How to Succeed in Big Data Analysis? Follow These 5 Steps

October 27, 2017

Currently, there’s a significant demand for analytical insights that not only improve understanding of customer behavior but also predict it. This trend highlights the need for data-driven decision-making that benefits businesses, leveraging new technologies such as Big Data and the Internet of Things (IoT), along with advanced analytical methods like machine learning, deep learning, and natural language processing.

Descriptive and predictive data analysis are fundamental to data analytics. Descriptive analysis aims to accurately represent reality as captured by collected data, while predictive analysis uses past records to forecast future trends. Statistical methods and artificial intelligence algorithms can process structured data, as well as text and images. However, the quality of analytical outputs largely depends on the quality, speed, scope, and volume of the processed data.

Through analysis, you can identify key moments leading to significant customer decisions, such as repeated failed calls in areas with good coverage but low base station capacity, which could lead to customer dissatisfaction and a potential switch of service providers. If operators can detect such issues from the data, they can act as valuable partners by addressing these technical problems promptly. Similarly, in banking, if a client frequently checks the exchange rates on a bank’s website, it might be wise to offer them related services.

5 Steps for Successful Big Data Analysis

Processing transactional, operational, or log data can greatly benefit large corporations if they know how to do it right:

1. A robust technical infrastructure is crucial, ensuring that systems can collect, transmit, transform, and store data efficiently. Technological advancements lead to near real-time solutions. With structured data, we can begin to perform various analyses and manipulations.
2. Long-term data storage or at least the ability to extract key information over time is necessary. Without historical data, building a quality predictive model is impossible. The advent of big data storage solutions, like cloud or Hadoop, has eliminated the constraints of having detailed records for only weeks, months, or at most a year or two. Large companies are increasingly managing their big data in-house.
3. Having ample records for analysis doesn’t automatically translate to a competitive advantage. To capitalize on this data, it’s essential to integrate analytical insights with current systems like sales, customer service (CRM, call centers), marketing, and product and service development.
4. Data scientists can now start building models to predict when and to whom to offer services. This journey to model creation is complex, but with effort, classification and predictive models can pave the way to a personalized approach.
5. Perfecting the model is not the final step; without a series of procedural and systematic improvements, one cannot expect advanced Big Data analysis to be beneficial.

The success of analysis processing largely depends on ensuring that the analysis is not just for show or filed away but has a clear purpose. This includes using the outputs to detect early opportunities and improve business outcomes. A skilled analyst can thus contribute to providing relevant services and more efficient customer care.