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The Portfolio Approach to Big Data

by Guillermo Vazquez and Sherrell Davis | SAPinsider, Volume 15, Issue 2

April 1, 2014

Discover the traits of a portfolio approach to big data — treating your data assets as part of a portfolio investment. In this way, you can focus on data that is necessary to answer key questions about your business and analyze only the data you need while improving your business performance.
 
1. With the variety of data companies possess, how can they make analytics consumable throughout the enterprise?

 

Businesses must learn to approach their analytics assets as part of a portfolio investment. The goal of the portfolio approach is simple: Focus on the data that is necessary to answer key questions about your business and analyze only the data you need while improving your business performance. With this approach, you bring in the asset owners from across the organization who can help you best leverage the required information. The portfolio approach can help organizations:

  • Understand the various analytics assets they possess, who owns them within the organization, and the insights each can deliver
  • Gain quick, accurate answers to critical business questions
  • Determine where best to make analytics investments
  • Establish greater discipline in the use of analytics

New business intelligence (BI) architectures based on SAP HANA in-memory technology allow the optimization, integration, and data federation required to exploit all of the available structured and unstructured data, in large volumes — big data, in other words — with fewer functional and technical resources.

2. What are EY’s customers saying (and doing) about imple­menting predictive models for analytics in their organizations?

 

We’ve found that the majority of our clients are already using descriptive and predictive analytics. However, the prescriptive and predictive models that they generate are not produced in structured and integrated environments, with common data created in the same periods of time or even from single data sources. This fact creates discrepancies among the business units that operate based on disconnected information silos. BI maturity-level assessments help recognize the analytical capabilities of our clients at the system level, not at the siloed business unit level. Organizations should be driven by top-down business information needs. This is where we help our clients recognize their integrated information requirements and help them integrate and leverage their current descriptive, predictive, and prescriptive analytics models using a BI architecture built to support their needs. Learning about SAP HANA in-memory BI architecture helps focus the dialogue among business and IT teams in this direction.

3. What is your overall take on the pace of innovation in the BI space and its impact on business?

 

Based on our internal assessments and research on the BI needs of our customers, we have found that the business requirements and transformations necessary for our clients to succeed can be achieved by leveraging their existing BI capabilities.

We see a need for mobile BI that can reach not only business units in the field but also customers. These services, offered in the cloud, are a must-have. There is also a need to streamline and make a simple BI architecture that can integrate transactional data with structured and unstructured data created outside the organization in a simpler business data warehouse. To support this, enterprises use integrated calculation engines such as those found in an in-memory database. Finally, we see BI as a single entity embedded in the data sources of social media and transactional systems, as well as complex enterprise performance systems for planning, budgeting, and controlling processes.

To learn more, visit www.ey.com/US/en/Services/Advisory/IT.

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Guillermo Vasquez
Guillermo Vazquez

Senior Manager and BI
HANA Lead
Ernst & Young LLP


Sherrell Davis
Sherrell Davis

BI Manager
Ernst & Young LLP



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