Industry leaders know that their challenges with data analytics are spread across 4 areas: help them better see 'the things they know they know' (aka confirmation), understand 'the things they don't know they know' (aka intuition), discover 'the things they know they don't know' (aka inspection)...and more importantly, discover 'the things they don't know they don't know' (aka revelation). This last category is often the most tragic for organizations. Luckily, we now have resources to help from Tom Davenport's latest webinar on Data Strategy to key research data points on chief data executives'priorities...
Over the years, Big Data pundits have come up with great one-liners to highlight the importance of data: "Data is the new gold" or "Data is the new bacon" are examples of such analogies. They are all great but there is one in particular that's been bothering me lately. If you're coming to next week's DataWorks event, be sure to attend the AtScale keynote on Wednesday to find out which one it is and why...
Production deployments continue to drive rapid pace of innovation
When we announced AtScale 5.0 you may recall that I was quite excited about the rich set of analytical capabilities included in the release, including multi-fact support and an improved design experience for complex models. These capabilities have been well received by our customers, and have helped them to put more use cases into production using the AtScale Intelligence Platform.
With the 5.5 release we’ve continued to provide new capabilities in working towards a modern BI platform for big data and we have also included a number of “production-ready” enhancements for our existing customers. Read on to learn about the key themes of AtScale 5.5!
Robust Analytic Use Cases
One of the key catalysts for AtScale’s rapid adoption among the world’s largest enterprises is our ability to support the complex business use cases and analytical models that these customers demand. Some examples of these use cases across multiple verticals are highlighted in table 1.
Table 1. BI on Big Data: More than just fast queries
The rapidly exploding demand for business intelligence on big data is nothing new - this trend is clearly indicated in the latest Big Data Maturity surveys (2015 and 2016). As shown in the graphic below, 75% of respondents are planning on deploying BI workloads on their big data platforms (with 73% of respondents already with some BI use cases deployed).
This past week, Tom Davenport, of the "Competing on Analytics" fame published an HBR article that he could have named: "What They Don't Teach You at Harvard Business School: Big Data Edition". The piece follows a long list of mainstream articles that have attempted to highlight the short-comings of "Old School Data Strategy". Here is what you might have missed and why it could matter to your company and career.
Every once in awhile, the ultimate question comes up: "What is the best analysis tool for BI on Hadoop?!". AtScale is not in the business of favoring one tool versus the other. We are in the business of making all of them work. There are indeed many reasons why business users and I.T. departments choose particular analysis tools. Here are a few things to consider.
Conducting exploratory data analysis or even basic business intelligence on Hadoop often requires input from data scientists who:
- Create models for related information across Hadoop.
- Structure databases to contain that data.
- Ensure different BI tools generate consistent results when referencing identical data elements.
How many data scientists you'll have to hire depends on the size of your organization and the composition of the data under its control. In this post we review how much it typically costs to hire a data scientist, the factors you'll have to consider when assembling a team and ways you can alleviate the workload placed on data scientists.
In the world of Business Intelligence and Big Data there continue to be a number of exciting innovations as new and improved options for processing large data sets appear on the market. You may be familiar with AtScale’s BI-on-Hadoop Benchmarks - where we focus on evaluating the top SQL-on-Hadoop engines and their fitness to support traditional BI-style queries. As we continue to work with customers who are navigating their journey to BI on Big Data, we are increasingly getting questions about the emerging cloud-based data processing engines.
In this blog post, we will take a deeper look at Google’s BigQuery, and how it stacks up in the BI-on-Big Data ecosystem.
CONTINUING OUR TRACK RECORD OF RAPID DELIVERY & INNOVATION
Today we announced the general availability of AtScale 5.0 and I couldn’t be more excited about the host of great new features that are included in this release. As we’ve continued to gain traction in a number of industries - ranging from healthcare to retail to financial services to telco to online- we continue to learn from our customers and use these learnings to feed directly back into our product features. With the release of 5.0, AtScale customers now have an even richer set of capabilities that they can use to derive business insights and value from their Big Data investments. I’ve included some of the highlights of the release in the sections below.
I’ve asked it before and I’ll ask it again. Wouldn’t it be great if you could easily analyze ALL your data from a Excel single file? We all know this isn’t feasible; especially when dealing with big data and complex business analytics needs.
In working at the intersection of Big Data and traditional Business Intelligence, the AtScale team has encountered a number of complex business analytics use cases that are difficult, if not near-impossible, to solve using typical table-based data models and SQL. Today, I’m going to share why and how complex analysis, like for multi-level metrics, is no longer as ‘difficult’ nor ‘near-impossible’ as it once was.