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, Engineering, and Cloud Operations 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!”

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The post 36 Analytics & Data Science Predictions from 22 Experts for 2024 appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

Source: Forrester

Enterprise technology analyst house Forrester Research has recently released the latest version of its Data Preparation Tools Wave Report for Q1 2017. In the 21-criteria evaluation of data prep solutions, Forrester researcher Cinny Little identifies the seven providers whom are most significant in the category – Alteryx, Datawatch, Oracle, Paxata, SAS, Trifacta, and Unifi Software – then researched, analyzed, and scored them. The Wave report details the findings and examines how each vendor meets (or falls short of) Forrester’s evaluation criteria and where vendors stand in relation to each other.

According to Forrester, data prep tools are now must haves, as their proprietary survey data has showed that analytics professionals seek low-friction access to data on-demand. They add: “Customer-obsessed firms align on key customer-centric metrics and take the actions that matter most on the insights they derive from data. Forrester projects that insights-driven businesses — companies that embed analytics and software deeply into their customer-centric operating model — will grow revenue at least eight times faster than global GDP.” That’s an impressive projection.

In order to help Big Data professionals select the right tools, The Forrester Wave Report outlines the current state of the market and separates the top providers into Leaders, Strong performers and Contenders. At Solutions Review, we’ve read the report, available for download here, and pulled a few of  the most important takeaways:

Business users require ease of use and scalable execution architectures

Data preparation is a pre-processing step that allows for the transformation of data before analysis to ensure quality and consistency, providing enterprises with maximum potential for Business Intelligence. Given the growing volumes and velocity of Big Data, integration acts as a significant barrier to the overall data preparation scheme. From a tactical perspective, generating data quality too remains a challenge.

Traditional Data Management techniques get in the way of analytical agility. As a result, business users are choosing tools that provide not only speed, but transparency and oversight that will provide scalability. Machine learning and cross-enterprise collaboration are also key features on many organizational wish lists.

Trifacta and Paxata are on a planet of their own

Forrester argues that these two providers offer the most comprehensive and scalable platforms of any of the providers covered in this report, citing self-service and speed as major advantages. These solution providers are staples in the Big Data software market, and have staked their respective claims on the throne of the rapidly growing data prep sector.

Trifacta leverages machine learning algorithms to automate data interactions that allow self-service data wrangling for analysts and business users alike. Not only is their platform top-of-class, but they offer an expansive list of customer programs and resources including a curriculum, certification program, and more. Paxata’s platform is based on a set of technologies that unite Data Integration, quality, governance, collaboration and enrichment. They combine a well-received user interface with machine learning, text and semantic analytics for quick speedy data connection. Customers enjoy Paxata’s usability and time-to-value.

Datawatch and Unifi each lead their respective niche

Datawatch has made semi-structured and unstructured data sources the priority, and as Forrester points out, they’ve been in this business long before the buzzword ‘Big Data’ was ever mainstream. Customers gave Monarch, their flagship offering, high scores for ease of use and automation capabilities. Unifi Software combines self-service data discovery and prep into a unified platform, and does it all in a patented six-step process that includes: connect, discover, cleanse/enrich, transform, and format. Machine learning capabilities enable the tool to learn from organizational actions and make recommendations to the user at each step. According to the report, Unifi’s natural language search is the strongest among all the vendors in this market.

Read Forrester’s Wave for Data Preparation Tools, Q1 2017.

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Analyst house Gartner has officially released the 2017 version of their Magic Quadrant for Business Intelligence and Analytics Platforms. IT-led reporting platforms are a thing of the past, with modern tools now accounting for the vast majority of available software solutions in the marketplace. As a result, stakeholders have a wealth of tools from which to choose, with traditional vendors who have upgraded their platforms on one side, and the emerging disruptors on the other. Gartner explains: “The crowded BI and analytics market includes everything from longtime, large technology players to startups backed by enormous amounts of venture capital.”

Gartner has been warning of the coming evolution in BI and analytics for some time, and in 2016 redesigned the popular report to more adequately represent the changeover. The Magic Quadrant provides ample evidence to suggest that agile BI is now the industry standard. This software sector is mature and saturated with intriguing options for buyers looking to expand their use of self-service BI. Providers included in this year’s report are assessed based on their support of five main critical capabilities (use cases):

• Agile centralized BI provisioning
• Decentralized analytics
• Governed data discovery
• OEM or embedded BI
• Extranet deployment

Oracle makes a triumphant return to the report after being left out in 2016. In addition, ThoughtSpot, Datameer, and Zoomdata also make an appearance. Platfora was acquired by Workday and is no longer sold as a standalone solution, and as such, has been axed. BeyondCore was acquired by Salesforce and is also no longer included. In addition, Datawatch and GoodData were excluded for no longer meeting Gartner’s inclusion criteria.

The 2017 Magic Quadrant features a lot of vendor movement, with many of the most prominent solution providers having their standing downgraded as they attempt to keep their head above water. With the exception of Tableau and Microsoft, who saw their positioning improve drastically among the market leaders, the household names have their work cut out for them if they wish to see improved standing in next year’s coverage. Alteryx, Logi Analytics, MicroStrategy and Pentaho all suffered major downgrades in standing.

Notable gainers include the likes of Sisense and Salesforce, whom now occupy spots among the market visionaries after spending 2016 as niche players, and TIBCO Software, a provider that has an upgraded position in their own sphere of influence. The placement of solution providers in this report speaks to the fact that traditional BI vendors have had a tough time adapting to the new landscape, with Gartner saying: “What is new this year, is that traditional BI vendors that were slow to adjust to the “modern wave of disruption struggled to remain relevant during the market transition.

If your organization is one of the many looking to adopt a modern data analytics tool, read how about how team organization can play a vital role in deployment.

Learn how to organize your team for modern analytics deployment.

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The Solutions Review team is proud to announce that the all new 2017 Business Intelligence Buyer’s Matrix Report is now available as a complimentary download to site visitors. Those who are looking for a new Business Intelligence solution to pair with their data-driven organization have the top-28 vendors in the space to compare and contrast, making the search for a new software offering that much easier.

This year’s product includes four new vendors. Top providers highlighted include: Alteryx, Birst, BOARD, Datameer, Domo, Dundas BI, Exago, GoodData, IBM, Information Builders, Izenda, Logi Analytics, Looker, Microsoft, MicroStrategy, Oracle, Pentaho (Hitachi), Phocas, Prognoz, Pyramid Analytics, Qlik (Thoma Bravo), SAP, SAS, Sisense, Tableau, TARGIT, TIBCO, and Yellowfin.

The Business Intelligence landscape is evolving in real-time, making the challenge of finding and deploying the right solution a difficult one. At Solutions Review, we put ourselves right in the middle of it all; making it a point to highlight the tools that have the greatest impact on today’s buyers and end-users while also looking ahead to tomorrow. Part of doing this is to continually update this resource in order to make the buying process as easy and as stress-free as possible for those in enterprise IT. The Buyer’s Matrix is organized by specific categories, which include: areas of focus, industries, features, services, and support.

By offering a comprehensive matrix-style report, the updated Solutions Review Buyers Matrix presents a wide range of features in multiple tables for a clear and side-by-side view of the best benefits each vendor has to offer. Readers will be given direct comparisons of the solutions providers and their offerings in an easy to understand report.

Coupled with the newest update of our Buyer’s Matrix, Solutions Review also offers a free Business Intelligence Buyers Guide, further enhancing the ability of the IT professional to make the right product decisions. In using these two tools in conjunction, solutions-seekers will be armed with all of the materials they need to ensure selection of the best software for their company.

Download the complete report.

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Gartner has officially released the 2016 version of their Magic Quadrant for Business Intelligence and Analytics Platforms, and boy has the market changed. According to Gartner, the enterprise analytics industry has surpassed the tipping point, and has nearly finished its evolution from a market based heavily on reporting to one that is more business-centric and user friendly, offering self-service analytics and allowing more users to get their hands on tools that drive insights. The majority of solution buying in BI and analytics now comes in the form of modern platforms which focus on user engagement, and this fact has essentially reorganization the vendor landscape.

As a result of this major shift in focus, Gartner has identified different perspectives with which to assess vendor performance. Centralized provisioning and tightly governed platforms are becoming a thing of the past, and are being counterbalanced and replaced by tools that promote analytical agility and business user autonomy. Thus, solution providers have had to shift gears. In response, Gartner has pivoted, focusing the 2016 Magic Quadrant on platforms with modern capabilities, something that the technology research giant has been threatening for several years.

Tableau, Microsoft and Qlik are the three lone-remaining leaders in this year’s Magic Quadrant. The three providers are grouped rather tightly, with Tableau leading the pack in ability to execute for a second-straight year on the heels of their newest product update. With the Challengers bracket now an empty black hole, the heavily populated Visionaries quadrant deserves extra attention. Alteryx is now on the verge of becoming a market leader, improving in both Magic Quadrant metrics just a short time after receiving $85M in new funding.

Another noteworthy development is the downward movement of tech giants IBM, SAP and SAS, who now all find themselves amongst the rest of the pack. Logi Analytics’ positioning took a considerable dive, now calling the middle of the Visionaries bracket home after a year as a Challenger. Logi’s regression comes as a result of eroding scores in the customer reference survey. Pentaho saw a slight dip in their ability to execute, though the company was able to cross the border from the Niche Players bracket with a bump in completeness of vision. ClearStory Data, a cloud and Spark-based tool offering self-service data preparation and leveraging machine learning, and BeyondCore, a vendor that touts a data discovery tool and high scores in market understanding, round out the Visionaries field, making their first appearances in the Gartner Magic Quadrant.

The Niche Players bracket includes four newcomers, Domo, Salesforce, Sisense and Platfora. It may come as a surprise to some that Birst is now a Niche Player after being well within striking distance of the Leaders column last year, but their positional downgrade comes at a time where Gartner is concerned about the complexity of their offerings and pedestrian marketing strategy. GoodData showed minor pullback in both Magic Quadrant metrics, though remains strong in the bracket behind only Birst and newcomer Domo. Board International showed minor horizontal improvement, finding itself clustered with Information Builders and Sisense in the middle of the bracket, just ahead of Yellowfin, who offers an easy-to-use platform, though some users complain of poor product quality and support functionality. Information Builders also saw a dip in standing and was a market leader just one year ago. On the heels of $30 million in new funding, Pyramid Analytics looks like a provider on the rise, scoring well on capabilities that support a governed self-service analytics environment and ease of use for end-users.

Read Gartner’s Magic Quadrant.

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Gartner recently released their Critical Capabilities for Business Intelligence and Analytics Platforms report, a companion resource to the Magic Quadrant study. Used in conjunction with the related Magic Quadrant, Critical Capabilities is an additional resource which can assist buyers of enterprise BI in finding the tools and solutions that will work best for their organizations. In the report, Gartner recognizes the top-24 vendors in the space and rates them based on their ability to deliver against important use cases.

According to Gartner, the enterprise analytics industry has surpassed the tipping point, and has nearly finished its evolution from a market based heavily on reporting to one that is more business-centric and user friendly, offering self-service analytics and allowing more users to get their hands on tools that drive insights. The majority of solution buying in BI and analytics now comes in the form of modern platforms which focus on user engagement, and this fact has essentially reorganization the vendor landscape.

With that said, Gartner does make it a point to clearly relay that organizations need to select a vendor based on the specific use cases that will impact their business. The companion Magic Quadrant places vendors in four brackets based off of their overall completeness of vision and ability to execute, but some of the solution providers who scored lower in those metrics may be able to handle a specific use case better than the top-scoring vendors in the report. However, while early entrants into the data discovery and self-service market are likely to be more forward-thinking than the BI mega-vendors, they could be weaker in areas needed for enterprise deployments such as broad delivery of reports and dashboards. For those reasons, it is best to evaluate vendor offerings on per-case basis instead of simply relying on solution provider standing within the Magic Quadrant grid.

In this report, Gartner defines 15 critical capabilities which support five important BI use cases, including: centralized BI provisioning, decentralized analytics, governed data discovery, OEM/embedded BI, and extranet deployment. These use cases support organizations in the building of their analytics portfolios, helping to transform IT from the chief “doer” to the “enabler”, thus making the business the primary “doer.” These critical capabilities enable BI leaders to support a wide range of data sources, features and business use cases. Each vendor receives a product score ranging from 1 to 5 based on the aforementioned use cases. In addition, Gartner also includes product and service ratings on the critical capabilities, one of which is compiled as a result of polling only end-users.

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Birst was the top scorer in governed data discovery and extranet deployment, also finishing second in the other three use cases, an impressive feat. Birst’s newest release, Networked BI, enables organizations to balance governance and agility inside a single platform, which according to Gartner should increase its overall utilization in governed data discovery use cases moving forward as Birst customers adopt it. ClearStory Data, a business-oriented cloud and Spark-based modern BI tool finished inside the top-five for each use case as well, the only other vendor to accomplish such a feat. ClearStory offers smart self-service data preparation tools that facilitate insights via machine learning to automatically infer semantics of dimensions and attributes of data. This is ClearStory’s first year featured in Gartner’s market reports, so they will be a vendor to track closely in the year ahead.

Well-known enterprise BI and analytics vendor MicroStrategy finished with one of the five-highest scores in three of the use cases. MicroStrategy has a governed data discovery capabilities and offers an enterprise-class tool that includes security, scheduling and distribution. They recently released version 10.3 of their flagship tool, adding even more enhancements. SAS also had three top-five finishes and scored the highest of all the vendors for its embedded Advanced Analytics capabilities.

Two other vendors of note who finished with at least two top-five scores in Gartner’s use cases include Logi Analytics and GoodData. Logi Analytics’s platform is comprised of three components, Logi Info, Logi Vision and DataHub. Logi’s largest use case is for OEM/embedded BI at 62 percent, the highest of any vendor included in the report, and of the surveyed reference customers, 38 percent use Logi’s tools in centralized BI provisioning. GoodData received an excellent score for its cloud BI functionality and given that it is used extensively in OEM and embedded BI scenarios, it’s no wonder that it had high marks in Gartner’s report.

Gartner holds the position that companies should initiate new Business Intelligence projects using a modern platform in order to take advantage of vendor innovation in order to bread collaboration between IT and business users, adding: “As the ability to promote user-generated content to enterprise-ready governed content improves, so it is likely that, over time, many organizations will eventually reduce the size of their enterprise system-of-record reporting platforms in favor of those that offer greater agility and deeper analytical insight.” However, business-specific use cases should be the driving force behind vendor selection, not provider scores or standing within any of Gartner’s market studies.

Read Gartner’s Critical Capabilities.

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Gartner has officially released the 2016 version of their Magic Quadrant for Advanced Analytics Platforms, a market that the technology and research giant expects to grow substantially in the coming years. By 2018, Gartner believes that more than half of large organizations around the globe will compete using Advanced Analytics and proprietary algorithms, causing disruption on a grand scale. In addition, Gartner believes that by 2020, this market will attract upwards of 40 percent of organizational net new investment in Business Intelligence and analytics.

According to Gartner, the Advanced Analytics industry was the fastest-growing of the analytics segment in 2014, showing 12.4 percent growth. Advanced Analytics tools have been around quite some time, and while the space has traditionally been considered mature and stable, increased excitement has been driven by a desire to use Big Data, Predictive Analytics and machine learning to address issues that used to be difficult to solve. Non-classical use cases are driving much of this interest, things like demand prediction, gaining insights surrounding service and product quality, fraud detection, the streaming of analytics via data in motion, and other types of predictive maintenance. Classical applications of heavily predictive tools is also increasing.

New vendors in this year’s report include Accenture, Lavastorm, and Megaputer. Solution providers that no longer meet the criteria for inclusion and have been replaced from the Magic Quadrant include Revolution Analytics, who was purchased in 2015 by Microsoft, Salford Systems, and TIBCO because they did not satisfy the visual composition framework inclusion criteria. Similar to Gartner’s 2015 edition, the vendors in this year’s report are spread evenly throughout the visual. Starting with the Leaders column, we see household names SAS and IBM once again leading the pack. IBM has certainly closed the gap SAS held a year ago, though each vendor did see a notable decline in their standing based on the vertical ability to execute metric.

KNIME and RapidMiner are hot on the trail of technology giants SAS and IBM, and for the second year in a row find themselves grouped close to one another inside the Leaders column. KNIME, a Swedish company, offers a free, open-source, desktop-based Advanced Analytics platform, and customers of their tool rave about the platform’s flexibility, openness and ease of integration with other existing solutions. RapidMiner, showing slight improvement in both metrics, offers a solution that is easy to use, receiving high scores for innovative features such as its “Wisdom of Crowds” and collaboration functionality. Rounding out the Leaders quadrant, Dell makes an appearance after having been comfortably entrenched as a Challenger in 2015.

SAP and Angoss are the lone members of the Challengers quadrant, with SAP holding a similar position as they did last year. Angoss showed impressive vertical movement having been a member of the Niche Players bracket this time last year, and the company holds better-than-average reference scores. With the exception of Predixion Software, who is new to the Visionaries bracket, the quadrant is made up of the same cast of characters that inhabited it in 2015. Alteryx had a foot in the Leaders door in last year’s report, but has fallen back a bit in 2016 and has been leapfrogged by Microsoft. Microsoft received the highest completeness of vision score amongst any of the vendors in this report behind their Cortana Analytics tool, which Gartner describes as “the best example of an analytics cloud marketplace.”

Alpine Data, formerly Alpine Data Labs, remains in a similar position amongst the Visionaries. Alpine has high reference scores for innovation and has been an early adopter of the Spark and Hadoop stacks. Predixion is headquartered in Orange County, California, and has benefited from the rise in IoT use cases in the last year. The three new vendors can all be found in the Niche Players quadrant of this year’s report, with returning providers FICO and Prognoz both tumbling in their standing. Lavastorm recorded high reference scores for Data Integration and manipulation due to customer satisfaction with the provider’s data pipelining capabilities. Megaputer, a vendor based in Indiana has a clear strength in its ability to analyze text in 14 languages that is used for both reporting and machine learning.

Read Gartner’s Magic Quadrant.

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Business Intelligence is the practice of using data to predict future outcomes. Though many companies with a goal of becoming data-driven have transitioned to using these kinds of tools in recent years, many organizations remain stuck in the stone age, still without a viable way to gain valuable insights from the growing volumes of data they generate and collect. In a recent column for Business.com, Larry Alton outlined how moving from legacy tools like Excel to leveraging Business Intelligence solutions would provide many companies with success in 2016. In this article, I’ll summarize the top six reasons why Alton believes you need a Business Intelligence tool.

1. Gain a visual overview of company health

Business Intelligence tools provide stakeholders with a comprehensive visual of what’s going on within their organization via the analysis of collected data. Individual reports and files once had their place long ago, but standard office software like Excel simply cannot handle the high-frequency volumes of data modern digital businesses amass. In the past, managers would spend too much time drawing conclusions from these types of legacy tools, whereas in today’s enterprise, Business Intelligence solutions automate those processes. Business Intelligence tools provide a consolidated and widespread view of a company’s health and wellness, all in one place, which is contrary to the way legacy solutions portray business vital data.

2. Leverage real-time insights for analytics

Most of today’s best Business Intelligence tools allow anyone within an organization to access real-time data, analytics, and insights. This makes the decision-making process within a company much more democratic, which can lead to deeper realizations. Managers can still choose who can access what information, but empowering employees to dig even deeper into complex volumes of data allows everyone to get more involved and provide their own opinions.

3. Save money

According to a study by Nucleus Research, organizations earn $13.01 for every dollar they invest on Business Intelligence and analytical solutions, up from $10.66 per dollar in 2011. There are certainly upfront costs associated with getting started, but there’s no question that the potential ROI is a game changer.

4. Save time

By improving operational efficiency and eliminating tasks that don’t move the needle, businesses can greatly decrease the time they spend searching for data to plug into analytics tools. According to a report from the Aberdeen Group, 93 percent of required information is available in real-time and on-demand in “best-in-class” companies with Business Intelligence solutions in place. Those same companies achieve a 95 percent on-time customer response rate.

5. Minimize redundancy

Business Intelligence tools automate analytical tasks that have traditionally been performed on a manual basis, freeing up employees to work on other mission-critical responsibilities that directly impact an organization’s bottom line.

6. Level the playing field

Companies that currently lack Business Intelligence tools are playing from behind, however, implementation of analytics tools can help them level the playing field. Business Intelligence tools are helping to transform businesses from reactive entities to proactive ones, allowing them to extract value from numbers and trends quickly, as opposed to having to sift through countless sources inside legacy solutions.

This post was inspired by a recent article on Business.com.

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