In previous blog posts I talked about how the ability of Novetta Entity Analytics to understand relationships between entities allows it to deliver more accurate entity resolution and analytics results. Today I’ll explain in more detail how Novetta Entity Analytics detects relationships within and between entities from many data attributes and sources, and why this awareness yields greater insights. Payload Attributes Provide Relationship Insights To understand relationships, Novetta Entity Analytics analyzes both the attributes used for entity resolution, and additional payload attributes that exist within the data but are not used during the entity resolution process. When analyzed in different combinations…
Strata+Hadoop World Recap: Hadoop Business Drivers and Challenges
A few short weeks ago at Strata+Hadoop World in San Jose, our team surveyed attendees of the show walking through the expo floor. We asked these attendees five simple questions to get some sense of the adoption of Hadoop, the business drivers behind it and challenges organizations are facing. The five questions we asked are these: 1. Has your organization invested in a Hadoop platform?[clear-line] 2. If yes, which best describes your Hadoop investment. 3. What business justification drove or is driving your implementation of Hadoop? 4. What is the most challenging aspect of big data and your ability to derive…
5 Critical Components in Novetta Entity Analytics
In my last post, I introduced you to Novetta Entity Analytics and how companies can use it to achieve specific business goals by connecting and analyzing internal and external data sources stored in Hadoop. In this post, I’ll discuss five unique capabilities in Novetta Entity Analytics that enable advanced analytics that weren’t possible before. 1. Source-Agnostic Model Connects Structured, Unstructured, Semi-Structured Sources First and foremost, Novetta’s source-agnostic entity model ensures that any data source registered in Hadoop’s HCatalog can be loaded into Novetta Entity Analytics for analysis. For unstructured and semi-structured sources not already registered in HCatalog, the software…
Hadoop Analytics: Combining Data Requires a Source-Agnostic Approach
In the last two blogs, I’ve talked about the complexities of combining data sources in Hadoop. Specifically I’ve covered: Why custom, source-specific scripts that combine data sources are problematic How using data integration or data science tools introduces too much uncertainty Why adding data from external sources is next to impossible Today, I’m going to discuss how Hadoop analytics is enhanced through source-agnostic technologies that make it easy to combine internal and external data sources. In addition to describing how source-agnostic methods work, I’ll also cover why Hadoop analytics need built-in intelligence and knowledge transfer capabilities, an understanding of relationships…
Hadoop Analytics: Even Harder With External Sources
In my last post; Hadoop Analytics: Not So Easy Across Multiple Data Sources, I discussed the issues organizations face when attempting to use Hadoop to store and analyze data from multiple internal sources. In this post, I’ll talk about the challenges and benefits of adding external data to the mix.[clear-line] Adding External Data Improves Predictive Analytics Organizations increasingly want to analyze third party data because these sources increase their visibility into the broader marketplace, help them predict future actions, and generate additional sales leads. Analyzing internal data alone provides historical perspective about customers and their purchases, which is useful for…
Hadoop Analytics: Not So Easy Across Multiple Data Sources
Hadoop is a great place to offload data for analytics processing or to model larger volumes of a single data source that aren’t possible with existing systems. However, as companies bring data from many sources into Hadoop, there is an increasing demand for the analysis of data across different sources, which can be extremely difficult to achieve. This post is the first in a three-part series that explains the issues organizations face, as they attempt to analyze different data sources and types within Hadoop, and how to resolve these challenges. Today’s post focuses on the problems that occur when combining…
