Skip to main content

Salesforce Acquires BeyondCore to Enable Analytics . . . and More

In October of 2014, Salesforce announced the launch of Salesforce Wave, the cloud-based company’s analytics cloud platform. By that time, Salesforce had already realized that to be able to compete with the powerful incumbents in the business software arena—the Oracles, SAPs and IBMs of the world—arriving to the cloud at full swing would require it to expand its offerings to the business intelligence (BI) and analytics market and to the enterprise mobile app market. Safesforce has realized this goal with Wave Analytics and Salesforce’s analytics app for Sales Cloud customers Wave App.

Since then, there have been many developments in the analytics field and especially in the areas of advanced analytics, where most major software vendors have expanded their analytics offerings to the cloud. These events signal Salesforce’s entry into the never-ending race for the ultimate intelligence solution—or not?

From Salesforce’s Perspective

Cloud-based analytics solution provider BeyondCore had the upper hand over similar vendors, and in August 2016 was acquired by Salesforce. The company fits most, if not all, the necessary requirements for fast integration within Salesforce platform.
But why the acquisition?, I asked myself as soon as I heard the news.

I mean Salesforce had already launched its analytics and mobile offerings, including major functional expansions and improvements in 2015 to improve dashboard capabilities and provide better integration with the existing platform as well as with a number of specific partner apps—e.g., Apttus Quote-to-cash Intelligence, FinancialForce ERP Web Apps, and Vlocity Communications Cloud Analytics.

So what was so appealing about BeyondCore that encouraged Salesforce to decide upon this acquisition? First, according to a post written by Arijit Sengupta, still the CEO of BeyondCore, it was during Gartner’s BI Summit that BeyondCore grabbed Salesforce’s attention while showing the neat integration capabilities of BeyondCore’s next version with the cloud software giant’s platform. For Salesforce, a particular point of interest is of course getting a hold of a product that has already a tight set of integration capabilities, which can mitigate user resistance to adoption.

Second, Salesforce knows that the analytics and BI realm is continually expanding. By the time the vendor got its hands on its brand new analytics platform, “disruptive” (boy do I love this term!) and other big vendors were already getting their hands dirty with new trending technologies entering the market: machine learning, enhanced pattern recognition algorithms, improved guided discovery techniques, and even cognitive and artificial intelligence functionality. Salesforce quickly realized that to remain competitive in this ever-changing space, it would need to expand its existing analytics platform to keep the pace with the rest.

From the User’s Perspective
The third reason for the acquisition is that BeyondCore’s offering is one to watch closely from the customer’s perspective, if integrated nicely within Salesforce’s platform. With proprietary smart pattern discovery technology, the start-up analytics company aims to ease the often laborious and error-prone process involved in finding useful insights. Specifically, it provides a way to exhaustively examine variable combinations and patterns within large datasets, and automate the statistical analysis process with ready-to-use patented models for defining mathematical relationships within the data.

BeyondCore’s analytics solution provides users with guided exploration and discovery features, along with recommendations to provide help within the complete analytics cycle, such as suggestions for action. A set of collaboration functionality features is available for sharing results and promoting analytics within a collaborative interface, rather than as an isolated data science bunker.

BeyondCore includes a broad set of advanced analytics features (figure 1) that serves to build the case for Salesforce´s interest and user appeal. The possibility of having an advanced analytics toolset within the Salesfoce platform will appeal to both users and Salesforce itself, which will enable the vendor to not only keep pace with industry leaders but also compete head to head with other players in the market.

Figure 1. Major functional features in comparison with other offerings (Source:

Expecting a Prompt Integration of BeyondCore with Salesforce
While the acquisition has already been completed, not much information has been shared on Salesforce’s internal strategy for fusing BeyondCore within the Salesforce platform.
I would expect Salesforce to promptly fuse all BeyondCore assets to fit nicely with the platform. A couple of these integrations appear to be worth exploring:

  1. Integration with the Salesforce Wave Analytics and Wave App portions of the cloud platform in order to naturally expand the analytics offering
  2. Integration with Salesforce’s new IoT Cloud Service, which would enable Salesforce to offer more robust and modern data management solutions that provide a wide variety of connected services and their analysis

In my view, the BeyondCore acquisition offers Salesforce more than just technology. Whether Salesforce succeeds in successfully integrating the BeyondCore solution will depend on how well the solution interacts with the rest of the Salesforce’s platform from a practical and business point of view.

So what?
Finally, I would expect to see Salesforce keep investing fervently in the analytics and space. Evidence of this is in Salesforce’s recent revelation, by CEO Marc Benioff, of the company’s continued investment in the realm of artificial intelligence (AI) and cognitive computing with its ‘Einstein’ project/product, Salesforce’s project to introduce AI to its customer relationship management (CRM) product aside from SalesforceIQ.

Certainly, for Salesforce and other vendors in this market, the road ahead will see many changes as, surprisingly, there’s still a lot of consolidation to be done in many areas of the analytics space. This is particularly true for creating a smooth path from analysis to decision making as well as for filling the gap between traditional analytics infrastructures and those emerging from new technologies—to enable faster, better, and more accurate decisions based on current, available data.
(Otiginally published in TEC's Blog)


Popular posts from this blog

Machine Learning and Cognitive Systems, Part 2: Big Data Analytics

In the first part of this series, I described a bit of what machine learning is and its potential to become a mainstream technology in the industry of enterprise software, and serve as the basis for many other advances in the incorporation of other technologies related to artificial intelligence and cognitive computing. I also mentioned briefly how machine language is becoming increasingly important for many companies in the business intelligence and analytics industry. In this post I will discuss further the importance that machine learning already has and can have in the analytics ecosystem, especially from a Big Data perspective. Machine learning in the context of BI and Big Data analytics Just as in the lab, and other areas, one of the reasons why machine learning became extremely important and useful in enterprise software is its potential to deal not just with huge amounts of data and extract knowledge from it—which can somehow be addressed with disciplines such as data

Next-generation Business Process Management (BPM)—Achieving Process Effectiveness, Pervasiveness, and Control

The range of what we think and do is limited by what we fail to notice. And because we fail to notice that we fail to notice there is little we can do to change until we notice how failing to notice shapes our thoughts and deeds. —R.D. Laing Amid the hype surrounding technology trends such as big data, cloud computing, or the Internet of Things, for a vast number of organizations, a quiet, persistent question remains unanswered: how do we ensure efficiency and control of our business operations? Business process efficiency and proficiency are essential ingredients for ensuring business growth and competitive advantage. Every day, organizations are discovering that their business process management (BPM) applications and practices are insufficient to take them to higher levels of effectiveness and control. Consumers of BPM technology are now pushing the limits of BPM practices, and BPM software providers are urging the technology forward. So what can we expect from the next

Teradata Open its Data Lake Management Strategy with Kylo: Literally

Still distilling good results from the acquisition of former consultancy company Think Big Analytics , Teradata , a powerhouse in the data management market took one step further to expand its data management stack and to make an interesting contribution to the open source community. Fully developed by the team at Think Big Analytics, in March of 2017 the company launched Kylo –a full data lake management solution– but with an interesting twist: as a contribution to the open source community. Offered as an open source project under the Apache 2.0 license Kylo is, according to Teradata, a new enterprise-ready data lake management platform that enables self-service data ingestion and preparation, as well the necessary functionality for managing metadata, governance and security. One appealing aspect of Kylo is it was developed over an eight year period, as the result of number of internal projects with Fortune 1000 customers which has enabled Teradata to incorporate several be