Critical success factors for big data analytics

Critical Success Factors For Big Data Analytics

1. As is the case with any other large IT investment, the success in Big Data analytics depends on a number of factors. Figure 9.4 shows a graphical depiction of the most criti- cal success factors (Watson, 2012).

The following are the most critical success factors for Big Data analytics (Watson, Sharda, & Schrader, 2012):

1. A clear business need (alignment with the vision and the strategy). Business investments ought to be made for the good of the business, not for the sake of mere technology advancements. Therefore, the main driver for Big Data analytics should be the needs of the business, at any level—strategic, tactical, and operations.

2. Strong, committed sponsorship (executive champion). It is a well-known fact that if you don’t have strong, committed executive sponsorship, it is difficult (if not impossible) to succeed. If the scope is a single or a few analytical applications, the sponsorship can be at the departmental level. However, if the target is enterprise- wide organizational transformation, which is often the case for Big Data initiatives, sponsorship needs to be at the highest levels and organization wide.

3. Alignment between the business and IT strategy. It is essential to make sure that the analytics work is always supporting the business strategy, and not the other way around. Analytics should play the enabling role in successfully executing the business strategy.

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FIGURE 9.4 Critical Success Factors for Big Data Analytics. Source: Watson, H. (2012). The requirements for being an analytics-based organization. Business Intelligence Journal, 17(2), 42–44.

4. A fact-based decision-making culture. In a fact-based decision-making culture, the numbers rather than intuition, gut feeling, or supposition drive decision making. There is also a culture of experimentation to see what works and what doesn’t. To create a fact-based decision-making culture, senior management needs to:

• Recognize that some people can’t or won’t adjust • Be a vocal supporter • Stress that outdated methods must be discontinued • Ask to see what analytics went into decisions

• Link incentives and compensation to desired behaviors

5. A strong data infrastructure. Data warehouses have provided the data infra- structure for analytics. This infrastructure is changing and being enhanced in the Big Data era with new technologies. Success requires marrying the old with the new for a holistic infrastructure that works synergistically.

As the size and complexity increase, the need for more efficient analytical systems is also increasing. To keep up with the computational needs of Big Data, a number of new and innovative computational techniques and platforms have been developed. These tech- niques are collectively called high-performance computing, which includes the following:

• In-memory analytics: Solves complex problems in near real time with highly accurate insights by allowing analytical computations and Big Data to be processed in-memory and distributed across a dedicated set of nodes.

• In-database analytics: Speeds time to insights and enables better data gover- nance by performing data integration and analytic functions inside the database so you won’t have to move or convert data repeatedly.

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• Grid computing: Promotes efficiency, lower cost, and better performance by processing jobs in a shared, centrally managed pool of IT resources.

• Appliances: Brings together hardware and software in a physical unit that is not only fast but also scalable on an as-needed basis.

Computational requirements are just a small part of the list of challenges that Big Data impose on today’s enterprises. The following is a list of challenges that are found by business executives to have a significant impact on successful implementation of Big Data analytics. When considering Big Data projects and architecture, being mindful of these challenges will make the journey to analytics competency a less stressful one.

Data volume: The ability to capture, store, and process a huge volume of data at an acceptable speed so that the latest information is available to decision makers when they need it.

Data integration: The ability to combine data that is not similar in structure or source and to do so quickly and at a reasonable cost.

Processing capabilities: The ability to process data quickly, as it is captured. The traditional way of collecting and processing data may not work. In many situations, data needs to be analyzed as soon as it is captured to leverage the most value. (This is called stream analytics, which will be covered later in this chapter.)

Data governance: The ability to keep up with the security, privacy, ownership, and quality issues of Big Data. As the volume, variety (format and source), and velocity of data change, so should the capabilities of governance practices.

Skills availability: Big Data is being harnessed with new tools and is being looked at in different ways. There is a shortage of people (often called data scientists) with skills to do the job.

Solution cost: Because Big Data has opened up a world of possible business improvements, a great deal of experimentation and discovery is taking place to determine the patterns that matter and the insights that turn to value. To ensure a positive return on investment on a Big Data project, therefore, it is crucial to reduce the cost of the solutions used to find that value.

Though the challenges are real, so is the value proposition of Big Data analytics. Anything that you can do as a business analytics leader to help prove the value of new data sources to the business will move your organization beyond experimenting and ex- ploring Big Data into adapting and embracing it as a differentiator. There is nothing wrong with exploration, but ultimately the value comes from putting those insights into action.