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.

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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. 

Learn more

Want to learn how IBM DataOps capabilities can create opportunities to monetize your data, increase data agility and build new models for AI?  Visit ibm.com/dataops.

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.

*Microsoft, Windows, Windows NT, and the Windows logo are trademarks of Microsoft Corporation in the United States, other countries, or both.

Rikke Jacobsen: Helping Danish companies build solid analytics foundations and prepare for change

This story is part of Analytics Heroes, a series of profiles on leaders transforming the future of business analytics.

Though Rikke Jacobsen has worked with IBM Cognos Analytics for more than 20 years, she still gets excited about the prospect of helping her clients make sense of their data — you can hear it in her voice. “I’m known as ‘Mrs. Cognos,’” she says with a smile. “People come to me when they want to build new reports or do any kind of data analysis.”

Rikke is the CEO of CogniTech, a Danish company offering solutions for data warehousing, business intelligence, budgeting and forecasting, financial consolidation, AI and machine learning. CogniTech is a newer player in the analytics world. Rikke founded the company in 2019, as part of a strategic buyout with five other veterans from worldwide software giant DXC, and 8 other employees from her department followed her and the 5 other partners to CogniTech. As the only IBM Gold Partner in the country, they’re steadily building a reputation for excellence.

Rikke acknowledges the challenges of working under lockdown, saying, “I have employees in four locations that I used to visit every other week. So now I try to touch base a lot and try to replicate that team spirit. Luckily my employees are used to working from home and they are all very responsible and independent. They know what I expect from them and I have full confidence that they do their best every day and help to give the customers the attention they need.” In the face of a global pandemic, Rikke speaks with enthusiasm and optimism about her industry. “Companies have so much data that they can be using better. That’s what I’ve been helping them with for two decades — and I still have work to do,” she laughs.

The need for better data analysis in unprecedented times

Rikke understands the huge challenges facing companies during the COVID-19 crisis. “It’s more important than ever to help get our customers access the right reporting information about their sales and the demands.” CogniTech hosts online workshops to help companies understand what they can get out of new, more agile planning tools like IBM Planning Analytics powered by TM1®. In these times, companies are looking at sales and also closely looking at their cost bases, to see where they can cut costs. This is where IBM solutions come in. End-to-end planning solutions help businesses avoid the pitfalls of siloed business planning and the associated errors from reconciling disparate plans, saving time and money.  Unifying data across the organization with real time refreshes lets people work with the latest data and prevent basing decisions on inaccurate data.

More transparency, improved sales

CogniTech helped the management team of a large Danish hardware store prepare some reports showing revenue, order amount, and gross profit by customer, product, and vendor. “The report was intended for management, but it was accidentally shared with salespeople in one department,” says Rikke. As it turns out, the happy accident lead to a friendly competition for who was earning more revenue, and who was leading on gross profit. And after six months they had raised their gross margin by 5%. Because of this, their client now shares sales data with all their employees.

The best IT investment ever made

Rikke is a real believer in the power of IBM’s analytics tools, and she shares that passion with her customers. Of one client in Denmark, she recalled, “I went up to help him implement IBM Cognos Analytics. We had spent three days together, and he had hardly said anything. Then one afternoon, I showed him reports that I built, and he suddenly said, ‘this has been the best IT investment I have ever made.’ So that was a really, really great day.” Rikke sees a lot of customers who spend too much time putting data into Microsoft Excel sheets, and she likes helping companies save time. She says “customers can save so many hours with Cognos. And on the planning side with Planning Analytics.”

Today, Rikke and her team are helping their clients on a difficult journey. “We work with a large food service company who caters to hotels and restaurants. So obviously, things are challenging for them right now. We helped them implement Cognos about twenty years ago and in the following years they also implemented Planning Analytics and SPSS.” Thanks to their agility, self-service capabilities, and built-in AI, these solutions helped this client to get an overview of the sales and inventory and it helps them in the difficult times with the major changes in market demand. They have also been able to make quick changes and work with Planning Analytics using sandboxes to simulate how the changes in the market demand will influence their business. Now, they’re seeing their retail business grow, which also creates a more valuable need for reporting and changes to their forecast.

Delivering value to clients

As a full-service partner, Cognitech goes the extra mile when it comes to implementation and training.  They help customers implement IBM Cognos Analytics and IBM Planning Analytics, build the models and data cubes and set it up so it’s easy for the client to input data and do the reporting and analysis. “People are often stuck on Excel for budgeting and planning so Planning Analytics is a huge win for clients.” Rikke and her Team shows them how connected the IBM tools are and how Cognos Analytics works for all the other parts of the client’s company. “When we show them the explorations and dashboards, and how Planning Analytics works with Cognos, they get really excited.” says Rikke.

“It’s not just about showing them how the tools work, but about how they are presenting the information, the design principles,” says Rikke. These solutions are specifically designed with the user in mind, so that they can be easy to adopt and deliver immediate value to the user.

Rikke is a big proponent of the self-service capabilities of Cognos. But she believes in first helping clients build the right data models. Rikke says, “[Business users] can’t just transition from an existing ERP system, take out the data, and build a data model,” she always recommends building a new data warehouse with new models — which they can build using tools like IBM Db2. The business sers can then build reports and create the analysis with the shared data models.

Being an analytics hero

Rikke is very proud to be nominated as an Analytics Hero, saying “I’ve had a burning passion in this field for 20 years, so it feels fantastic that IBM is recognizing that, and celebrating that.”

Her clients couldn’t agree more. Most recently, Rikke and her team helped Nukissiorfiit, Greenland’s energy and water supplier, transform their business and accelerate their mission to 100% sustainable energy using Cognos, Planning Analytics and AI forecasting.

Learn more about IBM’s analytics solutions, or read about other Analytics Heroes!

Five real-life Netezza performance server use cases

Right now, businesses are focused on getting the most out of their Data and AI platform without overspending to make sure it is operational and running productive workloads.  This means having an always-on, 24×7 system that can handle huge spikes in workloads when needed. Many customers have turned to Netezza for this reason, depending on its record of reliability and simplicity.

The newest member of the Netezza family is Netezza Performance Server for IBM Cloud Pak for Data. It is 100% compatible with existing Netezza appliances and migration can be accomplished with one simple command: nz_migrate. Existing applications will work ‘as is’ once pointed to the new Netezza.  Netezza’s ability to do deep analysis on petabyte-scale data volumes is combined with the data virtualization and data science capabilities of a data and AI platform, Cloud Pak for Data. Additionally, it is a containerized implementation and can run on any cloud. 

Many customers have already begun to take advantage of these features. Below, we take a closer look at five of those exemplary Netezza users and the use cases that have led them to success.

1. Netezza Performance Server in Manufacturing

A manufacturing customer in Europe wanted to maintain optimal inventory levels to meet demand for its made-to-order product. To do so, it needed to gain insights into the most popular combinations of product capabilities.

With Netezza Performance Server for Cloud Pak for Data, this manufacturing client was able to accelerate its reporting processes which, in turn, helped them to predict customer preferences more quickly. This enabled them to stock the optimal quantities of each product and effectively serve business-to-business customers. Ultimately the optimized stock levels drove increased sales.  This is made possible because of Netezza Performance Server’s blazing fast database engine. This is due to its architecture which is laid out and optimized to take full advantage of the hardware.

2. Netezza Performance Server in Finance

A large banking client with branches across the United States encountered regulations (such as protecting customer’s personal confidential information in a credit card transaction) that grew the number of rules it had to enforce on an increasing amount of data. The new rules-based decision-making processes were not fast, agile, or consistent enough to meet the demands of geographic expansion, increasing regulation, and new customer expectations.

By deploying a new, centralized rule engine on Netezza Performance Server for Cloud Pak for Data, this banking client greatly accelerated the creation, dissemination, and processing of new business rules, driving smarter and faster decision making.  As new regulations come into effect, the analytics required become more difficult to perform.  Netezza Performance Server shines in its ability run complex queries in an optimized way for quick execution times – improving time-to-insight and decision speed. 

3. Netezza Performance Server in Healthcare

A hospital in the United States wanted to improve patient outcomes but was struggling with combing through massive amounts of physiological data to extract insights as it arrived.

Netezza Performance Server helped the hospital build a new computing platform that could stream the data coming in and analyze thousands of vital sign readings in near real-time, enabling it to use proven predictive models to support clinical decision-making. In other words, clinicians could spot trends and look at risk indicators which enabled faster, more effective intervention. Netezza Performance Server does this by abstracting some of the complexities in today’s enterprise data. When users need custom reports that are often too difficult to put together with large data warehouse sets, it redefines what you can do with on the spot data analysis. 

4. Netezza Performance Server in Insurance

With regulatory reporting demands becoming more frequent and stringent, an insurance company wanted to facilitate compliance without reducing its ability to deliver high-quality customer service.

It deployed a hyper-converged system, Netezza Performance Server for IBM Cloud Pak for Data System, helping it to process many more records per day in much less time. This made faster reporting possible which facilitated regulatory compliance and laid a foundation for enhanced, data-driven customer services.  Netezza Performance Server takes advantage of SSD drives and FPGA’s to make sure that it is fully utilizing powerful hardware to execute large queries on large amounts of data in the quickest time. 

5. Netezza Performance Server in Retail

A large retailer in the United States who is known as a leader in speciality store brands and has a reputation for constantly providing a high level of customer service and a pleasant shopping experience needed to integrate data from multiple brands and channels into a data warehouse. It wanted to enhance decision making and provide faster access to actionable information about current production and inventory as well as future demand. 

Netezza Performance Server allowed this retailer to reduce reporting time from days to minutes and reduced supply chain logistics costs. Combining the sales, inventory and logistics information with a simple and fast data warehouse design meant that they could update all transactional data within minutes. To glean insights from data in various places you once needed to move, cleanse and combine the data then look for the insights. With Netezza Performance Server for Cloud Pak for Data, lightening fast analytics can be performed on all data without needing to move it thanks to data virtualization. The automated governance also helps ensure that the data is clean and ready to use.

These are just a few examples of the wide variety of use cases in which Netezza is used all over the world.  Netezza Performance Server for Cloud Pak for Data is optimized for a multi-cloud world built with a blazing fast engine, simple load and go capabilities that are optimized for your Data and AI journey. Learn how it can unlock the value of your data in brand new ways by reading our Netezza solution brief. Schedule a free one-on-one consultation with one of our data experts to learn how Netezza can help with your industry.

Accelerate your journey to AI.

Jumpstart your journey to AI expertise: recap of Data and AI Virtual Forum talent sessions on demand

Talent: It’s a key issue impacting today’s AI-hungry organizations. While AI skills are in high demand, organizations admit they’re hard to come by. In fact, the lack of talent scarcity has been called out as one of the top three hurdles to AI adoption, after data complexity, and a lack of trust in AI systems.

IBM’s first ever Data and AI Virtual Forum brings together AI leaders across a variety of disciplines to dissect the talent problem and offer up fresh solutions based on successful use cases. See our recommended sessions that focus on how to cultivate the right skills mix to help AI initiatives succeed.

All sessions can now be accessed here on demand.

Start with the keynote: Talent and Tech in a Post-Pandemic World with Daniel Hernandez, General Manager, Data and AI, IBM and Aaron Levie, Chief Executive Officer, Co-founder and Chairman, Box

In a world of rapid change, flexibility and agility are essential to maximizing the productivity of your talent base. Aaron Levie, Chief Executive Officer, Co-founder and Chairman, Box describes the new way of work that delivers results for your business and customers.

“We think the future of work in not going to be defined the by office you go into or the computer you’re using but really the ability to work from anywhere, with anyone, at any time, on any device and be able to share securely with people in and outside your business in real time.”

Get a head start in cultivating the talent and expertise your business needs with AI: Only as Good as the Ecosystem that Supports It where three IBM Business Partners describe their strategies to help clients start their AI journey as they empower their clients’ teams.

If you’re looking to understanding the concept and possibilities of AI critical no matter your role in an organization, check out Building AI Skills: A How-To Guide. Ana Echeverri, IBM AI Skills Learning and Certifications Lead, gives an overview of course work of AI and Data Science education. She guides you through easy-to-access education opportunities to start your knowledge journey here on the AI Learning Community

If you’re a CIO challenged with modernization agendas: How to save a half million dollars a minute with AI Ops covers how IT teams can address incident responses faster to minimize and even prevent outages altogether by making use of all their data and leveraging the power of AIOps.

IBM, Forrester, and IDG cover key success factors for AIOps, such as unifying and correlating all data, applying an intelligent overlay across disparate monitoring tools, and integrating AIOps into the ChatOps environment.

“…CIOs are challenges with modernization agendas. They need to find ways to free up that time for this higher value work and look at creating a more digital approach to everything their doing and to reaching their clients,” said Jessica Rockwood, IBM Vice President of Development, Multicloud Management and AIOps.

Over 30,000 IBM engagements have helped clients across all disciplines accelerate their journey to AI. With self-service analytics, predictive capabilities and natural language processing, modern analytics solutions are empowering users, allowing them to be more strategic and provide more value to their organization.

Change the analytics game: empower users with AI goes into detail about the analytics solutions infused with AI are transforming planning and analytics from the office of finance to sales, marketing, supply chain and beyond.  You’ll learn about IBM’s AI ladder and how AI infused analytics eliminate replication of efforts, helps harmonize all your data to more easily drive insights and get context for those decisions.

Accelerate your journey to AI.