How Do You Analyze Large Amounts of Data?



Business intelligence today involves the ability to take raw data and use it to make informed decisions, so how do you analyze large amounts of data in a useful and meaningful way?


There are many models out there and many expert opinions, but the proof is really in your bottom line.


Is your data analytics system producing measurable results in terms of increased quality of products and services? Is it resulting in streamlined processes that save you time and money? Are you seeing financial gain? Are your employees informed and knowledgeable about what big data means to your organization, and are they able to visualize past the numbers?


Those are the questions that define business intelligence when working with big data sets. It is important to have a data analysis system in place that is reliable and produces results that are meaningful and productive.


What Is Big Data?

The term big data refers to data sets that are incredibly large and growing exponentially over time. The sheer volume of information being collected today via social media, machine data, and transactional data require machine learning applied to data science to solve for patterns and anomalies that a business may find useful.


Big Data is big business! The term big data is also being used to describe the systems that manipulate the vast amounts of data being collected and create meaningful patterns out of the apparent chaos.


How Do I Get Experience Analyzing Big Data Sets?

If you don’t already have experience in analyzing big data sets, there is a significant learning curve. There’s more to analyzing big data than just applying our old systems to larger sets of numbers. The sheer volume of data is overwhelming without an appropriate system to manage the large data sets and create meaningful patterns of information.


Gaining experience in analyzing big data sets starts with collecting volumes of raw data, learning some fundamentals of data science, finding the system that works best for you, and finally transforming that into data visualization methods that make sense for the goals of your organization.


What Are Best Practices When Analyzing Big Data Sets?

There are some best practices when analyzing big data sets to produce valuable end results. Harvard Business School recommends a 6 step process for data management and and analysis:

  • Clean up your data. This refers to the process of eliminating repeat information and uncovering and correcting any errors. Essentially, you are taking the raw data and making it more meaningful.

  • Identify the right questions. Your questions should be easily measurable and very closely related to a specific business problem that you are trying to solve.

  • Break your data down into segments. Large data sets need to be broken down and organized into smaller, more manageable segments in order to be analyzed properly.

  • Visualize the data. It is important to create graphic representations of your data segments. This allows you to more easily identify and communicate patterns and trends. There are many data visualization tools that can help you to create engaging visuals.

  • Use your data to answer your questions. After the above steps of organizing and representing your data, you want to go back to your original question and interpret the data for answers. If the answers aren’t clear, you may need to try segmenting the data differently or try a different method of visualizing the data.

  • Supplement your data analysis with qualitative data. Qualitative data may come in the form of discussions, questionnaires, or interviews and can shed important light on the whys surrounding the data.

What Are the Best Ways to Analyze Big Data?

The best ways to analyze big data are the ways that answer the questions you have about your business performance clearly and efficiently. Some features of effective big data analysis may include the following:

  • Insights-as-a-Service: Service providers, like Teamworks, offer Insights-as-a-Service because most businesses simply don’t have the infrastructure or the training to give meaning to big data sets. IaaS is a method of gaining the business intelligence you need in meaningful format without having to analyze the big data in-house.

  • BI Dashboard Reporting: Business Intelligence Dashboard Reporting is a software suite that organizes big data into many different valuable displays. A dashboard can show your data in charts, infographics, and other visuals. It also offers predictive analysis and suggestions for improvement. In addition, dashboards are designed to offer real-time feedback.

Keep Reading: The top features of business intelligence tools


Getting Started with Teamworks

Here at Teamworks, we have all of the tools and strategies that you need to manage big data effectively. We stay on the cutting edge of business intelligence and offer models that are best suited for analyzing large amounts of data. Contact us today to get started with BI strategy, Insights-as-a-Service, and customized business intelligence dashboards.