BI software vendors choose their strategies: on-premise, cloud or hybrid approach?

Where should we deploy our analytics and business intelligence? In the cloud or on-premise? It is a question that is thrown at me frequently. Most software vendors expect only 20% of the enterprises run full cloud analytics in 2020. What about the rest, and what do you decide today: cloud analytics or on-premise? Is there an easy answer? No, it seems not ..... though ........ Hybrid Business Intelligence is !

The discussion about cloud analytics or on-premise analytics

Before understanding why there is a discussion for cloud analytics and/or on-premise analytics anyway, we need to recap on the market situation a bit. When it comes to analytics, our world has changed dramatically and is still in this changing process. It is all part of Digital Transformation: where we initially shifted into renewed business networks with things like video collaboration, mobile devices, connected things and what have you, we are now moving into insights-driven experiences using structured and unstructured data in real-time and online. This change is massive. Today we connect everything; devices, people, data and processes. Enterprises of course undergo similar experiences; it changes the way they are competing. Data is their key asset in this competition and as such enterprises make data their strategic differentiator. Data becomes the new gold.

In the same time technology also emerged. Besides in-memory computing, we also got stable cloud platforms that leverage in-memory computing and boost performance tremendously which was welcomed a lot in todays massive data generation. The cloud platforms hugely attract given maintenance and support efforts are limited down significantly and on top the platforms deliver the scalability enterprises need in this period of time. Analytics vendors got on top of this and provided cloud analytics tooling. BusinessObjects Cloud is one of the best examples that even went a step further in answering to the need enterprises by providing both monitoring, planning and predictive capabilities all in ONE tool; the so-called closed loop portfolio.

Yet ..... it is expected that in 2020 only 20% of the enterprises will run full cloud analytics. Why only 20%? That is either because not everybody applies to todays use cases to run analytics and business intelligence in the cloud, but also cultural differences make that certain areas do not feel ready to shift to cloud analytics. So what about all the others? They still run on-premise analytics highly successful today. Software vendors keep on innovating the on-premise software allowing it to handle the latest needs for agility, data volume and complexity, and real-time capabilities. And they succeeded in it. On long term we are pretty sure all enterprises will shift to cloud analytics, but that will take another 7-10 years. In the meantime analytics techniques need to further evolve to answer to the need of insights-driven experience and data-driven strategies. Now the question is, what should software vendors do? All focus on cloud analytics or remaining to only innovate on the on-premise?

Shift from descriptive analytics to predictive analytics

To answer our question whether software vendors should focus their developments on cloud analytics or on-premise, some other elements are important too. These are the intentions of enterprises and their strategy on data-insights. With enterprises using data as their core assets to be competitive, we see them shifting from the more traditional descriptive analytics (the typical monitoring) into predictive analytics. Simply said; where descriptive analytics answers the likes "what happened" and "how did it happen", the predictive analytics tell us "what could happen".

On the long term this shift we further evolve towards pre-emptive analytics, quantum analytics or even adaptive analytics:

  • pre-emptive analytics: analytics focussed on anticipating on certain outcome

  • quantum analytics: machine intelligence and mathematics are used to "ask" the data automated and most relevant questions for insights

  • adaptive analytics: the ultimate goal towards fully adaptive enterprises where enterprises can predict what they what analytics insights are important

This shift says a whole lot which we will discuss in other artciles, but one thing is sure: it will definately mean that we all need to bring analytics to where the data is created. That's the place where we can create the data intelligence algorithms, so that the enterprise can predict what it needs to ask in terms of analytics questions: adaptive enterprises. Everything must be analytics-enabled. We must have integration for predictive models and machine learning for a closed loop system. If we can all accomplish that, the world is ready for the opportunity of monetization and designing business models for subscribing to the outcomes of data.

Bringing the analytics to the data, means enabling almost any application with analytics. Given the future of applications is in the cloud anyway (companies will also start using applications to their needs - a typical cloud model), we can also conclude the future of analytics is in the cloud.

Enterprises however drive the speed and adoption towards adaptive organizations. They might shift iterative or in chunks. As such they also drive the bigger part of the decison of software vendors to either focus on on-premise or cloud. Therefore the immediate answer is the hybrid analytics focus: it must be! It means in parallel innovate the on-premise software tooling and in the same time evolve the cloud analytics offerings. At any time align them, and make them interoperable; cloud analytics must be able to interconnect with the vendor's on premise analytics and the other way around.

The longterm strategy of analytics vendors should be on cloud analytics. However the adoption of cloud analytics is at a spead that in 2020 we can expect 20% of all enterprises running full cloud analytics and the vast majority is still - partially - running on-premise. Digital Transformation however keeps on going and enterprises need to go forward using their data-insights as competitve advantage. Short and midterm strategy for software vendors can thus only be hybrid.

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