Kinaxis CTO on partnering with Google Cloud and the future of data transformation
- Discussing its new partnership with Google Cloud, the CTO of supply and demand planning vendor Kinaxis looks ahead to faster, easier data transformation and integration
One of the big themes of this week's Google Cloud Next conference has been quicker access to data across the enterprise. Supply and demand planning vendor Kinaxis yesterday expanded on this theme with the announcement of a partnership to host its RapidResponse SaaS offering on Google Cloud for customers that want this option. The goal is to make data available where and when it's needed, as Gio Pizzoferrato, CTO at Kinaxis, explains:
Both Kinaxis and Google, we share that same vision around data — data is only as useful as you can exploit the value of it in near real-time. And unlocking that value is sometimes what everyone focuses on, rather than the value [itself] ...
Sometimes it's such a Herculean effort to just get that data out of an ERP system, or get that disparate data in different formats and try to normalize it, and then once it's all normalized, bring it into a system like RapidResponse, and then start to extract the value.
We see some of the innovation that Google particularly has been announcing this week as key accelerators for customers who choose to use those technologies. RapidResponse being able to ingest those types of data will invariably unlock that value at a quicker pace, lowering the barrier of entry for planners and others who are using RapidResponse to get from a raw data set to an insight that's actionable much quicker.
Datasets in hours
While the Kinaxis product already provides its own built-in technologies to support capabilities such as concurrency and nested scenario planning, having the data platform also playing its part allows customers to move faster. He explains:
Sometimes in my old life in the data side, you could go days, weeks, even months before you get to a dataset that's ready for a data scientist or a planner to use and extract value. Now we're talking about hours, a day, for some of the most complicated datasets ...
Anything that can make the customer’s experience around how they manage data easier to use is something that we appreciate and we endorse, because that's a part of a customer's onboarding that is always the most difficult in any scenario that you go to deploy your technology. If customers are now getting help from these hyperscalers to do this type of activity, it just speeds up that whole synergy between the diagnostic capabilities that RapidResponse brings to the table for them to do all their planning.
It also enables them to see their data in a much more usable and quicker fashion that then leverages more capabilities out of RapidResponse.
The partnership with Google Cloud follows on from a similar arrangement with Microsoft Azure earlier this year. Most customer instances are hosted by Kinaxis, but the vendor wants to give customers a choice of hyperscaler options, particularly for those looking for a wider range of data residency and redundancy capabilities. There's also the potential to learn from some of the innovation happening at hyperscalers, in AI and other fields. There's plenty of scope to continue to automate and streamline the process of integrating and transforming data from different sources, he observes, even though some of the most ambitious goals are still some years away. He sets out his vision of the ultimate destination:
I would love to ... get to a point where data transformation and data integration and manipulation is now a voice command rather than a visual input through keys, where I can talk to a system and explain in [spoken language] what I want from the data, and the system is smart enough to understand how to transform, normalize it and then deliver it in a schema that can be consumed by the upstream system that needs it.
I think the public cloud providers, they're the ones that are going to drive that mandate and that's where we're going to leverage that strategic partnership.
Supply and demand planning is a classic example of a field where data needs to be brought together and analyzed from a wide range of different sources — largely due to historic application silos that have grown up in different functions over the years. This is another example of the growing trend towards adding an application-neutral data layer to perform that consolidation and analysis, making information more readily available for more efficient, accurate decision making.