How Data Is Helping To Resolve Supply And Demand Challenges

Perhaps one of the most sweeping outcomes of the 2020 pandemic has been its effect on the global supply chain. From consumer goods to raw materials, products are either unavailable for purchase or take excessively long to reach their destinations. Even common grocery items like baby formula are becoming hard to find, as reported by CBS in an April 2022 report.

Analysts predict that the major supply and demand crunches will have less impact in the future, per CNBC. However, businesses and buyers aren’t content to wait until early 2023 to feel less of a pinch. They want answers now, and they’re getting them in the form of innovative uses of data and technology.

As it turns out, data—when utilized thoughtfully—has value in smoothing out supply chain hiccups. Below are several examples of how data is being tapped to tackle post-pandemic procurement and delivery issues.

1. Data is revealing where companies should focus their resources to satisfy customers.

Nothing is as frustrating for shoppers as being unable to get what they want. To better allocate resources and anticipate needs, some brands are leveraging real-time data analytics. Understanding in-the-moment demands enables teams to pivot and respond.

An example of this type of process is Chipotle’s use of Semarchy’s data management tool. After “The Great Carnitas Shortage of 2015,” the company realized that it needed to make adjustments to its supply chain. By aligning operations, communications channels, and ordering platforms, Chipotle found it could more easily stay ahead of supply chain issues. This has helped the company meet customer experience assumptions and avoid snags.

2. Data is reducing friction from delays in service industries.

Many services that followed more traditional in-person models were forced to embrace digitization during Covid. Many found that their internal processes weren’t ready for the challenges or consumer expectations of online transactions, though. For instance, some small to mid-sized financial lenders realized that they didn’t have the workflows or tools to streamline application processing. As a result, they risked falling behind their bigger competitors.

Data-driven software solutions from entities like publicly traded MeridianLink have helped fill this gap. MeridianLink, valued at over $2 billion, designed a data-rich platform to gather and process loans rapidly. Their platform has enabled nearly 2,000 financial institutions to swiftly turn around consumer loan applications without causing friction.

Due to the improvement in efficiency backed by data, banks, credit unions, and mortgage lending houses can keep pace. In today’s strong real estate market, that’s a huge supply and demand advantage.

3. Data is freeing employees to concentrate more fully on supply chain management.

Overcoming major supply chain hurdles can only happen when thought leaders have the bandwidth to brainstorm. Regrettably, far too many of them are bogged down by repetitive tasks. If those tasks can be automated, they can take up far less time. The result is teams who can concentrate on solving high-level concerns.

For instance, consider digital pioneering company IBML and its Cloud Capture software. The software captures, identifies, and classifies information from any source such as a complex invoice or a standard customer return form. Once appropriately logged, the information becomes available to authorized users. This type of consistent data capture facilitates a less clunky document processing.

It also frees executives, managers, and supervisors to divert attention toward pressing supply chain concerns. The supply chain conundrum won’t be fixed overnight or even in a few months. Yet fresh, data-driven solutions can help companies undergo fewer stressors as a result of supply and demand interruptions.

Many businesses have yet to digitize their supply chain processes, but rather rely on paper-based exchanges. This can lead to very limited visibility and coordination, and processes being heavily disrupted in times of crisis. This can lead to a failure to anticipate and meet demand and consequent loss of revenue.

Digitization requires investment and change management, but if properly leveraged it supports visibility, collaboration and communication. Access to real-time data compared with historical data can help businesses to identify cost drivers, support demand-supply balancing, manage warehouse cost by way of stock optimization, optimize processes, and in turn, identify opportunities to lower costs.

This can result in an ecosystem which makes digitization and data sharing pay by improving economic and financial performance.The collection and analysis of data creates valuable visibility and understanding within the supply chain but also greater confidence in the analysis and decision making process.

It enables businesses to introduce governance mechanisms and business models to measure the demand signal across the supply chain. Data can be used to oil the wheels of the supply chain but to achieve these benefits collaboration and the sharing of data is required amongst participants across the supply chain or at least between critical parts of the chain.

Collaboration and data sharing require trust. This can be challenging, particularly where the parties in the supply chain are competitors.

Serenity Gibbons

By:

Source: How Data Is Helping To Resolve Supply And Demand Challenges

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12 Things You Should Know About Observability

Observability is the ability to measure the internal state of a system — an application, for instance, or even a distributed IT system) by examining its outputs, namely sensor data. While it might seem like a recent buzzword, the term originated decades ago.

(Fun fact: In-the-know types abbreviate observability to “o11y,” because there are 11 letters between the initial O and the final Y. Those are some cool m11s.)

Observability uses three types of telemetry data to provide deep visibility into distributed systems and allow teams to get to the root cause of a multitude of issues:

  • Logs — a record of events, e.g. what happened
  • Metrics — measured against a standard, e.g what changed by how much and over what period of time
  • Traces — where in the system did it happen

Now let’s take a look at those immutable rules to keep in mind when considering, adopting and improving an observability solution.

1. An observability solution uses all your data to avoid blind spots

The best way to solve a problem is to collect all the data about your environment at full fidelity — not just samples of data. Traditional monitoring solutions fall short when working with microservices-based applications because they randomly sample traces and often miss the ones you care about (unique transactions, anomalies, outliers, etc.).

When assessing observability solutions, look for those that do not sample and also retain all your traces, as well as populate dashboards, service maps and trace navigations with meaningful information that will actually help you monitor and troubleshoot your application.

2. Operates at speed and resolution of your software-defined (or cloud) infrastructure

Different use cases require different resolutions, depending on how critical they are (a.k.a. how many people are angry at you and/or how much it’s costing). As you start to collect data from more dynamic microservices running on ephemeral containers and serverless functions, you’ll need to collect data in different ways than you did in a virtual machine environment.

If you have microservices running on Kubernetes-orchestrated containers that spin up and down automatically in minutes, or serverless functions that instantiate for only seconds, you’ll need a much finer view. Plan for that need now, as you begin to adopt microservices, because it will be very difficult (and costly) to add it later.

3. Leverages open, flexible instrumentation and makes it easy for developers to use

Plan on using open, standards-based data collection from day one. Proprietary agents are difficult to maintain, degrade service performance and may be outdated before you know it. Choosing to rely on common languages and frameworks will give you the most flexibility not only in how you collect data, but also what cloud solutions you use.

4. Enables a seamless workflow across monitoring, troubleshooting and resolution with correlation and data links between metrics, traces and logs

Organizations manage multiple point tools. It’s not uncommon to find application owners flagging a performance issue with one tool, then contacting another IT operations team that uses a different tool to try to understand how the issue is impacting critical workloads and business performance.

Obviously, this doesn’t work when your actions are measured in seconds. Your observability solution should have all capabilities fully integrated, providing you with relevant contextual information throughout your troubleshooting.

5. Makes it easy to use, visualize and explore data out of the box

A completely fake statistic by a fictional analyst firm shows that most companies use only 12% of the capabilities their software systems provide. Now that’s a powerful made-up statistic. Observability should give you intuitive visualizations that require no configuration — like dashboards, charts and heat maps — and make it easy to interact with key metrics in real time. Your solution should also allow custom dashboards that can help keep an eye on particular services of interest.

6. Leverages in-stream AI for faster and more accurate alerting, directed troubleshooting and rapid insights

As much as we love humans, there’s no denying that cloud-native environments produce too much data for people to make sense of manually. Old-school alert triggers are often inaccurate, causing floods of alerts that frustrate on-call engineers. Observability solutions built with real-time analytics surface relevant patterns and deliver actionable insights before you need them. Look for solutions that are effective at baselining historical performance, performing sophisticated comparisons and detecting outliers and anomalies in real time.

7. Gives fast feedback about (code) changes, even in production

Observability is not just for operations and should be employed during development. Once code is deployed, teams need to understand what is happening within their applications as each release flows down the delivery pipeline. You can’t understand your pipeline, or correlate pipeline events with application performance and end-user experience, if you don’t understand what is happening inside your application. Observability delivers synthetic monitoring, analysis of real-user transactions, log analytics and metrics tracking, so teams can understand the state of their code from development through deployment.

8. Automates and enables you to do as much “as code”

The idea behind the “observability as code” movement is that you develop, deploy, test and share observability assets such as detectors, alerts, dashboards, etc. as code. Monitoring and alerting as code involves automated creation and maintenance of charts, dashboards and alerts as part of service life cycles. Doing so keeps visualizations and alerts current, prevents sprawl and allows you to maintain version control through a centralized repository, all without having to continuously manage each component manually.

9. Is a core part of business performance measurement

In the data age, you need to know what’s going on from development through delivery in order to measure business performance. Observability gives you a view into every layer of the stack, as well as key metrics tailored to your business needs. In cloud-native environments, small upticks in service usage can spiral, even creating increased latency for specific customers. It’s important to understand the KPIs by which your business is measured and how the teams within your organization will consume the data. Observability does that.

10. Provides observability as a service

Modern observability platforms provide centralized management so teams and users have access controls and gain transparency and control over consumption. Implementing clear best practices for observability across your business can not only cultivate a better developer experience, empowering them to work more efficiently and focus on building new features. It can also improve cross-team collaboration, cost assessment and overall business performance.

11. Seamlessly embeds collaboration, knowledge management and incident response

While incidents may be inevitable, a strong observability solution can mitigate downtime or even prevent it entirely, saving businesses money and improving the quality of life for on-call engineers. To respond to and resolve issues quickly (especially in a high-velocity deployment environment), you’ll need tools that facilitate efficient collaboration and speedy notification. Observability solutions should include automated incident response capabilities to engage the right expert to the right issue at the right time, all leading to significantly reduced downtime.

12. Scales to support future growth and elasticity

Have you ever heard the phrase “Duty Now for the Future”? It’s a Devo album from 1979, so it has nothing to do with observability. But the phrase does contain a relevant — immutable — truth. You need to invest now for your future needs and not just your current needs. The same is true for observability.

To meet the needs of any environment — no matter how large or complex — observability solutions should be able to ingest petabytes of log data and millions of metrics and traces, all while maintaining high performance. This ensures that your investments are future-proof.

Now that you’ve read about the benefits of observability and the characteristics of a modern observability solution, take the next step and find out more, including how to implement an observability solution that meets your needs now and in the future. Be sure to download 12 Immutable Rules for Observability.

Splunk Inc. turns data into doing. Splunk technology is designed to investigate, monitor, analyze and act on data at any scale.

Source: 12 Things You Should Know About Observability

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Reducing Risk When Migrating Mission-Critical Applications To The Cloud

Over the last decade, significant strides have been made in cloud computing, and today’s enterprises have substantial data and application footprints in the cloud. Many organizations are moving toward implementing cloud-based operations for their most crucial business applications.

A cloud-first mindset is usually a given for new companies and continues to gain traction for established enterprises. Still, existing legacy infrastructures and large on-premise footprints that don’t map easily to cloud architectures are slowing and even blocking faster adoption.

Organizations are poised to prioritize cloud investments over the next five years, according to the results of IDC’s Future of Operations research. The appeal includes the potential for an improved experience for customers, employees and suppliers/partners, better development agility, improved time to market and increased operational efficiency across organizations.

Although a pivot to the cloud could complete the evolution of the business from an operational and capital perspective, significant barriers to broader adoption still exist.

Cloud spending is on track to surpass $1 trillion by 2024, partly due to urgent changes to business operations driven by the pandemic, which accelerated cloud adoption timelines for many companies. And the results of recent research find that optimizing cloud costs tops companies’ 2021 priorities for the fifth year in a row.

Increasingly ambitious migration timelines are driving important decisions about moving critical applications without fully understanding the risks. Organizations are addressing application and data migration with largely ineffective one-size-fits-all solutions that don’t always meet expectations, often causing more problems than they promise to solve.

Others are moving with extreme caution when deciding which applications to keep on-prem and which to move, migrate or refactor. Mission-critical apps remain on the legacy infrastructure to assure control over the foundational data and safely maintain business as usual.

Moving from massive, on-prem data centers to the cloud presents a future filled with possibilities but also a level of risk due to the various unknowns within this significant paradigm shift. After all, a mission-critical app can be essential to the immediate viability of an organization and fundamentally necessary for success.

Although moving to the cloud is the way forward for many modern companies, migration can prove time-consuming and highly challenging, often with incomplete or unacceptable results. Successful migration can further business opportunities, but the risk of failure is considerable, and the high visibility that accompanies these major initiatives increases the level of exposure and consequences of said failure.

Mission-critical initiatives often cross the length and breadth of organizations, across low-level operational groups up to the C-suite and beyond. But just as all data is not created equal, neither are clouds or migration strategies.

Data Mobility Matters

With increasing cloud investments comes a growing need for more accessible data mobility. As more data moves to the cloud and strategies expand to occasionally include multicloud environments, there’s an expectation that underlying cloud resources deliver about the same level of performance as on-prem. But, often, the required type and volume of cloud resources are not available and deployment is difficult or impossible.

Performance is instrumental in determining where a mission-critical application should live and drives myriad scaling considerations and challenges. Sometimes, particular features, functionality and capabilities are lacking.

Perhaps the data primarily resides in a private or hybrid cloud to engage in cloud bursting on the public cloud when capacity needs to balloon. Longtime legacy challenges of architecting for the peak versus the average persist. Still, cloud decisions have forced IT leaders to relinquish a level of control over the physical infrastructure, significantly increasing risk.

Managing data mobility is challenging. To increase success, plan an approach that minimizes workflow disruptions of critical processes while ensuring sufficient capacity to support expected workloads and providing enough scalability to handle unexpected workloads. Managing random workload fluctuations requires a solid plan and a scalable, flexible and agile architecture to avoid those black swan events that are all too threatening.

Cloud Migration Considerations

Successful migration is not easy, but for many applications, it’s pretty simple to migrate to a platform as a service (PaaS) or managed service and be up and running fairly quickly. But for those performance-sensitive vertical stack monolithic applications running on the most expensive hardware for decades, moving can prove challenging and even impossible.

Ideally, refactoring enhances an application without negatively modifying external behavior and improving the internal architecture, as well as perhaps gaining cost efficiencies, maintenance or performance. But not all mission-critical applications are a fit for a refactor. Complexity, cost and the risk of disrupting a mission-critical app that’s performing as expected are valid reasons to leave some apps on-prem.

Others are constrained by performance requirements that aren’t achievable in the cloud with current offerings. There are fundamental limitations to the types of applications and databases that can quickly move to the cloud, and overhauling those solutions introduces significant risk, possibly resulting in critical delays and higher costs.

Solving Migration Problems

The best plan to mitigate the risks and improve the odds for cloud migration is to eliminate silos between multiple clouds and on-prem — regardless of type or location — facilitating a free flow of information in a simple, resilient, well-understood fashion. The next truth can’t be overstated:

Data is the new oil and should be treated as such. Just as trained specialists are leveraged to find and extract oil, specialized experts should be utilized when performing high-risk, business-changing moves regarding mission-critical data and the application stacks that access it. Ideally, the team migrating mission-critical applications should be proficient in enabling data mobility across environments without refactoring to reduce risk.

The question of cloud migration in 2021 is often no longer “if” but “when and how.” The material risk of maintaining the status quo can be significant, and avoiding moving mission-critical applications to the cloud is often no longer an option. A wise man once said, “What’s dangerous is not to evolve,” and this truism fully applies to an organization’s journey to the cloud.

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Source: Reducing Risk When Migrating Mission-Critical Applications To The Cloud

What An Ethical Data Strategy Can Look Like

That’s according to Angela Benton, the founder and CEO of Streamlytics, a company that collects first-party consumer data transparently and aims to disrupt the current model of third-party mining of data from cookies and other methods that raise privacy and ethics concerns. Most recently, she was named one of Fast Company‘s Most Creative People for helping consumers learn what major companies know about them and paying them for the data they create while using streaming services like Netflix or Spotify.

In the latest Inc. Real Talk streaming event, Benton explains that she founded the company with minorities in mind, particularly the Black and Latinx communities, because of the disproportionate way they’ve been affected by data and privacy. For example, she points to the recent controversy over facial recognition data being sold to the police, which has a much higher error rate when comparing data of Black and Asian male faces, which could potentially lead to wrongful arrests.

“That becomes extremely important when you think of what artificial intelligence is used for in our day-to-day world,” she says, noting that AI is used for everyday interactions like loan applications, car applications, mortgages, and credit cards. Using her company’s methods, Benton says, clients can secure ethically sourced data, so that algorithms won’t negatively affect communities that have historically suffered from discriminatory practices.

Here are a few suggestions from Benton for finding data ethically without relying on third-party cookies.

Do your own combination of data sets.

“How [Streamlytics] gets data is very old school,” Benton says. Instead of relying on tech to combine data points, she says, you can manually compare data you already own and make assumptions using your best judgment. You may have data from a Shopify website, for example, about the demographic of your customers, and then you can go to a specific advertiser, like Hulu, for instance, to then target people that fit that profile.

Use your data to discover new products.

You can also look to your data to find common searches or overlapping interests to get ideas for new products, Benton says. Often, she says, she receives data requests from small business owners to discover ideas that aren’t currently on the market, for example, a vegan searching for a vitamin.

This combination method surprised Benton when she presented clients with data. “I thought it was going to be more focused on just like, “How can I make more money?” she says. “But we are hearing from folks that they want access to data to use it in more creative ways.”

Don’t take social media data at face value.

Benton and her company purposely do not source social media data because she thinks the data leave too much out of the full picture. You may get a customer’s age and “likes” from a social media page, but that doesn’t tell you what they’re searching for or what their habits are.

Related:

Data Privacy: 4 Things Every Business Professional Should Know

5 Applications of Data Analytics in Health Care

Data Science Principles

“That’s not, to me, meaningful data. That’s not where the real value lies,” she says. “We’re not focused on what people are doing on social media, we’re focused on all of the activities outside of that.” She gave a scenario where a consumer is watching Amazon Prime, while also scrolling on Uber Eats to find dinner.

Data signals are happening at the same time, but they’re not unified. It’s up to businesses to connect the dots. To Benton, that’s more meaningful than what you’re posting and what you’re liking on social media.

Source: What an Ethical Data Strategy Can Look Like | Inc.com

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References:

“Datafication and empowerment: How the open data movement re-articulates notions of democracy, participation, and journalism”.

“Who Owns the Data? Open Data for Healthcare”.

“Note – The Right to Be Forgotten”.

“Big Data ethics”.

“Data workers of the world, unite”.

“Challenges and Opportunities of Big Data in Health Care: A Systematic Review”

Personal Data trading Application to the New Shape Prize of the Global Challenges Foundation

The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences

“Methodology – Global Open Data Index

The Open Knowledge Foundation

Data Or Processes: Where To Begin With Intelligent Automation

Over the past year, many clients I’ve spoken with have been looking for ways to make processes smarter, more adaptable and more resilient. According to our recent research, many companies see the combination of AI and automation — or intelligent automation — as key to achieving these goals.

Despite the promise of better operational performance with intelligent automation, a common question is where to begin: with the process itself or with the data that will power the process? The answer lies in identifying which outcome you’re trying to achieve. Getting the sequence wrong could counteract the very goal you’re pursuing.

The right starting point 

Here are two examples that distinguish when a process-led vs. data-led approach makes the most sense with intelligent automation:

How can we improve our operational efficiency?

Amid global uncertainty, supply chain disruptions and social distancing requirements, improving operational efficiency has become a priority for many businesses. The goal in this case is to improve speed and accuracy across the value chain, and achieve outcomes faster without cutting corners.

Adding data intelligence can significantly reduce errors, remove process hurdles and reveal where corrections are needed. But doing so requires a strong process automation backbone in order to shape when and how the data is applied. So in this case, a process-led approach is best.

For example, we’re working with a major insurance provider to improve customer lifecycle management. Typically, insurance customers who file a claim experience long decision times, a lack of visibility into decision making and repeated or disconnected requests for information submission.

Insurers can distinguish themselves by being fast, frictionless and responsive in how they handle claims. However, operating in a highly regulated industry and with overt risks around claims fraud, speed can never be a trade-off for accuracy and compliance.

A contributing factor to the insurer’s process challenges was the dependence on third-party systems and disparate data sources to make decisions. We helped the company implement an automated and fully integrated process for claims handling, which was then supported with AI and data modeling to segment customer profiles and personalize services.

The system has helped reduce the turnaround on claims capture by as much as 80% and shorten overall claims procedure times from 14 days to just two, all while maintaining the necessary high levels of accuracy and regulatory compliance. The insurer has also received positive customer feedback on the effectiveness and quality of services.

How can we be more agile in our product and service offerings?

Leading retailers have an impressive ability to recommend relevant products and anticipate customers’ next actions. Whether shoppers search for a needed item, browse relevant sites or interact with brands across different channels, digitally savvy retailers can connect the dots in real-time and make recommendations with a high degree of precision.

With so many factors and variables at play in dynamic online customer environments, companies need an agile approach that allows them to test the market, gather feedback and continuously improve in order to meet customer needs.

We’re working with an online fashion retailer to deliver this level of personalization. The company is well aware of the speed at which consumers’ tastes and styles change, and realized it needed to move swiftly to gain and keep customers’ attention.

Because it was vital to gain insights into consumer preferences, we took a data-led approach. We helped the retailer use existing data to gain a deeper consumer understanding. Using this insight, we then designed a process that segmented the brand’s customer base and enabled all interactions and product recommendations across channels like chatbots, email and social media to have the highest degree of relevance, timeliness and usefulness.

The combination of process improvements and data insights allowed for an integrated digital thread to run through all phases of the customer lifecycle, including product design and development, sales and after-sales. As a result, the retailer can now drive more relevant customer interactions and next-best offers, which in turn has improved customer mindshare, loyalty and revenue.

Accelerating the path to Intelligent Automation

To get the most out of intelligent automation, process and data need to work in harmony. Automated processes enable greater efficiency, while data enables better decision-making.

By coordinating these attributes — and having a clear outcome in mind — businesses can add intelligence to how and where they automate processes in a way that accelerates business outcomes while ensuring the quality of service is enhanced.

To learn more, visit the Intelligent Process Automation section of our website. View our latest webinar on Redesigning Work for the Post-Pandemic Age.

Chakradhar “Gooty” Agraharam is VP and Commercial Head of EMEA for Cognizant’s Digital Business Operations’ IPA Practice. In this role, he leads advisory, consulting, automation and analytics growth and delivery within the region, helping clients navigate and scale their automation and digital transformation journeys. He has over 25 years of consulting experience, working with clients on large systems integration, program management and transformation consulting programs across Asia, Europe and the Americas. Gooty holds an MBA from IIM, Calcutta (India’s Premier B school), and has executive management certifications from Rutgers, Henley Business School. Gooty has won reputed industry awards with MCA for his contribution to the digital industry in the UK and is a member of various industry forums. He can be reached at Gooty.Agraharam@cognizant.com

Source: Data Or Processes: Where To Begin With Intelligent Automation

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