Experimentation and Big Data

Big data deals with complex specifications that are difficult to understand and compares data based on a number of factors. But, when anticipating human behavior, computers, and algorithms fail to consider several factors. These factors can range from changing weather to moods to relationships that might influence customer-purchasing patterns.

Thus, all analysis results from big data won’t create value. Several ventures have failed due to faulty implementation of big data. For example, a company noticed that customers who purchased perishables also tended to purchase large-screen TVs. Based on this observation, the company made a significant investment in marketing activities directed at increasing purchases of perishables, in the hope that this would trigger more TV purchases. But while they sold more perishables, they didn’t manage to shift any more TVs, and the profits from selling extra perishables weren’t enough to cover the marketing investment. Thus experimentation is required in big data to validate the results which are received from analysis. And not just experimentation, but rapid experimentation. In today’s highly competitive business environment, companies are changing the way in which they operate. They need to innovate faster to gain results. It spring ups the need for rapid experimentation and prototyping.

These are the steps to follow while conducting an experiment:

Describe: You must list out all the objectives of the experiment. When there is a clear understanding of all the objectives, the results will be positive.

Develop: In this step, you must develop the strategy. For instance, a company with a products-driven driving force should build exceptional products and should experiment with markets that could benefit from their products. Alternatively, a company with a market-driven driving force should understand a particular market extremely well and experiment with products or services that would benefit their customers.

Refine: In this step, you must narrow down on select procedures. Experiments, ranging from simple A/B tests and interface changes, to more complex within-subject designs, can be included in experiments. Thus, refining is an important process.

Prove: You must demonstrate how the experiment will help in the analysis in how the data acquired from field and online experimentation can be used to target customers.

Scale: You must determine whether the experiment provides results in the user environment. You need to be confident that the testing environment is similar to the environment in which the insights will be deployed. Organizations that embrace big data for a competitive advantage expect faster results. You must remember that rapid experimentation is the core of analytics in big data. Read more about big data and how you can implement it in your own organization.