Secrets of developing and creating applications using Big Data

Applications are traditionally perceived as computing units designed and used to solve problems. It doesn’t matter whether this application is a CRM tool to help manage customer information, or a complex management system for supply chains – the problems they solve are often very specific. Applications are often developed with a relatively static set of input and output interfaces, and specially developed (or selected) protocols are used to communicate with the application.

Applications are also built around data

The data that the application uses to solve problems is stored by the data hosting platforms. These source data hosting platforms have been designed to provide optimal data storage and retrieval. In the process of storing and retrieving data, the application performs the calculation and displays the results.

One of the negative side effects of optimized placement and retrieval of structured data is that this data requires structuring according to a specific model (both on disk and during modeling and retrieval of information.) In the world of big data, applications must be able to pull data from hard structured elements, such as names, addresses, numbers, and birthdays, as well as free and unstructured data.

Defining and creating big data applications can be a difficult task given the complexity of structuring the underlying data. This lack of structuredness makes it difficult to pinpoint the tasks that a big data application should perform. This applies to communication interfaces, processing of unstructured and semi-structured data, and even communication with other applications.

Although a traditional application can solve a specific problem, a big data application is not limited to specific or planned problems. Its goal is to provide a working environment for solving many different problems. A big data application manages life cycles pragmatically and predictably. Big data applications can include batch processing components or processing components with large or small delays (or a component for real-time processing), or even a component for streaming processing. Big data applications do not replace traditional applications for solving specific problems, but complement them.

Take the CRM tool as an example

A traditional CRM tool can store customer information, purchase history, and customer loyalty levels. Given the limited resources of enterprises such as a customer service call center, during its maximum load, the CRM tool should determine which customers should receive the basic and which should receive the priority level of service. Typically, customers with higher loyalty receive priority service, and loyalty levels are determined in advance. These levels may depend on costs, their frequency, or other rules, and their definition usually depends on difficultly structured data.

However, if the CRM tool can define customer behavior as similar to customer behavior with high loyalty, even if it is outside the predetermined level, it will be able to make a more correct decision regarding the prioritization of resources and offer an appropriate call priority level.

Operating with data sets that apply to all aspects of the business, the big data application offers new possibilities for joining data sets, which was not previously possible. This provides the ability to create feedback loops for existing applications, which can help make them smarter. In the example above, a CRM provider can use big data applications to count and analyze trends that point to preferred customers and find those customers faster than previously possible. The Big Data application will continuously re-evaluate the performance of the predictive model based on changes in information about each client as they interact with traditional applications.

Measure results and reap the benefits

This applies to all the steps described above: measure, measure, and measure again. The only way to know the impact and performance of your big data application is to measure results. It can simply measure the amount of data collected compared to the number of records sent back to existing traditional applications, or conduct advanced A/B testing of behavioral models. In any case, the meaning is the same: the best way to find out if your big data application has reached its goal is to measure results.