Monthly Archives: September 2022

“Above the Trend Line” – Your Industry Rumor Central for 9/30/2022

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, financial results, industry alignments, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

Why CIOs Should Care About Blockchain

In this contributed article, Maria Colgan, distinguished product manager on the database team at Oracle, discusses how blockchain is starting to play a big – and growing – role in her work-life, and the world of databases where she spend much of her time. Blockchain is about much more than the Bitcoins of the world; its fundamental attributes of security, authenticity (what Blockchain folks call “immutability”), and strict recording of events can be applied to many applications, from financial data to contracts like property titles or government records.  

Data Sovereignty Extends Beyond Real Data

In this special guest feature, Omar Ali Fdal, CEO at Statice, discusses how data sovereignty does not obstruct innovation. Rather, it enables us to become even more independent and control our digital assets. And synthetic data has a big role to play in this transformation.

Turning insights into actions with IBM Business Analytics

We are living in the age of the unexpected. The pandemic, regulatory changes, economic questions, and human resource and supply chain challenges are just some of the disruptions that have impacted organizations. Disruptions will continue to surface unexpectedly, leaving broad and lasting impacts on organizations and their ecosystems. The result is an increased pressure to make smart decisions faster and often against a moving target.

Most organizations are now understanding the value of making decisions based on data insights rather than experience or intuition alone. However, the organizations that will navigate the unexpected successfully and win will do more than make data-driven decisions. These organizations will focus on how insights are framed, created, marketed, consumed and stored for reuse.

That’s where business analytics comes in.

What is IBM Business Analytics?

IBM is helping clients successfully navigate the age of the unexpected with IBM Business Analytics, an enterprise-grade, trusted, scalable and integrated analytics solution portfolio. It streamlines and extends enterprise reporting, self-service analysis and planning strategies across the organization to empower teams to better predict and shape future outcomes.

With the new IBM Business Analytics Enterprise (BAE), we are bundling together Planning Analytics with Watson, Cognos Analytics with Watson and the new Analytics Content Hub. This enables a single point of entry for planning, budgeting, forecasting, dashboarding and reporting. Now you aren’t just breaking down departmental and data silos, but analytic silos, too.

The capabilities of bundled business analytics

Planning Analytics with Watson addresses integrated business planning in extended planning and analysis (xP&A) including FP&A, HR, S&OP, Marketing, Project/IT planning and more. It’s the only planning analytics solution on the market that excels in all areas of continuous, integrated, predictive and prescriptive planning.

Next, IBM Cognos Analytics with Watson is a trusted AI co-pilot for business decision-makers who want to improve the impact of their business function by empowering every user to turn data into insights, and rapidly make business decisions. IBM is the only partner that can plan at the speed of your business and for the integrity of your environment, increasing accuracy and consistency with AI and prescriptive analytics capabilities you can trust.

And last but certainly not least, we’ll showcase the new IBM Analytics Content Hub in Business Analytics Enterprise, which is designed to break down organizational analytic silos and help you deliver all your analytics capabilities to your teams.

The benefits of business analytics

Most recently, review site G2 named Planning Analytics a “Leader” in their Fall 2022 report and Cognos Analytics a “Top 50 Analytics and AI” product for 2022. TrustRadius awarded both Planning Analytics and Cognos Analytics a “Top Rated” designation. Over the last couple years, a range of companies shared their feedback, leading to many of the improvements in the user experience, AI innovations and deployment options available today.

Organizations use analytics and AI to enhance decision-making that drives competitive advantage. Consider food packaging leader Novolex, who had to adapt their planning cycles during the COVID-19 pandemic. As shared in the case study, Violeta Nedelcu, Supply Chain Director at Novolex states, “Instead of taking weeks, the company can now process data within a few hours, taking two days for analysis, discussion and review, and provide clarity on the available capacity to proceed with new products and to support the current market.” Overall, Novolex was able to see a 83% reduction in forecasting processing times.

With business analytics, organizations in all industries, can experience the power of faster, better planning and analysis with data-driven precision. We look to continue helping organizations achieve successful implementations across their analytics cycle. As such, we have exciting new updates to our business analytics solution portfolio coming in the next month.

Register today for our Business Analytics launch event on October 25th to hear about the new Business Analytics Enterprise, including new deployment options and capabilities. You don’t want to miss out!

The post Turning insights into actions with IBM Business Analytics appeared first on Journey to AI Blog.

Synthesized Solidifies its Partnership with Deutsche Bank, Providing High-quality Synthetic Data for AI and ML Testing Purposes

Synthesized Ltd, a leading synthetic data generation platform, which provides engineering and data science teams a quick way to create and share trusted data through advanced machine learning and automation, announced that Deutsche Bank is investing in its next phase of growth and technology innovation development. 

Talend Data Health Barometer Reveals Companies’ Ability to Manage Data is Worsening Year-Over-Year

Talend, a global leader in data integration and data management, released the results from its second annual Data Health Barometer, a survey conducted globally among nearly 900 independent data experts and leaders. While a majority of respondents believe data is important, 97% face challenges in using data effectively and nearly half say it’s not easy to use data to drive business impact. The Data Health Barometer explores the disconnect between data and decision, which can impede enterprises and executives from supporting their strategic objectives through any economic conditions.

How Artificial Intelligence Can Improve Coding Audits

In this contributed article, Jacob Wilkinson, Director, Product Management at VisiQuate, takes a look at how medical coding audits are complex, time-consuming, and costly processes that historically have required significant investment of manual resources to complete, but, more recently, advances in artificial intelligence have made audits more efficient, accurate, and cost-effective. 

From principles to actions: building a holistic approach to AI governance

Today AI permeates every aspect of business function. Whether it be financial services, employee hiring, customer service management or healthcare administration, AI is increasingly powering critical workflows across all industries.

But with greater AI adoption comes greater challenges. In the marketplace we have seen numerous missteps involving inaccurate outcomes, unfair recommendations, and other unwanted consequences. This has created concerns among both private and public organizations as to whether AI is being used responsibly. Add navigating complex compliance regulations and standards to the mix, and the need for a solid and trustworthy AI strategy becomes clear.

To scale use of AI in a responsible manner requires AI governance, the process of defining policies and establishing accountability throughout the AI lifecycle. This in turn requires an AI ethics policy, as only by embedding ethical principles into AI applications and processes can we build systems based on trust.

IBM Research has been developing trustworthy AI tools since 2012. When IBM launched its AI Ethics Board in 2018, AI ethics was not a hot topic in the press, nor was it top-of-mind among business leaders. But as AI has become essential, touching on so many aspects of daily life, the interest in AI ethics has grown exponentially.

In a 2021 study by the IBM Institute of Business Value, nearly 75% of executives ranked AI ethics as important, a jump from less than 50% in 2018. What’s more, suggests the study, those organizations who implement a broad AI ethics strategy, interwoven throughout business units, may have a competitive advantage moving forward.

The principles of AI ethics

At IBM we believe building trustworthy AI requires a multidisciplinary, multidimensional approach based on the following three ethical principles:

  1. The purpose of AI is to augment human intelligence, not replace it.
    At IBM, we believe AI should be designed and built to enhance and extend human capability and potential.
  2. Data and insights belong to their creator.
    IBM clients’ data is their data, and their insights are their insights. We believe that data policies should be fair and equitable and prioritize openness.
  3. Technology must be transparent and explainable.
    Companies must be clear about who trains their AI systems, what data was used in training and, most importantly, what went into their algorithms’ recommendations.

When thinking about what it takes to really earn trust in decisions made by AI, leaders should ask themselves five human-centric questions: Is it easy to understand? Is it fair? Did anyone tamper with it? Is it accountable? Does it safeguard data? These questions translate into five fundamental principles for trustworthy AI: fairness, robustness, privacy, explainability and transparency.

AI governance: From principles to actions

When discussing AI governance, it’s important to be conscious of two distinct aspects coming together:

Organizational AI governance encompasses deciding and driving AI strategy for an organization. This includes establishing AI policies for the organization based on AI principles, regulations and laws.

AI model governance introduces technology to implement guardrails at each stage of the AI/ML lifecycle. This includes data collection, instrumenting processes and transparent reporting to make needed information available for all the stakeholders.

Often, organizations looking for trustworthy solutions in the form of AI governance require guidance on one or both of these fronts.

Scaling trustworthy AI

Recently an American multinational financial institution came to IBM with several challenges, including deploying machine learning models in the hundreds that were built using multiple data science stacks comprised of open source and third-party tools. The chief data officer saw that it was essential for the company to have a holistic framework, which would work with the models built across the company, using all these diverse tools.

In this case IBM Expert Labs collaborated with the financial institution to create a technology-led solution using IBM Cloud Pak for Data. The result was an AI governance hub built at enterprise scale, which allows the CDO to track and govern hundreds of AI models for compliance across the bank, irrespective of the machine learning tools used.

Sometimes an organization’s need is more tied to organizational AI governance. For instance, a multinational healthcare organization wanted to expand an AI model that was being used to infer technical skills to now infer soft/foundational skills. The company brought in members of IBM Consulting to train the organization’s team of data scientists on how to use frameworks for systemic empathy, well before code is written, to consider intent and safeguard rails for models.

After the success of this session, the client saw the need for broader AI governance. With help from IBM Consulting, the company established its first AI ethics board, a center of excellence and an AI literacy program.

In many instances, enterprise-level organizations need a hybrid approach to AI governance. Recently a French banking group was faced with new compliance measures. The company did not have enough organizational processes and automated AI model monitoring in place to address AI governance at scale. The team also wanted to establish a culture to responsibly curate AI. They needed both an organizational AI governance and AI model governance solution.

IBM Consulting worked with the client to establish a set of AI principles and an ethics board to address the many upcoming regulations. This effort ran together with IBM Expert Labs services that implemented the technical solution components, such as an enterprise AI workflow, monitors for bias, performance and drift, and generating fact sheets for the AI models to promote transparency across the broader organization.

Establishing both organizational and AI model governance to operationalize AI ethics requires a holistic approach. IBM offers unique, industry-leading capabilities for your AI governance journey:

  • Expert Labs for a technology solution that provides guardrails across all stages of the AI lifecycle
  • IBM Consulting for a holistic approach to socio-technological challenges

The post From principles to actions: building a holistic approach to AI governance appeared first on Journey to AI Blog.

DDN Simplifies Enterprise Digital Transformation with New NVIDIA DGX BasePOD and DGX SuperPOD Reference Architectures

DDN®, a leader in artificial intelligence (AI) and multi-cloud data management solutions, announced its next generation of reference architectures for NVIDIA DGX™ BasePOD and NVIDIA DGX SuperPOD. These new AI-enabled data storage solutions enhance DDN’s position as the leader for enterprise digital transformation at scale, while simplifying by 10X the deployment and management of systems of all sizes, from proof of concept to production and expansion.

insideBIGDATA Latest News – 9/26/2022

In this regular column, we’ll bring you all the latest industry news centered around our main topics of focus: big data, data science, machine learning, AI, and deep learning. Our industry is constantly accelerating with new products and services being announced everyday. Fortunately, we’re in close touch with vendors from this vast ecosystem, so we’re in a unique position to inform you about all that’s new and exciting. Our massive industry database is growing all the time so stay tuned for the latest news items describing technology that may make you and your organization more competitive.