Monthly Archives: November 2022

AWS Announces 10 New AI Features at AWS re:Invent 2022

At AWS re:Invent 2022, Amazon Web Services (AWS) announced 10 new features to its portfolio of AI services, and is excited to expand its offerings to more than 100,000 customers who currently rely on AWS for AI and ML initiatives. Please see below for a high-level overview of these new features.

The Key Role Missing in Most Data Science Teams

In this contributed article, Wendy Lynch, Founder of, shares her experience of working with small to large global clients on how to break down the communication barriers in an organization to deliver results. This often happens between the analyst teams and the business teams.

Call center modernization with AI

Picture this: A traveler sets off on a camping trip. She decides to extend her RV rental halfway through her trip, so she calls customer service for assistance, but finds herself waiting minutes, then what feels like hours. When she finally does get a hold of somebody, her call is redirected. More waiting follows. Suddenly her new plan doesn’t seem worth the aggravation. Now, imagine the same scenario from the agent’s perspective, dealing with a dissatisfied customer, scrambling for information that takes time to collect. Instances like these are far too common—the debacle ends up being costly for the company, and frustrating for both customer and agent.

Luckily, the AI solutions for customer service available now mean that customer care doesn’t have to be complicated. It starts with bringing AI into the mix and ends with more cost-efficient operations and more satisfied customers. 81% of customers say they want more self-service options. According to a Gartner® report, in 2031, conversational AI chatbots and virtual assistants will handle 30% of interactions that would have otherwise been handled by a human agent, up from 2% in 2022.1

So how can AI help fulfill customer expectations in today’s ever-demanding landscape?

When you deploy conversational AI in your call center, you get:

  1. Increased customer and agent satisfaction. Think of the example above—long wait times and unanswered questions can only lead to frustrated customers and agents and slower businesses. With leading natural language understanding (NLU) and automation leading to faster resolution, everybody wins.
  2. Improved call resolution rates. AI and machine learning enable more self-service answers and actions and help route customers who need live agent support to the right place – continuously analyzing customer interactions to improve response. Agents benefit from this assistance, too, empowering them to perform at their best when call traffic is high. Ultimately, improved resolution rates mean better customer experiences and improved brand reputation.
  3. Reduced operational costs. With the capabilities of AI-powered virtual agents, you can contain up to 70% of calls without any human interaction and save an estimated USD 5.50 per contained call. This is money saved for your business, and time saved for your customers.

Not all conversational AI platforms are built the same

On the lowest rung of the AI ladder, you have rules-based bots with limited response function. For example, you want to know if your telecom provider offers an unlimited data plan, so you call customer service and are given a set of basic questions following strict if-then scenarios—“…say yes if you want to review service plans; say yes if you want unlimited data.”

Climb up one rung, and there’s level two AI with machine learning and intent detection. You accidentally type “speal to an agenr”— but the virtual assistant understands your intention and responds properly: “I will connect you with an agent who can assist you.”

Then there’s IBM Watson® Assistant—the always-learning, highly resourceful virtual agent. Watson Assistant sits at the top—level three. Level three offers powerful AI that has unparalleled data and research capabilities.

The Watson Assistant deployed at Vodafone, the second-largest telecommunications company in Germany, exhibits level-three capacities—in addition to answering questions across a variety of platforms, such as WhatsApp, Facebook and RCS, Watson Assistant answers requests pulled from databases and can converse in multiple languages. It mines data, customizes interactions and is continuously learning. “*Insert Name*, transferring you to one of our agents who can answer your question about coverage abroad.” 

With Watson AI, you can expect more for your call center: 24/7 support, speedy response times and higher resolution rates. Seamlessly integrate your virtual agent with your existing back-end systems and processes, with every customer channel and touchpoint, without migrating your tech stack—IBM can meet you wherever you are in your customer service journey. Watson AI offers:

  • Best-in-class NLU
  • Intent detection
  • Large language models
  • Unsupervised learning
  • Advanced analytics
  • AI-powered agent assist
  • Easy integration with existing systems
  • Consulting services

All these features work in concert to redefine customer care at the speed of your business.

Why add complexity when you can simplify with AI? 

The expectation is that by 2031, conversational AI bots and virtual assistants will handle 30% of interactions, up from 2% in 2022.[i] To remain among the leaders, modern contact centers will need to keep up with AI innovations. Of course, like Watson, leading businesses are constantly learning, analyzing, and striving to become better.

Watson Assistant plugs into your company’s infrastructure, is reliable, easy to use and always there to provide answers and self-service actions. Take Arvee, for example, an IBM Watson AI-powered virtual assistant for Camping World, the number one retailer of RVs. When customer demand surged early in the global pandemic, Camping World deployed Arvee in their call center and agent efficiency increased 33%.  And customer engagement increased by 40%. Yes, 40%!

Overall, Watson Assistant helped streamline processes and create agent efficiency—and when calls did go to human agents, they could deliver higher quality personal service. Remember that aggravated customer from earlier? With the power and capabilities of Watson Assistant, she can enjoy her time camping—goodbye hold music, hello sounds of nature.


Learn more about call center modernization with AI

Find out more about IBM Watson® Assistant for Voice

Read more client stories


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How Jabil is building better, faster enterprise reporting

Jabil isn’t just a manufacturer, they are experts on global supply chain, logistics, automation, product design and engineering solutions. They are also interested in and invest heavily into the holistic application of emerging technologies like additive manufacturing, autonomous technologies, and artificial intelligence. They are a technologically motivated enterprise, so it’s no surprise that they would apply this forward-thinking view to their finance reporting as well.

Jabil is a sizable operation with 2,300 profit centers, over 250,000 employees in over 20 countries worldwide. And when you add to that the needs of their clients and partners in healthcare, aerospace, telecommunications, and consumer industries you can see why they would need a better, faster way to handle financial reporting. Finance reporting isn’t much use when it’s slow and inaccurate. That method leads to poor decision making and improper spend allocation. If an organization can only review its numbers toward the end of each month, there isn’t time to respond to irregularities or emergencies.

Jabil is a longtime partner and IBM Business Analytics (BA) portfolio user, but before they made the switch to BA almost 15 years ago, they were using excel and spending most of their financial planning time trying to determine which numbers were most true for planning purposes.

“We can bring in more investments and the IT team can handle it. We’re working in a weeks and months timeframe where others are taking months and years. We can’t even see where the bar used to be.”

– Patrick Patterson, Senior Manager Financial Systems, Jabil

IBM Business Analytics delivers efficient management of an incredibly complex supply chain

Switching to IBM Business Analytics gave Jabil the ability to gather and structure data to provide a central approach to management. The integrated solution automatically handles reporting, analysis, planning and forecasting. Now, leaders use the solution to define top-down financial and operational targets to influence annual planning and to develop a quarterly bottom-up 12-month forecast to capture a reliable forward view of revenue, expense, profit and working capital across all 2,300 profit centers.

It’s IBM Cognos Controller that offers Jabil a simplified automated consolidation process, removing their dependance on spreadsheets. The result is a single version of truth that management can rely on to anticipate performance gaps and focus on initiatives designed to steer business performance. The solution shifts time spent away from gathering data and report creation and allows for increased focus on understanding insights and acting on them.

Using IBM’s Business Analytics portfolio Jabil was able to cut three full working days from the process of creating SEC reports and cut four days out of pulling internal monthly financial numbers, providing important true numbers much earlier. With the ability to “steer the ship” in real time, Jabil can easily bring in new business. Since the solutions are scalable, new companies are onboarded and integrated into Jabil’s external financial reporting without much effort.

All of this provides leadership with the ability to see specific trends across their customer base and physical plants. Leaders can develop effective best practices, working practices and drill down on data to understand the particulars of any situation and respond effectively. Providing Jabil with total business analytics throughout their organization.

Keeping it small, keeping it connected

The first tool Jabil integrated was Cognos Controller and TM1 as part of an overall finance transformation. They had a small IT team and needed a powerful tool that could handle a variety of uses, like tax provisioning in treasury, reporting and modeling for finance and still scale. Cognos Controller proved to be durable, scalable and powerful enough to allow them to keep their IT team small and still support anything that comes their way.

In most organizations when an IT team finishes a project, they move on to another project and a support team takes over the initial project. At Jabil, the developers that build a project own it for the duration. From a skills point of view, developers are the ideal support staff to keep applications up and running. With IT and line of business integrated, the final piece of the puzzle was making sure everyone was looking at and using the right data.

With Cognos Controller and TM1 as the book of record, finance was able to implement a systemwide forced reconciliation to make sure the final data is always 100%. When each site submits their financials, there’s a reconciliation template that doesn’t allow finalization unless the numbers tie out. Everyone knows they can trust the system and that trust saves days of work—and it’s a truly automated system. Once actual numbers are in the system, Cognos sends out a mix of reports, around 80,000 a quarter, and since it’s a SOX controlled application, the data remains secure throughout.

The benefits include the ability to close out their books earlier. They went from day 15 reporting to day 5. The time savings is massive and having an application that takes data security seriously is a huge benefit. Overall, the solution turns a massive data collection effort into a push-button activity.

“Trust in data security is important, and puts Cognos Analytics and TM1 ahead of many other solutions.”

– Patrick Patterson, Senior Manager Financial Systems, Jabil

Knowledgeable partners drive success

For 15 years, IBM and Jabil have been integrating successful solutions with Cognos. No tool is perfect, and the leaders at Jabil understood that they would need a dedicated partner for the duration. A partner ready to invest in the tool and help with solutions and troubleshooting. IBM has been that partner. Working together Jabil and IBM have been able to work through every single major issue. The partnership allows Jabil to make important decisions faster and to fix problems quickly as they come, and Jabil is going to continue to invest in giving their employees the best tools possible.

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Stop Building Models, Start Training Data

In this special guest feature, Sanjay Pichaiah, VP of Product Growth at Akridata, highlights why it is time for data scientists to stop building models and start training data. The path to better models and greater model accuracy doesn’t lie exclusively with the model, even though that has been the greatest focus in recent years. To truly accelerate and increase model performance, we need to be focusing more on the training data sets we are supplying the models and stop hoping the data is good enough.

Embeddable AI saves time building powerful AI applications

Just a few weeks ago, IBM announced an expansion to their embeddable AI software portfolio with the release of three containerized Watson libraries. This expansion allows our partners to embed popular IBM Watson capabilities, including natural language processing, speech-to-text, and text-to-speech into their applications and solutions. But what is embeddable AI, and what are its uses?

Embeddable AI is the first-of-a-kind suite of IBM core AI technologies that can be easily embedded within enterprise applications to serve a variety of use cases. Think of embeddable AI as an engine. Planes and cars both use engines to make them go, but each engine accomplishes its purpose in different ways.

The analogy doesn’t end there either, because just like with a car or plane engine, it’s much easier to use something pre-made than to construct one yourself. With embeddable AI, you get a set of flexible, fit-for-purpose AI models that developers can use to provide enhanced end-user experiences—like, automatically transcribing voice messages and video conferences to text.

Unfortunately, many organizations still struggle to find talent with the skills to build and deploy AI solutions into their businesses. So, it’s crucial for companies that want to add specific AI capabilities to their applications or workflows to do so without expanding their technology stack, hiring more data science talent, or investing in expensive supercomputing resources.

To address this, businesses have found value in embedding powerful technology using specific models to harness AI’s potential in the way that best fits their needs, whether it is through domain optimized applications to containerized software libraries.

Differentiated solutions drive business success

IBM Research has added three new software libraries to IBM’s portfolio of embeddable AI solutions—software libraries that are not bound to any platform and can be run across environments, including public clouds, on-premises, and at the edge.

As a result, organizations can now use this technology to enhance their current applications or build their own solutions.

The new libraries include:

In addition to the libraries, the embeddable AI portfolio includes IBM Watson APIs and applications like IBM Watson AssistantIBM Watson DiscoveryIBM Instana Observability, and IBM Maximo Visual Inspection.

Embeddable AI libraries are lightweight and provide stable APIs for use across models, making it easier for organizations to bring novel solutions to market.

Partner solutions using embeddable AI

IBM partners are making use of embeddable AI in various ways and across different industries.

LegalMation, an IBM partner that helps the legal industry make use of AI and advanced technology, uses natural language processing to automate contract privacy. Contracts and agreements contain information that organizations want to be careful about. Usually, redacting information within a contract is a manual process involving a person going line by line to mark passages for redaction. Instead, LegalMation uses embeddable AI to create an automated solution. The legal company now uses a natural language processing tool to find and mark sensitive information automatically.

See how LegalMation also uses AI to reduce the early-phase response documentation drafting process from 6 – 10 hours to 2 minutes.

Language-training school ASTEX, based in Madrid, Spain, has seen student careers skyrocket after they completed its courses. ASTEX uses AI to streamline students’ onboarding experience, offer personalized learning plans and improve the program’s scalability by reducing its dependence on humans. IBM Business Partner Ivory Soluciones connected ASTEX with IBM because of the tech company’s expertise in AI solutions. Working closely together, ASTEX, Ivory, and IBM developed the ASTEX Language Innovation platform on IBM Cloud® with IBM Watson® technology.

Call recording service, Dubber, uses speech-to-text, tone analyzer, and natural language understanding to capture and transcribe a variety of verbal exchanges. The solution, powered by embeddable AI, automatically translates phone calls and video conferences into text and assigns each conversation a positive, negative, or neutral value, depending on the call. Users can then mine the data using simple keyword searches to find the information they need.

Now, with the addition of the new software libraries, new and existing IBM partners can embed the same Watson AI libraries that powers IBM’s market leading products with flexibility to build and deploy on any cloud in the containerized environment of choice.

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The Anyscale Platform™, built on Ray, Introduces New Breakthroughs in AI Development, Experimentation and AI Scaling

Anyscale, the company behind Ray open source, the unified compute framework for scaling any machine learning or Python workload, announced several new advancements on the Anyscale Platform™ at AWS re:Invent in Las Vegas, NV. The new capabilities extend beyond the advantages of Ray open source to make AI/ML and Python workload development, experimentation, and scaling even easier for developers.

Heard on the Street – 11/29/2022

Welcome to insideBIGDATA’s “Heard on the Street” round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace.

2023 Trends in Artificial Intelligence and Machine Learning: Generative AI Unfolds  

In this contributed article, editorial consultant Jelani Harper offers his perspectives around 2023 trends for the boundless potential of generative Artificial Intelligence—the variety of predominantly advanced machine learning that analyzes content to produce strikingly similar new content.

Maximize your data dividends with active metadata

Metadata management performs a critical role within the modern data management stack. It helps blur data silos, and empowers data and analytics teams to better understand the context and quality of data. This, in turn, builds trust in data and the decision-making to follow. However, as data volumes continue to grow, manual approaches to metadata management are sub-optimal and can result in missed opportunities. Suppose that a new data asset becomes available but remains hidden from your data consumers because of improper or inadequate tagging. How do you keep pace with growing data volumes and increased demand from data consumers and deliver real-time data governance for trusted outcomes?

It is imperative to evolve metadata management approaches to keep pace with the proliferation of enterprise data. This puts into perspective the role of active metadata management. According to Gartner, active metadata management includes a set of capabilities that enable continuous access and processing of metadata.

What is Active Metadata management?

Active metadata management uses Machine Learning to automate metadata processing and use the outcomes of that metadata analysis to help drive decisions through recommendations, alerts and more. In short, active metadata management makes data more actionable in real-time. It includes a set of capabilities that facilitate automated data discovery, improve confidence in data, and enable data protection and data governance at scale.

Common use cases for active metadata management

Improve data discovery

Research shows that up to 68% of data is not analyzed in most organizations. Knowing what data assets are available across the enterprise is key to improving data utilization. You can enable advanced data discovery with AI-driven recommendation engines that analyze active metadata and recommend new assets to data consumers based on their usage patterns.

Provide early indicators of data quality

Poor data quality is one of the top barriers faced by organizations aspiring to be data-driven. Most data quality management approaches are reactive, triggered only when consumers complain to data teams about the integrity of datasets. Active metadata management can help with proactive data quality management. Data observability capabilities help augment trustworthy data and detect anomalies in data pipelines, allowing IT teams to quickly surface and resolve issues before they impact the business.

Regulatory and compliance

The risks of non-compliance – legal penalties, loss of reputation and customer trust – are too big to be ignored. According to the Gartner Hype Cycle for Data Privacy 2021, more than 80% of companies worldwide will face at least one privacy-focused data protection regulation by 2023. Rather than responding to each challenge individually, a proactive approach to data privacy, protection and risk management is an opportunity for organizations to build customer trust. With active metadata management, organizations can enforce data policies automatically and implement data protection rules at scale for better compliance with new data regulations.

3 benefits of an active metadata management solution

A data fabric solution, like the one for trusted AI, connects the right data, at the right time, to the right people, from anywhere it’s needed. One of the key aspects of the IBM data fabric solution is the active metadata capabilities delivered by IBM Watson Knowledge Catalog for Cloud Pak for Data. This data catalog empowers data producers and consumers to understand, trust and protect data, and to use it confidently throughout its lifecycle.

Know your data

Ensuring that data is enriched with all the relevant context is critical for advanced data discovery and improved trust in data. Watson Knowledge Catalog helps data consumers find and understand data by offering a strong metadata foundation consisting of business terms, data classifications, and reference data backed by AI/ML-driven automation. With intelligent recommendations from IBM Watson and peers, users are empowered to find relevant assets from across the enterprise at scale. Furthermore, automated metadata enrichment built into Watson Knowledge Catalog uses machine learning to automatically assign business terms to data assets at scale. This helps users find data faster, decide if data is appropriate and can be trusted and how to work with data.

Trust your data

Complex data landscapes and resulting data silos place a time-consuming burden on data teams to govern data spread across distributed data environments and deliver trusted data.  To improve trust in data, Watson Knowledge Catalog performs data quality analysis to assign quality scores to data assets based on dimensions like data class and type violations, duplicate values, missing values, and suspect values. Custom data quality rules can then be defined to improve curation activities.  Furthermore, IBM’s partnership with MANTA brings automated data lineage capabilities to trace and analyze how data is moved and consumed across all your applications and data sources. This complements IBM’s acquisition of and its data observability solutions to facilitate trustworthy data by actively using historical trends and statistics to detect data anomalies in data pipelines so that IT teams can quickly surface issues before they impact the business.

Protect your data

Privacy is becoming a reason for consumers to purchase a product. A proactive approach to compliance governance reduces the time and effort to comply with new regulations. IBM supports dynamic enforcement of data protection policies using active metadata to provide automatic decisions to mask and protect data, thereby facilitating enterprise-wide data access with less risk of regulatory violations.


Want to try out the active metadata features that allow IBM to deliver integrated quality and governance capabilities? Check out the free trial.

Access the report to read why IBM is recognized as a Leader in the 2022 Gartner® Magic Quadrant™ for Data Quality Solutions.

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