In IBM Sterling OMS, Exception Handling is the procedure for managing deviations from the normal order processing flow – including incorrect pricing, missing information, inventory issues, stock shortages, payment issues, or shipping errors – which require immediate attention to preserve service quality and operational continuity. Retail businesses manage order processing and exception handling through manual entries and semi-automated systems. These tasks are typically divided among customer service teams, logistics staff, and operations managers, who rely heavily on traditional tools like spreadsheets and email communications.

The Strategic Impact of Automation

Order Exception handling procedures are crucial to maintaining competitive advantage and customer satisfaction. This traditional approach affects workload. A report suggests that employees spend around 30% of their time managing email alone, which involves communications related to order and exception management. In addition to being time-consuming, these manual processes are prone to errors that can affect your bottom line and customer satisfaction. With rising consumer expectations for quick service and flawless execution, automating these processes has become a strategic priority. Automation can transform every aspect of exception handling by improving efficiency and precision.

In IBM OMS, we have a reprocessing flag which makes the exception re-processible. And there is not out of the box automation process.

Automatic exception handling can be done in various ways in OMS including the following.

1. Writing a utility: We can write a utility to query all the alerts and exceptions and have all the possible solution for each exception. For example, getting cache issue because of multi thread while creating the order. In this case, simple reprocess will work. So, we need to specify the Error code inside utility to reprocess this exception.

In Utility, we must call the OMS rest API to get the exception and its details and then identify the solution and based on that reprocess as it is or modify the xml and reprocess.

Some time we must modify the input xml to fix the issues and reprocess with modified xml.

• Pros: This is the better automatic exception resolution in SAS environment. We are not allowed to query directly to database.
• For any changes to utility, we do not need a build.
• Cons: we need a separate environment to run this utility.

2. Writing an agent servers: We process the exception within the OMS. In this case we create an agent server in OMS. We will have to specify error codes and what to do for what error, fix the exception and reprocess or just reprocess depending upon the error code.

• Pros: This does not require a separate environment to run this utility, we can create OMS agent server to use this.
• Cons: This will be tied to the project workspace and if we need to change any code, it must be done using the build process.

3. Utility with database query: This can only be done in on-perm, Sas environment does not support the querying database directly. In this case we get directly query the database to get the exceptions and then reprocess or fix and reprocess depending upon specify error codes the exception using API.

• Pros: This is an easy and quick utility where you just write the database query and reprocess.
• Cons: we need a separate environment to run this utility

4. Reprocess when you get the exception – This automatic resolving exception has limitation as if it is not handled properly, it can cause the server to crash or not process the actual message. And since the risk of the implementation is too high, it is highly recommended to minimize this implementation or do it properly so that it never gets stuck in a loop.

• Pros: This does not require any overhead or utility to reprocess the exception.
• Cons: This can only be done for certain exception which we know can be fixed by reprocess

Advantages of Automation

• Operational cost reductions from minimizing manual labor and streamlining processes. Automation can cut operational expenses related to order processing by up to 40% by reducing the need for manual labor and decreasing the incidence of errors.
• Accuracy enhancements and lower error rates in order processing.

• Automated systems are highly scalable, allowing businesses to handle increased order volumes without proportionate staffing or manual workload increases.

Automation significantly improves customer satisfaction and loyalty by ensuring accurate, timely order processing and proactive exception handling. Automation not only brings substantial cost savings and operational efficiencies, but it also enhances the overall customer experience, paving the way for sustained business growth and success. Automation can be a valuable tool in managing order exceptions. By automating the process, we can reduce the risk of human error and ensure that exceptions are handled consistently. These benefits are not just specific to IBM Sterling OMS, but any OMS system can have these benefits by automating the processing of exceptions.

In today’s world, every retailer’s biggest challenge is to ensure the shopper’s loyalty, and retailers are constantly dealing with this. Retailers need an intelligent and efficient supply chain to deliver the product. Retailers who operate the order fulfillment without synced-up end-to-end order promising, risk losing shoppers, increased costs, and falling behind competitors who can meet customer demands more efficiently.

Order Promising ensures promises are kept, all the product in the cart, better customer experiences, and chaos is transformed into orderliness. IBM Sterling Intelligent Promising combines inventory and capacity visibility with sophisticated fulfillment decisioning to help the retailers to maximize inventory productivity, make reliable and accurate order promises, and optimize fulfillment decisions at scale.

There are plenty of benefits. They include:

1. Improved Customer Satisfaction: Accurate delivery estimates and reliable order fulfilment enhance customer trust and satisfaction. This leads to higher conversion rates and fewer cancellations.
2. Efficient Resource Utilization: Optimized inventory, production, and logistics such as consolidated shipping reduces wastage and operational costs.
3. Reduced Delays: Coordination between order promising and order management minimizes delays and ensures timely deliveries.
4. Enhanced Brand Reputation: Consistently meeting delivery commitments strengthens the brand’s reputation for reliability.
5. Lower Operating Costs: Better inventory control and resource allocation lead to cost savings.
6. Streamlined Supply Chain: A well-coordinated system ensures smoother and more efficient supply chain operation.

How This “Promise” can be Achieved

Adoption of cutting-edge technology enables retailers to ensure the most accurate ‘Promise’ to their Shoppers! IBM’s Sterling Intelligent Promising (SIP) solution offers greater certainty, choice and transparency across shoppers’ buying journey. It is designed to revolutionize order promising and fulfilment in the ever-evolving world of commerce.

It’s a SaaS platform that has the following three services.

1. Inventory Visibility
2. Promising
3. Fulfillment Optimizer

All the three services modules are independent, but they share a common single platform SIP.

1. Maximize inventory productivity: Use real-time inventory visibility to confidently expose inventory and maximize conversions, gaining granular control over inventory actions, such as safety stock setting based on configurable business rules. Improve inventory turns by applying additional context like channel, fulfillment type and labor availability when making available-to promise decisions.
2. Make and manage order promises: Improve conversion rates by confidently delivering order and delivery promises across every step of the shopping journey, including the product list page, product detail page, cart, and checkout. Automate the review of inventory, capacity, and costs to make informed promises, and harness powerful AI during fulfillment to simplify complex scenarios like orders with third-party services and support a wide range of fulfillment options.
3. Optimize omnichannel profitability: Set operating performance objectives and KPIs using real cost drivers (like distance, labor, capacity, and carrier costs) and profit drivers (markdown, stockout), so you can confidently make the best fulfillment decisions for your business objectives. By optimizing across thousands of fulfillment permutations in milliseconds, retailers can ensure balance between profitability and the best customer experience.

SIP is the future of OMS, it ensures that the customers receive their orders on time, with trust and loyalty. SIP can employ AI and predictive analytics to anticipate demand, optimize inventory, and offer customer-centric promises. In an increasingly complex supply chain environment, it collaborates with suppliers for synchronized commitments, helping businesses stay agile and responsive to market shifts. IBM Sterling Intelligent Promising is not just a solution for today but a strategic asset for the future.

In today’s highly competitive retail landscape, providing exceptional customer experiences is paramount to success. Customers now demand seamless interactions across multiple channels, personalized services, and real-time access to product information. To address these evolving expectations, IBM has developed the Sterling Next Generation Store Engagement solution, a powerful platform that helps retailers transform their in-store experiences and bridge the gap between physical and digital realms. In this blog post, we will explore how IBM Sterling Next Generation Store Engagement is revolutionizing retail experiences.

Seamless Omnichannel Integration:

One of the key strengths of IBM Sterling Next Generation Store Engagement is its ability to seamlessly integrate various channels. The solution consolidates data from different sources, such as online stores, mobile apps, and physical stores, creating a unified view of customer preferences, purchase history, and inventory availability. This holistic approach enables retailers to deliver personalized and consistent experiences across all touchpoints, whether customers are shopping online, visiting a brick-and-mortar store, or engaging through mobile devices.

Empowering Store Associates:

Store associates play a crucial role in enhancing customer experiences, and IBM Sterling Next Generation Store Engagement empowers them with the right tools and information. The solution provides real-time access to inventory data, allowing associates to quickly check product availability, locate items within the store, and offer accurate delivery timeframes. Additionally, the platform enables associates to access comprehensive product details, including specifications, and recommendations, thereby enabling them to provide expert guidance and personalized recommendations to customers.

Enhanced Customer Engagement:

IBM Sterling Next Generation Store Engagement offers a range of features that enhance customer engagement and satisfaction. For instance, the platform enables store associates to handle in store Pickup, Ship from Store and in store returns of online, POS and mobile orders, thereby ensuring a dedicated and uninterrupted shopping experience. Moreover, the solution leverages inventory visibility and SIM microservice modules to provide seamless customer experience with near real time product availability and accuracy among the store and DC networks thereby saving the sale and creating an omni channel realm.

Omni Channel Customer Expectations Illustration Simplified

Technical Overview:

IBM Sterling Next Gen Store Engagement is a single, monolithic front-end application that is built with Angular and Bootstrap framework. It leverages micro frontend architecture of the single spa framework that splits monolithic application into smaller and logical modules known as micro-front-ends, and at the same time keeps the user experience similar to using a single application. It supports multiple features that spans across store fulfillment and inventory management operations. Each feature is modeled as an angular feature module that contains multiple routes or views. The objective is to break up each feature module into individual angular application. Each single-spa-enabled Angular application has its own package.json and controls its own dependencies, so an application can be upgraded independently. The single-spa-enabled angular application uses a common set of components and services through shared widget libraries.

Micro-frontend Architecture and Evolving Technologies adapted for the solution

IBM Sterling Next Generation Store Engagement is a game-changer for retailers looking to create exceptional in-store experiences that seamlessly integrate the physical and digital worlds. The use of the latest front-end technologies is another advantage that makes the application more scalable and easier to expand and customize depending on the client’s needs. By empowering store associates, enhancing customer engagement, streamlining checkout processes, and providing valuable insights, the platform enables retailers to meet and exceed customer expectations. As the retail industry continues to evolve, solutions like the Next Generation Store will play a crucial role in helping retailers thrive in the digital era.

Reference:

IBM Sterling Store Engagement (Next-generation) – IBM Documentation

We use different formats (sequential, XML, etc.) as a source or target in ETL jobs. In some cases, HTML is needed to create the output file.

What is HTML?

HTML (Hypertext Markup Language) is a text-based approach and the foundation of a website. It is the backbone of a website that creates content in a structured and organized manner. HTML also provides a creative outlet for those interested in design and allows content to be structured to be easily accessible.

HTML Files/Documents:

The documents help to present the data in an organized manner; that is, they include images, headings, paragraphs, links, a footer, etc.

They also are different from text files (ordinary files).

1. In an HTML file, we can change the format as per our requirements, but in a text file, we can’t
2. HTML is capable of embedding media, videos, etc., but text files aren’t.
3. HTML commonly uses the internet to view the document, but text files have a wide variety.
4. HTML also uses text.

The HTML file size is greater than a normal text file as HTML uses tags.

Basic Structure of an HTML File:-

Like the sequential file component, the HTML file component is not available directly in the DataStage tool, we design the job and use it as a feature in the DataStage ETL tool.

Below are the following four components used to create the job to create a file in HTML format:
1. Row Generator
2. Transformer
3. Funnel
4. Sequential File

Here are the steps one needs to follow to create a file in DataStage

Procedure: –

1. Drag and drop the components from Palette: Row generator, Oracle Component,     Transformer, Funnel, and for output   File.

2. Connect the row generator with the transformer, the Oracle component with the transformer, then the funnel, and produce the output sequential file.
   Design the job to create the HTML file, as an HTML file consists of a header, body, and footer.

Configuration of the components:

1. Use the row generator component to create the first header of the HTML file and set properties as below: –

Link the Row Generator to Transformer and set the properties as below:-

Then Oracle component configuration as below: –

For HTML Footer, again use Row Generator and connected to Transformer as below: –

Transformer linked to Row Generator: –

Then above all multiple links connected to the funnel to produce a single output

Configuration Of Funnel: –

As needed to generate an HTML File(Header, Body, and Footer) so set it as Sequence funnel type in Properties of Funnel

Link the funnel to the Sequential output file and configuration as below: –

Execute the job:-

Procedure:-

1. Save the job (Ctrl + s)
2. Press (Ctrl+F5) to run the job and will get the below output: –

Conclusion: –

As mentioned above, we can establish an HTML file feature in DataStage.

HTML files are structured and constructed in a specific way. When we compare an HTML file to a text file, it offers advantages.HTML files can contain videos, media, and other elements that allow users to see the content more effectively. This HTML feature is generated by a task that already has a Sequential file with its own property. We can call the above job and use it in DataStage as the child job.

Happy Learning.

IBM has named its IBM Champions for 2023 and for the second year in a row, Eric Walk was named as an IBM Champion.  

What is an IBM Champion 

IBM Champions are individuals who possess in-depth knowledge and expertise around IBM products, services, and technology. They are committed to sharing their expertise with others to help them get the most out of IBM software, solutions, and services. 

IBM Champions provide answers to questions, gather content, organize user groups, and events, as well as assist others in the community to fully leverage the potential of their investment in IBM products and services.  

Meet Our IBM Champion, Eric Walk

To say that Eric Walk is experienced and well versed is an understatement. Eric has been at Perficient for 12 years and has worked on IBM projects for all 12 of those years. Eric enjoys getting to see his work make real impacts on business outcomes as well as designing and deploying products for clients. 

Although Eric has worked with IBM solutions for 12 years, the work never gets old because IBM software is always evolving. He says “the progress in just the last 5 years has been incredible, and there’s so much further to go. I’m excited to see the impact of the recent advancements in IBM software on our clients’ success.” 

A piece of advice Eric gives is to “think about the opportunity to maximize existing investment by taking your IBM software to the cloud as SaaS or PaaS. For those new to IBM, they’re not some old stuffy fossil, they’re doing innovative stuff and their platforms power the biggest businesses in the world for a reason.”

Eric is honored to be named an IBM Champion because it reinsures the strong working relationship between Perficient and IBM. 

This continued recognition is a testament to Perficient’s 20-plus years as a top IBM partner and my personal investment in the ecosystem over the last 12 years. We appreciate the close working relationship we have with IBM’s product and sales teams and their willingness to be open with us and incorporate our client’s feedback into their roadmaps and products over the years.

Champion IBM Solutions with Perficient 

We are a select group of elite Platinum Business Partners who can sell and service across all IBM software brands, which allows us to deliver comprehensive industry-focused and business-aligned solutions. Along with our IBM Champion, Eric Walk, we are also an award-winning partner that provides verified IBM solutions. 

Explore our IBM partner page to discover how we can assist you in creating value-driven and transformative solutions and your clients. And feel free to reach out we would love to hear how we can help you! 

Red Hat AnsibleFest 2022 took place October 18-19 showcasing new updates to Red Hat’s automation software. The Ansible Automation Platform is an open-source IT automation tool that automates provisioning, configuration management, application deployment, orchestration, and many other manual IT processes.

These capabilities, paired with current updates, improve Ansible’s offerings and give customers with what they need in order to modernize.

New Managed Ansible on AWS Offering

Red Hat introduced Red Hat Ansible Automation Platform’s availability in the AWS Marketplace, a digital catalog with thousands of software listings that makes it easy to find, test, buy, and deploy the software  on Amazon Web Services (AWS). Building on Red Hat’s goal to extend a common IT automation solution wherever organizations operate, this new offering enables customers to quickly automate and scale operations from their datacenter on AWS and out to the network’s edge.

New Managed Ansible on Azure Offering

Another noteworthy update is the implementation of Ansible on Azure at Azure Marketplace. This collaboration pairs hybrid cloud automation with the convenience and support of a managed application. Customers can experience Red Hat’s fully supported offering directly from the Azure Marketplace.

Event-driven Ansible

Automation allows us to give systems and technology speed and agility, while minimizing human error. However, when it comes to trouble tickets and issues, developers are often left to traditional and manual methods of troubleshooting and information gathering. One application of Event-riven Ansible is to remediate technology issues before near real-time, or at least trigger troubleshooting and information collection to find the root cause of an outage while support teams handle other issues. Event-driven Ansible has the potential to change the way IT teams respond to issues and illuminates many new automation possibilities.

IBM AI and Ansible

Red Hat and IBM Research collaborated on Project Wisdom, the first community project to create an intelligent, natural language processing capability for Ansible and the IT automation industry. Using an artificial intelligence (AI) model, the project aims to boost the productivity of IT automation developers and make IT automation more achievable and understandable for diverse IT professionals.

Perficient + Red Hat

Perficient’s middleware and application modernization expertise earned us the Red Hat 2020 Application Platform Success Partner of the Year and 2018 Rising Star Partner of the Year.

As a Red Hat Premier Partner and a Red Hat Apex Partner, we offer a modern approach to delivering application modernization as well as cloud implementations and migrations.

Red Hat provides open-source technologies that enable strategic cloud-native development, DevOps, and enterprise integration solutions to make it easier for enterprises to work across platforms and environments.

Healthcare organizations play a key role in offering access to care, motivating skilled workers, and acting as social safety nets in their communities. They, along with life sciences organizations, serve on the front lines of addressing health equity.

With a decade of experience in data content and knowledge, specializing in document processing, AI solutions, and natural language solutions, I strive to apply my technical and industry expertise to the top-of-mind issue of diversity, equity, and inclusion in healthcare.

Here are five questions that I hear commonly in my line of work:

1. What is the digital divide, and how does it impact healthcare consumers?

There are still too many people in this country who don’t have reliable access to computing devices and the internet in their homes. If we think back to the beginning of the pandemic, we can see this in sharp relief. The number one impediment to the shift to virtual school was that kids didn’t have devices or reliable internet at home.

We also saw quite clearly that the divide is disproportionately impacting low income people in disadvantaged neighborhoods.

The problem is both affordability and access.

The result, through a healthcare lens, is that people without reliable access to the internet have less access to information they can use to manage their health.

They are less able to find a doctor who’s a good fit for them. Their access to information about their insurance policy and what is covered is more restricted. They are less able to access telehealth services and see a provider from home.

All this compounds because we’re using digital and internet-connected tools to improve healthcare and outcomes for patients. But ultimately, the digital divide means we’re achieving marginal gains for the populations with the best outcomes already and not getting significant gains from the populations that need support the most.

2. How can organizations maintain an ethical stance while using AI/ML in healthcare?

Focus on intrinsic bias, the subconscious stereotypes that affect the way individuals make decisions. People have intrinsic biases picked up from their environment that require conscious acknowledgement and attention. Machine learning models also pick up these biases. This happens because models are trained on data about historical human decisions, so the human biases come through (and can even be amplified). It’s critical to understand where a model comes from, how it was trained, and why it was created before using it.

Ethical use of AI/ML in healthcare requires careful attention to detail and, often, human review of machine decisions in order to build trust.

3. How can HCOs manage inherent bias in data? Is it possible to eliminate it?

At this point, we’re working to manage bias, not eliminate it. This is most critical for training machine learning models and correctly interpreting the results. We generally recommend using appropriate tools to help detect bias in model predictions and to use those detections to drive retraining and repredicting.

Here are some of the simplest tools in our arsenal:

• Flip the offending parameter and try again.
• Determine if the model would have made a different prediction if the person was white and male.
• Use that additional data point to advise a human on their decision.

For healthcare in particular, the human in the loop is critically important. There are some cases where membership in a protected class changes a prediction because it acts as a proxy for key genetic factor (man or woman, white or Black). The computer can easily correct for bias when reviewing a loan application. However, when evaluating heart attack risk, there are specific health factors that can be predicted by race or gender.

4. Why is it important to educate data scientists in this area?

Data scientists need to be aware of potential issues and omit protected class information from model training sets whenever possible. This is very difficult to do in healthcare, because that information can be used to predict outcomes.

The data scientist needs to understand the likelihood that there will be a problem and be trained to recognize problematic patterns. This is also why it’s very important for data scientists to have some understanding of the medical or scientific domain about which they’re building a model.

They need to understand the context of the data they’re using and the predictions they’re making to understand if protected classes driving outcomes is expected or unexpected.

5: What tools are available to identify bias in AI/ML models and how can an organization choose the right tool?

Tools like IBM OpenScale, Amazon Sagemaker Clarify, Google What-if and Microsoft Fairlearn are a great starting point in terms of detecting bias in models during training, and some can do so at runtime (including the ability to make corrections or identify changes in model behavior over time). These tools that enable both bias detection and model explainability and observability are critical to bringing AI/ML into live clinical and non-clinical healthcare settings.

EXPLORE NOW: Diversity, Equity & Inclusion (DE&I) in Healthcare

Healthcare Leaders Turn to Us

Perficient is dedicated to enabling organizations to elevate diversity, equity, and inclusion within their companies. Our healthcare practice is comprised of experts who understand the unique challenges facing the industry. The 10 largest health systems and 10 largest health insurers in the U.S. have counted on us to support their end-to-end digital success. Modern Healthcare has also recognized us as the fourth largest healthcare IT consulting firm.

We bring pragmatic, strategically-grounded know-how to our clients’ initiatives. And our work gets attention – not only by industry groups that recognize and award our work but also by top technology partners that know our teams will reliably deliver complex, game-changing implementations. Most importantly, our clients demonstrate their trust in us by partnering with us again and again. We are incredibly proud of our 90% repeat business rate because it represents the trust and collaborative culture that we work so hard to build every day within our teams and with every client.

With more than 20 years of experience in the healthcare industry, Perficient is a trusted, end-to-end, global digital consultancy. Contact us to learn how we can help you plan and implement a successful DE&I initiative for your organization.

IBM Cloud Pak for Data- Multicloud Data Integration and Data Governance:

As we all know, IBM Cloud Pak for Data is a cloud-native solution that enables you to put your data to work quickly and efficiently. Let’s understand below features of IBM Cloud Pak for Data. I’ll also be discussing what practical experience I have gained while working on this through some detailed steps:

• Multicloud Data Integration with DataStage as a part of Data Fabric Architecture
• DataStage AVI (Address Verification Interface)
• Watson Knowledge Catalog – Data Governance Processes and Data Privacy

Multicloud Data Integration with DataStage:

IBM DataStage on IBM Cloud Pak for Data is a modernized data integration solution to collect and deliver trusted data anywhere, at any scale and complexity, on and across multi-cloud and hybrid cloud environments.

This cloud-native insight platform — built on the Red Hat OpenShift container orchestration platform — integrates the tools needed to collect, organize and analyze data within a data fabric architecture. Data fabric is an architecture that facilitates the end-to-end integration of various data pipelines and cloud environments through intelligent and automated systems.

It dynamically and intelligently orchestrates data across a distributed landscape to create a network of instantly available information for data consumers. IBM Cloud Pak for Data can be deployed on-premises, as a service on the IBM Cloud, or on any vendor’s cloud.

Source of above Data Stage diagram: IBM Documentation

Prerequisites: Need to have Data Stage Instance provisioned to perform the required tasks.

Below are the Tasks performed on Data Stage:

1. Created a project and added DB2 as a connection
2. Added data to the project. Data added from a local project sample file
3. Create a DataStage flow that extracts information from DB2 source systems
4. Performed steps using operations to transform the data using filters on Customer columns.
5. Compiled and ran the DataStage job to transform the data.
6. Deliver the data to Target – Project – Asset Tab, and Data asset Customers were present there.

 Prerequisites:

• Signed up for Cloud Pak for Data as a Service
• Added Data Stage Service Instance
• Also added Watson Knowledge Catalog and Cloud Object Storage services

 Below are the Tasks performed on Data Stage for Multiload Data Integration:

1. Created a Sample Project and associated with a Cloud Object Storage instance
2. Ran an existing DataStage flow that created a CSV file in the project that joins the two different Customer application data sets.
3. Edited the DataStage Flow and changed the Joint node settings, and selected the Email Address column name as Key
4. Added PostgresSQL Database to the DataStage Flow to get more Customer related information.
5. Added another Join Stage to join filtered application data
6. Added a Transformation Stage that created a new column by summing up two different Customer $amount columns.
7. Added MongoDB database to get more information related to Customer
8. Added a Lookup Stage and specified the range to get Customer information
9. Ran the DataStage flow to create the final Customer output file.
10. Created a Catalog so data engineers and analysts can access the relevant Customer Data.
11. Viewed the output file in the Project and Published it to a Catalog
12. In the Project->Asset Tab -> Now, you can view the data.

DataStage AVI (Address Verification Interface):

IBM’s Quality Stage Address Verification Interface (AVI) provides comprehensive address parsing, standardization, validation, geocoding, and reverse geocoding, available in selected packages against reference files for over 245 countries and territories.

AVI’s focus is to help solve challenges with location data across the enterprise, specifically addresses, geocodes, and reverse geocode data attributes. Data Quality and MDM have never been more critical as a foundation to any digital-minded business intent on cost and operational efficiency.

IBM cares about Quality addresses to avoid negative customer experience, Fraud Prevention, Cost of Undelivered and returned Mail, and maintaining key Customer Demographic data attributes.

Source of the above diagram: IBM Documentation

Prerequisites:

• Signed up for Cloud Pak for Data as a Service
• Added Data Stage Service Instance

Below are the Tasks performed on the Data Stage AVI Feature:

1. Created an Analytics Project in IBM Cloud Pak for Data
2. Added a Connection to the Project -> Selected DB2 and provided all DB and Host details
3. Added DataStage Flow to the Project. Below three Primary Categories appear
   1. Connectors (Source and Target Access Points)
   2. Stages (Data Aggregation, Transformation and Table lookup, etc.)
   3. Quality (Data Standardization and Address verification)
4. Added and Configured Connectors and Stages to the DataStage Flow
   1. Added a Source Connector from Asset Browser and selected address as an Input
   2. Added Address Verification from the Quality menu
   3. Added Sequential file to generate the .csv output
   4. Connected all the above 3 files from left to right
   5. Provided the required details and inputs for Address Line 1 and Address Line 2
5. Compiled and Executed the AVI DataStage Flow
6. Go to Project ->Data Asset->You would see a .csv file would be created
7. Open the .csv file and review the columns. Here you will see more columns added from the Address Verification Process
8. Please review the Accuracy Code String to see Verified versus unverified addresses.

Watson Knowledge Catalog:

IBM Watson Knowledge Catalog on Cloud Pak for Data powers intelligence, the self-service discovery of data, models, and more, activating them for artificial intelligence, machine learning, and deep learning. With WKC, users can access, curate, and share data, knowledge assets, and their relationships wherever they reside.

WKC’s below features were performed and tested.

• Data Governance processes include role assignment, access control, business terms, and classifications.
• Created a Centralized Data Catalog for Self-Service Access
• Created workflow to manage the business processes
• Mapped Business value to Technical asset

                                                                                                            Source of above Data Governance diagram: IBM Documentation

Prerequisites:

• Signed-up for Cloud Pak for Data as an Admin

Below are the Tasks performed on Watson Knowledge Catalog:

1. Click Administrator->Access Control->Created a New User Group
2. Added Users under New User Group:
   1. Quality Analyst
   2. Data Steward
3. Provided Pre-defined Roles – Administrator, Data Quality Analyst, Data Steward, and Report Administrator.
4. Go to Governance -> Categories –> Customer Information ->Customer Demographics subcategory to view the Governance Artifacts
5. Here you can explore the Governance Artifacts such as Address, Age, Date of Birth, Gender, etc.
6. Go to Governance -> Business Terms ->Account Number. Here you can view the business terms such as – Description, Primary Category, Secondary Category, Relationship, Synonyms, Classification, Tags, etc.
7. Go to Governance -> Classifications-<Confidential Classification. Here you can view the business terms such as – Description, Primary Category, Secondary Category, Parent/Dependent Classification, Tags, etc
8. Go to Administration -> Workflows ->Governance artifact management->Template file-> You will find different approval templated here, including publish and review steps.
9. Selected Automatic Publishing and provided Conditions (Create, Update, Delete, Import)
10. Saved and Activated it.
11. There were more things you could do in WKC, such as:
    1. Creating Governance Artifacts for Reference data to follow certain standards and procedures.
    2. Creating Policies and Governance Rules
    3. Creating Business Terms
    4. Creating Reference Data sets and Hierarchies
    5. Creating Data Classes – such as data fields or columns

Watson Knowledge Catalog – Data Privacy:

Here I have learned:

• How to prepare trusted data with Data Governance and Privacy use case of the Data Fabric.
• Created Trusted data assets by enriching them and with data quality analysis.
• The goal was how data consumers can easily find high-quality and protected data assets via a self-service catalog.

Prerequisites:

• Signed up for Cloud Pak for Data for data as a service with Watson Knowledge Catalog Services

Below are the Tasks performed on Watson Knowledge Catalog:

1. As a data steward – Created a Catalog by going to the Catalog menu with Enforce Data Policies
2. Created Categories by going to Governance ->Categories. This contains the Business Terms that we have to import later.
3. Added Governance ->Business Terms and imported the .csv file
4. Published the Business Terms.
5. Imported data to a Project by going to Projects ->Data Governance and Privacy Project->Assets->New Asset->Metadata Import ->Click Next->Select the Project->Select Scope and Connection
6. Selected Data Fabric Trial for DB2 Warehouse connection so the data can be imported and viewed as a table.
7. Enriched the Imported data by selecting Metadata Enrichment from the Assets tab. You can Profile the data, Analyze the Quality and Assign the terms. This will help the end-user to find the data faster.
8. Viewed the Enrich metadata
9. Published the enriched data to a Data Catalog.

Conclusion: IBM Cloud Pak for Data is a robust Cloud Data, Analytics, and AI platform that provides a cost-effective, powerful MultiCloud Data Integration and Data Governance solution.

Perficient and IBM recently partnered with BWG Connect to conduct a survey of 100+ healthcare professionals exploring technology adoption in the post-pandemic environment. Experts from these organizations joined a roundtable discussion to review what is top of mind for healthcare organizations in 2022 based on the responses to this study.

The panel was moderated by Aaron Conant, co-founder and managing director of BWG Connect, and a panel of experts that included:

• Brendan Fowkes: Health Plan, CTO, IBM
• Tom Lennon: Director, Healthcare Data & Analytics, Perficient
• Eric Walk: Director, Knowledge, Data & Content, Perficient

A Focus on Equity in Healthcare

Fowkes shared that in a study IBM conducted with 39 Blue Cross Blue Shields, equitable distribution of health was top of mind in every single organization. Similarly, participants in the BWG survey frequently brought health equity into their answers. Our panelists took some time addressing concerns around the use of automation furthering this issue:

Tom Lennon: As we start to push into AI and leverage AI, just because you’re using information around social determinants, it doesn’t mean that you’re going to drive equity. This is because the bias is inherent within AI until you can manage it and normalize. That bias is still going to be there. Learning how to use that and adapt it is important because that gives us the opportunity to help address some inequality. There’s also the opportunity that it could make it worse because AI doesn’t have the same feeling and natural intelligence as we would have when we’re worried about different groups within our population.

EXPLORE NOW: Diversity, Equity & Inclusion (DE&I) in Healthcare

Other Key Takeaways from the Research Study

The survey generated interesting data points around data privacy, communicating ROI, and gradual technology adoption, which the panelists also explored:

Privacy, Compliance, and Trustworthy AI 

The research study revealed that many healthcare professionals had questions and concerns about the safeguarding of protected health information (PHI). The moderator opened this discussion with a question to the other panelists about protecting sensitive data when using automation.

BF: We use the term “trustworthy AI” because, if there’s any violation of a patient’s trust, you’ve lost everybody, whether it’s the nurses, the call center agents, or the patients themselves. We start with that foundation and a core belief that everything has to be trustworthy from the start. There’s no hidden “whatever else.” You’re not borrowing the data to go do something else.

If you start with that foundation at your core belief, you can start to overcome any of these objections. You’re going to design a system with the proper security, privacy, and consent. Borrowing a little bit of what we’ve done on the academic side, you can use other examples to make sure that everything’s secure when you’re designing that trustworthy story.

EW: And how are you going to control the data? There are CRM options out there that have the proper certification security controls in place to allow you to load your PHI and secure it and control it appropriately. Even so, that may not be an option for any given organization. How can we take the AI and the machine learning models, and bring them to where the data is, so we have fewer concerns about shipping the data to other places for training and for executing those models? We can more tightly control how that data is being sent, where it’s being sent, and how it’s being used.

You’re going to have more challenges in terms of just making sure you understand the way each of those solutions controls and secures PHI because a good number of them are compliant in various ways. Or you can look to other options that allow you to maintain trust through the whole chain of custody of the data by using more complex and nuanced solutions.  That allows you to take your modeling and do it where and when you need it.

READ NOW: HIPAA Compliance and Protecting PHI

A Crawl-Walk-Run Approach to Technology Adoption 

Many see adopting this technology as a daunting task and a huge undertaking. The round table moderator addressed this by asking the other panelists how they are breaking down the process for clients who don’t think they have the bandwidth to begin any work with machine learning.

EW: You can do this use case-by-use case. You can tackle one challenge in one area with this kind of technology and slowly expand it. The upfront cost of dealing with security concerns and ethical concerns is lower than you might expect. If your use case is sufficiently simple, dealing with the additional concerns of more complicated use cases becomes incremental cost. The overhead there is not going to be huge and it’s not going to sink your plan to expand out into other areas.

BF: I call it “crawl-walk-run;” other people use “land-and-expand” or “start small.” Whatever metaphor you want to use, there’s a way to take something simple. An example was middle-aged men not taking blood pressure medication. If you looked at socioeconomic factors in that model, and we predicted not adherence, it would ship them a three-month supply and give them a call to tell them it’s coming. It’s not a complex prediction. It didn’t take a lot of work. What’s the generic cost, about 30 dollars to keep somebody adhering?

There still is true machine learning in the model. We did a whole scatter plot where we can look at all these different techniques and pick the right model that was most accurate. But it wasn’t a complex use case. It was just a different way of using the data they had and then helping them automate something to drive a benefit at the same time. So, it is all about crawl-walk-run.

EXPLORE NOW: The Healthcare Executive’s Guide to Intelligent Automation

See More!

This research study revealed key insights on the adoption of automation technology in the medical industry. Our panelists discussed how your organization can take advantage of these technological improvements by gradually adopting, communicating clear goals, and remaining compliant. Learn more about what industry professionals are prioritizing in 2022 by exploring the published research study and watching the full on-demand recording.

IBM introduced its IBM Champion program back in 2008 and has continued to recognize innovative thought leaders in the technical community that are helping others derive greater value from IBM software, solutions and services.

This year, one of the professionals being honored is Perficient’s Eric Walk a director of knowledge, data and content.

What Makes an IBM Champion?

IBM Champions are experts and thought leaders around IBM products, offerings, and technology. And they are driven to share their knowledge and expertise to help others. IBM Champions are often answering questions, creating content, running user groups and events, and helping others in the community to better understand the possibilities their investment in IBM offerings brings.

Candidates are entered into a nomination process every year and top contributors are selected and welcomed into the program.

Qualifications include:

• Evangelizes and advocates for IBM
• Shares knowledge and expertise
• Helps grow and nurture the community
• Expands reach across the IBM portfolio
• Provides feedback on IBM products and direction

Meet Your Perficient 2022 IBM Champion

We know we have great thought leaders here at Perficient, but we’re always proud when our experts are recognized by others for the work they’re doing in the community. And Eric Walk is no exception.

He has worked closely with our partner IBM for many years and is honored to be named an IBM Champion this year.

“Over the last 11 years, I’ve been working closely with IBMers and IBM technologies to solve our client’s problems and improve their information management and business processes,” Walk said. “Being an IBM Champion is not just recognition of my work to support various IBM product teams with feedback and go-to-market approach, but also the hard work across the whole Perficient family continuing to support and deploy IBM’s market leading solutions.”

For him, this is a great opportunity to grow his reach as a thought leader and to learn from the other Champions and the broader IBM Community.

Connect with Us

We are part of a select group of elite IBM Platinum Business Partners who can sell and service across all IBM software brands, which allows us to deliver comprehensive, industry-focused and business-aligned solutions. We are also an award-winning, certified Software Value Plus solution provider.

Check out our IBM partner page and find out how we can help you deliver innovative solutions that unlock value for you and your customers and transform your business.

Digital process automation (DPA), also called process orchestration, leverages digital technology to perform a task in order to accomplish a workflow or function, such as the loan process at a bank. With DPA, you can define event-driven business rules that provide a streamlined next step for all processes within your enterprise.

As enterprises continue to invest in automation, selecting the vendor with the right platform to meet your business’ demands is paramount.

The Forrester Wave: Digital Process Automation Software, Q4 2021

In The Forrester Wave: Digital Process Automation Software, Q4 2021, Forrester identified fourteen of the most significant providers that offer DPA software solutions for application development professionals. Forrester evaluated the top vendors in the market against twenty-three different criteria, covering current offering, strategy, and market presence. The report also provides analysis and comparisons of each provider to help companies find the right DPA solution for their needs.

In the report, Forrester recommends that application development professionals work with DPA software outfitted with a suite of features for robotic process automation (RPA), embedded task managed, artificial intelligence (AI), machine learning (ML), content intelligence, scalability, low-code development, and more. Using a general-use low-code solution is also recommended unless your organization has a particularly sophisticated use case.

Learn More About Perficient

Perficient is proud to partner with Pega, IBM, Appian, Microsoft, and ServiceNow, all of whom were Leaders in The Forrester Wave report, to address the key elements of intelligent automation with their best-in-class product offerings. Each enterprise’s needs differ, but we can help you determine the product or system for your particular use case. We also provide readiness evaluations, business case development, implementation and migration services, and rapid development and pilots.

Visit our Intelligent Automation page to learn about our partnerships and solutions.

Get the Report

To find out more, the report is available to Forrester subscribers and for purchase. You will learn which business challenges leaders are looking to solve with DPA, understand the capabilities critical for DPA vendors, and get detailed analyses of the top DPA vendors in the marketplace.

Perficient and Reuters Events recently hosted a virtual roundtable about how inventory transparency boosts consumer confidence. Industry experts Karie Daudt and Justin Racine, Directors of Commerce Strategy at Perficient, facilitated the discussion which featured eleven panelists from different enterprise brands including Lowe’s, Staples, Uber, and Fastenal. Each panelist discussed how their organization is using inventory transparency to exceed consumer expectations in the ever-evolving retail industry.

The Role of Order Management and the Future of Commerce

The dramatic shift to online shopping has made inventory transparency essential to providing a customer experience that sets brands apart from the competition. This includes informing consumers about the quantity of an item available and providing alternate choices when an item is out of stock.

Karthik Mahadevan, Senior Director, Product Management at Lowe’s, pointed out how inventory forecasting and the ability to promise, (ATP), provide accurate end-to-end visibility. When companies are familiar with their inventory, they can forecast how long it will take to get the products to customers and calculate the lowest delivery cost and fastest route. Meanwhile, the ability to promise, (ATP), builds ROI and customer loyalty. If a customer wants to buy a Samsung refrigerator and sees it is out of stock online but knows when it will be back in stock, Lowe’s won’t lose the sale to a competitor.

Adam Moriarty, VP, Inventory Management & Planning at Staples, mentioned the importance of inventory visibility depending on customer expectations. Some customers are willing to accept an alternate version whereas other customers are not as flexible. Staples provides good front-end information about inventory availability and is working towards providing more accurate delivery dates instead of a range of back-order dates.

Lauren Collett, Director of Sales at Uber, spoke to how Uber has pivoted to leverage drivers to partner with shippers to bring products directly to consumers. She emphasized the importance of determining the right information and data to provide to shippers to ensure customer satisfaction.

Jeff Hicks, VP, Sales at Fastenal, pointed out how technologies such as vendor-managed inventory, (VMI), allow companies to react to consumer demand more quickly. VMI helps with supply chain unreliability by serving as an extended warehouse. Fastenal is building a network of plans to give visibility into inventory using VMI.

A Roadmap to Better Inventory Visibility

Overall, the panelists agreed on the benefits of having an order management system, (OMS), in place. An effective order management system provides accurate end-to-end visibility into inventory and makes it easy to execute order fulfillment, provide a stellar customer experience, and maintain a competitive advantage. Inventory transparency empowers customers at the point of purchase and builds brand loyalty, ultimately boosting ROI.

To learn more about how inventory transparency delivers a smooth shopping experience read The Evolution of the Modern Supply Chain.