SAP TechEd 2013: How to Get Started with Big Data Without Boiling the Ocean ?

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How to Get Started with Big Data Without Boiling the Ocean ?

In order to compete in globally-integrated markets companies today need a comprehensive understanding of their customers, suppliers, products, competitors and more. This understanding requires a new use of information within organizations which promotes Business Analytics to an even higher significance.

For some companies information has already become their most valuable and differentiated economic good. Big Data can help to leverage the complexity of information to gain and maintain competitive advantages.

Within the SAP world Big Data meanwhile became a popular term too:

• Marketing has launched a fantastic campaign around Big Data in North America that will soon be kicked off in other regions too
• Leading SAP architects are busy outlining reference architectures tailored to Big Data use cases
• Recent announcements of strategic partnerships and acquisitions will strengthen SAP’s position in the market of Advanced Analytics
• Services units are getting ready to support real-life Big Data engagements through expert skills and new services
• Customer Engagement Teams are working on stunning show cases to demonstrate the art of the possible in the world of Big Data
• Sales teams are having great conversations about Big Data with our customers

In the past many companies considered Big Data to be relevant for web companies only, such as Amazon, Facebook or Google. 
However, the reality is that these days every company has more and more data available that could potentially help them to run better and companies must make a plan on how to leverage that data in order to stay ahead of the competition. 

But what would be the right approach to getting started with Big Data ?  Here is a 6 step approach that will help you to focus on the right things to do with the right priority:                                       

• Identify the Business Problem
• Develop your individual Big Data Roadmap
• Analyze your existing environment and identify the right technology
• Start smart and turn plans into action
• Plan to scale
• Execute

Identify the Business Problem: The current global economic conditions force companies to focus on investments with measurable outcome. Articulating the right business problem that can be addressed through data is critical before ever thinking about technical solutions:

• Understand the value a business case could create for the company
• Understand the source and complexity of the data that would support a business case
• Understand the high level solution design requirements from and end-user perspective
• Define measurable success criteria

Typical business cases are centered around:

• More intelligent decisions through new information from untapped data sources
• Quicker decisions by enabling organizations to make (real-time) decisions at the time they are needed

Develop an Individual Big Data Roadmap: In order to avoid burning money with a Big Data initiative without tangible benefits companies should develop a clear roadmap that helps them to focus on business value, technical requirements and priority.  In general a Big Data road map should be driven by their use cases and the value and priority they have for the company.
An important aspect in this context is to get the sponsorship from a business executive and setup a close business-IT-collaboration to make sure the Big Data Roadmap is aligned with business and IT objectives. 
It is recommended to start with existing data that companies have, understand and trust, but that they could not capture and analyze to a bigger extent in the past. 
By applying the latest technology and skill sets companies often can leverage their existing information management foundations to harvest “low hanging fruits”, to generate significant business value and to prove that data-driven decision making is the right way to go. Once an organization has matured from this stage it will be ready  to take on more complex Big Data scenarios (e.g. involving unstructured data or data from new and/or exotic data sources).

Analyze the existing environment and identify the right technology: Defining the use cases in step 1 enables companies to identify data entry and storage points, to outline end-to-end processes and  data flows and to define data quality requirements.  Based on that, a high level architecture can be outlined that will help to understand the ‘Big Data Readiness’ of an existing architecture as well as to document potential gaps and transformation processes that are to be discussed before any bigger deployment. A detailed assessment of technological dependencies will help to understand the impact new components might have on an existing environment.  For example might it be required to complement an existing BI solution with appropriate tools if considering to use Hadoop in the new architecture.  Beyond that, it needs to be considered that a new Big Data use case might also have an impact on the design of existing architecture components such as the BI interface when planning to consume the information in a different way than before (e.g. via a mobile interface).

Start smart and turn plans into action: The key element of this phase is to prove the value of data-driven decision support in an easy way and to take away lessons learnt that will help to further execute a company’s individual Big Data roadmap as outlined above.

This happens by selecting 1 or 2 key priority use cases from different business units that can be addressed during a Proof of Concept project, ideally using the company’s existing data.  These PoC’s should be executed in a very focused way, in a short period of time and under close collaboration of business and IT. In order to ensure ongoing buy-in from all relevant stakeholders successes should be properly communicated.

Apart from the potential business outcome a Big Data PoC should also contribute to the question how to integrate the required technology into an existing architecture and how to change existing data management and governance processes in the context of those use cases. Finally this phase should be concluded by documenting all lessons learnt that are relevant for the execution of the company’s Big Data strategy and should answer the following fundamental proof points: 

• Was the data analyzed enough to provide the required level of insights ?
• Are the architecture and tools appropriate to scale for the use case?
• Is the organization ready for Big Data and for the specific use case?
• What are the potential costs of an implementation ?
• Are there any skills issues that might remain un-addressed ?
• Are there any road blocks that might remain open ?

Plan to scale: After successfully proving the value of Big Data through the execution of PoC’s a company’s Big Data road map can be turned into reality.  A dedicated planning phase will allow to reflect the lessons learnt from the PoC’s and assess the future impact on architecture, organization and processes. The following elements should be covered in particular:

• Final to-be-architecture design and data flows
• Roadmap to existing data management and governance processes, Information ownership, Information Life cycle management in the context of Big Data
• Skill requirements, sourcing and skill development plans to ensure availability of resources
• Review of the business cases and validation of efforts, risks and benefits
• Identification of change management requirements for business processes and organizational structure

Execute:

Once all the steps before have been successfully done it’s time to execute.  In general, the execution phase of a Big Data roadmap isn’t any different than other roadmaps which have been executed in the analytics space before.  However, unlike traditional Business Analytics projects (BI, EPM, etc.), the most essential success criteria will be a close collaboration between business and IT and the involvement of the right skills at the right point in time.

SAP Business Analytics Service offers a range of services and skills that can support the development and execution of a company’s Big Data Strategy throughout all phases outlined above. I'll report Big Data as a services opportunity and the SAP services offered in one of my next blogs. Stay tuned!

In case of any specific questions don't hesitat to reach out to me directly.

Markus Tempel

Big Data Analytics Global Practice Lead

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