Big data analytics can improve your business decisions and enhance the touchpoints that your brand has with customers. This introduction to big data analytics will help you understand what big data is all about, so that you can apply it to further your business. Already, there are plenty of examples where big data is leading to better outcomes for customers and businesses. Here are a few examples of big data applications in various industries:

There is a clear pattern: Companies collect and analyze big data in order to help one or more of their stakeholders do things better. As a leader in big data analytics, Jumpshot’s solutions fit this pattern, too. We analyze the clickstream activity of our 100-million global consumer panel to provide companies with a complete understanding of their customers’ online journeys, including the 99% of the time that customers are not visiting their online brand properties.

The origins of big data analytics

Web analytics provides many organizations with their first glimpse into the power of big data analytics. In this technical introduction to big data on GitHub, Haifeng Li writes, “Looking back to web data analysis, the origin of big data, we will find that big data means proactively learning and understanding the customers, their needs, behaviors, experience, and trends in near real-time and 24×7.”

Today, even the neighborhood toy store is collecting web data in order to help the owner optimize the toy store’s website and win more business. Yet, the toy store faces a limitation with their data. It’s all about the toy store itself. Imagine what the owner could do with website data on all the toy stores in the region. This is an example of the difference between just using data and using ‘big data’. The scope of big data is larger and the insights are more powerful.

Preparing to use big data

Business leaders need to think very creatively about where big data can have the biggest impact on their organization. For this, it helps to have a framework for imagining how to use big data and how to apply the insights derived from big data analytics to further your business.

The first step organizations must take to become data-driven is proposed in the framework below, detailing how your organization needs to prepare to use big data analytics by deciding what to collect, how to collect it, how to partner for it, and how to consolidate it.

  1. Data strategy: A good data strategy will answer two questions: Which business decisions do you want to improve? And which customer touchpoints do you want to optimize? Then, you’ll need to figure out what data you need to collect in order to realize your data strategy.
  1. Data collection: You may be able to collect data yourself. It’s technically possible to collect data from any internet-connected hardware, software, or website that you control. Just make sure to follow all applicable laws and recommendations for responsible data collection. Your data collection points may include, but are not limited to, the following: websites, mobile apps, browser plugins, internet-connected sensors, and wearable devices.
  1. Data partnerships: Since you can collect data from hardware, software, and websites under your control, you may not need to form partnerships to access the data you need. However, if you require data about activity on other properties, you can partner to get it. There are various companies and research firms with data for sale and many organizations with free public data, too. A good starting list of data sources and APIs is available in the Big Data Landscape 2016 infographic by Matt Turck and Jim Hao of FirstMark Capital. Once you zone in on all the data you have and need you move on to address your data storage and integration needs.
  1. Data integration: Chances are that you don’t have just one source of data that you want to work with because you want to work with the data that you collected yourself, as well as the data provided through partnerships. As a result, you will need to find somewhere to store all of the data, such as a NoSQL database, data warehouse, Apache Spark, or Apache Hadoop. Once you’ve made your choice, you can have developers and data scientists pull the data in one source at a time, or you can speed up the process with a data integration platform.

Bottom line: Big data analytics can improve your business decisions, enhance your customer’s experience and further your businesses. This article outlines the nature of big data and presents a framework for organizations to prepare to implement big data analytics. In our next installment, we will dive into how to use big data so your organization reaps the benefits of analyzing it for insights and acting on those insights to increase ROI, so stay tuned!