Access the podcast to hear Splunk experts discuss how bitsIO’s datasensAI technology visualizes performance metrics and highlights opportunities to simplify infrastructure complexity while maintaining compliance. Discover how advanced AI/ML predictive analytics showcase unrealized value by streamlining data routing, archiving and usage workflows in the Public and Private Sectors.
Carnes White
Team, welcome back to the CarahCast, the podcast from Carahsoft. You can subscribe to get more information about our latest updates and technology across the government industry and otherwise, now very much in the commercial space too. My name is Carnes White.
I'm your host today. I'm the channel leader for Splunk and Cisco. And on behalf of our partner, Bits.io, we want to welcome you to today's podcast. Today, we're going to get to meet CTO and co-founder of Bits.io, Suman Gajavelly. We're going to focus a lot on how Bits.io is directly addressing some of these more critical challenges with their tool, Data Sensei, which is very much AI-backed solution, addressing knowledge gaps, addressing everything that you could think of from a customer learning perspective. And I think what's really critical about that from our side is, you know, for probably the past 16, 24 months or so, I'd say one of Carahsoft's greatest initiatives from the top is around training and enabling customers and enabling partners to get better access and use out of the tools that they're already buying.
If they're buying them, let's teach them how to do it. And I think that Bits.io has probably become one of our leading experts in this space. And I'm really excited to talk about how they're aligning to our side.
So, without any further ado, I'll hand it over to Suman if you want to tell us a little bit more about yourself and about Bits.io.
Suman Gajavelly
Thank you, Carnes, and thanks, Carahsoft, for the opportunity. I'm Suman Gajavelly, co-founder and CTO at Bits.io. We are really passionate about providing solutions on Splunk platform. We built solutions like Data Sensei, RAS AI, Resilify AI, a lot of the solutions that focus more on ROI for Splunk.
And we are one of the partners that just focuses on providing Splunk solutions and Splunk implementations. We won Partner of the Year three times for services. And then, you know, I was one of the Splunk Trust members three years in the past, which is so excited to be part of that group.
And then our team is very dedicated in providing solutions that get more ROI for customers. And we continue to do that with the partnership at Carahsoft.
Carnes White
Well, we sincerely appreciate it. And three-time award for the trust is no small feat by any means. And I think today we're going to be really talking about the solution set, really talking about Data Sensei.
And I guess I'm wondering if you can give us sort of a high level of what that really means. What are the problem spaces that Data Sensei is addressing? What are you doing today there?
Suman Gajavelly
Yeah. Great question. So, a lot of customers love the Splunk platform because it's a world-class tool.
It can solve the immediate problem of being secure. Cybersecurity is top of everybody's mind right now, right? And with AI, it makes it even more challenging.
So, with Splunk, I know a lot of customers think that it's a lot of cost. It's a cost center. But with our Data Sensei proprietary AI solution, we came up with this value proposition where you could not only just use Splunk as a SIM tool, but you could use it for IT ops, observability, and so many other use cases with our extensive knowledge of implementing at 300 plus customers who are Fortune 500.
And we could bring all that experience to customers to give them more ROI. What I would like to ask customers is, do you know what your ROI index is? I think everybody should know it's probably at 30 to 50%, but we want to get customers to 100% of ROI index.
Carnes White
So, let me ask you this, and I guess, how are you attacking this today? We're thinking about ROI. You're thinking about customer needs in this way.
Suman Gajavelly
How are you attacking it? Right. Yeah.
With the Data Sensei solution, as you can see, the Data Sensei has AI in the name itself. So, we want to make sure we are utilizing the top technology, which is AI, to get more insights into their Splunk investment. So, what we are doing currently is analyze their current state of what they have, what data sources they are bringing in.
Most of the times, every customer, they just send all their log data into Splunk, right? But majority of the times when we go implement for the customer, we see that only 30, 40% of the data being utilized. So, if you have 100 data sources, you might be only using 30 data sources out of them.
So, our Data Sensei solution will go give them the current state. You're using only 30%, you have 70% of the data, and we have AI that gives you one industry expertise. It brings all the knowledge from there.
And we have our own use case repositories, Splunk has a use case repository. So, we bring in all the data, combining their industry, we provide the use cases that they can implement on the data that they are not using. So, and if they still don't need some specific data, we kind of move the data, we work with the data pipelines using Ingest processor, Edge processor, and move that data over to cheaper storage and to make space for new, higher value data.
So, but still, even though the data is moved to S3, you still have federated search, federated analytics to view data from Splunk and S3. So, you get the complete visibility. So that way we are bringing more ROI up to 100% in some cases.
Carnes White
That's pretty impressive. And I'm hearing numbers like 50 and 70% ROI. So, when we're talking about numbers, and that's impressive, by the way, when we're talking about that, can you just walk me through a few scenarios?
What does that really look like at the customer level?
Suman Gajavelly
Yeah, so that's a great question as well. So, we actually worked with a lot of customers with Data Sensei. So, we worked with a healthcare customer.
We also worked with a retail customer where the immediate outcome they saw was knowing that they are only using so many data sources when they have thousands of data sources in their system. Like, oh, wow, we have this much data, but we could have used it for some other outcome. So that was the biggest eye opener for a lot of customers.
So, we go to the customer to take out the look at their current state, and then go with the security first. Everybody wants to be secure before they want to implement more features in their applications, right? So, we kind of look at the security, then IT ops observability, and then industry specific use cases.
So, and then when we go to the customer, we have a report that we go through and sit down with the customer, sit down with their SMEs. And then make sure they understand the outcomes that the report has. And some customers have gained 50% more ROI.
And it's not Bits.io telling them what to use, but also, it's teamwork. We collaborate with the customer's team and then come up with those use cases. So, a lot of these customers where we worked with, they actually saw 50% to 70% ROI on them.
Carnes White
Well, that's incredible to hear. I definitely am excited to promote that as well. So, I guess when we're talking about, I think maybe choices, we're talking about decisions and we're offloading that to AI.
I guess, how is Data Sensei in this instance identifying what's high impact? What's low impact? What can we move to different layers of storage, different levels of storage, cheaper storage?
How are we doing this without compromising the critical workflows, everything that's in place as it is now, whether security or just operational period?
Suman Gajavelly
Great question. That's probably top of customer's mind. How are we doing this?
So, we actually have the Data Sensei solution, looks at multiple things. It's user activity, what data is being searched often, what data is never touched, right? And so, there are so many aspects of this scoring.
So, we score each data source and then we come up with this. This could be high value because anything security, we kind of rank a little bit higher because security should be always that high value use case. And then a lot of customers have non-security use cases as well that they can utilize and where we, with the things I mentioned, AI would take all this repository that we have with even Gartner, we go up to Gartner level where we pull in all the data and then give them insights into this data can be high value.
This can give you insights where your business can see some revenue potential. So, we kind of give those use cases. And then some data sources, a few customers, they did not have need for it because of their industry.
Most of the security, yes, they will need them. But some data that they don't need, it could be offloaded to those cheaper storages. And then we use Edge Processor and Ingest Processor to actually filter out from the pipelines and it goes directly into this cheaper storage.
And then Federated Analytics comes on top of it and gives the full visibility, 100%.
Carnes White
That's definitely exciting and I'm excited to hear it as well. I think that one of the things that we run into or definitely hear about with Splunk is just the complexity of the data pipeline. Having it moving and having it going at all sometimes can be a feat, right?
These things are complicated. So how does Data Sense, one, simplify this, make it work, ensure that all this data... And we talked about tiers and security, for example, being one of the highest tiers here and most important to have accessible.
How are you sort of navigating that ease of use and also that operational excellence, if that makes sense? We're thinking about costs as well. We're thinking about critical data sets versus non-critical.
Suman Gajavelly
Right, yeah. So, the Ingest Processor, Edge Processor, they are great tools to manage the pipelines. And one of the first steps we do before we even touch the data pipelines, once the data is away from Splunk, it's kind of hard to...
I know we can have it in the cheaper storage, but it's kind of hard to get to the data as fast as if it was in Splunk. So, we kind of sit down with customers and look at the data and then tell them the value of this data is security or the value of this data is observability. And we kind of don't...
We would love to automate this process, but we don't because customers need to understand they need to be coached and mentored on what value that data has. And then that's when we actually take that data out and use Ingest Processor, Edge Processor to sometimes even reduce a volume as 70%, for example. So, we can take the data that's repeating in every event, and then we kind of take it as a lookup back end.
So, you're kind of reducing the ingest from the get-go, but at the search level, you could actually join that data, merge that data, that way your ingest is less than the original data. So, we do a lot of creative things with the Ingest Processor, Edge Processor. Splunk has been working on multiple features.
So, kind of customers see a lot of value with that. And then federated analytics also makes it easy that you have data sitting in Splunk, data sitting in S3, all visible in one place.
Carnes White
Definitely impressive. Definitely important. And I think definitely the customers are going to understand and appreciate it.
I guess then... So, if we're looking at a customer, if we're looking at where these things... Obviously, these things have to start somewhere, right?
What does that look like? What is the lifecycle? Where does this thing start?
Where does it go?
Suman Gajavelly
What have you seen there? As I mentioned, everybody needs to know, every customer needs to at least know what their AutoAI index is. So, I think every customer wants this is known.
Everybody wants to know this. So once a customer decides that they want to know their AutoAI index, and the first step they have to do is I think it's their time. I know they are busy.
The customer is always busy. They have a ton of stuff to do. And what we have is we have about a day, four to eight hours, I would say.
That's how much time we need from the customer. And then we kind of collect all the data from their audit logs. And then in a couple of days of time, we will go back and set up another meeting with them to go over the current state, where they are, what's their AutoAI index look like, what are their critical data sources that they need to keep and monitor and get insights out of that data.
And then we come to an agreement that, hey, this is the data that we really absolutely don't need. We don't see any value. And that's when we kind of help them discussing about Ingest Processor, Edge Processor to deal with the data pipelines and then actually move that data over and then have the federated analytics done on top of it.
So not much commitment from their side, about four to eight hours. But we do all the work back behind the scenes and get them a report that will hopefully get them 50 to 70 percent more AutoAI than what they have currently.
Carnes White
I think that's super exciting. And I think definitely a seller's, one of the questions we're asking is, hey, where does this thing get started? How flexibly can we initiate something like this?
And with a four-to-six-hour time period, I think it's an incredible place to get that started. So, it's definitely exciting for me. And when I think about our sellers and folks who want to go talk about this, knowing where to start and you can do it so easily, I think it's definitely going to be important.
So, then I guess let me ask you this, because it's clear the expertise in Splunk and what that means and how that works is so obvious. But what is that Bits.io stamp? What is Bits.io doing in particular that's really driving it home?
Suman Gajavelly
Yeah. So, as I mentioned, I love to talk about our three times partner of the year. It just kind of proves that we are a trusted advisor to Splunk and Splunk's customers.
We have done 300 plus implementations. We have learned a lot doing those implementations. Every customer is different.
Every customer uses different assets, different vendor products. So, we have kind of extensive experience. So, we bring that.
That's why we have this use case repository of our own, bringing all that expertise into one place. And on top of it, AI kind of enhances our expertise and our recommendations, bringing data from Gartner and other places. So, and we have certified people.
We have about 50 plus consultants who just focus on Splunk day in, day out. So, they know what they are doing. And another unique thing with Bits.io is we actually always coach and mentor these customer teams. So, once we are done with our project, they are self-sufficient. They are able to follow the best practices and have their Splunk running smooth and efficiently. And with the solutions like these, Data Sensei, we are helping customers shift their mindset from cost center to high value center.
Right. So that's another thing. So Bits.io is a top partner for Splunk, and we always want to help customers. And we have examples of one of the things I wanted to mention was we have seen multiple customers go from Splunk to other cheaper products, but they again came back because Splunk is a world-class tool. It does the job well. So, we always recommend customers to at least try Data Sensei to see if they are getting the value, if they can improve the value, ROI index at least 50%, I would say.
And then we are positioned well in the services. We have consultants that have experience over 10 years. So, they can give all the insights into where they can improve.
Carnes White
Well, it's an exciting story. We're happy to hear it, and we're even more excited to tell it. Team, thank you for listening to our guest today, Suman Gajavelly.
Don't forget to add a like, a comment, subscribe to CarahCast. Be sure to listen to our other podcasts as well too. If you want more information about Bits.io or what they can do for your organization, please visit the website in the link below. And we hope you have a great day. Thank you for listening.