RESEARCH & RESOURCES

Building the Business Case for Big Data

What do companies do when they need the power of analytics to be increased with decision support architecture from enterprise data? They built business cases. Here are five business components to address in your own business case.

By Krish Krishnan, CEO, Sixth Sense Advisors, Inc.

[Editor's note: Krish Krishnan is conducting a session at the TDWI World Conference in Orlando (December 8-13, 2013) entitled Building the Business Case for Big Data in Your Data Warehouse. We asked him to explain what enterprises are including in their business cases and to discuss what key issues he'll cover in his seminar.]

Big data means different things to BI professions. Although there is considerable awe and admiration for the data in this spectrum, there is a fear of the unknown, the uncertainty of the value, and the doubt of successful insights and innovation from the exercise of integrating big data into the data warehouse. Why is the FUD factor driving us in the world today? The answer is the inability of different generations of users in the enterprise to connect on the platform and leverage the benefits that the insights will provide.

Think about what transpired over the years as computing power and analytics have evolved in your organization. What did you do to bring the latest technology to help you establish an advantage over the market competition and bring customers into your organization? You built a set of business cases that proved the value of the data and the analytical information that it helped create and manage.

What did the companies such as Yahoo, LinkedIn, or Starbucks do when they needed the power of analytics to be increased with decision support architecture from enterprise data? They built business cases to solve data management, storage, and analytical requirements, then created platforms that helped satisfy the business requirements. At the same time, they integrated enterprise data that provided critical business insights and several competitive advantages.

Why does big data need a business case in your organization? In order to create the situations that can be solved with enterprise collaboration, data sharing, customer innovation, and the ability to bring analytics into the enterprise platform as a real solution, you need to create a business case along with the desired KPIs and analytics.

What business components get addressed in the business case document? In the case of big data, this question requires a more complex answer because there are no requirements, unlike the data warehouse. The business case will consist of several components for the backend data processing:

  • User needs: What is the business solution requirement>? For example: "The hospital needs to integrate data from patient management, disease management, lab records, and financial data to do patient financial analytics."

  • Business benefits: How will the business benefit from the solution? For example: "Patient financial analytics will enable the hospital to determine the financial needs of the patient and enable a better outcome of payments from insurance, government, or patient themselves."

  • Value: What business value will be delivered from meeting the need? For example: "This need will deliver the financial burden value of the hospital and determine the overall performance of the hospital."

  • Outcomes: Discuss the anticipated outcome: For example: "There are two key outcomes from implementing patient financial analytics. One is the improved health of the hospital from a financial perspective, the overall general ledger and account payables and receivables. The other is better financial situation of patients as they are discharged from the hospital, providing solutions for any hardship encountered."

  • ROI: What is the return on investment and how soon can it be seen or observed? For example: "The patient financial analytics system will provide ROI on day one for the hospital and the patient. The financial impact will be observed as the system completes three months of production execution."

In our session, in addition to the backend, we will discuss the visualization and analytical needs from the user perspective of data discovery and analysis. We will discuss new-age tools like R and RHadoop and how these tools can be integrated with visualization tools and can execute Mahout algorithms.

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Krish Krishnan is the CEO of Sixth Sense Advisors, Inc., an independent management and technology consulting organization, and a TDWI World Conference session leader. He can be reached at info@sixthsenseadvisors.com.

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