Three Reasons Your Data Analytics Strategy Must Include Hadoop 1

Three Reasons Your Data AnalyticsStrategy Must Include HadoopOvercoming Current Limitations

IntroductionHadoop has emerged as the leading software framework for the storage and analysis of big data. Early adopters such as Facebook, Twitter and Yahoo successfully built custom analytics using Hadoop to tackle their big data analytic challenges. However, many other organizations struggle to get their Hadoop projects off the ground since the Hadoop lack’s end user tools and has a steep learning curve.

Datameer offers the only end-to-end big data analytics application native to Hadoop designed to make big data simple for everyone. This paper explains the compelling advantages of Hadoop-based analytics, Hadoop’s challenges for end-users and how Datameer overcomes these challenges.

“Hadoop promises to become a ubiquitous framework for large scale business intelligence, but right now it is difficult for many developers to use. I see tremendous value in Datameer’s approach - making Hadoop accessible to more users who need scalable analytic power for their organization’s big data requirements.”Shawn Rogers, VP of ResearchBusiness Intelligence Enterprise Management Associates

1. Cost Effective Scale from Terabytes to Petabytes of DataAccording to the analyst firm Wikibon, unstructured data doubles each year while data production will be 44 times greater in 2020 than it was in 2009. Businesses who are able to harness the power of that data have distinct advantages. According to McKinsey data, those that use Big Data and analytics effectively show productivity rates and profitability that are 5 – 6 percent higher than those of their peers. McKinsey analysis of more than 250 engagements over five years has revealed that companies that put data at the center of the marketing and sales decisions improve their marketing return on investment (MROI) by 15 – 20 percent. That adds up to $150 – $200 billion of additional value based on global annual marketing spend of an estimated $1 trillion.

According to a 2013-2014 Gartner survey of companies, big data analytics offer a range of opportunities with customer-oriented activities in sales and marketing leading the way.

2Three Reasons Your Data Analytics Strategy Must Include Hadoop

Hadoop, designed for the cost effective storage and processing of large volumes of data, linearly scales to 6000 servers and petabytes of data. Hadoop utilizes clusters of commodity hardware and provides a powerful and cost-effective alternative to expensive, traditional data warehouse and BI database servers.

Implementation costs can be high for Hadoop because this open source framework is complex and offers no end-user tools for data loading, analytics and visualization. Datameer provides pre-built, easy to use functionality across the entire analytics process including data loading and integration, data analytics and data visualization. Datameer dramatically decreases IT resource requirements, implementation time and ongoing administrative costs.

2. Inclusion of Structured and Unstructured DataIn the context of data warehousing, business intelligence and analytics, unstructured data refers to information that either does not have a data model or has one that is not easily usable by traditional business intelligence applications. Common examples include Word documents, call detail records, clickstream data, log files, email and social media data.

According to Gartner, enterprise data will grow 650% in the next five years - 80% will be unstructured. It is clear that enterprise analytics must address both structured and unstructured data as well as correlations between the two in order to gain true insights into customer behavior, operations and fraud and compliance issues. However, traditional data warehouse and business intelligence technologies are optimized solely for structured data stored in relational database tables.

One of the distinct advantages of Hadoop is that it is designed for the storage and processing of both structured and unstructured data. Pre-built data models are not required because users leverage the power of low-cost commodity hardware. Datameer extends this flexibility by using “schema on read“ for its analytics as well as pre-built, end-user focused tools for its data loading and integration, analytics and visualization that brings analysis of all relevant structured and unstructured data to every user.

“Analyzing and processing the huge volumes of data that Trustev uses, and then delivering a result in real time wouldn’t be possible without the data analytics provided by Datameer.”

Chris KennedyCo-founder and CTOTrustev

Data from Gartner's Big Data Webinars, March 2013 and 2014

Marketing and salesgrowth

0% 5% 10% 15% 20% 5% 5% 5%

Operational andfinancial

performance

Risk and fraudreduction

New product andservice innovation

Monetizing data(directly/indirectly)

Percentage of Respondents

20132014

Which Is the Biggest Opportunity for Big Data?

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3. Fast Integration of Multiple Data SourcesEnterprises are facing a proliferation of data and data sources. Business users need access to data regardless of its location, whether it comes from sources such as relational databases, call center logs and Web logs or new social media sites such as Twitter — data outside a company’s systems and infrastructure. They also need to integrate new data sources as the need arises for additional analysis.

Hard-coded data and schema mappings required with traditional data warehouses and business intelligence systems make it difficult to respond to constantly changing data requirements in a timely manner. These requirements also make bringing in new data sources difficult and slow as new models must be built anytime a new data source is added.

While Hadoop requires no pre-built data models, it lacks data loading and integration tools for end users. Datameer provides complete Hadoop data loading functionality including 50+ pre-built connectors for structured and unstructured data. With the Datameer wizard-based integration approach, users simple have to specify where the data is, what data needs to be ingested and on what schedule.

In addition to data loading, Datameer also provides automated functions for exporting data and analysis from Datameer to data stores such as data warehouses and business intelligence tools. This makes it easier for everyone to extend and share their analytics.

ConclusionBusiness users work under tight deadlines to provide answers to pressing business questions. Yet, today’s business environment and analytic requirements are ever changing and users need fast and agile access to all relevant data sources for analytics.

Overstretched IT departments simply can’t keep up with user demand and the backlog of requests continues to grow as data from inside and outside the enterprise grows. While business users wait for relevant analytics from traditional systems, companies miss out on opportunities that can positively impact their bottom line.

Don’t let your company get behind in accessing and using information assets for strategic advantage. Consider Datameer and Hadoop to remove the barriers for:

• Cost effectively scaling for big data requirements• Integration and analysis of all your structured and unstructured data• Agility in responding to dynamic business requirements with rapid data integration

Ready to learn more? Simply contact Datameer at 650-286-9100 or email us as sales@datameer.com. A number of resources are also available at www.datameer.com including informative weekly webcasts and a free Trial Edition.