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dotData Insight: Melding the Power of AI-Driven Insight Discovery & Generative AI

By Walter Paliska

Introduction Today, we announced the launch of dotData Insight, a new platform that leverages an AI-driven business signal discovery engine augmented with GenerativeAI - deriving business hypotheses beyond uncovered signals. dotData Insight directly explores millions of possible data signals from convoluted enterprise data, frees data analysts, business intelligence professionals, and power users from weeks or months of repetitive trial-and-error effort, and delivers valuable and unseen insights.  https://vimeo.com/891270844/da759762de?share=copy Move From a Single-Threaded Analytics Process to  Multi-Threaded Signal Discovery  Business Intelligence (BI) systems have been around for decades, yet according to VentureBeat, 90% of executives still struggle to use data to make decisions. The problem is inherent in how BI systems were developed - and in how they were intended to be used. BI systems are ideal for providing business users with information on “what” happened to the business. Whether in scorecard systems, dashboards, or static reports, they provide a static snapshot…

6 Predictive Analytics Use Cases In Advertising

By Walter Paliska

6 key ways advertisers can benefit from predictive analytics and how to get started Predictive Analytics, in its simplest definition, is just the process of using historical data to detect patterns and build predictions for future outcomes based on similar patterns repeating in the future. In advertising, this might include examples like using historical performance data from digital advertising campaigns on Google to build predictions of how similar campaigns might perform in the future. Unlike other forms of analytics - like dashboarding and Business Intelligence (BI) - also known as “descriptive analytics,” predictive analytics relies on mathematical algorithms to predict future outcomes. It’s a powerful tool for optimizing advertising spend. Why Predictive Analytics in Advertising? Predictive analytics, driven by Machine Learning, can be a powerful tool for data-heavy industries. The world of advertising, especially digital advertising, relies heavily on data but has recently been subjected to seismic shifts due to…

The Evolution of Analytics, BI, and the next big thing in AI

By Walter Paliska

Why automating feature discovery and engineering will be a game-changer for enterprise AI Updated August 3, 2022 Data Everywhere Large enterprises have long known that data is at the heart of rapid decision-making and better long-term organizational health. Most organizations’ challenge is not deciding if leveraging data for decision-making is useful but how to do it. Anyone who’s been around the BI industry long enough knows that for years the big goal was “self-service BI.” The idea was that business users would somehow become dashboard mavens who could easily bypass and displace business analysts and developers to flood the enterprise with dashboards. Platforms have increased in complexity, and the reality is that analytics is still primarily performed by skilled analysts with in-depth knowledge of data management and visualization techniques.  Fast forward to 2021, and the same conversation can be had about AI and Machine Learning - and how they will…

Five Critical Predictive Analytics Mistakes (and How to Avoid Them)

By Walter Paliska

The business world is increasingly in love with all things AI. Included in this is the increasing demand for predictive analytics among enterprise companies. In fact, according to research firm Markets & Markets, demand for predictive analytics is expected to grow to an impressive US$28B by the year 2026. Forecasts are often educated guesses, but if demand for data scientists (the specialists needed for most predictive analytics projects) is any indication, the estimates might just be on target. In fact, in 2021, the demand for data scientists, as measured by job openings, grew by over 250% over 2020. Yet, with all the need for machine learning and predictive analytics, the reality is that over 87% of machine learning projects still fail. The past five years have seen a flurry of activity in the world of machine learning and predictive analytics with new tools that promise to make predictive analytics simple…

How Inventory Management With AI Can Boost Your Business

By Walter Paliska

Inventory forecasting is a critical business requirement for any enterprise that relies on the movement of supplies to and from warehouses. Whether it’s a manufacturing operation, logistics vendor, large distributor, or a large retailer, managing optimal inventory is a critical function of efficient operations management by creating the right balance between profit and cost. While there is a multitude of advantages in properly managing and moving inventory, there are three core benefits that most businesses highlight and that inventory management with AI can boost:

Is No-Code AI Really Worth The Effort?

By Walter Paliska

No-code, low-code & automation The idea of “no-code” software has become increasingly popular in a variety of fields. The world of AI and Machine Learning (ML) development is no different. Platforms that attempt to make the process of developing AI and ML models more intuitive, less “code-heavy,” and more ubiquitous are gaining in popularity. The challenge of developing AI and ML models is one that screams for no-code or low-code solutions. AI failure rates are notorious – whether it’s VentureBeat reporting 87% failure rates for data science projects in 2019 or Gartner reporting in 2021 that only 53% of AI projects make it into production – even in AI-experienced organizations. While there are many challenges to successfully moving from “experiments” to “ROI” in the world of AI and ML, one of the biggest obstacles is the sheer complexity of the development process. In the world of AI and ML development, “No-Code” and “Low-Code” solutions…

Explainable AI: Moving Beyond Intuition

By Walter Paliska

Why companies struggle with AI adoption, and how to change. The Challenge of Explainability The rapid growth and adoption of Artificial Intelligence (AI) and Machine Learning (ML) within the enterprise is well known. In fact, according to a 2020 report by O’Reilly Group, 85% of surveyed companies are evaluating or using AI in production. However, the harsh truth is that adopting or evaluating AI vs. benefiting from it can be far different challenges. A 2018 KPMG report found that only 35% of surveyed executives had a high level of trust in how their companies used data, analytics, or AI. A late 2020 report by Wakefield Research went even further and found that nearly 75% of CEOs still make decisions primarily based on gut instinct. While there are numerous aspects to why organizations and executives struggle with using data for decision-making, in the world of AI, a significant hurdle comes down…

Simplify Your ML Workflow With AutoML 2.0

By Walter Paliska

AutoML platforms offer “no-code” AI development, but the devil is in the details. For most Business Intelligence professionals, the world of AI and Machine Learning(ML) seems a bit out of reach. The challenge is not so much in whether the technology is useful or not, but rather in the effort required to add AI and ML technology to their BI stacks. The requirement from business users is undoubtedly there. Whether it’s to predict customer churn, model marketing campaign performance, forecast sales, identify clients at high risk of defaulting on receivables, or countless other applications, adding AI/ML to your BI stack can provide immense value. The average BI professional’s problem is that although they are highly skilled at manipulating data and creating sophisticated visualizations, applying the additional data optimization and statistical mathematics necessary to build effective AI/ML models is not within their skill-set. Enter AutoML Workflows The promise of AutoML is…

AI Automation for BI Professionals

By Walter Paliska

What if you could tell that an essential robotic process in your manufacturing line was about to break down? What if your finance department could provide you with a list of customers most likely to default on their payments? What if your marketing department could rank the planned campaigns in their budget based on the likelihood of success? The answer to these, and countless other questions, are at the heart of predictive analytics. As the world of Business Intelligence (BI) continues to evolve, describing "what happened" through dashboards and reports is no longer sufficient. To provide genuine value, modern BI professionals must frequently deliver dashboards and reports that can help line-of-business users make better, smarter decisions faster. Of course, the challenge is that most BI systems do not have predictive analytics capabilities "out of the box," and purpose-built predictive systems are often too limiting or designed for use-cases that are…

How Automation Solves the Biggest Pain Points in Data Science

By Walter Paliska

While most of the attention in the world of AI and Machine Learning is on the algorithms themselves, most data scientists often worry not about the outcome, but instead on the steps involved in arriving at that outcome. The reason for this is simple: building AI and ML models is tedious, complicated, requires a multitude of subject matter experts, and is a highly manual process. In our blogs, we have often highlighted the multiple steps necessary to build useful AI and ML models through data science. Today's article focuses on what data science teams can do to accelerate the building of models, while still achieving the goal of building valuable AI/ML models. As a refresher, below is an illustration of the complexity and multi-step nature of the data science process. To understand the benefits of automation in data science, we first have to know where the most manual work is…

Disrupting Businesses with AI Automation

By Walter Paliska

AI is upending the business world and disrupting industries. The question is how can organizations leverage the transformative power of AI? How have the economic upheavals created in the past few months impacted AI development efforts? How does a business continue to invest in AI without making massive investments in additional talent? At dotData, our vision is to bring disruptive scale and speed in AI development through automation. We deliver production-grade models in a matter of days compared to the traditional AI development process that takes months. Intelligent business decisions and powerful insights are within your reach. Join us @VentureBeat #Transform July 15th to the 18th, 2020 to learn how AI automation enables organizations to execute more AI projects with fewer resources,  adding unprecedented value and causing disruption! Related Articles

Take Advanced Analytics into Overdrive with AutoML 2.0

By Walter Paliska

The term “Advanced Analytics” was coined by the Gartner Group and is defined as the “...autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations.” Advanced analytics, by definition, requires the use of advanced techniques like data mining, machine learning, pattern matching, and other sophisticated manipulation of data in an effort to gain greater insights. The most broadly used category of advanced analytics is also known as predictive analytics. Predictive analytics itself is not new, but has traditionally been the exclusive domain of data scientists and highly skilled statisticians due to the extremely complex mathematical models required to effectively build predictive dashboards. While many organizations can benefit from predictive analytics, only a few are able to create and deploy dashboards powered by predictive algorithms, due to the high cost of…