It's been a year since Tibco Software introduced Hyperconverged Analytics at its 2020 virtual user conference, and, since then, the concept has been the guiding principle for the vendor's product development strategy.
Hyperconverged Analytics is the blending of data science, streaming data capture and visual analysis in a single environment. Until a year ago, the vendor offered all three capabilities, but they were separate parts of the vendor's platform.
Meanwhile, over the past 12 months, Tibco, founded in 1997 and based in Palo Alto, Calif., has worked to mix the capabilities, adding data science and streaming data capture features to Spotfire, its main business intelligence platform, adding more visual analysis to its data science tools, and so on.
In May 2021, for example, the vendor released an update that better enabled users to embed data science workflows in Spotfire.
In addition to prioritizing the melding of what were once considered disparate capabilities, Tibco acquired IBI -- formerly Information Builders -- in October 2020, and a significant focus since the deal closed in January has been integrating IBI's analytics capabilities, including WebFocus, IBI's main BI tool, with Tibco's.
With Tibco Now 2021 being held this week from Sept. 27-30, Nelson Petracek, the vendor's CTO, recently discussed the evolution of Hyperconverged Analytics over the past 12 months and the new capabilities Tibco plans to unveil during its user conference.
In addition, he spoke about the progress of the IBI integration, analytics market trends he's now seeing and what capabilities Tibco has on its roadmap that might be revealed in a year during Tibco Now 2022.
It's been about a year since Tibco introduced Hyperconverged Analytics at Tibco Now 2020. In the 12 months since then, what has been the response from customers?
Nelson Petracek: When you look at it from the customer standpoint, we're seeing this convergence of visual analytics, streaming and data science more and more across the customer base. It's not just about creating the next report or pretty dashboard. It's about creating combined analytical experiences. The ability to take a rich, immersive visual experience and combine that with deeper insights through augmented intelligence/machine learning (ML) and data science -- and then also do that in real time -- is something we find that, as organizations become more mature in their data journey, they're starting to realize is becoming more important. We've actually seen an uptick in interest and activity around the combination of those capabilities. It goes along with the broader data strategy that most organizations are putting in place. Digital transformation initiatives have accelerated, and so we see people advancing toward this notion of Hyperconverged Analytics.
Going back a bit more than a year now, what led Tibco to develop Hyperconverged Analytics?
Petracek: It was definitely the direction that we could see our customers, prospects and partners going. When we were looking at the use cases and the problems our customers were trying to solve, it was more than just one of the capabilities under Hyperconverged Analytics. It wasn't just data science, it wasn't just visualization and it wasn't just streaming. They were really trying to create a broader analytical capability that combined elements of all three. In many cases, you'll find an organization will start with one, but their plan is to move and provide additional capabilities with the others. When we talked with more and more customers, we could see that they were taking a little bit of this and a little bit of that and they were putting together these broad analytical applications. That really led to us taking a step back and trying to figure out where Tibco could help, look at our strengths and then bring together this broader concept.
Nelson PetracekCTO, Tibco
How has Hyperconverged Analytics guided Tibco's product development strategy over the past 12 months?
Petracek: There's a combination of things. When you look at the individual products themselves, they've got their own roadmaps. But one of the main themes that was included in the roadmaps was interoperability -- making it easier for people to consume each capability within each of the products. For example, in Spotfire, we've made it much easier for people to define streaming components. With a couple of clicks you can have Kafka data streaming into Spotfire without really having to know what Kafka is or the details behind it. It's the same thing with data science -- just making it easier for people using Spotfire to invoke AI and ML models as part of their overall analytics pipeline. These little things make a huge difference and make it easier for people to consume each product's capabilities in other products, and make it a much more seamless combined experience.
That's a large part of the roadmap and also will be going into 2022.
What additions to the analytics platform does Tibco plan to announce during Tibco Now 2021?
Petracek: A large part of it is the continued evolution of each of the product areas under our Connect, Predict and Unify pillars. If you look at the Connect portfolio, it's around continuing to build upon the Tibco Cloud story, so providing additional capabilities when it comes to integration services on Tibco Cloud, the further evolution of our API story, the further evolution of our messaging story. It really is around continuing that journey on Tibco Cloud and enabling people to consume our capabilities in different and expanded ways.
On the Predict side, it's the further evolution of Hyperconverged Analytics. You'll see additional work we're doing in Spotfire, in data science and streaming to make it easier for people to build these rich analytical experiences that encompass the capabilities under Hyperconverged Analytics. You're also going to see the inclusion of IBI into that story. We acquired IBI and IBI had some great strengths in a number of different product areas, so when you look at our Predict and Unify pillars, you will hear announcements that relate where and how IBI fits into each of those pillars and how those products are evolving to support Hyperconverged Analytics.
Speaking of IBI, how has the integration of IBI into Tibco progressed in the 11 months since Tibco acquired IBI in late 2020?
Petracek: From an acquisition and integration standpoint, most of that is already done. It was probably one of the acquisitions, in terms of its fit and alignment to what Tibco already had, that had the least conflict. It was a direct fit. It was really a great augmentation to what Tibco's capabilities were at the time. Being able to integrate the IBI capabilities into our own capabilities was actually fairly straightforward. The overall process was very streamlined and was done in a relatively short period of time.
We're already at the point where each of the products has integration points to some of the other Tibco capabilities; the product teams have revamped their roadmaps and they're now directly aligned to where Tibco, as an organization, is going. That part of it went very smoothly.
In terms of analytics capabilities and how it fits into Hyperconverged Analytics, what made IBI a good fit for Tibco?
Petracek: When you look at areas like the Predict portfolio from Tibco, WebFocus was a key product area for IBI and continues to be a key product area going forward. For us, it nicely fits that space between Jaspersoft and Spotfire. It fits nicely in the middle and augmented our capabilities when it comes to being able to build interactive reporting capabilities or rich visual and analytical experiences. When you look at some of the other areas, IBI brought a whole capability around master data management, a platform that encompasses a number of data integration capabilities required to support MDM right from data ingest through things like data quality, and then representing master data in different forms and making that available through vertical-specific applications. That has immediate benefits to our customers. Data quality is an area we see as a growth opportunity.
Looking beyond Tibco, what are some analytics trends you're seeing?
Petracek: One of the trends we're seeing is that people know how to build models, but there are two challenges. One is on the input side and one is on the output side. On the input side, you can build the greatest models in the world, but if you feed them bad data, that's not going to help. So, there's a renewed interest around things like data governance, data quality and data security. AI and ML are still very important, but there's more to it than just building the models. The quality of the data, and the governance and processes around the data, are also very important. That way you get your model better data, which makes your model more accurate and, from there, you're going to get better outcomes.
On the output side, since there are so many models being built, organizations are having trouble operationalizing them all. How do you deploy them into production, how do you monitor them, how do you know when it's time to go back and rework that model, how do you deploy them at the edge, how do you deploy them in the cloud and how do you deploy them in an application? There's the whole operational side of this now -- the ModelOps -- so there's a question about how to make it easier for organizations to operationalize ML models.
Finally, what can you share about the roadmap for Tibco's analytics platform and what we might be talking about a year from now?
Petracek: Next year, we're still going to be talking about data and all the problems that are associated with and related to how organizations can get more value out of their data. We're also going to be talking about more integration points as a way to provide better ways to capture data and better ways to expose information to more people. In terms of Tibco, being able to facilitate both the collection of data and the exposing of data in a governed manner, and exposing high-quality data, will be capabilities we're going to build upon.
We'll work on how we can make it simpler for organizations to build analytical pipelines and analytical workflows end to end, and that might be analytics at the edge through more advanced techniques like computer vision all the way through operationalization. The notion of helping organizations build out their data fabrics is something else. That's getting into the pipelines and how to provide capabilities at each stage, whether it's data ingest, preprocessing, data quality checks, storage, making sure metadata and data catalogs are in place -- that story around data fabric is something we're going to continue to provide capabilities toward.
Editor's note: This interview has been edited for clarity and conciseness.