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Big Data is changing the face of OEM

AudreyCox

We all know the reality: Big Data is here to stay and those that embrace it have the upper hand. A recent IDC forecast shows that the Big Data market will grow at a 26.4% compound annual growth rate to $41.5 billion by 2018 or six times the growth rate of the overall information technology market. Additionally, by 2020, IDC believes that line‐of‐business buyers will drive analytics beyond its historical sweet spot of relational (performance management) to the double‐digit growth rates of real‐time intelligence and exploration/discovery of the unstructured worlds.

To manage the incredible volume, velocity, and variety of the data, several technologies have emerged. Because most (>80%) of the data is unstructured (email, text, IM, log files), it is the job of Big Data analytics to combine the “known” with the “unknown” to deliver value in ways never before possible. From data monetization to customer retention to compliance to track optimization, enterprises that embrace this emerging technology are changing the dynamics of business in every vertical market. 

 

An OEM opportunity 

All of this brings a major opportunity for OEM software vendors. OEMs profit by creating analytic data management features or new applications that put customers on a faster path to better, data‐driven decision-making. To get such competencies, many embedded software providers attempt unorthodox approaches with row‐oriented OLTP databases, document stores, and Hadoop variations for heavy Big Data analytic workloads of today’s enterprise. Alternatively, some companies attempt to build their own Big Data management systems. But such custom database solutions take thousands of hours of research and development, require specialized support and training, and may not be as adaptable to continuous enhancement as a pure‐play analytics platform. Both approaches are costly and often outside the core competency of businesses that are looking to bring solutions to market quickly. 

 

Today’s analytics requirements 

As enterprises discover new ways to monetize their data, traditional transaction‐oriented, memory‐intensive, and basic analytical platforms struggle. Customers need an analytical OEM software platform that can:

  • Manage huge data volumes: The selected database should provide limitless scale at minimal cost. Today, the scale may be gigabytes or terabytes; tomorrow, petabytes and beyond. 
  • Deliver fast analytics: Expectations are high; waiting for results is not acceptable. A software vendor should provide the load and query performance to ensure timely and relevant analytics. The solution should provide ways to optimize queries without asking to tune, and it should be flexible enough to run ad-hoc
  • Embed machine learning: With the volume and velocity of data combined with the goal of predictive and prescriptive analytics, embedded machine learning algorithms are critical. An analytics platform that iteratively learns from new data eventuates software solutions to a new level of intelligence. 
  • Handle user-defined functions (UDx): Having an analytical platform interface that connects to hundreds of applications, data sources, ETL, and visualization modules is also critical. What it doesn’t connect to out of the box, the UDx can easily integrate. UDx is a clear differentiator for software vendors looking to add variety and improved data insight to their core offerings. 
  • Support data scientists: The new class of data scientists use tools like Java, Python, and R to create predictive analytics. The underlying database makes it easier to leverage these programming languages. Data scientists will appreciate an analytics platform that delivers the tools to spot trends, uncover anomalies, and anticipate the unexpected. 
  • Use highly advanced analytics: Many platforms analyze a single table or a simple lookup, but very few analyze dozens of tables, hundreds of data types, dimensions, and attributes from numerous sources over several years. 
  • Require minimal administration: Enterprises have been conditioned to administer databases for optimal performance. The new paradigm is the analytics platform that tunes and manages backup and interface without heavy oversight. 

 

Deploying HPE Vertica-based solutions 

Commercial software applications are delivered to customers in a variety of ways and Vertica offers the deployment and licensing flexibility required to build:

  • Appliances
  • Installed Software Solutions
  • In-house hosted solutions
  • Cloud-hosted solutions

Vertica runs on low‐cost industry‐standard Linux® servers. It's fast performance, shared‐nothing architecture and compression enable it to run on much less expensive hardware than any other database, which lowers solution development and hosting costs. In addition to this, Vertica supports VM environments and runs on the Amazon cloud, allowing solution providers to bring new offerings to market faster and less expensively without any in‐house data center costs. It also enables new solutions with micro life spans such as marketing campaign analytics licensed for the duration of a campaign. 

HPE offers flexible licensing programs to ISVs designed to support licensing model and foster mutual growth and profitability. Vertica has more than 100 ISVs in networking, security, marketing, retail, financial services, sales, healthcare, utilities, and insurance embedding. The ability of Vertica to extract greater insight out of enterprise data leads to additional license revenue and a much closer customer relationship. 


Learn More about HPE’s OEM Program

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About the Author

AudreyCox

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