Yesterday, Gartner published the 2017 Magic Quadrant for Business Intelligence. The MQ research for BI has been in existence for close to a decade; it is THE document of reference for buyers of Business Intelligence technology.
Wouldn’t it be great if you could load all of your data from a single file into an Excel pivot table for easy analysis?
Unfortunately, this approach isn’t usually viable when dealing with complex business analytics and big data. Take for example a typical use case found inthe world of healthcare insurance. A large insurance provider has 10s of millions of members, and processes 100s of millions of claims a year. As flexible as Excel is, we all know it won’t handle this volume or velocity of data.
As a result, more and more enterprises store large data sets in big data platforms like Hadoop. And while Hadoop provides a low-cost and performant approach to store and process this information, there is still the challenge of supporting the many types of analytics required on claims and member data sets. But why? Why and how, with all of the advances in technology, can a simple calculation cause so much complexity?
Yes, there are actually ways to 'Do Big Data Analytics Right'.
Leaders and innovators in the Big Data space have learned the hard way, and now those of you looking to dip your toe, or jump head first, into the BI on Big Data waters can capitalize on their early experiences. Let go of the fear or ego or whatever may be holding you back and take the chance to learn from those who took the early adopter risk.
I am a software engineer and I like abstractions. I like abstractions because done correctly an abstraction will factor complexity down to a level where I don’t have to spend any brain cycles thinking about it. Abstraction lets me work with a well thought out interface designed to let me accomplish more without having to always consider the system at a molecular level.
It turns out business people also like abstraction. This shouldn’t be surprising as businesses model complex real world concepts where the details matter. From calculation to contextual meaning, abstraction helps with correctness and understanding.
Topics: Semantic Layer
With every New Year come new trends. As we experience changes across the world of Analytics and Big Data, we took a look at sources we follow and trust and put together a Top Five 2017 BI and Big Data Trends to Watch.
- Big Data, no longer just Hadoop -- Tableau
- Consider the Cloud -- Tech Republic
- Query performance Matters --Information Management
- Business-driven Apps drive Value for Data Lake -- MAPR
- Big Data becoming part of Business Fabric -- Datanami
Read on to hear why, how and where BI and Big Data leaders in the space say enterprises are, or should be, focusing, to stay ahead of the curve and succeed with BI and Big Data.
Topics: 2017 Big Data TRends
As 2016 draws to a close, and the AtScale Blog continues to grow, it is easy for a few fantastic posts to have been overlooked over the year. With this in mind, we present to you … Five BI on Big Data Blogs You May Have Missed This Year, But Shouldn't … that offer unique insights to expand your thoughts and ability to drive success in your BI and Big Data journey.
Is your Big Data ‘mature’? You may be puzzled by this question, since many in the industry have been saying ‘Big Data is Dead’ for years. But Big Data is far from dead, and instead technologies and solutions that make up the Big Data space are maturing at an ever increasing rate. From traditional players like Teradata, to open-source Hadoop, to new Cloud players like Google Big Query, the Big Data space is doing more to help companies manage and gain insights from their exploding and morphing data than at any other point in history. So what?
Digital transformation is a broad term that has various meanings by application, but in general, it means that more and more of what organizations, people, governments do is happening in computers, mobile devices and networks. As a result, the way things are done is changing, especially in the way things are connected. So in this new world of data flying everywhere, being generated and consumed, where does one stop for a second to take a look at what’s going on?
Congratulations! Your Hadoop cluster is up and running. Your data feeds work; your team knows how to manage the cluster, and expert users mine the data with Hive, Pig, Spark. But your executives aren’t satisfied. “Where is the business value?” they ask. “Why don’t we see more people using Hadoop?”
Big Data analytics leader previews industry’s first platform to enable unified business intelligence for Teradata, Hadoop, Google Dataproc and BigQuery
San Mateo, CA, November 17, 2016 – AtScale, the first company to provide enterprises with a fast and secure self-service BI platform for Big Data, today announced a significant expansion of its services, from BI on Hadoop to BI on Big Data.