Big Data. Big Strategy. Big Results.

If you talk to five people working on Big Data projects, you'll hear about fifty kinds of Big Data they're using. Sometimes volume is the biggest issue, sometimes unstructured data, sometimes ordinary data flooding the system in real time. Support for Big Data can mean many different things to different people.

No matter what Big Data means to you, Information Builders can help you manage and analyze it. Hadoop distributions from Apache, Cloudera, MapR, and Hortonworks? Check. Massive structured data from Teradata, Netezza, and Greenplum? Of course. Unstructured content from social media, blogs, and comment streams? No problem. Streams of data from the Internet of Things? Absolutely. NoSQL databases like MongoDB and InfoBright? That and much more.

Managing Big Data

People often ignore data quality when they store Big Data. A few outliers won’t be a problem in a large data set, the idea goes – we’ll just ignore those on the output, or cleanse them when the time comes.

But if you don't profile the data's quality, you can't know that there will only be a few outliers. And if you do need to clean the data later, you will have lost a lot of contextual information about the time, people, and technology that collected the data. The time to clean data is as close to the point of generation as possible. The same goes for Master Data Management -- having Big Data silos with unmastered data will make it more difficult to relate your Big Data efforts to the larger enterprise.

iWay Software technologies from Information Builders enable you to capture, cleanse, and master big data as it's being derived from its sources, ensuring that you have trustworthy data that fits into the enterprise context.

Analyzing Big Data

Big Data was supposed to open up huge volumes of data to analysis. Instead, it often requires administrators to spend a lot of time subsetting data and loading it into in-memory databases.

Using the WebFOCUS Business Intelligence and Analytics platform and iWay Software, you can go directly to the data you most want to see, whether targeting normalized databases or semi-structured data in Hadoop. You can also use special features for specific data sources, such as directly accessing MapR's rebuilt, high-performance Hadoop Distributed File System.

Data can be processed on load or retrieval for special situations, such as sentiment scoring in social media analytics or enrichment and data blending with other sources for data discovery. Since everything is part of the same platform, you can even do things like use predictive analytics as a selection criteria in an ETL job -- enabling you to get the data that's most likely to be important for your follow-on analytics.

We also don't limit you to in-memory databases. Big data analytics, by definition, often use data sets that would blow out the capacity of even the largest desktop computers an analyst might have. A columnar data store with 90-plus percent compression enables you to get more data into the local system while still having as much of it in memory as possible. Occasional disk I/O is far more effective than out-of-memory conditions are.

Regardless of the diversity of your data galaxy, Information Builders covers your needs for big data analytics and management so that you can unlock new insights and accelerate innovation.

 

Companies using Information Builders solutions for Big Data