jKool Blog

14 02, 2017

How You Can Improve Customer Experience With Fast Data Analytics

By |February 14th, 2017|Fast Data, Financial Services, Middleware, monitoring, Real-time Analytics, streaming analytics|0 Comments

In today’s constantly connected world, customers expect more than ever before from the companies they do business with. With the emergence of big data, businesses have been able to better meet and exceed customer expectations thanks to analytics and data science. However, the role of data in your business’ success doesn’t end with big data – now you can take your data mining and analytics to the next level to improve customer service and your business’ overall customer experience faster than you ever thought possible.
Fast data is basically the next step for analysis and application of large data sets (big data). With fast data, big data analytics can be applied to smaller data sets in real time to solve a number of problems for businesses across multiple industries. The goal of fast data analytics services is to mine raw data in real time and provide actionable information that businesses can use to improve their customer experience.
“Fast data analytics allows you to
turn raw data into actionable insights instantly”
Connect with Albert Mavashev
Co-author, CTO & Evangelist at jKool

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13 12, 2016

$1.9 trillion dollars of economic value could be created by the use of IoT devices and asset tracking solutions

By |December 13th, 2016|Internet of Things, Real-time Analytics, transaction tracking|0 Comments

Using sensors, tags, and other IoT devices to track goods through the global supply chain is one of the fundamental use cases for the Internet of Things, and also one of the most impactful.

The enormous potential value created by these asset tracking technologies is due to the incredibly broad array of benefits that companies […]

12 04, 2016

Big Data in Finance Can Improve Retention and Returns

By |April 12th, 2016|Big Data, Big Data Analytics, blog, Real-time Analytics|0 Comments

The amount of data that banks have flowing through their systems is massive. However, most banks only use a fraction of the data. Some leading banks are able to use big data by gathering customer data to provide a better understanding of more efficient ways to sell, retain customers, and of course attract new ones. […]

6 04, 2016

5 Ways the Internet of Things Will Change Big Data

By |April 6th, 2016|Big Data, Big Data Analytics, Internet of Things, Real-time Analytics|0 Comments

Another Internet of Things article? Well, what about how Big Data and the Internet of Things work together. Before the Internet of Things became a driving force in organizations, big data was quite frankly, pretty big. There were massive amounts of  data being generated by billions of networked sensors and devices. How were businesses going to keep up […]

25 03, 2016

The Future of Machine Learning: Trends, Observations, and Forecasts

By |March 25th, 2016|Big Data, Big Data Analytics, blog, Data Science, Internet of Things, machine learning, Real-time Analytics|0 Comments

The topic of Machine Learning  has increasingly gained popularity over the last few years. Although it’s not a new science, it continues to gain momentum. Allowing computers to find hidden insights that can guide better and faster decisions in real time without the use of human intervention allows this science to continue to grow. […]

29 02, 2016

How Big Data Is Changing the World of Soccer

By |February 29th, 2016|Big Data, Big Data Analytics, blog, Internet of Things, Real-time Analytics|0 Comments

Whether you’re watching a sporting game, participating in one, or simply playing on a fantasy team.  Sports as you know, has helped shape today’s culture. Fans not only in the United States but across the globe share their need in knowing the advanced statistics and information of their favorite sporting team.  The media outlets […]

25 02, 2016

3 Reasons Root Cause Analysis is Fundamentally Flawed

By |February 25th, 2016|devops, Java, operational analytics, prediction, Real-time Analytics|0 Comments

Always the same final answer to all WHYs

Root-cause analysis = ask WHY until the answer is ‘i don’t know’, meaning that root-cause analysis inevitably arrives @ UNKNOWN or UNKNOWABLE:

Oversimplified example:

Boss: Our application is slow and end-users are unhappy, why?
You: Database is slow
Boss: Why?
You: Because our report query is taking too long.
Boss: Why?
You: Database server is out of memory.
Boss: Why???
You: I think Bob […]

22 02, 2016

Gartner shakes up annual ranking of business analytics tools – TechTarget

By |February 22nd, 2016|Data Science, operational analytics, Real-time Analytics, Spark, streaming analytics|0 Comments

Open source tools, such as R, Python and Spark, are growing in prominence. The number of data sources available to businesses is increasing. And the need for complex event processing and
streaming analytics related to the Internet of Things is growing.

[…] Read the source article at searchbusinessanalytics.techtarget.com
Original Author: edburnstt

12 02, 2016

10 tips for chief analytics officers – ZDNet

By |February 12th, 2016|Apps, Big Data Analytics, Real-time Analytics, streaming analytics, time-series data|0 Comments

Real-time and streaming analytics are getting lots of attention, but keep the real decision time in mind, advised Bill Franks, CAO at Teradata. The IRS, for example, doesn’t worry about real-time because it has weeks to detect fraud before it cuts …

So if a customer’s Web query is followed by a phone call and, […]

11 02, 2016

What does APM have to do with Fast Data?

By |February 11th, 2016|Apps, Big Data, Big Data Analytics, Fast Data, Java, log analytics, operational analytics, operational intelligence, performance monitoring, Real-time Analytics, streaming analytics, transaction tracking|0 Comments

Fast Data is about processing high velocity data in real-time as it happens. Think of Fast Data as Big Data in real-time.

So what does APM have to do with Fast Data?

Everything.

APM is all about processing lots and lots of data as close as possible to real-time.
Tracking transactions, analyzing logs, sampling metrics, figuring out relationships, […]