Imagine you are buying a car.

And your car does not have any dashboard. So, what will happen?

Similarly, when you buy a storage array, you need to have an, uh, a very world-class

observability tool because buying a storage without observability is like driving a car

without a dashboard. You may keep moving, but you do not know when you are going to run out

of fuel, when you-your car is going to overheat, or you are headed for a

breakdown. Now, having said that, now that uh, I have established that we

need a world-class observability tool when you buy a storage box. What are the critical

operational questions that this tool can answer for you? And that I call it the seven

pillars of observability. So what are those pillars? Number one:

availability. Is my uh,

storage infrastructure available? Does it have enough availability for my applications? This is

the very basic question a storage admin needs to know. Hence that is the first pillar.

Second: performance. Is my

storage uh, infrastructure performing well? Does it give enough resources to my applications? How is

it doing with respect to latency and IOPS? These are the critical questions that a world-class

observability tool can answer once the storage array is configured. Now I moved into a very

critical one. The third one: capacity. Do

I have enough capacity now? What is that I was capacity I was using in the past? When is

my uh, my box running going to run out of capacity? How can I order more capacity when it's going to run

out? Next, and the very important pillar in the

recent days has become security. Is my storage infrastructure secure? Do I h, do I have

enough safety against the ransomware attacks? Is my security posture configured correctly? How do I

know this tool can answer those questions and make me safe? The next one:

inventory. What does my inventory look like? How many block storage devices I have? How many file

storage devices I have? How many of them are hyperconverged? All these critical questions so that I

can plan my storage infrastructure better can be answered when I know what my inventory has,

right? Cost. What is the current cost I am paying for my

storage infrastructure? How much is my cost going to escalate? Is my cost going to go out of uh,

a limit? Only my world-class observability tool can help, and hence this is a very critical part.

And last but not the least, sustainability.

Is my storage infrastructure doing well with respect to power consumption? What is

it taking for carbon emissions? Is my infrastructure aligning with my carbon goals?

What does the data say? Now, this is where my storage uh, observability tool will

help align my box. Now, having answered all the critical operational

questions via seven pillars of observability, let's look at use cases where the observability tool is a

must-have for storage boxes. The use cases that can help the admin to manage your storage

infrastructure better. What are those use cases? End-to-end visibility. In a

storage infrastructure, there are many components, from the host to switches to the storage box. And you

want to know all the paths from your applications and till the storage, what's exactly the

thing. So it helps teams understand how storage systems, applications and infrastructure are

interconnected to eliminate b-blind spots. Next, proactive issue

detection. By monitoring health performance and anomalies in real time, this enables early

detection and faster resolution of if-issues before the impact. Right?

Performance optimization. For a big time a storage admin has a problem: How is my

latency? How is my IOPs? How is my throughput? Now, if you have world-class observability tool, it can

provide insights into the workloads, bottlenecks and utilization patterns, ensuring optimal

performance and resource allocation. Next, capacity planning and cost

management. How am I doing with respect to capacity? With a world-class storage observability

tool, the usage trends and predictive analytics, a storage admin can plan the storage

growth, avoid overprovisioning, which is critical, and reduce unnecessary costs.

Right? In a data center today, incommon, data centers, they're generally multi-vendor

and hybrid environments. A world-class storage observability tool simplifies management across

different storage vendors and if whether it is on prem or cloud,

ensuring consistency and reducing complexity. It improves

reliability by continuously monitoring, reducing the downtime risks,

and enhances resilience against failures because of multiple upgrades or multiple

security threads. With all this, what you have in your hand is data-driven

decision-making. A world-class ob ... storage observability tool translates raw storage metric

into actionable insights

so that it can empower IT leaders to align their storage strategy with

their upcoming goals. I will go ahead and talk about the role of

AI in data storage observability, right? AI plays a huge part when

it comes to ease of storage observability. Observability is about managing

the data the storage array produces. And the entire infrastructure produces exabytes of data.

It's manually impossible to observe the data with m ... with a manual

way. Hence, the storage admins leverage AI in order to make storage observability and

management better.How, how does it help? Let's, let's go to the use cases. The first one: anomaly

detection.

AI algorithms in observability tools can learn normal storage behavior and automatically

detect unusual patterns. Example, sudden latency spikes, abnormal I/O or capacity

anomalies before they become critical issues. Second:

predictive analytics,right?

AI can forecast storage growth, performance trends

and proactively do capacity planning, enabling

proactive management and risk mitigation. Third

is root cause analysis. A pain

point for all storage admins. Instead of manually sitting through logs and

metrics, storage admins can leverage artificial intelligence, which can correlate

signals and across the infrastructure layers to

quickly pinpoint to the root cause behind the performance issues

or availability issues. And thus, it can help make storage

admins' life a lot simpler. Intelligent automation.

We all want our storage boxes to be plug and play, and AI can help us achieve that. AI can recommend

or even trigger actions based on the

data, observability data, and do load balancing,

or cache optimization without any human intervention, thus

reducing downtime and any manual operational effort.

Now, another big pain point for storage admins is a lot of noise.

All the storage boxes produce a lot of alerts because of all the components inside it. And in

large environments, storage admins are flooded with alerts. Here, AI comes to your rescue.

AI can filter the false positives and

prioritize what is critical and surface

out only what truly matters, thus achieving noise reduction.

Workload optimization. Are my

workloads running in the most efficient manner in a storage box? Well, it's very

difficult to figure out in a manual way, but observability tools with AI can analyze

user usage patterns and can suggest optimal data placement across

the entire storage box to-to ensure that the tiering is in

there to balance cost and performance. With all this in place,

AI is also learning from the data that the storage boxes are producing,

enabling in what I call as self-learning insights. So over the time, AI

adapts to the unique capacity characteristics of your storage box

and it then uses those characteristics of your workloads and storage systems

in order to improve the observability outcomes. With, now that we have gone through the role of

AI in storage observability, let's see about how agentic AI Ops

can help in storage observability much better. Now, before going there, I would

like to talk about four stages of storage observability, right? Now, first is

monitoring, the basic monitoring of a storage box. Second

is when I start observing the data that the storage box

produces and giving insights to the admin.

Third one is AI Ops, that I leveraged AI in order to make the storage

observability experience a lot better for the admin. And then, finally,

agentic AI Ops, where I have asked few agents

to do the work for me, which with normal AI I was unable to do. Now

what are those use cases where agentic AI Ops can take the

observability experience a notch better, right? Now, the first one: autonomous

monitoring and response, right? Now, what does this

mean? Based on the data that the observability tool is getting from the storage, it

can start responding real time to the storage admin. I am getting a message

that my drive is not available. So what do I do? What is my action?

So it can, in a chatbot kind of manner, provide immediate response and thus help storage

admins' life a lot easier. Second, a goal-driven response,

or goal-driven

operations, I must say. An admin can define the goals that

are needed from the observability, and when the goals are reached, the ... the relevant

agent starts responding with the admin, hence achieving the goal-driven operations. For

example, if he needs a performance latency less than

10seconds. So every time it exceeds 10 seconds, it goes and autocorrects

and thus, helping the admin, not having to sit through and monitor through thousand alerts and

doing those actions, rather trusting the agent to go see the goal and take the necessary

actions. Right? Now, next and the most critical and which is currently

not appreciated by all the users, is self-healing infrastructure. Now,

a storage box is a complicated piece of hardware with lots of components in it,

and lots of software that interact with each other to give the best data storage experience. So

it is going to come up with issues regularly. When the issues come up, admins run around in a team to

figure out how do I resolve the issues? But, if

I can, if I am able to outsource it to agents and then they can

self-correct themselves, I, we're basically achieving self-healing infrastructure. So whenever,

let's say, for example, there is a storage drive. And that failed, right? What can I do? A

controller went for a toss. What can I do? A back plane had some issue. How can I

heal it? So, all the actions and then automated manner,if it, it goes

for a toss, how can I self-heal them so that I can provide a best storage

experience for my admin. And last, and the biggest pain point, which is still not

solvable by AI completely, where agentic AI can really be a lifesaver is uh, it's in lifecycle

management. Now, a storage admin who has 50 to 100 storage

arrays has to regularly upgrade patches and the new releases, the new security

fixes. And every storage vendor has a different lifecycle, different release

timelines. So, today, admins maintain everything in an Excel sheet,

manually, interact with their team: what-what time it is coming up, then they have a

downtime, they have to communicate. And it is a management nightmare. Rather, you can outsource

all of it to agents. The observability tool can release the agents based on

the certain storage boxes, and they can achieve a complete lifecycle management

experience, where the agents will automatically track the amount of arrays, their release

timelines, and depending on each of the release timelines, they will take the necessary upgrade

actions and send an email once that is

done. If the upgrade has not taken place, they can send a communication

on wh-what the next upgrade should be and how it can be achieved, providing

a complete automated way of asset lifecycle management, eliminating hours and hours of manual uh,

effort that the storage admin puts today. And here agentic AIOps can be really a lifesaver.