Make better use of your data science tools and resources by taking an intelligent approach.

Every company in this digital world wants to employ data science and analytics methods to be the most “data-driven”. A study by McKinsey also states that more than 50% of CEOs feel that they’re leading their company with analytics. But there’s a difference in how companies use data analytics. Some collect data and analyze it periodically whereas others make data analysis a core part of their business processes. It’s a general assumption that the volume of data might more or less be the same, but what matters is how quickly a company can turn data into insights. This becomes critical when a company is working with siloed data.

• Enterprises Look for these 8 Modern Data Architecture Characteristics
• Businesses Cope with Non-tech Challenges of Big Data Analytics 
• Decoding the Five Pillars of an Enterprise Data Integration Journey 

The Cause of Friction

When organizations work with siloed data, different teams, like IT, cybersecurity, software,  will have different their own tools and data sets. Ideally, this should generate multiple data sets with the same conclusion. But there are one too many, there is bound to be chaos and confusion. There are several studies that highlight companies using multiple monitoring tools that do the same thing, one or 11. This causes friction in the overall team performance, adds up additional and unnecessary costs, and affects the project. According to Gartner, only 20% of the analytics projects will successfully finish their tasks by 2022. By then, there will be more such tools in the market.

COVID-19 worsened the situation as cross-functional teams couldn’t collaborate easily. This made it harder for teams to working in segregated silos with no proper resolutions in place. In this cut-throat business world, a company’s standing in the industry depends on agility. Data can significantly contribute to a company’s growth, as long as the silos are broken. How can this be achieved? By arming teams with the large raw data sets first which they can use as a part of their tasks.

According to Iain Chidgey, vice-president of EMEA at Sumo Logic, “Centralizing data handling and analysis, and then getting more teams involved around the one set of data should help companies make more of their data with different teams.” If a retailer is running an e-commerce website, its business operations team will be notified of all the abandoned carts. Then comes the urgency to inspect the cause as it directly affects the business. The same data can be used by the IT teams to understand if the dissatisfaction is the result of a poorly made infrastructure like show page load times or a bug that keeps crashing the website. Then the software development team will use this data to find a solution and monitor the website. If the issue was security-related, the cybersecurity and compliance team will take the needful actions. Not only will be approach be cost-effective, but it will also help the website maintain a record of all the issues and the impact it had on the business. It will help generate performance and developmental reports and improve the business optimization process.

The crux of the issue is to eradicate data silos. By doing that, all teams will work in tandem, use the same data source to tackle business problems and more profitable business over time. Whether the teams work remotely or not, a single data set will keep chaos away and generate data across all business domains. If companies want to shift more of their focus towards data science and analytics, this is the right way forward.

The post THE SIGNIFICANCE OF ELIMINATING SILOS FROM DATA SCIENCE PROCESSES appeared first on Artificial Intelligence.

Xilinx today introduced the latest evolution of its Vivado Design Suite software, Vivado ML Editions, to help developers in the electronic design automation (EDA) sector leverage machine learning algorithms and techniques to bring greater efficiency to the design process.

The EDA industry is dealing with trends toward lower-nanometer architecture with increasinging complexity and numbers of transistors. It has “decades of data and engineer know-how” on design processes, but processes can be slowed by the massive numbers of iterations required to get the designs right and the lack of Quality of Results that come through those iterations, according to Nick Ni, director of marketing, Software and AI Solutions at Xilinx.

Companies in EDA also don’t have much of their own expertise in machine learning (ML), a technology that could help these processes become faster and more efficient. That’s where Xilinx can step in, having built up a lot of its own ML expertise in recent years, in part through the 2018 acquisition of China’s DeePhi Tech, a company focused on ML and optimization of neural networks, Ni said.

Jim McGregor, principal analyst at Tirias Research, agreed, telling Fierce Electronics via email, “Xilinx is in a unique position in that it has a large history of information and works with most of its customers on designs. Combined with Xilinx’s expertise in ML, the company has a unique ability to implement ML for the benefit of customers using its technology.”

Vivado ML Editions, available now, leverages machine learning-based algorithms to accelerate design closure, Ni said, and software features such as logic optimization, delay estimation and intelligent design runs, which drastically reduces timing closure iterations. ML Editions also includes an Abstract Shell feature which decouples compilations from platform interfaces. Ni said that’s important because it helps speed up compilation, while also allowing EDA companies to keep their intellectual property under wraps as they expose some design aspects to customers and partners during the design process.

These capabilities combine to allow ML Editions to deliver 5x to 17x faster compile time, and average breakthrough quality of results (QoR) improvements of 10% on average and all the way up to 50% on complex designs, compared to the company’s existing Vivado software.

“Without something like intelligent design runs, you are doing a lot of manual runs,” Ni said. “With IDR it’s a push-button process,” which allows those iterations to be completed, for example, in a day instead of a week or more with manual processes.

National Instruments is one of the first users of ML Editions, and in a statement, Robert Atkinson, the company’s principal hardware engineer, described the IDR functionality as “a game changer. By offering a push-button method for aggressively improving timing results, it generates QoR suggestions that bring maximum impact and deliver expert quality results with a reduction in user analysis – especially for tough-to-close designs.”

Kevin Krewell, also principal analyst at Tirias, called ML Editions a “really good example of applied machine learning in design tools. Using machine learning, the tools can converge on an optimal solution many times faster than traditional heuristics and with a better result.”

McGregor added that while the EDA sector is just beginning to use ML technology, usage will continue to grow. “You will eventually see ML throughout the semiconductor design, manufacturing, and even lifecycle management process,” he said. “Even though it is piecemeal now, adding ML to both hardware and software tools will provide a significant benefit and provide a competitive advantage for both the technology provider and the customer.”

The post Xilinx infuses EDA processes with machine learning appeared first on Artificial Intelligence.

In a digital age, it only takes a few years for research into cutting-edge areas like predictive analytics modelling and artificial intelligence to find practical uses in everyday business contexts. Areas like general usability, user interface, and semantics that are changed to empower a broader cross-section of potential users.

Business-focused users keen to leverage statistical methods may not be capable of or comfortable with interacting in a pure text terminal in Python or R, for example. After all, open-source code is exactly that: can you blame someone for not wanting to make an investment decision based on code found freely on the internet? Business users need an easy way to interface with powerful analytics and need a trusted brand that stands behind them. That’s exactly why Minitab has changed the game, making machine learning easy for everyone.

Today, business users do not have to compromise to do advanced analytics. For statistical analysis, predictive analytics, and machine learning, there is a 50-year-old powerhouse called Minitab.

Over the past couple of years, Minitab has revolutionized the market by bringing the world’s most advanced data gathering, processing, visualizations, and analysis to the masses. Most recently, Minitab broke the barrier to putting “Data Science” into the hands of business professionals. And unlike others who promised this in the past, Minitab delivers.

That’s because, across the company’s product portfolio, there is a strong emphasis on usability and business outcomes – Minitab is deployed by a broad cross-section of the business community.

For decision-makers tasked with a business process or operational improvements, access to data and the ability to use it to achieve clear goals is critical. The lifeblood of today’s organizations is information, so using it to examine what was, what is, and what might happen can result in in lowered costs, higher revenues, and more efficient, timely actions for long-term strategy.

The talk may be of variables and predictors in mathematics and statistics — for the business’s Change Manager, it’s data sources, outcomes, and results. The phraseology might be different, but the required data processing and analysis remain the same. Don’t be intimidated by the term “Machine Learning.” All it refers to is learning from your data, which is effectively what data analysis is. Don’t believe us? Try it yourself.

With Minitab at the core of your data operations, there is immediate “plug and play” access to hundreds of data sources via included connectors that allow companies to access the data silos, repositories and applications across the network and in the cloud. It’s not surprising that Minitab was the highest-rated data integration tool according to Gartner Peer Insights.

Coupled with the statistical core of the Minitab data analysis platform, professionals can get a full picture of their data by leveraging archived information and real-time data streams as they happen in any organization.

Data is cleaned, transformed, and presented, providing the basis for predictive analytics modelling and insights into existing work processes.

As companies begin to scratch the surface of the data resources they have, hidden relationships between events and variables are uncovered. Factors that were never apparent can surface, even to the objective observer, and visual relationships and correlations emerge. Insights gained help both data specialists and line-of-business experts to determine how best to achieve the company’s objectives.

For line-of-business managers and non-data scientists, the visual language of Minitab helps show and correlate the various factors at play. It allows them to create predicted outcomes to proposed changes to operations in safe modelling environments.

The beauty of the Minitab portfolio is its design for use in practical settings. The platform’s openness and user interface mean it can be used in multiple verticals and unexpected use-cases: manufacturer Tate & Lyle used AI techniques and plotted thousands of variables to refine its sweetener consistency for better customer experience, for example. Where one might least expect it, Minitab’s statistical power is creating change.

In a vast range of industries, advanced analytical modelling, analysis, and machine learning algorithms are being deployed by organizations to improve outcomes in thousands of scenarios. At one time, this type of statistical analysis was only seen in finance and high-end medical research and pharma, but not today. Minitab is making real, meaningful differences in thousands of settings.

It integrates with both cloud and on-premises applications and services, from marcomms to stream processors. Minitab installs locally or is now available as a SaaS, ready to be accessed from anywhere with an internet connection.

To learn more about the Minitab suite of offerings and begin leveraging its accessible power to affect change, start your journey here.

The post Improve Your Business’s Processes with Predictive Analytics and Machine Learning appeared first on Artificial Intelligence.

International Business Machines Corporation IBM recently updated Watson Studio with AutoAI capabilities.

As the name suggests, AutoAI enables enterprises to automate complex arduous processes, comprising optimizing, designing and other related tasks by leveraging robust AI capabilities.

Markedly, innovation in machine learning (ML) and improvement in data set quality drives development of AI tools. IBM rolled out AutoAI with an aim to free up time for data engineers to design sturdy ML models to expedite AI deployment.

AutoAI capabilities function in sync with Watson Machine Learning to accelerate AI processes considerably. AutoAI is available in “Watson Studio on the IBM Cloud.”

IBM is leaving no stone unturned to strategically infuse AI capabilities across its offerings, which hold promise. AutoAI is anticipated to boost adoption of IBM Watson Studio services, consequently bolstering the company’s top line in the days ahead.

Noteworthy Features

With AutoAI, data scientists can utilize “hyperparameter optimization capabilities” which facilitates development of AI models. Moreover, AutoAI is loaded with robust enterprise data science models, including gradient boosted trees which allow users to upscale ML deployment processes.

In a bid to empower users with deep learning (DL) capabilities, AutoAI allows access to IBM Neural Networks Synthesis (NeuNetS). Notably, NeuNetS utilizes AI to aid development of DL model and automates data synthesis across custom-designed neural networks. Further, NeuNetS provides users with the flexibility to prioritize accuracy or speed of the data model.

Increasing Spend on AI Favors Growth Prospects

Huge volume of data is being generated through a wide range of sources. In a bid to harness this data constructively, the companies intend to leverage AI to provide specific solutions and enhance solutions’ capabilities. However, deployment of AI tools is not trouble free.

For instance, per an IBM study, Shifting Toward Enterprise-Grade AI , conducted last year, 63% of surveyed individuals cited “lack of proper technical skills was a prime challenge to AI implementations.”

Moreover, according to a Forrester study, 60% of surveyed candidates claimed management of data quality to be one of the headwinds in delivering AI applications.

Markedly, IBM’s new Watson AutoAI capabilities provide companies with robust data models, consequently accelerating their AI processes.

We must note that the number of companies utilizing enterprise AI is increasing, which favors the prospects of AutoAI tool in Watson Studio. Per a report by IBM, customer satisfaction is the primary reason behind around 77% of organizations focusing on AI investments.

Further, IDC data projects worldwide spending on AI systems to hit $79.2 billion by 2022 at a CAGR of 38% from 2018 to 2022.

The move is expected to strengthen the company’s position in machine learning as a service (MLaaS) market, which is envisioned to hit $8.31 billion by 2023 per a MarketReportsWorld report, as revealed by MarketWatch.

The aforementioned secular trends reinforce IBM’s growth prospects in the longer haul.

Competition a Woe

Rising competition from Amazon Web Services (AWS) and Microsoft Azure, which dominate the cloud infrastructure services market, is a headwind. Notably, the cloud players are strengthening their IT infrastructure with advanced ML capabilities. For instance, Microsoft recently rolled out additional functionalities to Azure Machine Learning.

According to latest first quarter data from Synergy Research, IBM Cloud is classified as “strong niche player” with comparatively “lower growth rates.”

Zacks Rank & Key Picks

IBM carries a Zacks Rank #3 (Hold).

Some better-ranked stocks worth considering in the broader sector are eGain Corporation EGAN , Rosetta Stone Inc. RST and j2 Global, Inc. JCOM , each sporting Zacks Rank #1 (Strong Buy). You can see the complete list of today’s Zacks #1 Rank stocks here .

Long-term earnings growth rate for eGain, Rosetta Stone and j2 Global is pegged at 30%, 12.5% and 8%, respectively.

Today’s Best Stocks from Zacks

Would you like to see the updated picks from our best market-beating strategies? From 2017 through 2018, while the S&P 500 gained +15.8%, five of our screens returned +38.0%, +61.3%, +61.6%, +68.1%, and +98.3%.

This outperformance has not just been a recent phenomenon. From 2000 – 2018, while the S&P averaged +4.8% per year, our top strategies averaged up to +56.2% per year.

The post IBM Watson Studio’s Latest AutoAI Tool to Automate Processes appeared first on Artificial Intelligence.