Rishidot Research

deciphering the modern enterprise

Machine Learning

Simplifying Data Analysis: Accelerite Launches ShareInsights 2.0

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Accelerite, the Silicon Valley based company focussed on Hybrid Clouds and Big Data, today announced the next version of their data analytics product, ShareInsights 2.0. ShareInsights is their self service data analysis product focussed on taking the grunt work out of business users and make it easy for them to gain critical insights needed for their organization. Their product offers data preparation (ETL), OLAP, visualization and collaboration in one single interface, making it an end to end stack for data analysis.

Market Landscape

Ever since big data infrastructure became mature, mainly driven by open source technologies, the focus has shifted to data analytics and machine learning. From offering superior customer experience to gaining critical business insights from disparate sources to developing product roadmaps, data analytics is becoming the core competency of any modern enterprise. The biggest pain points felt by modern enterprise decision makers were in data analysis, data transformation and data collection. The big expectation from the decision makers is to have a tool that seamlessly breaks down the data silos across disparate set of data sources. One of the biggest asks from business to enterprise decision makers is a self service analytics tools which takes the groundwork out of getting the data ready for analysis.

Company and Product

Shareinsights is Accelerite’s big data analytics tools that is focussed on offering business users a self service tool to gain insights from large volumes of data. Accelerite also has Rovius hybrid cloud platform and Concert for IoT. Unlike Rovius which is an acquisition, Shareinsights was built from ground up with a single goal of simplifying data analysis and take any pain out of business users that can slow them down. The key focus in their product is speed whether it is the speed of getting started with an analytics product using their platform or the platform speed itself. The platform can run on Hadoop or Kafka, easily integrating with your existing tools in your organization. The platform accelerates the lifecycle from infrastructure operator to business user, by streamlining the flow from data preparation to transformation to visualization.

With Shareinsights 2.0, it is easy to process large volumes of data from multiple sources, whether it is a CSV file or API from a third party service. It is easy to blend data from across multiple functions inside the organization and gain insights beyond what is usually available in legacy analytics tools. They have also included vast library for machine learning algorithms, making it easy for even business users to run machine learning models on top of data and gain critical insights.

Customer Expectations

The typical customer expectation from a tool like Shareinsights are:

  • Integration with multiple data sources
  • Powerful filtering capabilities in the UI to blend data across various sources and functions to gain insights
  • powerful machine learning capabilities

Shareinsights 2.0 appears (from the demo we used for our evaluation) to solve these needs and we expect the tool to meet the needs of modern enterprises. We will continue to watch their progress and talk to customers in the future to gain better insights on their product in the future.

Competitors

Panoply, Tableau Software, AtScale and others

Briefing Notes: Insight Engines Takes AI To Enterprise IT

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Insight Engines, the San Francisco based startup focused on making machine data actionable, announced the general availability of Cyber Security Investigator and, also, showcased how Amazon Alexa can be tapped to query from Cyber Security Investigator. In this note, we will do an analysis on this announcement.

Market Overview

AI in enterprise is relatively new. Even though enterprises are slowly embracing machine learning and other AI models to dig deeper into their customer data and make business decisions, there is very little progress in using AI to take advantage of machine data. There are plenty of analytics solutions that helps Operations teams optimize their decision making process. But the market is still in infancy when it comes to using ML to automagically do operations or use AI technologies like NLP to develop a better user experience for the machine data. Imagine how DevOps can be done more optimally if developers or even other stakeholders like business users can take advantage of NLP to interact directly with machine data. These are just beginning and we can’t imagine what AI can do to autonomic computing with our current understanding of the landscape.

SWOT Analysis

Strengths

  • Powerful NLP engine to handle machine data, add context and give customization options to users
  • Starting with Splunk data (with an investment from Splunk, of course) gives them an easy on ramp to enterprise IT
  • Their initial focus on Security will help them get the attention of IT decision makers
  • Autopilot feature that provides a more pro-active approach to security might serve as model for future autonomic computing platforms

Weakness

  • They are a startup pushing a newer technology in the enterprise market. The barrier to entry for startups in enterprise is high. However, their partnership with Splunk should help them
  • They provide on-premise deployments which makes it difficult for the learning engine to continuously improve. But they are tapping into the anonymized meta data to help the learning engines learn from user behaviors. This will work well with modern enterprises but they may have trouble convincing enterprises in highly regulated verticals. It is not a weakness for Insight Engines alone but for any company trying to build AI systems that can be deployed on-premises. To overcome this, Insight Engines, as a pioneer in this space, has to convince the customers to share their metadata with them. It is a potential weakness but also an opportunity for them to emerge as thought leaders

Opportunities

  • Insight Engines has the first mover advantage and they are attacking low hanging fruits (machine data and security) with a more powerful NLP engine. They can easily broaden their product portfolio going forward
  • As we embrace modern stacks with deeper and deeper levels of automation, ML and AI are going to be the next wave of innovation. The early innovations in this kind of autonomic IT will come around user interface and user experience. Insight Engines is well positioned to take advantage of the trend

Threats

  • Many established players collect lot of machine data (including Splunk) and it is a logical next step for them to attack the low hanging fruits like NLP for UI/UX. Though it is a threat, it is also an opportunity
  • Open source NLP engines can come and disrupt the market. OSS need not come from the traditional IT companies but also from end customers who develop ML and AI engines for their internal use. There is also an opportunity for Insight Engines to lead here but OSS by startups is not easy.

Conclusion

Insight Engines is an interesting startup in the up and coming field of AI in enterprise IT. It is too early in the market but offers potential opportunities for enterprises to do IT more optimally and optimize the use of human power in house by involving more stakeholders and by reducing the learning curve.

Google’s Enterprise Ambitions – Google Cloud Next ’17 Report

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Even though Google is one of the pioneers in Cloud Computing, they were late to enter the enterprise market. In the last couple of years, they started focussing on enterprise customers and, in the recent Google Cloud Next Conference at San Francisco, they showcased their determination to go after the enterprise market. Compared to last year, this year’s event was a big affair with a slew of announcements on new products and features being the highlight of the event.

Targeting the enterprise

In this conference, Google tried to appease enterprise customers by attempting to speak the language they like to listen. Whether it is talking about multi-cloud or partnering with SAP or talking about the engineering support options, Google tried to appeal to enterprises moving to cloud. One of the criticisms about Google Cloud was they appeal to vendors like Snapchat and Evernote but not much to traditional enterprises. They tried to negate this by lining up vendors like HSBC, Colgate, Schlumberger, Disney, The Home Depot, etc.. Listening to all these customers, I saw a common thread on their interest with Google Cloud. It is about the potential for Machine Learning workloads aided by powerful big data offerings from Google.

Google’s enterprise push focussed on

  • Large datacenter footprint: They announced support for new regions worldwide such as California, Montreal, and Netherlands.
  • Security: With the announcement of Identity Aware Proxy and Data Loss Prevention API, along with making other security features in GA, Google is promising enterprises that they can trust Google cloud.
  • Infrastructure reliability: Google highlighted 99.999 percent uptime to give confidence to enterprise customers on the robustness of their infrastructure. Rishidot Research strongly advises their clients to focus on resiliency in their application architectures rather than worry about infrastructure reliability.

I have long been advocating that Google’s path to relevance in the cloud is through Machine Learning and AI. I heard the same from various enterprise customers in this conference. One of Google’s strengths is in big data and, with the announcements related to Machine Learning, they are positioning themselves as the go-to cloud for ML workloads. Google’s machine learning engine and Google Vision API is now generally available. As a part of Vision API, Google is exposing the metadata as a service so that it helps app developers to use the API to gain Google Photos like detection capabilities. This along with the Video Intelligence API puts them as the top cloud destination for ML and AI workloads. Expect to see more startups and enterprises flocking Google Cloud for their ML and AI needs.

SWOT Analysis

Strengths

  • Data Center investments is their asset and the fact that their regions are connected by a private network gives them an edge and enterprise credentials
  • Machine Learning and AI are their strengths and will give them an edge over both AWS and Azure
  • Google is well positioned to offer the best in class security with their assets and expertise. But jury is out on whether it is enough to convince enterprise customers

Weakness

  • They are yet to gain widespread enterprise traction. We would love to see customers moving “all in” with Google cloud
  • Even though they have beefed up Google App Engine, it is yet to attract significant attention
  • Their multi-cloud pitch shows their weakness in the cloud market. Even though multi-cloud is fast becoming a reality, a public cloud provider using the pitch in high decibels is more indicative of their challenges in the market
  • They are still in a weak spot compared to AWS when it comes to Functions as a Service. After seeing the success of all the AWS Lambda sessions in the last re:invent, I expected Google to come out swinging. Even though their announcement regarding Firebase integration with Google Cloud Functions offers promise, they have a long way to go before they can catch up with AWS Lambda

Opportunities

  • Even though AWS has the runaway lead, the infrastructure market is huge and tons of legacy targets available for both Microsoft and Google. Google is positioning themselves to gain significant portion of the remaining cloud market
  • With the success of Tensorflow in the community, Google has the potential to attract a significant share of Machine Learning workloads. With their advantages in AI, they have an opportunity to become the cloud of choice for not just ML and AI focussed startups but also the enterprise customers
  • Their inter-region network and security focus will help them gain credibility with the enterprises

Threats

  • Google’s go to market strategy to attract enterprise customers is still not very convincing. Yes, Google cloud’s top leadership is packed with proven enterprise leaders from VMware, Red Hat, etc. but there is a lack of clarity on their approach. They are neither taking the AWS approach to enterprise customers nor taking a traditional enterprise path. They seem to be playing a middle ground and it runs the risk of not being attractive enough for enterprises
  • I love their Engineering Support announcement and how they are trying to incorporate AI into customer success. But some of the requirements for their support model could be upsetting the enterprise customers and may come back to bite them. I fully understand why these requirements are needed from a support logistics point of view but we will have to wait and see if it works

Conclusion

Google has started its journey to lure enterprise customers to their cloud. They are definitely growing up in this path but they still have to go a long way before emerging as a strong player. The next two years will be critical for Google Cloud to convince enterprises to trust their cloud. The key to their success lies in convincing enterprises that they are the destination for most of their workloads than giving a message that they are one of the providers in the multi-cloud era. We will have to wait and watch whether they can be a credible contender to AWS and Azure

Competitors

Amazon Web Services, Microsoft Azure, IBM Bluemix, Oracle Cloud, Digital Ocean

Disclosure: Google paid for my travel and stay during the conference

SWOT Analysis Source: https://github.com/rishidot/SWOT/blob/master/Google/Google-Cloud.md

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