Hi. Welcome to Ingram Micro.

Please choose your role, so we can direct you to what you’re looking for.

If you’d like to learn more about Ingram Micro global initiatives and operations, visit ingrammicro.com.

5 Types of Big Data Analytics That Every VAR Should Know

June 15, 2017

In the digital age, different types of big data analytics are driving the business intelligence of every industry, including retail, healthcare, financial services, manufacturing, hospitality, and government. While big data application examples are numerous, VARS that plan to make it a part of their offerings to their clients must start with an understanding of five types of big data analytics.

#1: Predictive Analytics
Predictive analysis identifies past data patterns and provides a list of likely outcomes for a given situation.

#2: Diagnostic Analytics
Data scientists turn to this technique when trying to determine why something happened. It is useful when researching leading churn indicators and usage trends among customers.

#3: Descriptive Analytics
Descriptive analysis examines what is happening in real-time based on incoming data by enabling organizations to convert big data into useful bite-sized nuggets. By utilizing dashboards, organizations can monitor the results through a process known as “data mining” that almost every organization uses in some capacity.

#4: Prescriptive Analytics
IDC predicts that by 2020, 50 percent of all business analytics software will incorporate prescriptive analytics built on cognitive computing technology. Prescriptive analytics translates a forecast into a feasible plan for the business and helps users identify the best steps to implement. It is commonly recognized as the culmination of business analytics and the third leg in the stool that includes predictive and descriptive analytics.

#5: Outcome Analytics
Also referred to as “consumption analytics,” this technique provides insight into customer behavior that drives specific outcomes. This analysis helps businesses have a more complete picture of customers and their interaction with products and services such as through embedded sensors for Internet of Things applications.

Practical Application of Big Data Analytics

For VARS, going beyond an understanding of big data analytics in working with their clients is about setting the stage to obtain answers about projects such as:

  • What is the goal, what is the business problem, and what is the value of solving the problem?
  • What questions are you trying to answer?
  • What are the deliverables?
  • What will you do with the insights?

Harnessing big data and analytics can deliver immense value to businesses by providing context for collected information and a big-picture view of the organization. The bottom line is that by turning complex data sets into actionable intelligence through one or more of these five analysis methods, VARs’ clients can make better business decisions.

In order to make the case that VARS are best positioned to architect big data infrastructures for the digital age, they must have strong partnerships with integrator and solution communities like System ArchiTECHS. Having manufacturer and distributor partnerships with leading companies that are the leading big data technology solution providers is crucial to VARs having access to the technology solution building blocks for big data analytics.

Technology Solution Building Blocks for Big Data Analytics

Among the building-block solution-provider examples that System ArchiTECHS partnerships can harness for VARS are Intel microprocessors. Specific technology solutions like the Intel Xeon processor E7 v2 family are providing big data storage solutions for businesses across a wide spectrum of industries that are harnessing big data via infrastructure. This is in addition to their, storage, and networking solutions.

The recent news of the completed merger of System ArchiTECHS partner Microsemi with PMC-Sierra makes Microsemi an even bigger asset to VARs working with different industries creating big data infrastructure. Microsemi data center solutions and chips are key tools for big data analytics for the communications, security, aerospace, and industrial markets.

Microsemi’s merger with PMC-Sierra brings semiconductor and software solutions for storage, optical, and mobile networks. This includes a wide variety of big data storage solutions such as Host Bus Adapters, RAID Adapters, and SAS Expander and Cables.

In addition, other System ArchiTECHS partners can help VARS create all-SSD (solid-state drive) storage arrays and software-defined networks for big data using Samsung SSDs from partners like Crucial. It’s all about getting the support from System ArchiTECHS members in architecting solutions as well as the support from their many big data technology solution providers. This empowers VARS with the tools and support that they need in order to ensure that clients get systems with the expected performance results needed in order to grow their customer base, build better products, and change customer outcomes for the better.

VARs must be able to speak the language of big data analytics when consulting with clients on architecting data warehousing and server solutions. These solutions must be customized to meet specific needs today and possible expanded needs tomorrow.

For example, solutions like SanDisk flash products (enterprise-grade SSDs) can be integral to contributing toward achieving higher and faster performance for big data business analytics. The knowledge and technology solutions surrounding the different types of big data analytics are crucial to helping VARs bridge the practicality use and understanding gap with clients.

VARs: Bridging the Big Data Gap with Clients

VARs must be capable of showing how clients can use these different types of big data analytics in order to help optimize business strategies and assess whether or not predicted business outcomes are worth pursuing. Having this practical knowledge that is integrated with data system design and use possibilities is a key part of educating clients on specific solutions and their application so that the knowledge barrier does not hold companies back from adopting more advanced analytics techniques.

VARs must understand and cultivate experience and knowledge in creating data warehouse architecture that combines a traditional enterprise data warehouse with technologies such as Hadoop clusters and NoSQL database systems. Those VARs that are partners with System ArchiTECHS have access to the knowledge base, skilled peers, and, most of all, technologies for architecting advanced data systems that can make practical use of the different types of big data analytics. Ultimately, with the support of VARs, clients can utilize big data in order to further the business in tangible, bottom-line-growth ways like never before.