Optimize your business intelligence solution on IBM Cloud Pak for Data

At a point in the not-too-distant future, AI will comprise an integral part of everyday business tools. But you need not wait for tomorrow, because, IBM Cognos® Analytics puts AI in the hands of users today to help them prepare, analyze, visualize their data and share insights across their organizations to foster more data-driven decisions. Cognos Analytics users have single-click access to advanced analytics and forecasting. They can ask questions in plain language and receive actionable answers in natural language.

IBM Cognos Analytics offers a way for organizations to reduce costs with self-service intelligent analytics capabilities, helping users with automated data preparation and best visualizations recommendations, improving results and driving better analysis and reporting. With built-in AI, users can find the insights they need and create accurate analyses without the help of IT staff or data scientists. This helps users to more quickly find and share the right answers in compelling visualizations that everyone can understand.

IBM Cognos Analytics combined with IBM Cloud Pak for Data

For even more powerful – and faster – data preparation, analysis, and report creation, IBM Cognos Analytics can be combined with IBM Cloud Pak® for Data, a fully integrated data and AI platform. Cloud Pak for Data modernizes how businesses collect, organize and analyze data and infuse AI throughout their organizations. Cloud native by design, the platform unifies market-leading services spanning the entire analytics lifecycle.

From data management, DataOps, governance, business analytics and automated AI, Cloud Pak for Data helps eliminate the need for costly, and often competing, point solutions. At the same time, it provides the information architecture needed to implement AI successfully. Building on the streamlined hybrid-cloud foundation of Red Hat OpenShift, Cloud Pak for Data takes advantage of the underlying infrastructure optimization and management.

Spend more time on higher-value work, less time searching for data

Forward-thinking businesses aim for greater process automation, agility, transparency and the power to make more data-driven decisions. Using AI, predictive analytics and cloud, professionals can change the way they work, increasing time spent on higher-value tasks versus searching for hard-to-find data and answers. The combination of IBM Cognos Analytics with Cloud Pak for Data makes the advice “accelerate your journey to AI,” more than just a catchphrase. These powerful solutions together can help provide enterprises a true competitive edge.

Deploy on any cloud or multi-cloud model

IBM Cognos Analytics on Cloud Pak for Data is designed for easy deployment and maintenance, allowing organizations to start quickly and focus time on collaborating with other stakeholders and analyzing data to propel the business forward.

Cognos Analytics on Cloud Pak for Data can run on any cloud or multi-cloud environment such as Amazon Web Services (AWS), Azure, Google Cloud, IBM Cloud and private clouds. The flexibility to deploy anywhere prevents vendor lock-in.

Give your organization the edge it needs to:

  • Execute data prep, exploration, analysis, and reporting with greater accuracy
  • Infuse AI into your analytics
  • Shift time to high-value work instead of searching for hard-to-find data and answers
  • Allow more self-service analytics without reliance on IT staff
  • Make more data-driven decisions for a greater competitive edge

For more information, check out the on demand analytics on Cloud Pak for Data webinar or visit the Cognos Analytics homepage.  

You can also try it out for yourself with the no-cost 7-day trial, IBM Cloud Pak for Data Experiences.

Accelerate your journey to AI.

Crédit Mutuel: Lessons learned building the bank of tomorrow

Overlooking the European Parliament buildings in Strasbourg, France, lies a very unique factory. But don’t waste time searching the city proper for evidence of its operation. Strolling through Strasbourg streets, you wouldn’t hear the hum of machinery or sense any vibrations under your feet. This is a very unique factory indeed. This is the Cognitive Factory, the largest in the world.

Five years ago, Crédit Mutuel began a “sweeping digital reinvention” partnering with IBM to formulate new ways of working to better serve the bank’s 12 million customers. With a team of engineers, data scientists and line-of-business experts, IBM services and Crédit Mutuel inaugurated the Cognitive Factory, an innovation hub dedicated to infusing AI across all lines of business in Crédit Mutuel. This welding of people and technology leveraged IBM Cloud and Watson to propel the bank to the forefront of AI technology.

Laurent Prud’hon is the Cognitive Factory Leader. From Euro Information, the FinTech IT group of Crédit Mutuel and where the Cognitive Factory deploys, Prud’hon heads a team of 150+ people dedicated to the development of innovative AI solutions. From combatting financial crimes, to augmenting customer experience with virtual assistants, he is innovating creative AI solutions for Crédit Mutuel employees and customers. 

In the recent IBM Data and AI Virtual Forum, Laurent Prud’hon discussed with Jean-Philippe Desbiolles, IBM Global VP of Data, Cognitive and AI, on how betting big on artificial intelligence can help financial services emerge stronger after crisis.

Watch the session replay or keep reading for highlights from that conversation.

Behind the curtain of the Cognitive Factory

Right from the start, Crédit Mutuel bet big on AI with the Cognitive Factory.

“We decided not to do small proof of concepts in labs to experiment or to see how things will work,” said Prud’hon.  “We said it will work, it has to work, and we invested right from the beginning and joined in partnership with IBM.”

The focus wasn’t only on the algorithms or the technical deployments of AI in business. The Cognitive Factory under Prud’hon’s leadership sought to scale AI across the entire organization: within applications, in processes, and deployed to all 30,000 of Crédit Mutuel’s advisors through a variety of use cases.

To achieve these goals, Crédit Mutuel’s Cognitive Factory was a coalition of diverse people and skillsets from project managers to classical developers; cognitive analysts to information architects. This assortment of talent proved vital to execute on the Cognitive Factory’s core missions:

1)         Build novel cognitive solutions

2)         Expand capabilities, skills, tooling and talent

3)         Establish a cognitive platform on which to build AI products

4)         Manage forces of change management with effective communication

How AI helps financial services emerge stronger
COVID-19 is accelerating AI initiatives as companies work to address isolation brought on by the pandemic. A pertinent use case to today’s challenges with disconnection is email analysis, which had been deployed by the Cognitive Factory in Crédit Mutuel since 2017. The challenge was to help the advisors answer 7 million client emails per month quickly and efficiently. From a computer point of view, email has the qualities of a blackbox: IT systems cannot automate the unstructured data (text, images, etc.) commonly found in email communications.

By analyzing customer emails with AI, Crédit Mutuel advisors could discern intent of customers, their needs, and what exactly they were asking for. Knowing these insights helped automatically route correspondence to the best people. It provided shortcuts. Improved efficiencies and quality of responses. With the right combination of AI automation and human empathy, the email analyzer saved Crédit Mutuel over 2 minutes per email. Multiplied by 7 million emails per month, the solution resulted in a significant time savings.

As Prud’hon explains his team wished to maintain the “rich relationship” between humans, but use AI to assist conversations, make them more efficient, and bring people closer together.

“The cognitive solution will not replace the human. It augments the relationship, Prud’hon says.  “With AI assisting to augjent conversations between humans, you are close even when remote –more important now than ever.”

For more insights from Laurent Prud’hon, watch the session replay. Then continue the conversation: attend our Data and AI Virtual Forums and accelerate your journey to AI.

To learn more on this topic, download the white paper, A point of view Financial services: Data and AI at the core, to dispel AI myths in financial services, ready for a robust DataOps and data platform redesign, and drive AI rapidly at scale.
 

How Watson Studio streamlines AI model development: Q&A with a lead architect

Data science and AI teams can now use model development and deployment in IBM Watson Studio to simplify and scale AI across any cloud while simultaneously automating the AI lifecycle. There’s an interesting story behind the development of the product, and I sat down with IBM’s Distinguished Engineer, Thomas Schaeck to learn about the architectural considerations under the hood and the benefits organizations gain with Watson Studio.

Thomas Schaeck, IBM Distinguished Engineer, is a lead architect behind IBM Watson Studio.1. Thomas, what’s your background? How did you get into data science?

I joined IBM right after finishing University at the Karlsruhe Institute of Technology in Germany. I first worked as a C/C++ developer, then started with Java soon after it came out. Then I led architecture and technical strategy for products such as WebSphere Portal, standard Java Portlet API and web services, IBM Connections, and IBM OpenPages.

Thomas Schaeck, IBM Distinguished Engineer, is a lead architect behind IBM Watson Studio.

In 2015, I joined the IBM Cloud team, where we started a new project to enable data scientists to analyze data on the IBM Cloud, initially using Jupyter Notebooks. I was immediately fascinated by the possibilities of data science over enterprise data, and that we could enable teams of data scientists in enterprises across the world to quickly get new insights from data and to create and train models — making predictions to help businesses serve their customers better and faster.

2. What challenges led IBM to build Watson Studio? How have your teams expanded Watson Studio?

Created a version for customers’ private clouds Integrated with Watson Knowledge Catalog to allow customers to access data with intelligent cataloging and active metadata and policy managementEnabled models created and trained in Watson Studio Projects to be deployed for online or batch scoring using Watson Machine Learning. Included these features in Cloud Pak for Data. Established Watson Studio Desktop to enable visual or programmatic model development from anywhere What’s AutoAI and why did it win an award?Best Innovation in Intelligent Automation award.

3. What’s decision intelligence and how does Watson Studio enable it?IBM Decision Optimization can then be used to take optimization targets and constraints into account and identify which offers and channels will deliver the optimal revenue/expense ratio in winning business from those customers.

We integrated IBM Decision Optimization with Watson Studio to enable decision intelligence. This feature is included in Watson Studio Premium for Cloud Pak for Data .

4. What are customers achieving with Watson Studio?

I

wanted to speed and scale AI development, so it modernized and centralized its data science tool landscape using IBM solutions such as Watson Studio.

This is now an open platform for all our data scientists,” says Lufthansa’s Head of Data and Analytics.

, an expert in mobile text recognition, used IBM Deep Learning Service, an element of Watson Studio, to teach smart phones to read, enabling data to be processed up to 20 times faster than previous solutions.

A lead data scientist at Fifth-Third-Bank says Watson Studio “allows us to explore many more models and run many more iterations in the same amount of time, helping us get results the business needs, fast.”

In 10 minutes, Honda R&D can use a solution that includes Watson Studio to analyze over a million documents and highlight examples of driver behavior.

Wunderman Thompson, a global creative agency, uses Watson Studio and IBM Cloud Pak for Data to release data from silos and predict post-Covid19 strategies, drawing from anonymized data on 270 million people and $1.1 trillion in transactional data. Its machine learning pipeline increased the performance over its previous models by 200 percent or more.

5. What challenges did your team need to solve to deliver Watson Studio?

What do you think of the many different architectural approaches to building a platform in the areas of cloud, apps and data? For instance, some solutions take a cloud-led approach, others come from an analytics-specialist angle, and there are many new tool vendors.

Model Operations (ModelOps) is a megatrend. What is IBM is doing to synchronize apps and AI development cadences?

In working with IBM Research, how did you crack the code to bring advanced research into an enterprise data science and AI platform?

IBM has many products and innovative ideas in progress. How are they being synchronized to develop optimum solutions?

Do you have advice for people who want to grow their skills in data science or are just starting?

Join the IBM Data Science Community for free and get a complimentary month to explore the popular IBM data science courses on Coursera.

Learn more about AutoAI by reading about it here, and try a guided AutoAI tutorial that helps you build a binary classification model.

Start with Watson Studio Cloud at no cost, without having to install anything.

Take this 45-minute product tour and get hands-on experience predicting customer churn and optimizing offers to keep customers.

Explore the potential of AI combined with human intelligence here, and see a short video playlist about what IBM Data Science can do for you.

And join me in a Data and AI Innovation Exchange webinar about ModelOps—and what you need to consider in automating AI lifecycle management. Register here.

Scaling AI at Lufthansa: Combined talents help the airline raise efficiency

In the airline industry, timing and synchronization are everything when it comes to the customer experience. Mitigating unforeseen circumstances against customer expectations and good old supply and demand are all issues well within the wheelhouse of AI’s predictive capabilities.

It’s no wonder that Deutsche Lufthansa AG, Germany’s largest airline, recognized early on that with the right data and AI strategy, it could enhance the customer experience and better empower its employees while achieving operational excellence.

In less than two years, the airline has quickly moved from AI proof-of-concepts to scaling data science projects further into the organization, moving past constraints, such as how much test data they could include in their models. They did it thanks to a partnership with IBM that brought forth deep expertise and solutions inherent in IBM’s prescriptive method the AI ladder – together with Lufthansa’s migration of AI services to the IBM Cloud.

It all started with the IBM Garage methodology

The rules and regulations of an airline that operates all over the world are infinitely complex—from baggage allowances for specific routes and status levels—to visa requirements for passport holders from one country traveling to any other. No agent can know all the answers.

Since early 2019, an IBM Garage team has been collaborating daily with Lufthansa employees – quickly testing and piloting new AI-based business ideas and services. The Lufthansa AI Studio’s first project integrated IBM Watson products, including Watson Assistant and Watson Explorer in the Service Help Centre.

Previously disparate data sources are now searchable in natural language and aviation terms to more easily address close to 100,000 customer queries annually. Watson manages, searches, analyzes and interprets the various relevant and connected data sources, such as Microsoft SharePoint and internal ticket systems.

The rise of a modern data science platform on IBM Cloud

Once the AI Studio’s muscles started to build, the conversation at Lufthansa turned to modernizing the company’s data science platform to bring all the disparate projects under one virtual roof – boosting the cache and effectiveness of its data science group and tying their activities closer to the needs of the business.

Data scientists and data engineers often struggle spending too much time maintaining their projects and not enough time on proving their business value. At Lufthansa, all of the above was true, and it was also compounded by limited scalability, lack of access to public software updates, plus a need for security improvements. What they needed was a tool inside the data science pipeline to monitor, build and scale models. The IBM Data Science and AI Elite team (DSE) and IBM Software Services joined the Lufthansa team in a two-day Design Thinking Workshop to build out a data science platform that would offer a single environment where data scientists could experiment with new techniques, and quickly roll out models with monitoring and modeling already in place.

Over a 10-week engagement, the DSE set up a new operational workflow to support the development of new data science projects using Watson Studio and Watson Machine Learning to create an open platform on a public cloud using PaaS and SaaS. This gave Lufthansa scalability and flexibility to handle mission critical workloads and accelerate the deployment of those projects in production.

Lufthansa Data Scientists worked with the DSE to prototype three use cases to help the airline run smarter and more efficiently – helping avoid delays, better predict boarding time and avoiding long queues at check-in counters.

The Lufthansa data science team can now develop new use cases in Watson Studio, while making improvements to the old ones. Aside from the three use cases, Lufthansa data scientists can now push out other projects – mostly to further increase passenger experience or to support operational or strategic decisions from employees.

The data science platform allows data scientists to work with new data sources. Or, by virtue of being open source, they can work more collaboratively or in their preferred language – or take advantage of other data science capabilities in IBM Watson Studio such as Auto AI and Watson Machine Learning for model development and deployment. Together with IBM Watson OpenScale, used for bias and drift mitigation during runtime, all of are available as PaaS and SaaS services on IBM Public Cloud or as microservices through the IBM Cloud Pak for Data platform available on any cloud.

Do you have the right talent to put AI to work?
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What Ventana says about the future of finance and analytics

Ventana Research is a leading benchmark research and advisory services organization, providing some of the most comprehensive analyst and research coverage for business.

Gaining the most benefit from a finance system requires an assessment of an organization’s unique needs to identify challenges and areas of improvement. The Ventana Research report Change in the Office of Finance: Evaluating Barriers to Digital Transformation investigates finance systems, practices, needs and potential benefits.

Importance of Finance IT and tangible results

In the fifteen years Ventana Research has delivered research and analysis for the finance industry, their reports found a significant shortening of the monthly and quarterly financial close process. They also note forward momentum in automation and communication across teams via new technology and finance tools. Within organizations, leadership teams have noticed that such improvements signify that the bar is now higher, and that what was once considered average performance for both monthly closing and business performance is now subpar.

With more sophisticated and affordable technology available, organizations recognized the need for analytics capabilities. Ventana Research concluded that 76 percent of participants understand that analytics are critical for improving their finance teams’ overall performance. Add to that the discovery that roughly 40 percent of participants’ financial departments were poorly prepared to positively transform their performance.

Ventana Research confirms widespread overreliance on using spreadsheets exclusively and a hesitation to embrace new technology. Despite advances in technology, Ventana Research consistently sees evidence that finance departments are slow or reluctant to change. Even with a growing array of solutions widely available to enhance the effectiveness of analytics by integrating with spreadsheets, and spreadsheets from disparate sources in order to create a bigger, more complete picture, organizations are slow to embrace them.

In a typical organization, the finance department contains five divisions: Accounting, Financial Planning and Analysis (FP&A), Corporate Finance, and Tax. Ventana Research concludes there must be a sixth group: Finance IT, a division responsible for seeking, researching, evaluating and implementing analytics solutions critical to improving the business.

When it comes to Finance IT and creating finance analytics, 22 percent of respondents said their process works very well, an increase from only 3 percent four years ago. As a result of advances in analytics software and easier access due to broader availability, organizations now appear more inclined to use analytics to improve their performance. Ventana Research shows 32 percent of participants use analytics significantly compared to 14 percent four years ago. This increase may also be due to greater availability: in 2014, 26 percent of senior executives reported full availability of finance analytics; today that number has nearly doubled to 46 percent. Businesses recognize that to be successful in today’s market, financial analytics has gone from a “nice to have” to a necessity.

The quality of financial planning and analytics is improving

Ventana Research finds a correlation between the type of software a company uses for budgeting and financial planning and how well their organization functions. Of companies that use a dedicated third-party solution, 66 percent said they have a budgeting and planning process that works very well, compared to 36 percent of those reliant exclusively on spreadsheets. Clearly having an analytics solution makes a world of difference in efficiency and accuracy.

Of participants surveyed, 76 percent cite analytics as critical for improving their performance. Significantly, 34 percent rate the skills of the people creating finance analytics in their company as excellent compared to only 14 percent in 2014. As both the analytics solutions and the skills of those who manage them become more advanced, they provide more value to their organizations.

New technology for an evolving department

In recent years the capabilities of analytics solutions have been enhanced by the integration of artificial intelligence (AI). Ventana Research notes that AI is a “technology that’s already available and has the potential to have a greater impact on how the finance department operates over the next 10 years than it has over the past 50.” AI currently automates an increasing volume of repetitive work, enabling a new generation of finance and accounting executives to provide their workforce with more efficient tools. Robots aren’t about to take over finance and accounting, but automation with AI will transform jobs, shifting time and attention away from repetitive tasks to work that requires insight, judgment and experience.

How IBM helps

Using the power of AI, IBM® has developed its own analytics suite of software: IBM Planning Analytics powered by TM1® and IBM Cognos® Analytics. Available on cloud, on-premises, and on IBM Cloud Pak® for Data, each offers scalability, ease of use, and economy. Whether an organization employs ten or 10,000, IBM Planning Analytics and IBM Cognos Analytics have the means to scale easily.

IBM Planning Analytics helps organizations plan more efficiently, allowing users to test what-if scenarios to provide a full view of possibilities. And it integrates directly with Microsoft Excel, allowing users to work with a familiar interface, enjoying the benefits of an enterprise planning solutions while eliminating the inherent risks of spreadsheet planning.

IBM Planning Analytics and IBM Cognos Analytics align with Ventana Research findings, with both at the forefront of the finance department need to help users track trends, monitor and manage performance, quickly acquire insights, and uncover root causes to make better business decisions. By taking this customer-first approach, IBM tools help solve the problems many businesses address on a daily basis.

Learn more about the Ventana Research Benchmark Report.

Northern Europe’s energy hub looks to IBM Garage and Cloud Pak for Data to design a green energy future

IBM Garage helps Danish power company Energinet envision how machine learning models built using IBM Cloud Pak for Data can amp up renewable energy goals

COVID-19’s devastating impact on health and the global economy also has a silver lining: an opportunity to tackle climate change.

Enter wind and solar – rapidly growing sources of renewable, affordable and available energy in Europe. As the EU mobilizes to support green energy projects as part of its economic recovery strategy, machine learning and AI are well positioned to help the continent’s energy and utility companies adapt and evolve their existing asset infrastructure and operational practices to meet increasing demand.

Wind farms and solar arrays are formidable yet fragile feats of engineering – prone to wide and varied environmental forces – from the sun- and wind-scorched Canary Islands to the blustery North Sea to the bone-chilling Arctic circle. Managing peak loads and distribution amidst unpredictable weather while keeping systems running poses tough challenges to maintaining a balanced and resilient electric grid.

In the last two years, IBM has applied data and AI solutions to renewable energy management projects at Spain’s Red Electrica, Nukkisiorfiit in Greenland and James Fisher in the UK. Recently, IBM was able to demonstrate to Denmark’s Electric Transmission System Operator (TSO) how machine learning capabilities in IBM Cloud Pak for Data could accelerate a faster transition to green energy –  meeting the need for utility asset performance management, reliability and operational excellence.

Watch how it happened:

Energinet operates and develops large transmission grids that form the backbone of the country’s electrical supply for 5.8 million citizens – with interconnectors that transmit power between Denmark and surrounding countries Sweden, Norway, the UK, Germany and the Netherlands.

The country aims to rely 100% on renewables by 2030 –  so for Energinet, the challenge has been three-fold: provide citizens with increasing levels of green electric power resilience and security of supply – at a price point that all can afford.

Energinet knew it needed a fresh approach and new thinking to re-write its energy future –  so it engaged IBM Garage on a three-month pilot project to design a “virtual operator” that could estimate risks to the grid based on large simulation data amounting to 400 terabytes.

The team’s goal was to deliver an easy-to-use interface capable of modeling different scenarios – both real and hypothetical – such as the impact on the system of taking equipment out of service during a certain period of time.

The solution was deployed using IBM Cloud Pak for Data, handling terabytes of simulation data within Watson Studio’s Machine Learning capabilities to evaluate the transmission system’s ability to withstand shocks under fluctuating power flows coming in and out of the country.

After implementing a hybrid cloud platform architecture, the joint team layered machine learning and artificial intelligence on top of terabytes of “N-1” data, consisting of historical facts about energy flow and overload situations.

The user interface reveals detailed risk probability instances, displaying the chances of an operational limit violation. Informed by years and combinations of past operational and environmental conditions encountered by the Energinet transmission system, the trained model risk profile allows the solution to provide robust decision support capability for the Operations Center, with “look-ahead” scenario generation.

Users can see where operational limits might occur, accept risk or initiate interventions to increase maintenance efficiency and identify and validate the most critical infrastructure needs.

Learn how IBM Garage combines startup speed and enterprise scale to tackle tough problems, ignite innovation and spark creativity.

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iKure + IBM: Trusted data brings resilience to rural communities

It could be said there’s really no wealth but health itself, but in rural India, some 840 million people are challenged by obtaining the healthcare they need. For the average citizen, just getting to a medical appointment might require a day-long journey. Inadequate infrastructure and a lack of specialists and oversight challenge community resilience outside India’s urban areas.

With the aggressive Covid-19 pandemic, India has had to consider how telemedicine can support healthcare systems in resource-poor regions with facilitated teleconsultations led by community healthcare workers.

When the pandemic hit, iKure’s Sujay Santra was well-positioned to address continuity of care for rural patients with a telemedicine platform that facilitates critical contact between rural patients and specialists.

But it goes beyond just setting up a video consultation and writing a prescription.

Thanks to a 2019 project with IBM, iKure now has an AI platform based on pre-built models available through IBM Cloud Pak for Data to analyze patient data captured from devices, hospital visits and home-based interactions with community health care workers. This is one strategy iKure employs to help specialists better manage care for patients – especially those who remain under shelter in place orders today.

Watch the video:

Santra launched iKure in 2010 after his father, who lived in a small town in West Bengal, was diagnosed with heart disease and taken to a local doctor. When his condition didn’t improve, Santra took him to a cardiology clinic in Bangalore, where he discovered that the local doctor had prescribed his father the wrong medicine.

Santra felt technology could help prevent such calamities – and began to envision an organization that could provide the best medical healthcare to people living in rural India. The idea: take healthcare beyond health facilities and hospitals to the doorstep, both providing last mile healthcare and eventually driving critical changes in public healthcare.

Today, iKure is a leading for-profit, primary health care provider providing affordable and accessible healthcare delivery to the most remote parts of India, covering close to 9 million across 7 Indian states, plus Africa, Malaysia and Vietnam.

Ensuring last mile healthcare through a hub and spoke model

The organization employs community health workers – mostly women – from the villages, who are provided smart phones loaded with the iKure application and medic bags that have various essential point-of-care devices to measure blood pressure, EKG and hemoglobin levels, enabling them to capture patients vitals at home.

When this data is entered into the iKure application, the system signals whether the patient is within normal range. On other days these patients can visit clinics, or the “spokes” which operate twice a week, where they can access doctor consultations, vision-testing machines, medicine and other essential tests. If they need access to other pathology tests or specialist consultations, they can reach those services at a “hub” only 12 miles away.

“Through a combination of a layered approach right from the hub to the spoke to the last mile of community health workers, we can provide a comprehensive sustainable primary healthcare to the remotest parts of India,” says Santra.

With the pressing demand to find ways to meet the needs of the beneficiaries without risk of Covid-19 exposure, iKure can take advantage of a Wireless Health Incident Monitoring System (WHIMS) to screen and monitor patients at their doorsteps, as well as for those visiting iKure’s hub clinics. This technology allows healthcare workers to facilitate the interactions between patients and doctors – and in turn, helps doctors manage patients’ health profiles, diagnoses, and prescriptions, through treatment plans created over multiple sessions.

The key to patient monitoring is iKure’s wearable Braveheart patch, which captures several points of data such as EKG, body movement, skin reactions and others – data transmitted into routers. Once iKure captures these parameters, a cardiologist can detect symptoms of a heart condition.

Managing the case load: Building a decision support system for cardiologists

Even with these accomplishments, Santra still faced a giant hurdle: capturing and sharing patient data would be only one part of the solution. With 1000 patients generating a large amount of data from every interaction, iKure embarked on a pilot with IBM to demonstrate how AI could help cardiac specialists better manage patient care by ranking more severe cardiac cases first. (The data iKure captures per patient is extensive, amounting to 152 parameters, each given a score based on severity. From there iKure presents doctors with the top 10 patients who need care immediately).

“The IBM team from US, Singapore and India were working as one team with iKure to make the lives of our doctors and our teams easier in terms of how we can prioritize the patients based on the of severity based on their other conditions – so we could identify the patients early on, in terms of cardiac management as well as prevent heart attacks and saving many, many more lives.”

iKure’s solution has helped bring trust and confidence to stakeholders and health officials alike — and allowing them to expand the platform outside India, including setting up an additional 200 hubs in the next four years.

The flexibility of Cloud Pak For Data’s ability to apply AI capabilities to such a wide variety of data streaming through various devices, platforms and integrations has been integral to iKure’s success and health of its patients – and as Santra says, with a better data repository they may be able to predict unknowns and prepare for other unprecedented events – including a future health crisis.

“With platforms like the one we have worked on with IBM we would never been able to treat patients beyond a basic level,” Santra said.

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How DataOps enabled Standard Bank to gain data quality and agility during changing market conditions

DataOps journey

Our data journey with IBM started when needing to respond to regulatory changes. We have since evolved to applying the technology to various business cases; most recently adapting to COVID-19 market conditions and now looking to the future for building new use cases with DataOps and AI. The journey begins with seeking to improve our data management, in the areas of data quality, governance, business reporting and customer experience. 

Regulatory market changes

The first market force challenges were regulatory -the Basel Committee on Banking Supervision (BCBS) 239, as well as the local regulatory standards and guidelines for record keeping to support Anti-Money Laundering. This regulatory requirement evolved as our first use case for end-to-end data management. A regulatory fine escalated this to a burning platform. We were investing tens of millions of dollars on data fixes in disparate places and we needed a disciplined data lifecycle approach that was sustainable. Improved data management required us to modernize our data operations with a data integration platform, governance catalog, and software tools for analyzing, cleansing and integrating data. By implementing a suite of IBM DataOps software with two-week agile implementation sprints, we now have a data catalog with its metadata to meet regulatory and compliance requirements— and embedded data quality and governance.

After meeting regulatory requirements, we then expanded on new use cases to improve our business reporting and customer experience, realizing data agility was as much of an asset as money in the bank. For business reporting, we changed the aggregation of our data sources, transforming from batch delivery styles and using static dashboards to persona-based access for near real-time reporting. Our branches improved their seller’s productivity as business performance monitoring reports provided metrics and insights on their marketing tactics. It also allowed for improvements in data modeling and provided our service teams the agility to make decisions for improving the customer experience.

Our data catalog, combined with our master repository for client data, became our single source of truth with embedded governance, capabilities to share a common business glossary of terms and definitions, and the ability to track data lineage. Software tools provided the capabilities to understand, cleanse and transform our data, while also analyzing the quality, structure, format and related relationships. Enabling the capabilities for self-service access to our data was critical to success, neither did we know how important it would be for our future—the agility needed to adapt to market forces.

Building agility into our culture

Also contributing to our success was creating an agile culture of continuous improvement, refining the criteria for data quality and business rules and definitions for the data catalog, conducting two-week sprints, standup’s and implementing metrics dashboards. We are beginning to see more cultural changes across our business units, such as data stewardship, data owner accountability and self-service access evolving across more job roles.

COVID-19 market forces

In 2019, our second use case focused on how to monetize our data for new business opportunities, versus just a focus on mitigating bank risk. For example, we mapped 52 key metrics to our target data sources and created dashboards to measure our success, while delivering metrics reporting in 24 hours. With COVID-19, our relationship bankers are working remotely and the ability to provide self-service access to dashboards has kept our teams engaged to maintain our productivity and business continuity metrics. These interactive dashboards have helped adjust to this new way of working and keeping our teams focused on meeting business commitments. The metrics reports with trusted, high-quality data have helped prepare Standard Bank for the implications of COVID-19 and the agility needed to navigate and pivot to new market conditions.

During COVID-19, we rapidly responded by creating a marketing tactic that leveraged client records with small business or student loans. Clients were advised their monthly loan payments would be deferred for three months due to COVID-19. We know our clients appreciated our level of responsiveness and especially not having to contact the bank directly to determine eligibility— a competitive differentiator in the markets we serve.

AI opportunities

What’s next for Standard Bank is to prepare for our digital future and build use cases for business opportunities with AI. Building a foundation for AI capabilities can help us leverage the API economy, as we can partner and ingest data from third party sources to create new revenue models. We will continue to focus on building our data agility and resilience as we partner with IBM and other entities on AI technologies— so we can be prepared for future disruptors. 

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IBM earns 13 top ranks in BARC’s The Planning Survey 20

BARC survey is the largest of its kind

Once again, IBM received top ranks in its five peer groups in the Business Application Research Center’s The Planning Survey 20. IBM beat out competitors in performance satisfaction, planning functionality and competitiveness in the “Enterprise Software Vendors” peer group and was named a leader in both flexibility and ease of use. This year’s praise adds to a long history of consistently high marks in the BARC survey.

BARC is a leading European consulting firm specializing in business software. The Planning Survey 20 is based on findings from the world’s largest and most comprehensive survey of planning software users, conducted from November 2019 to February 2020. The survey is designed to help planning software buyers make informed purchase and vendor selection choices.

IBM is a leader in performance satisfaction, among other KPIs

Highlights for IBM in the BARC study can be found at The Planning Survey 20: IBM Planning Analytics Highlights. The graphic shown below summarizes many of the strong current performance scores for IBM’s planning software, IBM Planning Analytics.

Click for larger imageEighty-six percent recommend IBM

Customers say it best. BARC reports that 86% of IBM Planning Analytics users say they would definitely or probably recommend their planning product to other organizations. One customer, a line of business leader in finance commented: “Great tool, extremely flexible and fast to develop scalable applications.”

Overall, 88 percent of respondents said they are either “somewhat satisfied” or “very satisfied” with Planning Analytics. A customer at a public sector organization with over 2,500 employees offered these insights about why IBM Planning Analytics is superior to spreadsheet planning:

“. . . It has the flexibility to build complex workflow that goes beyond the standard workflow features we have seen in other tools. It also covers statutory reporting, including non-trial balance data. It carries out mapping from the different charts of accounts used by our 40 partner organisations. It allows us to meet strict deadlines for revision of forecasts, consolidation and reporting …. It allows us to do things that would be impractical with spreadsheets.”

Praise for unprecedented scalability

BARC’s The Planning Survey 20 is a detailed quantitative report that puts software vendors under a microscope. BARC employs a deep list of criteria that reflects the key features users consider when choosing planning software, including performance satisfaction, data handling, number of users and requirements, to name just a few. IBM Planning Analytics performs well for many of these key criteria.

For instance, for large capacity data handling, nearly 40% of surveyed users chose Planning Analytics – 54 percent more than for the average planning tool. And on average, 475 employees per organization use Planning Analytics – 55 percent more employee users than for the average planning tool.

Integration with Microsoft Excel

An important feature of Planning Analytics is its tight integration with Microsoft Excel. Users comfortable working in spreadsheets can easily adopt a more modern, robust planning solution without a steep learning curve. Significantly more survey respondents reported high satisfaction with Planning Analytics as compared to Excel (42 vs 15 percent). Respondents also said they regularly gained more business benefits with Planning Analytics than with Excel (index of 6.2 vs 3.2). These benefits included:

  • Increased transparency of planning
  • Improved integration of different sub-budgets
  • Improved integration of planning with reporting/analysis.

About IBM Planning Analytics

Planning Analytics is an AI-infused integrated planning solution that automates planning, forecasting and budgeting. This solution accelerates planning cycles and leverages scenario planning to help users analyze the impact of decisions before making them. It is designed to deliver insights quickly so that users can adjust plans in real time. The helps clients realize time and cost savings by automating labor-intensive spreadsheet-based work. Customers can choose between on-premises, on cloud or on IBM Cloud Pak for Data, and access the same data from anywhere at any time.

Find out more

This blog covers just a few findings from BARC’s The Planning Survey 20. Get the full IBM highlights report for more details about the survey methodology and why customers are so satisfied with IBM planning software. Learn how IBM Planning Analytics can advance your business here.

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