Harnessing Big Data

We live in an era of information excess, in which information and data are omniscient and persistent. Today, our lives have been digitised. We generate and manage a large volume of data of various types, including streaming data, mobile data, social media content, day-to-day communications, banking transactions, weather logs, and so on. Making sense of all of the available data has become critical.

Big Data analysis has become the talk of the town in this context. People generally discuss Big Data Analysis at various levels of abstraction and comprehension. They discuss real-time and advanced analytics, frameworks, and products, which is usually a bad idea. So, let us take a step back and consider what Big Data actually means.

Big Data is defined by three data characteristics: volume, variety, and velocity. Making sense of terabytes, if not petabytes, of data is what volume is all about. Variety refers to a diverse set of data generated from sources such as social media feeds, videos and audio files, emails, sensor data, and other raw data. Bringing up data from real-time data sources such as websites, ATMs, point-of-sale devices, and other sources is referred to as velocity. Thus, Big Data analysis can be defined as the process of analysing large amounts, types, and speeds of data in order to uncover hidden patterns, correlations, and other useful information.

Amazon and Netflix rely entirely on big data analytics to develop and optimise their products. They know where and what changes to make to their platform by monitoring and analysing customer behaviour, needs, and wants. For example, recommendations are an important part of both of their products, and they can make significant improvements by monitoring how users react to AI-generated recommendations.

However, the application of big data to product innovation and optimization goes far beyond what Amazon and Netflix are doing. Companies can use data-driven changes to their products to set up automated pipelines that capture, sort, and process large datasets.

Customer experience is one of, if not the, most important use cases for big data analytics. Companies can gain a better understanding of customer behaviour by using big data to process years of data and metrics such as user time spent, conversions, and abandonment rates. Data scientists can link changes in customer acquisition and retention to new feature launches, marketing campaigns, design changes, and so on, and use this information to reinforce actions that resulted in positive consumer behaviour while eliminating those that resulted in negative consumer behaviour.

Big data analytics is a tremendously powerful tool with virtually limitless applications. However, the key to effectively harnessing and utilising data is to focus on the outcome rather than the process. If your company is only collecting and processing data without a specific goal in mind, you risk becoming sidetracked and failing to capture any impact.

Though big data can be highly automated and cloud-based, the data science required to get there is still labor-intensive, which means business executives must be selective in their use and take a very goal-oriented approach to the entire process.