in response to events that have already taken place.”

Justin Borgman, Co-Founder and CEO at Starburst

Two hot topics, data products & data sharing, will converge in 2024

“Data sharing was already on the rise as companies sought to uncover monetization opportunities, but a refined method to curate the shared experience was still missing. As the lasting legacy of data mesh hype, data products will emerge as that method. Incorporating Gen AI features to streamline data product creation and enable seamless sharing of these products marks the pivotal trifecta moving data value realization forward.”

Mike Carpenter, VC Advisor for Lightspeed Venture Partners

AI to Drive Real-Time Intelligence and Decision Making

“Next year will be foundational for the next phase of AI. We’ll see a number of new innovations for AI, but we’re still years away from the application of bigger AI use cases. The current environment is making it easy for startups to build and prepare for the next hype cycle of AI. That said, 2024 is going to be the year of chasing profitability. Due to this, the most important trend in 2024 will be the use of AI to drive real-time intelligence and decision-making. This will ultimately revolutionize go-to-market strategies, derisk investments, and increase bottom-line value.”

Brian Peterson, Co-Founder and Chief Technology Officer at Dialpad

Influx of data talent/AI skills 

“As businesses continue to embrace AI, we’re going to see not only an increase in productivity but also an increase in the need for data talent. From data scientists to data analysts, this knowledge will be necessary in order to sort through all the data needed to train these AI models. While recent AI advancements are helping people comb through data faster, there will always be a need for human oversight – employees who can review and organize data in a way that’s helpful for each model will be a competitive advantage. Companies will continue looking to hire more data-specific specialists to help them develop and maintain their AI offerings. And those who can’t hire and retain top talent  – or don’t have the relevant data to train to begin with – won’t be able to compete. 

Just like we all had to learn how to incorporate computers into our jobs years ago, non-technical employees will now have to learn how to use and master AI tools in their jobs. And, just like with the computer, I don’t believe AI will eliminate jobs, more so that it will shift job functions around the use of the technology. It will make everyone faster at their jobs, and will pose a disadvantage to those who don’t learn how to use it. ”

The commoditization of data to train AI

“As specialized AI models become more prevalent, the proprietary data used to train and refine them will be critical. For this reason, we’re going to see an explosion of data commoditization across all industries. Companies that collect data that could be used to train chatbots, take Reddit for example, sit on an immensely valuable resource. Companies will start competitively pricing and selling this data.” 

Wayne Eckerson, President at Eckerson Group

“Within five years, most large companies will implement a data product platform (DPP), otherwise known as an internal data marketplace, to facilitate the publication, sharing, consumption, and distribution of data products.”

Helena Schwenk, VP, Chief Data & Analytics Officer at Exasol

FinOps becomes a business priority, as CIOs analyze price / performance across the tech stack

“Last year, we predicted that CFOs would become more cloud-savvy amidst recession fears, and we watched this unfold as organizations shifted to a “do more with less” mentality. In 2024, FinOps practices the financial governance of cloud IT operations, as the business takes aim at preventing unpredictable, sometimes chaotic, cloud spend and gains assurance from the CIO that cloud investments are aligned with business objectives.

As IT budgetary headwinds prevail, the ability to save on cloud spend represents a real opportunity for cost optimization for the CIO. One of the most important metrics for achieving this goal is price/performance, as it provides a comparative gauge of resource efficiency in the data tech stack. Given most FinOps practices are immature, we expect CIOs to spearhead these efforts and start to perform regular price/performance reviews. 

FinOps will become even more important against the backdrop of organizations reporting on ESG and sustainability initiatives. Beyond its role in forecasting, monitoring, and optimizing resource usage, FinOps practices will become more integral to driving carbon efficiencies to align with the sustainability goals of the organization.” 

AI governance becomes C-level imperative, causing CDOs to reach their breaking point

“The practice of AI governance will become a C-level imperative as businesses seek to leverage the game-changing opportunities it presents while balancing responsible and compliant use. This challenge is further emphasized by the emergence of generative AI, adding complexity to the landscape. 

AI governance is a collective effort, demanding collaborative efforts across functions to address the ethical, legal, social, and operational implications of AI. Nonetheless, for CDOs, the responsibility squarely rests on their shoulders. The impending introduction of new AI regulations adds an additional layer of complexity, as CDOs grapple with an evolving regulatory landscape that threatens substantial fines for non-compliance, potentially costing millions.

This pressure will push certain CDOs to their breaking point. For others, it will underscore the importance of establishing a fully-resourced AI governance capability, coupled with C-level oversight. This strategic approach not only addresses immediate challenges, but strengthens the overall case for proactive and well-supported AI governance going forward.”

Florian Wenzel, Global Head of Solution Engineering at Exasol

Expect AI backlash, as organizations waste more time and money trying to ‘get it right’

“As organizations dive deeper into AI, experimentation is bound to be a key theme in the first half of 2024. Those responsible for AI implementation must lead with a mindset of “try fast, fail fast,” but too often, these roles need to understand the variables they are targeting, do not have clear expected outcomes, and struggle to ask the right questions of AI. The most successful organizations will fail fast and quickly rebound from lessons learned. Enterprises should anticipate spending extra time and money on AI experimentation, given that most of these practices are not rooted in a scientific approach. At the end of the year, clear winners of AI will emerge if the right conclusions are drawn.

With failure also comes greater questioning around the data fueling AI’s potential. For example, data analysts and C-suite leaders will both raise questions such as: How clean is the data we’re using? What’s our legal right to this data, specifically if used in any new models? What about our customers’ legal rights? With any new technology comes greater questioning, and in turn, more involvement across the entire enterprise.”

Nick Elprin, Co-Founder and CEO at Domino Data Lab

An army of smaller, specialized Large Language Models will triumph over giant general ones

“As we saw during the era of “big data” — bigger is rarely better. Models will “win” based not on how many parameters they have, but based on their effectiveness on domain-specific tasks and their efficiency. Rather than having one or two mega-models to rule them all, companies will have their own portfolio of focused models, each fine-tuned for a specific task and minimally sized to reduce compute costs and boost performance.”

Generative AI will unlock the value and risks hidden in unstructured enterprise data

“Unstructured data — primarily internal document repositories — will become an urgent focus for enterprise IT and data governance teams. These repositories of content have barely been used in operational systems and traditional predictive models to date, so they’ve been off the radar of data and governance teams. GenAI-based chat bots and fine-tuned foundation models will unlock a host of new applications of this data, but will also make governance critical. Companies who have rushed to develop GenAI use cases without having implemented the necessary processes and platforms for governing the data and GenAI models will find their projects trapped in PoC purgatory, or worse. These new requirements will give rise to specialized tools and technology for governing unstructured data sources.”

Kjell Carlsson, Head of Data Science Strategy and Evangelism at Domino Data Lab

Predictive AI Strikes Back: Generative AI sparks a traditional AI revolution

“The new hope around GenAI drives interest, investment, and initiatives in all forms of AI. However, the paucity of established GenAI use cases, and lack of maturity in operationalizing GenAI means that successful teams will allocate more than 90% of their time to traditional ML use cases that, despite the clear ROI, had hitherto lacked the organizational will.”

GPUs and GenAI Infrastructure Go Bust

“Gone are the days when you had to beg, borrow and steal GPUs for GenAI. The combination of a shift from giant, generic LLMs to smaller, specialized models, plus increased competition in infrastructure and also quickly ramping production of new chips accelerated for training and inferencing deep learning models together mean that scarcity is a thing of the past. However, investors don’t need to worry in 2024, as the market won’t collapse for at least another year.”

Forget Prompt Engineer, LLM Engineer is the Least Sexy, but Best Paid, Profession

“Everyone will need to know the basics of prompt engineering, but it is only valuable in combination with domain expertise. Thus the profession of “Prompt Engineer” is a dud, destined, where it persists, to be outsourced to low-wage locations. In contrast, as GenAI use cases move from PoC to production, the ability to operationalize GenAI models and their pipelines becomes the most valuable skill in the industry. It may be an exercise in frustration since most will have to use the immature and unreliable ecosystem of GenAI point solutions, but the data scientists and ML engineers who make the switch will be well rewarded.”

GenAI Kills Quantum and Blockchain

“The unstoppable combination of GenAI and Quantum Computing, or GenAI and Blockchain? Not! GenAI will    be stealing all the talent and investment from Quantum and blockchain, kicking quantum even further into the distant future and leaving blockchain stuck in its existing use cases of fraud and criminal financing. Sure, there will be plenty of projects that continue to explore the intersection of the different technologies, but how many of them are just a way for researchers to switch careers into GenAI and blockchain/quantum startups to claw back some of their funding?”

Arina Curtis, CEO and Co-Founder at DataGPT

Data and Business Teams Will Lock Horns Onboarding AI Products

While business user demand for AI products like ChatGPT has already taken off, data teams will still impose a huge checklist before allowing access to corporate data. This tail wagging the dog scenario may be a forcing function to strike a balance, and adoption could come sooner than later as AI proves itself as reliable and secure.”

Businesses Big and Small Will Prioritize Clean Data Sets

“As companies realize the power of AI-driven data analysis, they’ll want to jump on the bandwagon – but won’t get far without consolidated, clean data sets, as the effectiveness of AI algorithms is heavily dependent on the quality and cleanliness of data. Clean data sets will serve as the foundation for successful AI implementation, enabling businesses to derive valuable insights and stay competitive.”

Doug Kimball, CMO at Ontotext

Shift from How to Why: Enter the Year of Outcome-based Decision Making

“In 2024, data management conversations will experience a transformative shift and pivot from “how” to “why.” Rather than focusing on technical requirements, discussions next year will shift to a greater emphasis on the “why” and the strategic value data can bring to the business. Manufacturers recognize that data, once viewed as a technical asset, is a major driver of business success. Solution providers that deal with these needs are also seeing this change, and would be wise to respond accordingly.

In the coming year, data strategy and planning will increasingly revolve around outcomes and the value/benefit of effective data management, as leaders better understand the key role data plays in achieving overarching business objectives. Manufacturers will also reflect on their technology spend particularly those that have yielded questionable results or none at all. Instead of technical deep dives into intricacies like data storage and processing, crafting comprehensive data strategies that drive lasting results will be the priority.

Next year, manufacturers will move beyond technical deep-dives and focus on the big picture. This strategic shift signals a major change in the data management mindset for 2024 and beyond, ideally aligning technology with the broader objectives of the business such as driving growth, enhancing customer experiences, and guiding informed decision-making.”

Christian Buckner, SVP, Data Analytics and IoT at Altair

AI Fuels the Rise of DIY Physics-based Simulation 

“The rapidly growing interaction between Data/AI and simulation will speed up the use of physics-based simulations and extend its capabilities to more non-expert users.”

Mark Do Couto, SVP, Data Analytics at Altair

AI Will Need to Explain Itself

“Users will demand a more transparent understanding of their AI journey with “Explainable AI” and a way to show that all steps meet governance and compliance regulations. The White House’s recent executive order on artificial intelligence will put heightened pressure on organizations to demonstrate they are adhering to new standards on cybersecurity, consumer data privacy, bias and discrimination.”

Molham Aref,  Founder and CEO at RelationalAI

2024: the Rise of the Data Cloud to Advance AI and Analytics 

“While data clouds are not new, I believe there will be a continued emergence and a clear distinction made between data clouds and compute clouds in 2024. With compute clouds like AWS or Azure, we have had to assemble and stitch together all the components needed to work with AI. So with data clouds, like Snowflake or Microsoft Fabric, users have it all pre-packaged together in a single platform, making it much easier to run analytics on data needed to build AI systems. The rise of the data clouds will offer a better starting point for data analytics and Artificial Intelligence (AI) and Machine Learning (ML).”

Dhruba Borthakur, Co-Founder and CTO at Rockset

In 2024, Enterprises Get A Double Whammy from Real-Time and AI – More Cost Savings and Competitive Intelligence 

“AI-powered real-time data analytics will give enterprises far greater cost savings and competitive intelligence than before by way of automation, and enable software engineers to move faster within the organization. Insurance companies, for example, have terabytes and terabytes of data stored in their databases, things like documentation if you buy a new house and documentation if you rent. 

With AI, in 2024, we will be able to process these documents in real-time and also get good intelligence from this dataset without having to code custom models. Until now, a software engineer was needed to write code to parse these documents, then write more code to extract out the keywords or the values, and then put it into a database and query to generate actionable insights. The cost savings to enterprises will be huge because thanks to real-time AI, companies won’t have to employ a lot of staff to get competitive value out of data.”

The Rise of the Machines Powered by Real-Time Data and AI Intelligence

“In 2024, the rise of the machines will be far greater than in the past as data is becoming more and more “real-time” and the trajectory of AI continues to skyrocket. The combination of real-time data and AI make machines come to life as machines start to process data in real-time and make automatic decisions!”

Zandra Moore, CEO at Panintelligence

“The AI rush will continue into 2024, at least in the SaaS sector, whose products are the gateway through which most people and businesses will access AI. More than half of SaaS companies plan to progress new AI innovations by the end of 2024.”

“Following 2023’s Generative AI spree, AI strategies will shift in 2024. The focus is moving to more savvy innovation. 2024 will be the year of ‘pragmatic AI’. Our research indicates that SaaS companies will embrace Deep Learning, Predictive Analytics and Causal AI in 2024. “

“While one in six vendors are currently testing new Generative AI functionality ahead of planned launches, more than a quarter are testing Predictive Analytics to help users predict future outcomes based on historical data.”

“Causal AI, which helps understand data relationships and decision-making processes, also looks to gain prominence, addressing the need for transparent AI models. The number of SaaS vendors using this technology will double in 2024.”

“The number of SaaS vendors using Deep Learning technologies could also double. Almost a fifth of SaaS vendors are testing neural networks capable of learning complex patterns and representations ahead of target launch dates next year.”

Jonathan Friedmann, CEO at Speedata

“Since 2023 was all about the mainstreaming of AI and the crushing demand for specialized infrastructure (even from the category leaders), in 2024, we will see a reckoning for capacity to support other specialized workloads. The collective crisis standing in the way of business innovation is no longer just big data and the quality, compliance, and privacy concerns that come with it. It’s now big-time processing to unblock the teams, initiatives, and workloads within each particular domain. 

We have seen the GPU boom. But what comes next? Faced with enormous capacity constraints – including data center space and energy, as well as budget and performance – enterprises will have to strongly consider their future needs to efficiently and strategically do more with less. In the next year, we’ll start to see the shift to dedicated hardware for dedicated workloads to accelerate processing and break the cycle of scaling the footprint of generic compute across every conceivable industry and endeavor.”

Register for Insight Jam (free) to gain exclusive access to best practices resources, DEMO SLAM, leading enterprise tech experts, and more!

The post 44 Analytics & Data Science Predictions from 24 Experts for 2024 appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

A schedule of events for Insight Jam LIVE on December 7 (formerly BI Insight Jam), the annual element of Solutions Review’s Insight Jam, an always-on community for enterprise technology end-users, experts, and solution providers.

What is Insight Jam?

Think of the Insight Jam as a continuous, ongoing, interactive tech event.

The Insight Jam will always be here when you need answers to the questions that matter to your organization and your career. We’ve partnered with the leading industry experts, thought leaders, and analysts to live-stream a never-ending collection of Roundtable Events, Breakout Sessions, and Expert Podcasts.

And Insight Jam is built on a community platform that powers unlimited discussions, posts, and polls that will bring you deeper into the enterprise technology conversation.

Your Insight Jam journey starts here and starts now. We encourage you to dive in, explore, share, and engage. Let’s challenge ideas, bring new perspectives and elevate our knowledge together.

Join the Fastest-Growing Enterprise Tech Software End-User Community

Solutions Review is the largest software buyer and practitioner community on the web. Our Universe of Influence reach is more than 7 million business and IT decision-makers, as well as C-suite and other top management professionals. Our readers primarily use us as an enterprise technology news source and trusted resource for solving some of their most complex problems.

Our collection of vendor-agnostic buyer’s resources helps buyers and practitioners during the research and discovery phase of a buying cycle. This critical stage of information gathering is where buyers narrow down the field of solution providers to a short-list they plan to engage. The mission of Solutions Review is to make it easier for buyers of business software to connect with the best providers.

Event Details: Insight Jam LIVE on December 7, 2023

11:00 AM: Executive Roundtable: Data Mesh & Data Fabric Architectures featuring panel moderator Samir Sharma on LinkedIn and YouTube

12:00 PM: Executive Roundtable: How to Use AI to Enhance Data Engineering featuring panel moderator Robert Eve, as well as Prashanth H. Southekal, PhD, MBA, ICD.D on LinkedIn and YouTube

1:00 PM: Executive Roundtable: Data Data Management Automation Automation: Tools & Paradigms (presented by Satori) featuring panel moderator Wayne Eckerson on LinkedIn and YouTube

2:00 PM: Executive Roundtable: Scaling AI to Advance Analytics for Big Data Workloads featuring panel moderator Shawn Rogers, as well as Sanjeev Mohan and Dave Cameron on LinkedIn and YouTube

3:00 PM: Executive Roundtable: Metadata Management Challenges & Solutions featuring panel moderator Dr. Irina Steenbeek, as well as Susan Walsh on LinkedIn and YouTube

4:00 PM: Executive Roundtable: Data Observability & the AI Moment: Tools and Practice (presented by Monte Carlo) featuring panel moderator Robert Eve, as well as Mark Diamond and Dan Power on LinkedIn and YouTube

And that’s not all: Register for Insight Jam (free) to gain early access to all the exclusive 2024 enterprise tech predictions, best practices resources, and DEMO SLAM videos!

The post What to Expect at the 5th Annual Insight Jam LIVE on December 7 appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

Solutions Review’s Expert Roundtable with BARC, Sigma Computing, Monte Carlo, and Databricks is entitled: Revolutionizing AI: Innovative Approaches to Cloud Stack Modernization.

What is an Expert Roundtable?

Solutions Review’s Expert Roundtables are exclusive webinar events for industry professionals across enterprise technology. Since its first virtual event in June 2020, Solutions Review has expanded its multimedia capabilities in response to the overwhelming demand for these kinds of events. Solutions Review’s current menu of online offerings includes the Demo Day, Solution Spotlight, best practices or case study webinars, and panel discussions. And the best part about the “Expert Roundtable” series? They are free to attend!

Why You Should Attend

Solutions Review is one of the largest communities of IT executives, directors, and decision-makers across enterprise technology marketplaces. Every year over 10 million people come to Solutions Review’s collection of sites for the latest news, best practices, and insights into solving some of their most complex problems.

With the next Expert Roundtable event, the team at Solutions Review has partnered with BARC Research, Sigma Computing, Monte Carlo, and Databricks to showcase real-world examples of success and help you understand new strategies for augmenting your existing data and analytic investments to innovate, better leverage AI and realize greater ROI.

This panel explores how the intersection of data observability, analytics insights, and the utilization of data science skills leads to a more competitive edge and drives innovation. This often requires new solutions and new best practices to achieve.

Speakers

Moderator:

• Shawn Rogers – Industry Analyst, Analytics Strategy @ BARC

Featured Speakers:

• Stipo Josipovic – Director of Product Management @ Sigma Computing
• Bryce Heltzel – Product Manager @ Monte Carlo
• Keon Shahab – Senior Solutions Architect @ Databricks

About

BARC has helped thousands of companies worldwide in selecting software to meet their strategic requirements and deliver business benefits. BARC Reports are focused on helping companies find the right software solutions to align with their business goals. Their reports feature insights into market developments and dispense proven best-practice advice for software evaluation.

Sigma Computing offers a no-code business intelligence and analytics solution designed for use with cloud data warehouses. The product features an intuitive, spreadsheet-like user interface that provides users with the familiarity of Excel. Guided data warehouse access ensures that data remains secure, compliant, and in context.

Monte Carlo’s data observability platform utilizes best practices and principles of automatic application observability and applies them to data pipelines. This provides data engineers and analysts with visibility across all data pipelines and data products. Monte Carlo also offers machine learning that gives users a holistic view of an organization’s data health and reliability for important business use cases.

Databricks is one of the most widely used advanced analytics platforms in the world. Databricks offers a unified analytics platform that allows users to prepare and clean data at scale and continuously train and deploy machine learning models for AI applications. The product handles all analytic deployments, ranging from ETL to model training and deployment.

FAQ

• What: Revolutionizing AI Innovation: A Modern Approach to Cloud Stack Modernization
• When: Tuesday, September 14, 2023, at 12:00 PM Eastern Time
• Where: Zoom meeting (see registration page for more detail)

Register for Solutions Review’s Expert Roundtable FREE

The post What to Expect at Solutions Review’s AI Expert Roundtable on September 14 appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

Solutions Review’s Solution Spotlight with Alteryx is entitled: Achieving Meaningful Business Impact with AI, ML & Analytics.

What is a Solutions Spotlight?

Solutions Review’s Solution Spotlights are exclusive webinar events for industry professionals across enterprise technology. Since its first virtual event in June 2020, Solutions Review has expanded its multimedia capabilities in response to the overwhelming demand for these kinds of events. Solutions Review’s current menu of online offerings includes the Demo Day, Solution Spotlight, best practices or case study webinars, and panel discussions. And the best part about the “Spotlight” series? They are free to attend!

Why You Should Attend

Solutions Review is one of the largest communities of IT executives, directors, and decision-makers across enterprise technology marketplaces. Every year over 10 million people come to Solutions Review’s collection of sites for the latest news, best practices, and insights into solving some of their most complex problems.

With the next Solutions Spotlight event, the team at Solutions Review has partnered with leading analytics, data science, and automation vendor Alteryx. Through case studies and practical examples, Alteryx’s Field Chief Data & Analytics Officer, Heather Harris, will help you learn the keys to capturing the business impact or your analytic solutions.

Speakers

• Heather Harris, Field Chief Data & Analytics Officer

About Alteryx

Alteryx powers analytics for all with the award-winning Alteryx Analytics Cloud Platform. With Alteryx, enterprises can make intelligent decisions across their organizations with automated, AI-driven insights. More than 8,300 customers globally rely on Alteryx to democratize analytics across use cases and deliver high-impact business outcomes. Alteryx AiDIN is the industry’s first engine that combines the power of AI, machine learning, and generative AI with the Alteryx Analytics Cloud Platform to accelerate analytics efficiency and productivity.

FAQ

• What: Achieving Meaningful Business Impact with AI, ML & Analytics
• When: Thursday, July 27, 2023, at 12:00 PM Eastern Time
• Where: Zoom meeting (see registration page for more detail)

Register for Solutions Review’s Solution Spotlight with Alteryx FREE

The post What to Expect at Solutions Review’s Spotlight with Alteryx on July 27 appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

The editors at Solutions Review highlight what’s changed since the last iteration of Gartner’s Magic Quadrant for Analytics and Business Intelligence Platforms and provide an analysis of the new report.

Analyst house Gartner, Inc. has released its 2022 Magic Quadrant for Analytics and Business Intelligence Platforms. Gartner defines the marketplace as enabling less technical users, including businesspeople to “model, analyze, explore, share and manage data, and collaborate and share findings, enabled by IT and augmented by artificial intelligence (AI). ABI platforms may optionally include the ability to create, modify or enrich a semantic model including business rules.”

Gartner notes that the market is represented by an emphasis on visual self-service for end-users, as well as augmented AI to deliver automated insights. However, that augmentation is largely shifting from the analyst to consumers and decision makers. These platforms are also beginning to capture more information about user behavior and interests in order to deliver the most impactful experience possible. The researcher expects this will be a trend that continues as analytics and business intelligence tools get integrated into personal productivity tools.

Many of the best business intelligence solutions are adding capabilities for users to create no-code automation workflows and applications. As a result, the markets for data science and machine learning software and cloud data analytics tools are diverging from that of traditional BI platforms. This is good for the consumer, as it pushes many of the providers to improve their analytics features while helping organizations maintain a balance between control and agility while scaling multipersona, advanced analytics tools, and new and emerging use cases.

Gartner highlights the following providers in the analytics query accelerators market: Microsoft (Power BI), Salesforce (Tableau Software), Qlk, Google (Looker), Domo, ThoughtSpot, Sisense, Oracle, TIBCO Software, SAP, IBM, SAS, Yellowfin, Tellius, Amazon Web Services (AWS), MicroStrategy, Alibaba Cloud, Pyramid Analytics, Zoho, and Incorta.

Gartner adjusts its evaluation and inclusion criteria for Magic Quadrants as software markets evolve. As a result, Incorta, Tellius, and Zoho were added to the report. BOARD International, Infor (Birst), and Information Builders were removed for no longer meeting the inclusion criteria.

In this Magic Quadrant, Gartner evaluates the strengths and weaknesses of 20 providers that it considers most significant in the marketplace and provides readers with a graph (the Magic Quadrant) plotting the vendors based on their ability to execute and completeness of vision. The graph is divided into four quadrants: niche players, challengers, visionaries, and leaders. At Solutions Review, we read the report, available here, and pulled out the key takeaways.

For the second-straight year, Microsoft (Power BI), Tableau (now a part of Salesforce), and Qlik dominate the market. While Microsoft touts massive market reach and a comprehensive product roadmap, its new “goals” capability, an alignment with other productivity tools, and good price-to-value ratio keep it atop the list. Salesforce (Tableau) recently dropped new Slack integrations and an improved NLQ experience. That combined with a business user-centric approach and an “in” to the Salesforce ecosystem has its arrow pointing straight up. Qlik added a slew of new capabilities in 2021 (including technology buys) and has plans to go public soon.

Google (Looker) and Domo are the lone market Challengers for 2022. Google continues to integrate Looker with its other products (namely Google Data Studio) in the near term, and new extension frameworks are a fully hosted development surface that enables developers to build data-powered apps. Domo mostly sells directly to lines of business due to its ease of use. The company is also popular in multi-cloud scenarios where an organization chooses non-Microsoft clouds. Domo has lots of momentum in the space, according to Gartner.

ThoughtSpot remains a stone’s throw away from attaining Leaders status but introduced several key feature updates (Everywhere, developer playground, custom action, pre-builds for KPIs). The company has additional plans for a notebook-style SQL workspace and digital workplace tools integration. ThoughtSpot is a major player in the augmented analytics market. Beyond ThoughtSpot, several “mega-vendors” are also Visionaries again for 2022: Oracle, SAP, SAS, and IBM.

Three established and top-rated vendors Sisense, TIBCO, and Yellowfin are Visionaries this year. Sisense is unique due to its developer framework, multipersona tools, and composable vision. Sisense is also excellent for embedded BI. TIBCO offers tools for data science, visual data discovery, and streaming analytics. Its advanced data preparation is a differentiator as well. Yelllowfin is making strategic investments in augmentation tools like guided NLQ. The firm also enhanced its application development experience in a recent refresh.

New entrant Tellius rounds out the Visionaries class, offering a strong supper for multiple personas, augmented analytics, and scalability that make it an interesting choice. The Tellius Platform combines AI and machine learning with a search interface for ad hoc exploration so users can ask questions about their business data, analyze billions of records, and gain automated insights. The company recently launched Live Insights, which offers AI-guided insights from cloud data warehouses without moving data.

Along established brands in the global enterprise cloud market like AWS and Alibaba Cloud, MicroStrategy and Pyramid Analytics headline notable names among Niche Players. MicroStrategy “excels at scalability, manageability and security desired by IT” and offers “rich BI and reporting functionality” according to Gartner. The firm also launched a new containerized, microservices-driven platform architecture and continued its accumulation of popular cryptocurrency Bitcoin in 2021. Pyramid Analytics is a top choice in financial services, insurance, retail, and manufacturing, solving for a breadth of use cases and offering direct query and full-featured deployment options.

Incorta and Zoho debut as Niche Players and round out the representative vendors included in Gartner’s Magic Quadrant. Incorta features a Direct Data Mapping engine which provides real-time aggregation of complex business data without needing a data warehouse. Users can drill from top line, aggregated KPIs to supporting transaction records with one click. Zoho features a drag-and-drop designer, as well as different visualization tools to drill down to specifics. A “smart” assistant called Zia can answer questions in the form of reports and KPI widgets via AI, machine learning, and natural language processing.

Read Gartner’s 2022 Magic Quadrant for Analytics and Business Intelligence Platforms.

The post What’s Changed: 2023 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

Solutions Review editors examine the buy side of the build vs. buy analytics debate and offer an additional resource from Qrvey for evaluation.

Many factors go into the build vs. buy analytics debate, and even more depends on your customers’ needs. How to choose the best embedded analytics tools can be made easier if you have the right guidance and perspective, but it’s not always easier to plug-and-play with analytic software. It’s also one of the reasons why our editors recommend reading Embedded Analytics for Saas Providers, which highlights quite well one side of the conversation.

Embedding analytics into existing workflows helps business users gain access to the capabilities they need without having to go outside of the environments they use daily to do so. Users are often rewarded with faster, more informed, and more efficient decision-making, which can lead to more actionable insight.

Embedded BI: Build vs. Buy Analytics

The process for buying embedded analytics or a standalone tool are very different. Buyers should be aware that embedded BI requires analysis flexibility and ease of analysis for non-technical users. Other major factors include the processing of embedding seamlessly into the host application, lifecycle management, and distribution at scale.

Whether the build vs. buy analytics discussion for your embedded project is coming now or in the future, there are some key embedded BI use cases you need to know: self-service analytics, governed data democratization, and increasing user adoption. Per MIT, companies that are more likely to share data between users are the ones that reap the rewards, as innovation with analytics is a nearly direct result of this democratization.

As someone who works with data, you already know the value of analytics. The more metrics, insights, and analytic types you add to your embedded products, the more engagement they will receive. The decision then comes to whether or not you have the stomach to build your own in-house analytics system or invest in an existing platform that can easily be added into your software.

Embedded analytics are flexible enough to serve users in many industries. And there are more than a few markets specifically where interest in embedded BI tools is high, like in e-commerce, finance, and marketing.

There are a lot of good reasons to avoid building your own analytics, and many are well-known. However, it’s not understood just how high the opportunity costs are, and it’s true that building is more expensive than one might think. Sometimes, the right build vs. buy analytics decision for product managers is the most obvious one, whatever that means in your specific situation.

Read Qrvey’s Embedded Analytics for SaaS Providers: Build vs. Buy

The post Build vs. Buy Analytics: Examining the Buy Side Approach appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

Solutions Review presents its fourth annual BI Insight Jam community web event! Our editors will live blog all the news, tips, and insights in this space.

The BI Insight Jam is a data management and analytics community web event. Solutions Review editors are bringing the best and brightest minds in the industry together for a one-day social media gathering. Participants will include industry analysts, experts, influencers, practitioners, and software solution providers tweeting under the hashtag #BIInsightJam.

Solutions Review is hosting this free virtual gathering as a way to provide our site audience with guidance, best practices, and advice on various topics related to data management and analytics as we enter 2023. The tagline for the event is This year’s event theme is Cloud Data Architectures: Best Practices for Managing, Analyzing, and Governing Data in the Cloud featuring an exclusive best practices guide by industry analyst Philip Russom Ph.D.

Wondering what’s in it for you? Join us for the BI Insight Jam to get advice on data management and analytics software buying, best practices for piloting new and emerging technologies, and find out what the future will bring. It will also be a top-notch networking event featuring many of the foremost thought leaders in the field. Uncover insights personalized to your interests. From analysts to IT and the Public Sector, there will surely be something for everyone.

Our editors will reveal a new featured “insight” every 15 minutes throughout the day. Coverage begins December 15th at 8:30 AM EST, and here’s what to expect.

12/15/2022, 07:26 PM

And click below to learn more about @SolutionsReview's virtual events like #BIInsightJam and #DataDemoDay:https://t.co/4h79uOZAom

— Timothy "Tim" King @ #BIInsightJam (@BigData_Review) December 16, 2022

12/15/2022, 06:54 PM

A Special thank you to our 4th annual #BIInsightJam sponsors for making this event a success!

@CelerData: https://t.co/SgAo9A27A4
@DataStax: https://t.co/MDoqKPo1wm
@denodo: https://t.co/thga0jCVMY
@montecarlodata: https://t.co/pjUt6wWokshttps://t.co/suS3b2Carr

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 06:12 PM

You can also follow our Solutions Review Insight Jam showcase page on LinkedIn where we have links to all the #BIInsightJam sessions, articles, and experts. https://t.co/XYo4Kktx4K

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 05:30 PM

A #BIInsightJam exclusive: 95 (!) #DataManagement & #BigData predictions from 51 experts for 2023:https://t.co/68UwymzaHf

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 04:44 PM

For Dave’s final take, @matillion‘s VP of Product answers the question: What does #BusinessReady #Data actually mean? #BIInsightJam pic.twitter.com/CuQ3mZ96VN

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 04:12 PM

Not just #DataProductivity for the sake of it: @matillion ‘s VP of Product Dave Langton offers some useful advice on doing #Data tasks. #BIInsightJam pic.twitter.com/ENRjoTUJ5k

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 02:56 PM

Our 3 PM EST (and final) #BIInsightJam panel, moderated by infoVia Founder and CEO Michael Magalsky starts in just a few minutes. The topic: #DataIntegration: #DataEngineering & #DataPipeline Automation Best Practices at 3 PM EST:https://t.co/3EQ0M4lQLJ

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 02:36 PM

According to @edge_delta‘s Director Riley Peronto, companies will begin analyzing #Observability #Data with much more frequency in 2023. #BIInsightJam pic.twitter.com/RssIt7C4vh

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 02:26 PM

“It is becoming very important for people to have skills over degrees.” @Yugabyte Founder and CTO @karthikr offers commentary on the importance of #OnlineLearning in 2023 for #BIInsightJam: pic.twitter.com/gbTwc170EK

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 02:23 PM

Founder and CEO of @TDengineDB Jeff Tao offers a 2023 prediction on #OpenSystems for #BIInsightJam: pic.twitter.com/PJQmWMHeh3

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 02:13 PM

#RealTime (#Data) is going mainstream in 2023, according to @RocksetCloud Co-Founder and CTO @dhruba_rocks. #BIInsightJam pic.twitter.com/bP8Vkt58GH

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 01:45 PM

Our 1 PM EST #BIInsightJam panel, moderated by @ClassificationG (and sponsored by @montecarlodata) starts in just a few minutes. The topic: Mastering #DataIntegrity & #DataQuality in the #Cloud Age.https://t.co/MDnDIfBG0Y

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 12:51 PM

While you wait, a recommended read from #DataVirtualization pioneer (and #BIInsightJam panel moderator) @RobertEve on emerging #BigData use cases:https://t.co/I0tp8KXC9A

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 11:57 AM

Our Noon EST #BIInsightJam panel, moderated by @shawnrog starts in just a few minutes. Considerations or Evaluating #Cloud #DataPlatforms at Noon EST:

Sponsored by @DataStax and @CelerData.https://t.co/bWjuW8v0mu

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 11:39 AM

Solutions Review names 3 Vendors to Watch in #DataScience & #MachineLearning 2023:

CC: @rungalileo @integrateAI @TredenceInc https://t.co/vdeq8iCUZe

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 11:35 AM

Solutions Review names 9 #DataManagement (#BigData) Vendors to Watch for 2023:

CC: @anomalo_hq @ChaosSearch @datafoldcom @datometry @dryviq @TheModernDataCo @Onehousehq @Percona @Syniti https://t.co/UbipJYCVK6

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 10:57 AM

Watch the #BIInsightJam panel on YouTube here. Starts in just a few minutes:https://t.co/UU6qh8cZCx

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 10:53 AM

The first #BIInsightJam Executive Roundtable is set to begin – moderated by industry analyst @Philip_Russom. Check out his panel preview below, and gain access to an exclusive #BestPractice guide below: https://t.co/SnrQSg03Qf

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 10:29 AM

More #BIInsightJam insights : Solutions Review announced this morning the release of its new 2023 Buyer’s Guide for #MetadataManagement and #DataCatalog Tools:https://t.co/rcqswnKD1a

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 10:22 AM

Representative vendors: @1010data @Alation @awscloud @ataccama @cloudera @dremio @druvainc @ExperianDQ @googlecloud @HPE @HitachiVantara @IBM @immuta @Informatica. #BIInsightJam

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 10:16 AM

Representative vendors: @Altair_Inc @alteryx @anacondainc @databricks @dataiku @DataRobot @DominoDataLab @googlecloud @h2oai @IBM @knime @MathWorks @microsoft @RapidMiner @SASsoftware @TIBCO. #BIInsightJam

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 09:59 AM

Representative vendors: @1Spatial @adeptia @boomi @celigoinc @CleoNeverStops @denodo @Equalum @fivetran @HitachiVantara @IBM @Informatica @jitterbit @keboola @matillion. #BIInsightJam

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 09:18 AM

Representative vendors: @Altair_Inc @alteryx @awscloud @AnswerRocket @BoardSoftware @Domotalk @HitachiVantara @IBM @incorta @BirstBI @googlecloud @MSPowerBI @MicroStrategy @Oracle. #BIInsightJam

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 08:47 AM

You can also watch the #BIInsightJam Executive Roundtable streams on #LinkedIn starting at 11 AM EST:https://t.co/XYo4Kktx4K

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 08:43 AM

Keep tabs on all the day’s insights and bookmark our #BIInsightJam live blog :https://t.co/YGubtPXfId

— Timothy “Tim” King @ #BIInsightJam (@BigData_Review) December 15, 2022

12/15/2022, 08:23 AM

<blockquote class=”twitter-tweet”><p lang=”en” dir=”ltr”>Welcome to <a href=”https://twitter.com/SolutionsReview?ref_src=twsrc%5Etfw”>@SolutionsReview</a>&#39;s 4th annual <a href=”https://twitter.com/hashtag/BIInsightJam?src=hash&amp;ref_src=twsrc%5Etfw”>#BIInsightJam</a>! Follow along as we drop <a href=”https://twitter.com/hashtag/DataManagement?src=hash&amp;ref_src=twsrc%5Etfw”>#DataManagement</a> &amp; <a href=”https://twitter.com/hashtag/Analytics?src=hash&amp;ref_src=twsrc%5Etfw”>#Analytics</a> resources, best practices, and predictions from <a href=”https://twitter.com/hashtag/BigData?src=hash&amp;ref_src=twsrc%5Etfw”>#BigData</a> experts for 2023 throughout the day. Schedule:<a href=”https://t.co/suS3b2Carr”>https://t.co/suS3b2Carr</a></p>&mdash; Timothy &quot;Tim&quot; King @ #BIInsightJam (@BigData_Review) <a href=”https://twitter.com/BigData_Review/status/1603380257649827842?ref_src=twsrc%5Etfw”>December 15, 2022</a></blockquote> <script async src=”https://platform.twitter.com/widgets.js” charset=”utf-8″></script>

12/15/2022, 08:05 AM

Tim King: Welcome #BIInsightJam!

The post Solutions Review’s Fourth-Annual BI Insight Jam: Event Live Blog appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .