• Carl Bowen
  • Blog
  • September 1, 2020

Predictive Analytics 101: What is it, Why now, and How to Get Started?

TL: DR: Predictive Analytics is using historical and real-time data to generate useful insights and predicting critical outcomes in the future. A large number of organizations are leveraging this AI-powered technique to reduce risks, improve operations, cut business costs, and increase the bottom line.
Gartner defines Predictive Analytics (PA) as a form of advanced analytics which examines data or content to answer the question “What is going to happen?” or more precisely, “What is likely to happen?”, and is characterized by techniques such as regression analysis, multivariate statistics, pattern matching, predictive modeling, and forecasting. Grandview research recently estimated that the global market for PA is growing at a CAGR of 23.2% and projected to grow to $23.9 Billion by 2025. Initially, the purview of a few visionary companies, PA is rapidly gathering momentum in the market.  Several industries such as banking, financial services, insurance, and manufacturing are using PA to forecast demand, prevent customer churn, and perform anomaly detection for predictive maintenance. 
According to this Forbes article on the state of AI and ML, advanced algorithms and predictive analytics are among the highest-priority projects for enterprises adopting AI and machine learning in 2019. As this illustration from Dresner Advisory Services shows, PA is a top technology initiative for business intelligence (BI).  Since reporting, dashboards, data integration, and visualization are mature BI capabilities, augmenting BI with AI for predictive modeling is becoming critically important.
Technologies and Initiatives strategic to business intelligence
The focus of BI has been analyzing historical performance – what happened in the past and to an extent what can be done to prevent a recurrence using traditional analytics. Predictive analytics, on the other hand, offers the ability to look into the future and enable organizations to take appropriate actions. It can enable organizations to solve very complex business problems, manage risks, and identify new opportunities. PA is ideal for solving complex multivariate problems such as IoT time-series data or situations where traditional approaches such as classical statistical techniques cannot be applied. For example, timely predictions can identify financial fraud, predict buyer behavior, enable dynamic pricing, or detect a faulty component from going into a final product. By providing real-time dashboards, predictive analytics can improve the performance of BI platforms. Due to advances in cloud computing infrastructure and technological breakthroughs in AutoML platforms (that hide all the technical complexities with AI-Powered Automation), PA is very much affordable for even small and medium businesses without huge analytics or IT teams.
Organizations must leverage predictive analytics, an advanced form of analytics, and data science automation to gain greater agility and faster, more accurate decision-making. The best way to get started with predictive analytics is to use automated machine learning (AutoML) tools. A key hurdle in analytics and AI/ML has been finding talent – data scientists, data architects, machine learning engineers skilled in building, testing, and deploying ML models. That’s where automation can play a big role. AutoML platforms allow non-data scientists to automatically build, validate, and deploy predictive models at the touch of a button. The new breed of AI automation platform empowers BI professionals to leverage their data skills and bring predictive analytics to BI stack.
However, those organizations that don’t have any analytics capability or in-house data science expertise will require more than just an AutoML platform. The software platform needs to be bundled with training, use-case co-development, as well as a fully-managed SaaS environment as a predictive analytics program. A dedicated program will be ideal for companies with a strong BI practice and that wish to add predictive analytics capabilities without adding or growing expensive resources and data science teams. The BI and analytics leaders at these companies may have prioritized predictive analytics use cases but either don’t have a budget for hiring more data scientists or simply don’t have in-house data science skills. CIOs, BI, and analytics leaders should look for the following features to accelerate the predictive analytics journey:

  • AI-Focused data preparation that includes data value cleansing, statistical data profiling, and data re-architecting
  • AI-Powered Feature Engineering with feature hypothesis, query generation, and feature relevance validation
  • Model Production with prediction and retraining endpoint generation, model package, and containerized model generation.

The goal of a predictive analytics program should be to leverage existing resources, in-house BI teams, data engineers, or business analysts, and allow them to add more value with AI + BI quickly. Predictive analytics is bringing exciting new opportunities to digital-ready enterprises. A fast start program that is designed to provide everything organizations need to get started with AI is the best approach to incorporate predictive analytics in business applications. 

Read More
  • Carl Bowen
  • Blog
  • August 20, 2020

How Automation Solves the Biggest Pain Points in Data Science

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 involved, and how automation could benefit.

Traditional Data Science Process

 

Getting the Data: Your First Roadblock

At the heart of all AI/ML models is data – lots of data. In most cases, the data necessary to build AI/ML models live in disparate systems, often made by different vendors, each with its own set of data tables, structures, and fields of data. Before you even get to your AI/Ml model, you most likely started by building a data warehouse to collect, consolidate, and normalize all those systems into one unified repository – problem solved? Not quite. The problem with most data warehouse deployments is that IT departments do not build them with a single purpose in mind. It’s rare to build a data warehouse just to build AI/ML models; instead, it typically supports a host of initiatives from ad-hoc reporting to business intelligence to AI/ML development. That’s where the problem for AI/ML begins. Because data warehouses need to support a broad range of use-cases, much of the data prep and data cleansing needed for AI/ML use-cases is missing. Connecting to your data warehouse and prepping and cleaning your data with AI/ML development in mind is the first step in the manual data science process. Automation can provide significant time savings by identifying and correcting gaps in data and problems that may skew models.
 

Feature Engineering: The 800 Lbs. Gorilla

Although data prep and cleansing are significant hurdles in the AI/ML development lifecycle, they pale in comparison to the effort needed to build feature tables. For data science, the challenge is that even in a data warehouse, information is available in relational tables that combine data in modular ways to allow for a multitude of use-cases and reporting scenarios. The data science workflow, on the other hand, requires a purpose-built flat table with all relevant columns of data needed to build the AI/ML models. Building this flat table is the most critical – and most time-consuming – step in the data science process. Beyond the technical complexities of building SQL code that efficiently combines multiple columns from multiple tables, there is also the challenge of the usefulness of the columns retrieved. For example, when developing a customer churn model, having a column of data that quantifies how much revenue a customer is worth would be beneficial, but having a column that identifies a client’s main phone number, on the other hand, might not be necessary. This type of hypothesis, test, validation, and repeat process is a critical part of building a useful feature table and requires much time and the involvement of subject matter experts that are familiar with the data and can provide guidance as to which columns might give the most benefit.
Automating the feature engineering process is the ultimate goal of any data science automation platform. Early attempts at this were limited to using already built “features” to derive new ones. For example, a feature table with total revenue and number of orders might help create “average order value” through a simple mathematical equation. This type of feature creation is valuable but ultimately does not save any time in developing the original feature table necessary. However, recent advances in AutoML platforms have allowed the creation of new platforms that can leverage AI to build feature tables “on the fly.” This new type of “AutoML 2.0” products connect directly to your data warehouse and leverage AI to dynamically develop and evaluate millions of possible feature table combinations. Even with this approach, however, it’s essential also to have a second, critical step: feature evaluation. That’s because using “brute force” to build every possible feature is not valuable, since most features will be of little or no use. Again, an AI-based evaluation and scoring system can help the system determine which AI-discovered features are most likely beneficial to our model.

AutoML: Optimizing the Machine Learning Process

When discussing data science automation, most people immediately think of Automated Machine Learning – or AutoML. AutoML, however, does not address the entire spectrum of challenges in the data science process. Where traditional AutoMl does provide benefit is in the selection and optimization of Machine Learning Algorithms. With a feature table built, AutoML tools can help  select ML algorithms, train and validate models and provide visual displays to compare results in a fraction of the time needed to perform the same task by hand. But the focus should not be limited to  algorithm tuning  and optimization,  the traditional area of automation for data science. . Instead the goal should be to leverage automation to streamline the end-to-end data science process from data collection, preprocessing to building models accurately and efficiently. The final piece of the puzzle, often overlooked, is the model deployment and operationalization. Many customers use AWS orchestration software or have custom software for operationalizing ML models. Whether it is container based deployment at the edge, REST APIs or code generation, operationalizing ML models is the last frontier that determines if AutoML will deliver value. 

Putting it All Together

As discussed earlier, the data science process has many elements, some of which are easier to automate than others. While we have not covered all the possible automation available to data scientists, we have discussed some of the most critical – automating data management and the creation and evaluation of features in preparation for ML selection. By focusing on automating feature engineering and data prep, data science teams can dramatically reduce their development times and develop AI/ML models in days – instead of months.
 

Read More

dotData Launches dotData Stream – Containerized AI Model for Real-Time Prediction

Highly Scalable and Effective AI/ML Container, Easily Deployable Either in the Cloud for ML Orchestration or at the Edge for Intelligent IoT
SAN MATEO, Calif., July 7, 2020 /PRNewswire/ — dotData, a leader in full-cycle data science automation and operationalization for the enterprise, today launched dotData Stream, a new containerized AI/ML model that enables real-time predictive capabilities for dotData users. dotData Stream was developed to meet the growing market demand for real-time prediction capabilities for use cases such as fraud detection, automated underwriting, dynamic pricing, industrial IoT, and more.
dotData Stream performs real-time predictions using AI/ML models developed on the dotData Platform, including feature transformation such as one-hot encoding, missing value imputation, data normalization, and outlier filter. It is highly scalable and effective – a single prediction can be performed as fast as tens of milliseconds or even faster for microbatch predictions. Its deployment is as easy and simple as launching a docker container with AI/ML models downloaded from the dotData Platform with just one click. An end-point for real-time predictions becomes immediately available. In addition, dotData Stream can run in cloud MLOps Platforms for enterprise AI/ML orchestration or at the edge servers for intelligent IoT applications.
JFE Steel, one of the world’s leading integrated steel producers, recently implemented dotData to support the deployment of intelligent IoT in their manufacturing plants.
“After testing several leading autoML platforms, we chose dotData as we were impressed with dotData’s autoML 2.0 full-cycle automation of ML processes, including automated feature engineering on our manufacturing data,” said Mr. Kazuro Tsuda, Staff General Manager, Data Science Project Dept. JFE Steel Corporation. “JFE Steel has a vision to deploy various AI models to implement Cyber-Physical Systems in our steel manufacturing plants. dotData Stream will be a key component to realize our vision and JFE Steel is looking forward to expanding its partnership with the dotData team.”
“We are seeing an increasing demand for real-time prediction capability, which has become an essential necessity for many enterprise companies. dotData Stream allows our customers to leverage AI/ML capability in a real-time environment,” said Ryohei Fujimaki, Ph.D., founder and CEO of dotData. “We are honored and excited about our partnership with JFE Steel. Their intelligent IoT application is the perfect use case to demonstrate the ability of dotData Stream, and we are fully committed to supporting their vision to adopt AI/ML in smart manufacturing and achieve the full potential of Industry 4.0.”
dotData provides AutoML 2.0 solutions that help accelerate the process of developing AI and Machine Learning (AI/ML) models for use in advanced predictive analytics BI dashboards and applications. dotData makes it easy for BI developers and data engineers to develop AI/ML models in just days by automating the full life-cycle of the data science process, from business raw data through feature engineering to implementation of ML in production utilizing its proprietary AI technologies. dotData’s AI-powered feature engineering automatically applies data transformation, cleansing, normalization, aggregation, and combination, and transforms hundreds of tables with complex relationships and billions of rows into a single feature table, automating the most manual data science projects that are fundamental to developing predictive analytics solutions.
dotData democratizes data science by enabling BI developers and data engineers to make enterprise data science scalable and sustainable. dotData automates up to 100 percent of the AI/ML development workflow, enabling users to connect directly to their enterprise data sources to discover and evaluate millions of features from complex table structures and huge data sets with minimal user input.  dotData is also designed to operationalize AI/ML models by producing both feature and ML scoring pipelines in production, which IT teams can then immediately integrate with business workflows. This can further automate the time-consuming and arduous process of maintaining the deployed pipeline to ensure repeatability as data changes over time. With the dotData GUI, AI/ML development becomes a five-minute operation, requiring neither significant data science experience nor SQL/Python/R coding.
For more information or a demo of dotData’s AI-powered full-cycle data science automation platform, please visit dotData.com.
About JFE Steel Corporation
JFE Steel is a steelmaker engaged in the total steel-making process, taking iron ore raw material and turning it into final products. Boasting one of the world’s greatest capacities for steel production, JFE Steel satisfies customers by producing steel under a corporate philosophy of “contributing to society with the world’s most innovative technology.” The company also contributes to environmental protection by developing reduced-impact ironmaking processes and high-performance steel materials.
Official web site: https://www.jfe-steel.co.jp/en/company/about.html
About dotData
dotData Pioneered AutoML 2.0 to help business intelligence professionals add AI/ML models to their BI stacks and predictive analytics applications quickly and easily. Fortune 500 organizations around the world use dotData to accelerate their ML and AI development to drive higher business value. dotData’s automated data science platform accelerates ROI and lowers the total cost of model development by automating the entire data science process that is at the heart of AI/ML. dotData ingests raw business data and uses an AI-based engine to automatically discover meaningful patterns and build ML-ready feature tables from relational, transactional, temporal, geo-locational, and text data.
dotData has been recognized as a leader by Forrester in the 2019 New Wave for AutoML platforms. dotData has also been recognized as the “best machine learning platform” for 2019 by the AI breakthrough awards, was named an “emerging vendor to watch” by CRN in the big data space and was named to CB Insights’ Top 100 AI Startups in 2020. For more information, visit www.dotdata.com, and join the conversation on Twitter and LinkedIn.

Read More
  • Walter Paliska
  • Blog
  • May 14, 2020

AutoML: How Do You Measure Return On Investment?

So your company has decided to invest in an Automated Machine Learning (AutoML) platform. Excellent – AutoML promises that it can help accelerate and automate much of your data science process. At first blush, the return on investment (ROI) for your technology purchase would seem simple: Measure how many data science projects your team could produce on average before your platform purchase, and then measure again afterward. If your results are anything like what our clients have seen, you will likely measure ROI in terms of time: many of our clients are finding that they can deliver data science projects 10X to as much as 32X faster than they could manually. While those numbers are high, however, there are other even more powerful means of measuring ROI that will be even more meaningful and valuable to your business. Leaders should think beyond cost savings and look at developing sustainable competitive advantages enabled by AI.

AutoML: How do you measure ROI?

AutoML ROI Starts with Value to the Business

One of the most revealing stats recently published by VentureBeat shows that nearly 87% of data science projects never make it into production. While there are multitudes of technical reasons why these failures are common, there are also underlying business reasons. Chief among them is that there is often a disconnect between your data science team and your line of business users as to the purpose of the data science project. Creating a tighter alignment between your business units and your data science and BI teams is fundamental to drive ROI from data science effectively.

Investing in AutoML in and of itself will not remove this potential roadblock. An automated data science practice is still flying blind if the people driving the practice don’t know the business challenges they are trying to solve. In understanding the value of the project, the ability to measure ROI increases. If, for example, your team is working on applying machine learning to reduce churn rates, it will be valuable to empower the team with knowledge about the value of each customer that churns out. What is the financial impact of lowering your churn rate from 3% to 2.5%? What will the gains be on the business? Tying your data science projects to concrete financial implications will not only help motivate your teams but will also give you direct means of measuring ROI.

Measuring ROI: Lower Development Costs

One of the first, and most obvious steps, in measuring the ROI of your AutoML investment is to measure the changes in capacity for your team. However, how exactly do you achieve that? Most organizations have a relatively good measure of how long it takes (on average) to complete a data science project. For most businesses, the most complicated – and time-consuming – part of the process is known as feature engineering. During feature engineering, your team must carefully assemble fields from different tables, often sourced from multiple systems – and use statistical means to create “feature tables.” Tables of data that will be iteratively processed by machine learning algorithms to evaluate which features work best with appropriate models.

With the right data science automation platform, your team might be able to reduce its average development life-cycle from three months to as little as three days. When calculating on a “man-day” basis, a three-month project lifespan means sixty person-days worth of effort (20 working days per month x 3 months). Measuring the productivity gains achieved through automation, the team in this example has decreased development life-cycles by 20X (60 days / 3 days). To monetize this return, measure the “average cost” of your data science team in terms of salary, and you’ll quickly see how fast your investment will be paid back. Let’s assume in our example that we have 5 team members with an average annual salary of $150,000 per team member – that means a daily rate of $3,125. An AutoML platform, in this example, you would be saving seventeen days’ worth of development, a savings of $53,125 just on the first project!

Going Beyond Cost For Proper AutoML ROI

Having arrived at a basic calculation for return on investment that is based on productivity, we are ready for the next phase: Measuring ROI based on value to the business. After all, your data science team is not working in a vacuum. A bank might be leveraging machine learning to identify fraud, a retailer might be analyzing product inventory optimization, and an insurance provider might be attempting to find ways of lowering customer churn. Each of these scenarios has very high dollar-values associated with them. In the world of Software as a Service (SaaS), for example, customer churn is a critical metric.

Let’s assume your company has 3,000 customers, each paying on average $5,000 per year in software licenses. At an annual churn rate of 8%, that means a net loss of $1.2M in annual revenues EACH YEAR. Decreasing that churn rate to just 7% – nets an additional $150,000 per year in revenue. Reaching a more palatable 5% churn rate, nearly halves the annual revenue loss and nets the business an additional $450,000 in annual revenue. This example, of course, is a bit simplistic – but you get the point.

New Possibilities: Additional Revenue Streams

For many organizations, the reality is that hiring data scientists is a financial and practical impossibility. These businesses, however, typically already have BI and analytics teams in place that are responsible for creating reports and dashboards. Empowering these BI teams with tools that allow them to build predictive analytics reports and dashboards, can provide a significant benefit to the business. For example, ML can predict additional insurance products to sell for existing customers, create better efficiencies in supply chain and operations at pharmaceutical companies, improve processing times between lenders and consumers with smarter underwriting decisions and can be leveraged to predict customer churn and more efficiently manage cashflow.

Measuring the ROI of your AutoML investment must move beyond the simple view of data science productivity and must be measured against the business outcome including generating new revenue streams – incredible gains that can be had by minimizing fraud, lowering customer acquisition costs, reducing churns, optimizing marketing campaigns and the myriad of other use-cases that are possible with Machine Learning and Artificial Intelligence powered by AutoML.

Read More
  • Walter Paliska
  • Blog
  • April 29, 2020

dotData’s AI-FastStart™ Program Helps BI teams Adopt AI/ML with AutoML 2.0

Today dotData is thrilled to announce dotData AI-FastStart™, our new exclusive program aimed at helping Business Intelligence professionals with the adoption of AI and Machine Learning (ML) powered Business Intelligence (BI) solutions – regardless of the amount of expertise or infrastructure readiness of the organization. With AI-FastStart™, BI teams can quickly move from zero to a fully operational AI/ML experience in ninety days (90) or less.

AI-FastStart™ was born as a direct response to a rapidly changing BI & Analytics world. AI/ML has become a critical technology investment but most organizations still suffer from scaling AI/ML practices. BI+AI (a.k.a. citizen data scientists) is no longer a “nice to have” but must become the new approach to scale AI/ML for organizations. dotData AI-FastStart™ makes AI/ML adoption simple, easy and fast.  The program was designed around four core principles: The right platform, education, providing fast time-to-value, and to be easy to deploy and implement. We provide the best software, host it on the best possible platform, bundle the right depth and amount of education for unlimited users, and tailor services to enable an operational first use case in as little time as feasible. Whether your BI team has no experience with AI/ML, or are full experts, dotData AI-FastStart™ will help them become more proficient, more successful and will ultimately provide an exceptional predictive analytics foundation for your organization for years to come.

Award-winning AutoML 2.0 Platform: Eliminate Technical Barriers

The AI-FastStart™ program provides full access to our award-winning dotData Enterprise platform: The world’s first full-cycle AutoML 2.0 data science automation platform to automate and simplify all distinct parts of AI/ML development –  dotData Enterprise is a new approach to AI/ML development and was called “…AutoML’s best kept secret…” by Forrester. dotData Enterprise requires no coding or visual programming but simply works by providing a prediction target and datasets necessary. The platform performs all the technical heavy lifting to automate the entire process. With dotData Epterprise AutoML 2.0, BI professionals can avoid having to learn AI/ML technical complexities and significantly ramp up the adoption speed. 

infographic showing New Data Science Automation process

dotData’s AutoML 2.0 with AI-powered feature engineering

AI-FastStart™ will allow BI teams to onboard unlimited users, develop unlimited use-cases and models, and will let them leverage the full feature set of our platform, including our renown AI-Powered Feature Engineering and Automated Machine Learning that automatically builds, evaluates and presents features and ML models directly from relational data sources.

AI Essentials Training: Learn Our BI+AI Best Practices

AI-FastStart™ provides 12 complimentary “AI Essentials” training courses to help users get up to speed with concepts, ideas, and best practices. AI/ML is not just about technical knowledge, but the ability to design BI+AI use cases is critical. The AI Essentials develops your AI/ML muscle to accelerate your continuing BI+AI careers.  The program is not merely classroom lectures but it will provide full tutorial use-case to help users learn with practical and working examples.  All classes will be held by dotData’s global Data Science team, bringing decades of combined experience in delivering successful AI/ML projects. dotData’s AI essentials training will be open to an unlimited number of attendees, giving BI leaders an opportunity to cross-train multiple team members for redundancy and long-term skills scalability.

Co-Development of Your Use Cases: Measurable Impacts

Nearly 90% of AI/ML models never leave the lab. For the ongoing success of any BI+AI journey, BI teams must present measurable business value to your line of business teams. In the AI-FastStart™ program, our world-class data scientists support your team and co-develop the first use case during the first 90 days. As mentors and trusted partners, our team will help you from a clear AI/ML project definition, guide you through AI/ML model development using dotData Enterprise, and assist in AI/ML implementation and value-justification.  Better still, as your AI/ML needs grow, the dotData team is always available and ready to become an extended part of your development organization – as you need us.

Fully Managed Enterprise-Grade Hosting: Get Started on Day One

The AI-FastStart™ program leverages an enterprise-grade hosting environment fully managed by dotData, it’s designed to help you get started on day one. dotData is certified as an AWS Machine Learning competency partner, the highest technology partnership available from AWS, giving AI-FastStart™ participants an Enterprise-grade SaaS environment with enterprise-level security, scalability, and availability that complies with AWS best practices. dotData’s team will manage the full infrastructure and provide free software upgrades seamlessly and at no additional charge.

Risk-Free Rapid Start: No Up-Front Financial Commitment

We know that investing in AI/ML may seem technically complex and risky. At dotData, we are confident that with the combination of our award-winning software with our experienced and certified professionals your BI team will be able to become proficient and productive quickly and efficiently. We are so confident, in fact, that we are willing to provide the entire AI-FastStart™ program with a 45-day risk-free guarantee – get started, and if you don’t think the software and program are right for you, cancel within 45 days without penalties.

Get started today by visiting our AI-FastStart™ program page or by filling out this form. Then come on board and let’s build some amazing AI solutions together.

Read More

dotData Launches AI-FastStart Program to Make it Easy for BI Teams to Adopt AI/ML through AutoML 2.0

All-inclusive services bundle includes 1-year license to dotData Enterprise plus hosting, training and support.

SAN MATEO, Calif., April 27, 2020 – dotData, focused on delivering full-cycle data science automation and operationalization for the enterprise, today announced dotData AI-FastStart™, a new all-inclusive bundle of technology and services that includes a one year license to a fully-hosted version of dotData’s autoML 2.0 platform, plus training and support.

Available exclusively to North American customers who are not existing dotData clients, the dotData FastTrack program is designed to empower business intelligence teams to quickly and efficiently add AI/ML models to their BI stacks and predictive analytics applications. At the core of the new program is dotData’s full-cycle data science automation platform, dotData Enterprise, which accelerates ROI and lowers the total cost of model development by automating the entire data science process that is at the heart of AI/ML.

“We are seeing a huge demand for AI and ML capabilities in the market, but finding that many companies either do not have the internal resources to launch a data science program, or don’t know how to get one started,” said Ryohei Fujimaki, founder and CEO of dotData. “The AI-FastStart™ program was created as an all-inclusive bundle to help enterprises fast-track AI/ML deployments, and immediately realize value from their data.”

The dotData AI-FastStart™ Program includes:

* 1 year full license to the 3-compute node version of the award-winning dotData Enterprise AutoML 2.0 platform
* Full hosting by dotData on an enterprise-grade secure cloud infrastructure
* 12 remote training sessions for an unlimited number of users
* Support from dotData’s data science team to onboard and co-develop the first AI use-case
* “Worry free” cancellation for any reason within 45 days of sign up
* Discounts on additional years of licensing and on additional computation nodes in year one

dotData provides AutoML 2.0 solutions that help accelerate the process of developing AI and Machine Learning models for use in advanced predictive analytics BI dashboards and applications. dotData makes it easy for BI developers and data engineers to develop AI/ML capabilities in just days by automating the full life-cycle of the data science process, from business raw data through feature engineering to implementation of ML in production utilizing its proprietary AI technologies. dotData’s AI-powered feature engineering automatically applies data transformation, cleansing, normalization, aggregation, and combination, and transforms hundreds of tables with complex relationships and billions of rows into a single feature table, automating the most manual data science projects that are fundamental to developing predictive analytics solutions.

dotData democratizes data science by enabling BI developers and data engineers to make enterprise data science scalable and sustainable. dotData automates up to 100 percent of the AI/ML development workflow, enabling users to connect directly to their enterprise data sources to discover and evaluate millions of features from complex table structures and huge data sets with minimal user input. dotData is also designed to operationalize AI/ML models by producing both feature and ML scoring pipelines in production, which IT teams can then immediately integrate with business workflows. This can further automate the time-consuming and arduous process of maintaining the deployed pipeline to ensure repeatability as data changes over time. With the dotData GUI, AI/ML development becomes a five-minute operation, requiring neither significant data science experience nor SQL/Python/R coding.

For more information on the dotData AI-FastStart™ program or for a demo of dotData’s AI-powered full-cycle data science automation platform, please visit dotData.com.

About dotData

dotData pioneered AutoML 2.0 to help business intelligence professionals add AI/ML models to their BI stacks and predictive analytics applications quickly and easily. Fortune 500 organizations around the world use dotData to accelerate their ML and AI development to drive higher business value. dotData’s automated data science platform accelerates ROI and lowers the total cost of model development by automating the entire data science process that is at the heart of AI/ML. dotData ingests raw business data and uses an AI-based engine to automatically discover meaningful patterns and build ML-ready feature tables from relational, transactional, temporal, geo-locational, and text data.

dotData has been recognized as a leader by Forrester in the 2019 New Wave for AutoML platforms. dotData has also been recognized as the “best machine learning platform” for 2019 by the AI breakthrough awards, was named an “emerging vendor to watch” by CRN in the big data space and was named to CB Insights’ Top 100 AI Startups in 2020. For more information, visit www.dotdata.com, and join the conversation on Twitter and LinkedIn.

MEDIA CONTACT:
Jennifer Moritz
Zer0 to 5ive for dotData
jmoritz@0to5.com
917-748-4006
###

Read More
  • Ryohei Fujimaki, PhD.
  • Blog
  • April 23, 2020

AutoML 2.0: Is The Data Scientist Obsolete?

As originally seen on Forbes Cognitive World, our CEO – Ryohei Fujimaki PhD was a primary contributor for this article.  In case you’ve missed it, we’ve reposted from the Original Article below.

 

It’s an AutoML World

The world of AutoML has been proliferating over the past few years – and with a recession looming, the notion of automating the development of AI and Machine Learning is bound to become even more appealing. New platforms are available with increased capabilities and more automation. The advent of AI-powered Feature Engineering – which allows users to discover and create features for data science processing automatically – is enabling a whole new approach to data science that, seemingly, threatens the role of the data scientist. Should data scientists be concerned about these developments? What is the role of the data scientist in an automated process? How do organizations evolve because of this new-found automation?

 

traditional-data-science

AutoML 2.0, More Automation for Data Science

First-generation AutoML platforms have focused on automating the machine learning part of the data science process. In a traditional data science workflow, however, the longest and most challenging part is the highly manual step known as feature engineering. Feature engineering involves connecting data sources and building a flat “feature table” with a rich, diverse set of “features” that is evaluated against multiple Machine Learning algorithms. The challenge of feature engineering is that it requires an elevated level of domain expertise to “ideate” new features and is very iterative as features are evaluated and rejected or chosen. New platforms, however, have recently emerged that provide additional capabilities and automation aimed at solving this challenge. Platforms with “Automated Feature Engineering” capabilities now allow for the automated creation of feature-tables from relational data sources as well as flat files. This ability to “auto-generate” features in the data science process is a game-changing capability. Suddenly, the “citizen” data scientists – Business Intelligence (BI) analysts, data engineers, and other technically savvy members of the organization with deep domain knowledge – can become valuable contributors to an organization’s development of ML and AI models. Through Automated Feature Engineering, BI teams can suddenly develop sophisticated predictive analytics algorithms in days, significantly accelerating their productivity with minimal help from data scientists.

Automating Data Science: Democratization

One of the chief benefits of AutoML 2.0 platforms is true data science democratization. When data science automation can accelerate and automate the process of discovering and creating features, it allows for a more diverse and abundant group of users to contribute to the data science process. Automation of feature creation allows the “citizen” data scientist to create incredibly useful, highly optimized use-cases. Because citizen data scientists typically have a high degree of “domain expertise,” they can focus on use cases that are of high value to the organization with minimal if any assistance from the data science team. The added benefit of enabling citizen data scientists is that it allows the business to expand their use of data science without having to worry about hiring armies of data scientists. The ability to empower new data science contributors is especially significant given the difficulty organizations in the US have had in hiring data scientists, as examined in a 2018 LinkedIn study. With economic uncertainty facing the global community, enabling a new class of AI/ML developers with minimal investments becomes a game-changing value proposition to maintain or increase competitive advantages.

Automating Data Science: Productivity, Not Replacement

Any conversation of AutoML 2.0 platforms, however, is misplaced if the focus is on replacing or displacing the data scientist. Most data scientists see feature-engineering as one of the most significant obstacles to their work. Automation can only help to accelerate the process by providing incredible productivity boosts that would not be otherwise possible without automation. By leveraging AutoML 2.0, data scientists can often accelerate their work dramatically – from months to days. Besides, the use of AI-based feature engineering in AutoML 2.0 platforms, allows data scientists to discover features that they would have never considered. AI-based feature engineering automatically builds, evaluates, and exposes features by combining data from multiple columns, often across different tables and sources. The ability of AutoML 2.0 to self-discover features allows data scientists to explore the so-called “unknown unknowns,” the features the data scientists would have never even considered because of either lack of time or lack of domain expertise.

AutoML 2.0: Creating A More Productive, More Inclusive AI/ML Program

Rather than being a threat to the livelihood of data scientists, AutoML 2.0 platforms are, in fact, an enabling technology that helps accelerate and democratize the data science process. AutoML 2.0 provides the acceleration and automation necessary for data scientists to be more productive, giving them the ability to scale their work and providing an even more significant benefit to the business. This two-fold advantage of democratization and acceleration of the data science process are the most significant selling points of AutoML 2.0 platforms and the key to scaling the data science process in the modern organization.

Read More
  • Carl Bowen
  • Events
  • April 13, 2020

AutoML 2.0 – From BI to BI+AI in 5 Minutes (Online Event)

Adding AI, Machine Learning, or Predictive Capabilities to your BI stack has typically meant finding a data scientist or, even worse, learning about data science yourself.  Join dotData’s CEO, Dr. Ryohei Fujimaki, as he discusses the latest advancements in Automated Machine Learning, AutoML 2.0, and how it can help any business intelligence professional add AI and ML capabilities to their dashboards in record time.

Webinar Registration: AutoML 2.0 – From BI to BI+AI in 5 Minutes

Live Webinar Scheduled: April 29, 2020 @ 11:00am PT

Key Takeaways:

  1. What is Augmented Analytics – and how can it help?
  2. See how adding AI/ML to your BI stack is done today, and why it fails;
  3. Learn how AutoML 2.0 can accelerate your predictive BI work to days,
    not months.

 

Read More