Summary

• Article Source: Big Data for Dummies, Chapter 17
• Authors: Judith Hurwitz, et. al

Motivation

The benefits of big data to business are significant. But the real question is how do you make big data part of your overall business process so that you can operationalize big data? What if you can combine the traditional decision making process with big data analysis? How do you make big data available to decision makers so that they get the benefit from the myriad data sources that transform business processes? To make big data a part of the overall data management process requires that you put together a plan. In this chapter, we talk about what it takes to combine the results of big data analysis with your existing operational data. The combination can be a powerful approach to transforming your business.

Making Big Data a Part of Your Operational Process

The best way to start making big data a part of your business process is to begin by planning an integration strategy. The data — whether it is a traditional data source or big data — needs to be integrated as a seamless part of the inner workings of the processes.

Can big data be ancillary to the business process? The answer is yes, but only if little or no dependency exists between transactional data and big data. Certainly you can introduce big data to your organization as a parallel activity. However, if you want to get the most from big data, it needs to be integrated into your existing business operating processes. We take a look at how to accomplish this task. In the next section, we discuss the importance of data integration in making big data operational.

Integrating big data Just having access to big data sources is not enough. Soon there will be petabytes of data and hundreds of access mechanisms for you to choose from. But which streams and what kinds of data do you need? The identification of the “right” sources of data is similar to what we have done in the past.

• Understand the problem you are trying to solve
• Identify the processes involved
• Identify the information required to solve the problem
• Gather the data, process it, and analyze the results

This process may sound familiar because businesses have been doing a variation of this algorithm for decades. So is big data different? Yes, even though we have been dealing with large amounts of operational data for years, big data introduces new types of data into people’s professional and personal lives. Twitter streams, Facebook posts, sensor data, RFID data, security logs, video data, and many other new sources of information are emerging almost daily. As these sources of big data emerge and expand, people are trying to find ways to use this data to better serve customers, partners, and suppliers. Organizations are looking for ways to use this data to predict the future and to take better actions. We look at an example to understand the importance of integrating big data with operating processes.

Healthcare is one of the most important and complex areas of investment today. It is also an area that increasingly produces more data in more forms than most industries. Therefore, healthcare is likely to greatly benefit by new forms of big data. The healthcare providers, insurers, researchers, and healthcare practitioners often make decisions about treatment options with data that is incomplete or not relevant to specific illnesses. Part of the reason for this disparity is that it is very difficult to effectively gather and process data for individual patients. Data elements are often stored and managed in different places by different organizations. In addition, clinical research that is being conducted all over the world can be helpful in determining the context for how a specific disease or illness might be approached and managed. Big data can help change this problem. So, we apply our algorithm to a standard data healthcare scenario.

1. Understand the problem we are trying to solve:
   a. Need to treat a patient with a specific type of cancer
2. Identify the processes involved:
   a. Diagnosis and testing
   b. Results analysis including researching treatment options
   c. Definition of treatment protocol
   d. Monitor patient and adjust treatment as needed
3. Identify the information required to solve the problem:
   a. Patient history
   b. Blood, tissue, test results, and so on
   c. Statistical results of treatment options
4. Gather the data, process it, and analyze the results:
   a. Commence treatment
   b. Monitor patient and adjust treatment as needed

Figure 17-1 illustrates the process.

This is how medical practitioners work with patients today. Most of the data is local to a healthcare network, and physicians have little time to go outside the network to find the latest information or practice.

Incorporating big data into the diagnosis of diseases

Across the world, big data sources for healthcare are being created and made available for integration into existing processes. Clinical trial data, genetics and genetic mutation data, protein therapeutics data, and many other new sources of information can be harvested to improve daily healthcare processes. Social media can and will be used to augment existing data and processes to provide more personalized views of treatment and therapies. New medical devices will control treatments and transmit telemetry data for realtime and other kinds of analytics. The task ahead is to understand these new sources of data and complement the existing data and processes with the new big data types. So, what would the healthcare process look like with the introduction of big data into the operational process of identifying and managing patient health? Here is an example of what the future might look like:

1. Understand the problem we are trying to solve:
   a. Need to treat a patient with a specific type of cancer
2. Identify the processes involved:
   a. Diagnosis and testing (identify genetic mutation)
   b. Results analysis including researching treatment options, clinical trial analysis, genetic analysis, and protein analysis
   c. Definition of treatment protocol, possibly including gene or protein therapy
   d. Monitor patient and adjust treatment as needed using new wireless device for personalized treatment delivery and monitoring. Patient uses social media to document overall experience.
3. Identify the information required to solve the problem:
   a. Patient history
   b. Blood, tissue, test results, and so on
   c. Statistical results of treatment options
   d. Clinical trial data
   e. Genetics data
   f. Protein data
   g. Social media data
4. Gather the data, process it, and analyze the results:
   a. Commence treatment
   b. Monitor patient and adjust treatment as needed

Figure 17-2 identifies the same operational process as before, but with big data integrations.

This represents the optimal case where no new processes need to be created to support big data integrations. While the processes are relatively unchanged, the underlying technologies include the applications that will need to be altered to accommodate the impact of characteristics of big data, including the volume of data, the variety of data sources, and the speed or velocity required to process that data.

The introduction of big data into the process of managing healthcare will make a big difference in effectiveness to diagnosing and managing healthcare in the future. This same operational approach process can be applied to a variety of industries, ranging from oil and gas to financial markets and retail, to name a few. What are the keys to successfully applying big data to operational processes? Here are some of the most important issues to consider:

• Fully understand the current process.
• Fully understand where gaps exist in information.
• Identify relevant big data sources.
• Design a process to seamlessly integrate the data now and as it changes.
• Modify analysis and decision-making processes to incorporate the use of big data.

References

[1] Judith Hurwitz, et. al, Big Data for Dummies, John Wiley & Sons, Inc, 2013.