big data

Simplifying Data Analysis: Accelerite Launches ShareInsights 2.0

Krishnan Subramanian · December 12, 2017 · Leave a Comment

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: Cloudsoft AMP

Krishnan Subramanian · April 9, 2013 · 1 Comment

This is a briefing note prepared by me on Cloudsoft AMP, a DevOps platform services player offering autonomic management of applications.

Overview:

Cloudsoft AMP is an enterprise application management platform that helps automate the process based on business and performance needs. Deploying an application on any cloud or PaaS is just one part of the application lifecycle management. There are many other aspects of the lifecycle that are equally important. For example, management, monitoring, governance, portability, etc. play a critical role in enterprise IT. Cloudsoft AMP adds a layer of abstraction to platforms/platform services making autonomic management of applications much easier in enterprise IT environments.

Read the report below

Disclosure: Cloudsoft Corporation is a client and sponsor of Deploycon

To download the briefing note, you need to sign up as a free subscriber. Check out this page for signing up as a free subscriber. Once you sign up for your account and log in, you will see a download link to the briefing note.

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Download Link: https://rishidotver3.wpengine.com/?s2member_file_download=RishidotResearchBriefingNotesCloudsoftAMP.pdf
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Yes, Virginia, There is a Role for IT in Big Data

lmacvittie · July 18, 2012 · 1 Comment

Much like cloud and other technology that is used by IT and only later given a catchy, marketable name, IT has been involved in collecting, analyzing, and making decisions based on big data for, well, ever.

Whether it has focused on quality of service, bandwidth management, or performance, IT has collected big operational data and used it to make decisions that improve the quality and security of service delivery. So it was somewhat bemusing to read Joe McKendrick’s blog on big data and discover that most organizations consider data analysis a business function, not an IT function.

Today, 95% of businesses do not consider data analysts a part of their IT staff. Instead, companies are now distributing that expertise to line-of-business groups throughout the company. The majority of respondents (58%) report data management is now embedded throughout their business as a dedicated function.

— Yes, There is ROI in All That Big Data: Survey

While we certainly don’t give the title “data analyst” to the folks in operations who can glance at a CPU utilization chart and immediately deduce that a memory leak is occurring and causing the system to overprovision resources as a compensatory measure, that’s exactly what these seasoned IT operators are doing – data analysis. It just so happens that they’re focused on operational data, not business data. They’re aggregating across the whole of the data center (and increasingly the cloud) and analyzing operational trends in order to address operational issues like performance, resource consumption, and security as quickly as possible.

Big Operational Data, as it were, has been the foundation of improving operations for as long as log aggregators and inline monitoring solutions have been deployed in the data center. Much in the way we marvel at the ability of old school assembly programmers to peruse a Matrix-like screen full of hex codes and point at two instructions as the root cause of a problem, so do operators today analyze logs and output and charts produced by a menagerie of infrastructure services and rapidly deduce through their analysis from whence a problem originates.

The Convergence of BIG Business and Operational Data

Big operational data has the potential to add significant value to the business. In a world where micro-seconds of delay translate to lost revenue and customers, it is imperative that operations be able to quickly track down the causes of delay and redress. That means analyzing operational data and making decisions to reroute traffic, tweak policies, or turn on services that will improve performance, resource utilization or security.

Additionally, some big data is equally valuable to both IT and the business. Browser, location, identity, device form factor. These pieces of data are pertinent to operations as it enables the codification of policies regarding access and security and to business to understand the habits and preferences of its (potential) customers. This data exists across a variety of systems in the data center (and without) and must be used by both operational and business data analysts alike if organizations are to fully take advantage of big data.

There are plenty of ways in which IT can take advantage of big operational data – from more tailored delivery policies to improving the bottom line through better provisioning of resources and optimizing performance. Doing so, however, requires analysis of the data, either formally or informally. Data analysis, whether of operational or business data, is still analysis.

There is a role for IT operations in big data, and big data has a role in IT operations.