Sympathy vs. Empathy: The Key Differences & Social Uses

The suffix -pathy comes from the Greek word for “suffering,” pathos. The U.S. medical system is built around pathology, which simply means diagnosing suffering and treating disease. Similarly, mental health professionals find social connections critically important to the ways that people cope with and overcome suffering, grief, and trauma. Words like sympathy, empathy, and even apathy describe the nuanced differences between the very complex social connections and reactions humans display when we are suffering or when we witness others in pain.

While subtle behavioral differences might seem obvious to therapists, counselors, and psychologists, it’s not so easy for everyone else. So we spoke to Atlanta-based therapist Habiba Zaman, LPC, NCC, Pepperdine University professor of psychology Steven M. Sultanoff, Ph.D., and licensed clinical psychologist Bruce L. Thiessen, Ph.D., about simple ways to define sympathy and empathy—and their relationship to compassion.

Defining sympathy.

“Sympathy is when you understand someone else’s suffering and feel sorrow or pity for the experience they are facing,” Zaman explains. “It involves having a value judgment on someone else’s experience.”

While often well intentioned, this value-judgment-centered response often creates a palpable distance between the person in pain and the person who is listening. So, Zaman says, sympathy is often extended when a person doesn’t necessarily relate to, fully comprehend, or appreciate the circumstances of suffering facing someone they know or love.

“The emotion of sympathy is my experience of (reaction to) your situation. Sympathy lacks understanding,” Sultanoff adds. “When you are sympathetic, you get caught up in your own emotional reaction to how you are experiencing the world. This, for the most part, does not demonstrate any understanding of the person in distress.”

He notes that sympathy can create a barrier to understanding that can be activated because a sympathetic person may shift focus away from the person in distress to focus on themselves instead. Sympathy is the emotional reaction of the listener, who might say things like “I feel so sorry that this is happening to you,” or “I get so angry just listening to your story.” Other common ways it can show up are as pity (e.g., “I feel so bad for you”) or even as envy (e.g., “I’m sorry for your loss, but I sure wish I had as much time with my loved ones as you did”).

Defining empathy.

“When one expresses empathy, one draws upon personal experience, in relating to another person in the midst of a similar experience or hardship,” Thiessen explains. “An example of an empathetic statement might be, ‘I also have recently lost a loved one and know what it feels like to experience that deep sense of sorrow and grief.”

He says that this sense of commonality is a key differentiator between empathy and sympathy.

“Empathy is the ability to feel intimately and see the other person’s perspective. It is not just to understand what they are going through but rather, being able to walk in the other person’s shoes,” Zaman explains. “It is being able to say, ‘I am here to feel with you’ and let you know you are not alone.”

She adds that empathy is best defined by how the listener connects with the person in pain. Without judgment, an empathetic person would try to create and hold space for a person’s feelings and experiences. Empathy, which can be taught and honed over time, involves honoring how a person in pain sees their own situation, even if that is not how others might view it.

Understanding the key differences.

When it comes to understanding the key differences between empathy and sympathy, there are both internal and external factors to keep in mind. Sympathy and empathy are largely distinguished by external behavioral and performative aspects, which most people believe are a reflection of how the listener internally feels about the person who is suffering. Instead, the experts say that the difference is more about the relationship between the listener and the sufferer.

On the outside, sympathy often appears socially distant, like a one-off message of condolences, with no follow-up. Zaman says this is because sympathy lacks intimacy, but there may be situational reasons why that might be the case. In certain corporate settings or power structures, it might be appropriate to emotionally withhold to maintain decorum or to preserve group dynamics that extend beyond just the listener and the person in pain. Social dynamics and the appropriateness of displaying curiosity toward a person in pain might make a listener moderate their naturally emotive behavior.

“Sympathy is used in social situations where there isn’t an intimate connection between two people. It would be perfectly appropriate in a corporate environment to experience sympathy from coworkers or a boss. A card or flowers that share in acknowledging grief is perfectly acceptable and is expected in those environments because anything more could be perceived as inauthentic, unless that initial and genuine connection is there,” Zaman says.

Meanwhile, she says, that very same gesture of sending a card and flowers might be wholly inadequate for lifelong friends. Thus, the relationship and social context between the people involved is very important.

Also, no matter how close or distant the relationship, Sultanoff says that empathy is an internal experience of feeling caring, concern, and understanding toward another human being or living creature that is best shown through active and reflective listening.

“Responding by repeating back (but not parroting) what you heard from the other person, while especially attending to their feelings, demonstrates focus on the person and letting go of your own internal distractions,” he says.

In an attempt to be empathetic, a person who genuinely wants to help might share problem-solving advice, but Sultanoff says that this behavior does not necessarily show empathy for the other person’s immediate emotional state. In many ways, the difference between sympathy and empathy is the desire to understand the experience of a person who is suffering, not necessarily the drive to stop their suffering.

What about compassion?

“Both empathy and sympathy, when coming from a place of sincerity, are sensations and open expressions of compassion,” Thiessen says. After all, compassion, which simply means “to suffer together,” is an expression of caring and warmth.

He says that compassion from empathy typically comes from sharing similar experiences with another person, but compassion from sympathy can be just as useful. “For example, the act of researching the types of suffering experienced by an abused child might increase a person’s sympathy for abused children, regardless of whether or not the researching party had ever been a victim of child abuse,” offers Thiessen.

And this ability to extend emotions beyond one’s own personal experience is good because compassion allows humans to be motivated to alleviate harms they, personally, have never experienced.

“Expressions of compassion, be they in the form of empathy, or sympathy, or some palpable act of kindness, can be experienced as a healing balm on the psyche and the soul,” Thiessen says.

Moreover, that emotional inspiration can spark activism, philanthropy, or public advocacy in the service of moral causes that are far-reaching and socially impactful. In this way, actively cultivating compassion can allow an observer in one situation to be a force for change in many others.

The bottom line.

In the simplest of terms, empathy is an internal emotion that is directed outward toward another person, Sultanoff says. It demonstrates a true understanding of the other person, without any personal biases interfering with that understanding. Sympathy, however, is internally directed.

If you are watching someone in mourning or grief, empathy is focused on understanding the person in pain, while sympathy is focused on your reaction to watching that person deal with their pain. “From a mental health perspective, empathy is very healing, and sympathy is not,” Sultanoff says.

“Generally, it feels better to be the recipient of empathy than simply sympathy, because it allows for a point of connection and intimacy. Also, an expression of sympathy may be more difficult to trust unless it is coming from a genuine relationship and a place of genuine concern,” Thiessen summarizes.

All that said, both feelings can serve socially positivity purposes when tied with compassion and action.

Nafeesah Allen, Ph.D.
Nafeesah Allen, Ph.D.

Nafeesah Allen, Ph.D., is an American writer and independent researcher with a particular interest in migration, literature, gender identity, and diaspora studies within the global South. She completed her Ph.D. in Forced Migration from the University of the Witwatersrand in Johannesburg, South Africa. She completed a postgraduate diploma in Folklore & Cultural Studies at Indira Gandhi National Open University in New Delhi, India. She completed a Masters of International Affairs at Columbia University in 2009 and graduated cum laude from Barnard College at Columbia University in 2006.

Originally from New Jersey, she has lived in Spain, India, Mozambique, Angola, and South Africa. She speaks four languages (reads in three), but primarily publishes in English. Her writing placements range from popular trade magazines like Better Home & Gardens, Real Simple, and Whetstone to academic journals like Harvard’s Transition Magazine, the Centre for Feminist Foreign Policy, and the Oxford Monitor.

Source: Sympathy vs. Empathy: The Key Differences & Social Uses | mindbodygreen

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How To Manage Teams Effectively

[Source image: Eugene Mymrin/Getty Images]

For centuries, managers have been befuddled by team dynamics, particularly why some team members hit it off in a heartbeat while others simply don’t. Moving from anecdotes to data-based best practices took humankind all the way until 2012, when Google embarked on a two-year journey to find an answer to what drives effective teaming once and for all.

What they found laid the foundations for modern corporate team building as we know it today.  The results of Project Aristotle, as Google called the study, are simple: After hundreds of interviews and two years of data-crunching, the study found that the best performing teams share five particular features in common, namely psychological safety, dependability, structure and clarity, meaning, and impact.

All of this sounds self-evident today, which is partially testament to how far and wide the findings have trickled down through MBA courses and leadership self-help books over the past decade. Some of Google’s findings were markedly more counterintuitive, including why consensus-driven decision making, co-location, individual performance, and extroversion seem to have little to no impact on their effectiveness.

Even though Project Aristotle wrapped up long before anyone had heard of the metaverse or quiet quitting, the study is well worth any manager’s time today. First, given everything we know of millennials and Gen Z, it’s clear that many of the five tenets that are driving performance have become only more important over time, particularly psychological safety, meaning, and impact.

It is equally clear that our workplaces have adopted practices that are failing employees miserably by over-managing for structure and clarity to the detriment of all other drivers of teaming success. Not knowing where employees are and what they are doing presents real discomfort. In response, many managers have doubled down on clarifying roles and processes in a way that would make Frederic Taylor look like a slouch.

Daily check-ins and check-outs, software assisted monitoring, and hourly production breakdowns have become increasingly prevalent ways for managers to regain a resemblance of control over their minions who might or might not be logging in from Aruba at any given moment.

At the same time, managers across disciplines have adopted Agile ways of working in hopes of replicating the successes the process has brought to the field of IT development in particular. There is much good to be said about Agile for good reason, but when applied with the wrong intentions, it can quickly turn into micromanaging on steroids.

The first casualty of over-compartmentalization and overmanagement of work is the ability of employees to self-actualize and find meaning and impact in their work.Omnipresent monitoring and hourly timesheets also wreak havoc on dependability, making it a matter of rote compliance instead of a carefully cultivated sense of trust and accountability.

Now to the good news: Hybrid, and full-remote workforces are absolutely compatible with effective teaming. Here are two ways to help you push the pendulum back toward a more balanced approach across the five drivers in case you find yourself having gone too deep on structure and clarity.

Trust

The first step is bringing trust back to the equation.The world didn’t collapse even though everyone worked from home, and your team will be just fine even if you don’t know exactly how many minutes your staff worked on any given task or how many keystrokes they produce per minute.

In fact, they’ll be far better for it. Leaning in on the flexibility and adaptability of modern ways of working will foster psychological safety and promote engagement and ownership in ways that will have far greater dividends than monitoring and oversight ever could.

People are fundamentally reciprocal, and the best way of building trust is to exhibit trust. One particularly effective method is for managers to focus on setting goals and milestones and giving the team the autonomy to seek out the best way to accomplish them. Tolerating failures and the occasional missteps is critical for making the autonomy feel real, and it goes a long way in making employees feel safe to explore their roles.

Another equally powerful method is to transform stands-ups and check-ins from reporting to supporting events. Recasting regularly scheduled meetings as opportunities for helping team members overcome blockers instead of simply checking on progress made is by far one of the most powerful moves a manager can do to breed trust and self-actualization.

Co-location is another practice that is ripe for transformation. Biting the bullet and going fully flexible might feel risky, but there is no reason to believe that any arbitrary pre-established mix of in-office and at-home days works better than allowing teams to find the norms that work best for them.

Trusting your teams to work effectively from wherever they are is the right choice in particular because of how it immediately casts dependability in terms availability and outputs instead of physical presence.

Instead of the default option, the office should be recast as an enticing alternative to which teams naturally gravitate to when they need to share physical spaces for collaboration and team building.

Brown bag seminars, in-office baristas, and a few Pelotons can go much further than you think, and putting the emphasis on tangible moments of collaboration is key.

Self-actualization

Second, you should ensure that the roles and processes set in place leave space for self-actualization and personalized ownership of one’s work. If you’re using Agile, be cognizant that a 100-meter dash doesn’t come anywhere close to the sense of meaning and impact that running a marathon brings.

Not everyone’s work will be foundational, but everyone’s contributions should feel necessary and acknowledged as contributing to goal deeper than wrapping up a two-week work package. The trick is to make sure that even when working on their own units each employee can visualize a clear pathway from their work to the grand “why” of it all.

This is where the importance of setting goals mentioned earlier becomes particularly clear. The best thing a manager can do when running multiple sprints is to maintain a cohesive narrative of how the work contributes to the larger organizational goal.

Often this means distilling vision statements, contractual milestones and even change requests into meaningful arguments about why the work we do today matters. This may not always be simple to pull off, but who said being a good manager was supposed to be easy?

Ultimately, what particular steps you end up taking matter far less than the sincerity of your commitment to making effective teaming a priority for your organization. As for now, it’s more than likely to be pushing back on the overemphasis on structure and clarity.Alexander Puutio is an adjunct professor at NYU Stern where he explores the interplay between business leadership and our society at large.

By Alexander Puutio

Source: How to manage teams effectively

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The Demand for A.I. Is Huge. The Hurdles to Widespread Adoption Are Even Bigger

As artificial intelligence continues to disrupt industries, the demand for systems that can interact more naturally with people has greatly increased. Problem is, creating such a system is no easy feat. During a keynote presentation at 2022’s Annual Conference on Neural Information Processing Systems, Juho Kim, an associate professor at the Korea Advanced Institute of Science & Technology (KAIST) laid out the four main challenges facing the development of new A.I.-powered tools.

In doing so, he explained how incorporating robust opportunities for feedback will allow developers to create systems that are even friendlier to us humans.

1. Bridging the accuracy gap.

According to Kim, one of the most difficult aspects of creating artificial intelligence is aligning its functionality with how users expect it to work, and bridging the gap between the user’s intention and the system’s output.

Kim says that most A.I. developers are focused on expanding their systems’ datasets and models, but this can widen the accuracy gap, where the system can function very well but only for people who already know how to use it.

One solution? Incorporate human testers. By analyzing feedback from real people, developers can identify errors and unintended biases.

2. Incentivizing users to work with A.I.

Kim says, “unfortunately, one of the most common patterns we see in Human-A.I. interaction is that humans quickly abandon A.I. systems because they aren’t getting any tangible value out of them.” One way that Kim says developers can solve for this issue is by prioritizing “co-learning” when creating their systems.

By giving ample opportunities for humans to provide feedback on A.I. systems, people can learn how to better use the system, and the A.I. can learn more about what its users actually want from it. Kim says this kind of two-way learning incentivizes people to stick with these systems.

3. Considering social dynamics.

A major issue for today’s A.I. systems is understanding context. As an example, Kim described a system used by his students: Students enter a chatroom to have conversations with each other, and an A.I.-powered moderator watches their chat and can recommend questions to keep the conversation going.

While Kim says it may be tempting to just have the A.I. moderator participate in the conversation, asking questions to the students directly instead of just recommending topics of conversation, this wouldn’t encourage the A.I. to learn more about the social dynamics of the conversation. Instead, by giving users the ability to either accept or reject the A.I.’s recommendations, the system can learn more about what kinds of questions should be asked when.

4. Supporting sustainable engagement.

Kim says that A.I. tools are often used only once or twice by a given user, so it’s key to design experiences that can adapt over time to stay relevant to users’ needs. Kim described an experiment he ran with his class in which they developed an A.I. system that could edit the appearance of websites to make them more easily readable.

According to Kim, the class could’ve made a system in which the A.I. just automatically creates what it thinks is the perfect website design, but by giving users the ability to edit and tinker with the layout, the A.I. can learn more about what that specific user is looking for and provide a more personalized experience going forward.

By Ben Sherry

Source: The Demand for A.I. Is Huge. The Hurdles to Widespread Adoption Are Even Bigger | Inc.com

.

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How Microsoft Makes Money: Computing and Cloud Services

Microsoft Corp. (MSFT), one of the world’s biggest tech companies, sells personal computing devices, cloud systems and services, software, and other products.1 With products geared toward both consumers and businesses, Microsoft competes in a broad range of industries against companies including Apple Inc. (AAPL), Amazon.com Inc. (MZN), International Business Machines Corp. (IBM), and Oracle Corp. (ORCL).

Key Takeaways

  • Microsoft sells computing devices, cloud systems and services, software, and other products to consumers and businesses.
  • The company’s intelligent cloud segment is the largest source of profit, as well as the fastest-growing.
  • The COVID-19 pandemic has had positive impacts on certain aspects of Microsoft’s business, including its cloud business and productivity tools.
  • Microsoft announced in mid-January plans to acquire popular video game company Activision Blizzard for $68.7 billion.

Microsoft’s Financials

Microsoft announced in late January financial results for Q2 of its 2022 fiscal year (FY), the three-month period ended Dec. 31, 2021. Net income rose 21.4% to $18.8 billion. Quarterly revenue expanded 20.1% year-over-year (YOY) to $51.7 billion. Microsoft uses operating income as its profit metric for gauging the performance of its individual business segments. Operating income for the quarter grew 24.3% YOY to $22.2 billion.

Revenue for the fiscal second quarter benefitted from strong growth in Microsoft Cloud and other cloud services.3 Microsoft also noted in its quarterly filings that the COVID-19 pandemic continues to have an impact on its business operations and financial results, but that some of the effects have lessened. The company said that its commercial and consumer businesses have benefitted from demand for its cloud and productivity tools.

Microsoft’s Business Segments

Microsoft divides its business into three reportable segments, breaking out results by both revenue and operating income: productivity and business processes, intelligent cloud, and more personal computing. These segments are categorized according to both product type and customer demographic. Productivity and business processes, for instance, includes products across multiple platforms and devices relating to productivity and communication. And more personal computing focuses on products designed with end-users, developers, and IT professionals in mind.5

Productivity and business processes

Microsoft’s productivity and business processes segment includes a portfolio of products designed to enhance corporate productivity, communication, and information services. One of its major products is Microsoft’s Office software suite, including both the commercial and consumer divisions. The segment also includes business solutions products such as dynamics, as well as the professional networking site, LinkedIn.5

In Q2 FY 2022, productivity and business processes generated $15.9 billion in revenue, comprising 31% of Microsoft’s total revenue. This amounted to an increase of 19.3% from the year-ago quarter. Operating income for the segment grew 24.4% YOY to $7.7 billion in Q2 FY 2022, accounting for less than 35% of the total.6

Intelligent cloud

The intelligent cloud segment comprises all of Microsoft’s public, private, and hybrid server products as well as cloud services for business. These include Microsoft Azure, SQL Server, Windows Server, GitHub, Enterprise Services, and more.5

For Q2 FY 2022, Intelligent Cloud generated $18.3 billion in revenue, accounting for over 35% of total revenue. Up 25.5% compared to the year-ago quarter, Intelligent Cloud was the fastest-growing revenue segment in the company’s fiscal second quarter. It was also the fastest-growing segment in terms of operating income, which was up 26.3% YOY to $8.2 billion. Intelligent Cloud operating income accounts for just under 37% of Microsoft’s total operating income, making it the most profitable of the company’s three segments.6

More personal computing

Microsoft describes its more personal computing segment as consisting of products and services aimed at putting “customers at the center of the experience with our technology.” The Windows operating system, surface device, gaming products, and search and news advertising are all included in this segment.

In Q2 FY 2022, more personal computing generated $17.5 billion in revenue, comprising about 34% of total revenue. While revenue grew 15.5% YOY for the segment, operating income rose 21.8% YOY to $6.4 billion. More personal computing accounts for about 29% of the company’s total operating income.6

Microsoft’s Recent Developments

On Jan. 18, 2022, Microsoft announced a plan to acquire video game developer Activision Blizzard for $68.7 billion. The deal is expected to close in fiscal year 2023. According to Microsoft, the acquisition will make it the third-largest gaming company by revenue, behind only Tencent and Sony.7

How Microsoft Reports Diversity and Inclusiveness

As part of our effort to improve the awareness of the importance of diversity in companies, we offer investors a glimpse into the transparency of Microsoft and its commitment to diversity, inclusiveness, and social responsibility. We examined the data Microsoft releases to show you how it reports the diversity of its board and workforce to help readers make educated purchasing and investing decisions.

By: Nathan Reiff

Source: How Microsoft Makes Money: Computing and Cloud Services

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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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