Chapter 6. Managing Investments and Monetization of Data

	"In God we trust, all others must bring data."
	--Edwards Deming

Data has always been considered useful, but rarely has any value been attached to it. It was never an asset class by itself. Big Data is changing this; it is redefining how data acquires and generates value. Until recent times, businesses were valued on their assets, current income, potential growth, and similar financial parameters. Today, the IPOs of Big Data age companies, such as Facebook and Twitter, are showing us that investors are willing to pay a premium of hundreds of billions of dollars over physical assets and actual income for the value of data these companies have access to. Facebook went public on 18 May 2012 at a share price of $38, leading to a market capitalization of $104 billion. Before the IPO, it had already crossed 1 billion subscribers. In 2011, Facebook made profits of $1 billion. So, investors really paid for the potential value of the enormous...

Understanding how data creates value

Advancements in computing technologies have reduced the cost of collection, storage, and processing of data; advancements in analytics have increased the value of data. Earlier, raw data did not have any inherent value. Once it was processed, the resultant information was useful to take decisions and for usage in other business processes. So, the value lay in the information that was derived from the raw data. Now, we can process all data to create new insights and new businesses. Therefore, the influence of raw data today is significantly more than ever before. Unlike physical assets, the value of data is perpetual. The more we use it in varied ways and create newer insights, the more its value increases. In his Seven Laws of Data Science, Dr. Jerry Smith postulates, "The value from combined independent data is greater than the combined value of each data alone." Every time we pair new data sets leading to further newer insights, the value of those data...

Capturing the value of data

In the previous section, we discussed how data creates value. Now, let's find out what the value of data is and how to capture it; we will now move from understanding the process to understanding the math. The crux of monetizing Big Data lies in capturing the value created by data as correctly as possible. In going down this path, it is important to remember that this monetization process is not an exact science, so we will have to work with some assumptions and approximations.

Identifying the value

Data leads to insights and insights lead to tangible business benefits. In the example of retail store customer information from the previous section, the data will lead us to stocking and promotion decisions, which will most likely result in new customers coming in or existing customers visiting more frequently and customers buying more. The insights and resulting actions will lead to tangible business benefits in terms of higher sales and profits. It is not very difficult...

Understanding and capturing your Big Data costs

Big Data does not necessarily mean big costs; in fact, it is the other way around. Leveraging Big Data reduces your costs significantly in two major areas—storage and processing. In the other areas, the differences are less substantial. Before we develop a comprehensive framework to capture the total cost of Big Data initiatives in your organization, let's start with a simpler discussion around the various spend categories in Big Data:

• Procurement of or instrumentation for data collection (can be substantial on a case-by-case basis)

• Hardware investments or rental costs for storage and processing of data (usually much less when compared to traditional technology approach)

• Software licenses to build and run your environment (a lot of which is available freely)

• People-related costs for those engaged in analytics, development, and maintenance of the ecosystem (usually, a small number of very specialized resources)

• Physical infrastructure and general...

Monetizing your Big Data

There is no argument that Big Data creates tremendous value in real money. Monetizing this value helps in many ways:

• Creates other potential sources of revenue for your business

• Develops better appreciation around Big Data in your organization

• Helps better manage your Big Data investments

• Establishes a deeper sense of ownership in your Big Data team

Earlier in the chapter, when we discussed valuation, we only considered the benefits to the business. Subsequently, we looked at the investments involved with realizing those benefits using Big Data. When you are pursuing monetization, you need to subtract the costs from benefits to understand the true value of data. So, in the remainder of this section, when we talk about value, we are referring to the net impact value.

There are many ways you can monetize Big Data; we will discuss three of them in this section:

• Direct business impact

• Selling data externally

• Valuation of Big Data

Direct business impact

Direct business impact is...

Managing your Big Data investments

One of the best ways to capitalize on the uniqueness of Big Data is to manage investments as if you are doing so for a hedge fund. Using this approach gives you lot of flexibility over normal investment management techniques followed in corporations, and these flexibilities will help address the evolving and unique nature of Big Data:

• Normal micro level monitoring and governance processes around technology investments can be modified

• You can start or stop the investments at any point

• Choose where to invest, and you might not have to justify your choices to the rest of the company

• Track the returns of each project, but retain the flexibility to report and justify returns for the entire portfolio, allowing you to experiment a bit

• Reinvest the returns into more Big Data initiatives to generate more value

Just as hedge funds employ four key investment strategies (global macro, directional, event-driven, and relative value), similarly, you can classify your Big Data...

Summary

In 1992, in the Wall Street Journal, the great management guru Peter Drucker said, "From being organized around the flow of things and the flow of money, the economy is being organized around the flow of information". Today, nothing can be closer to the truth than this prophecy.

This was a crucial chapter for us to understand the financial aspects of Big Data. We learnt about how Big Data creates value in the near term and in the longer term through the business benefits it brings to bear. Big Data is less expensive, not free. Many people have the misconception that by putting in a Hadoop cluster, they have started leveraging Big Data. However, there are many other aspects to it; we learnt about many of them and how they impact Big Data projects monetarily. We touched upon a key topic of creating a context diagram to understand, analyze, and visualize Big Data costs with respect to the various aspects and phases of a project. Finally, we worked on ideas that lead to monetization of...