What Is Big Data Analytics?
An introduction to Big Data Analytics, with a clear definition and practical examples of how Big Data can be used to benefit your business.
Big data analytics allows you to analyze large amounts of data found in large datasets. These datasets can include a variety of different sources. When you use big data analytics, you can find patterns, trends, and other insights in big data. In short, you can make the most of all the data you have available.
In this blog, we’ll give you a definition of big data and explain how big data analytics can make a difference. Learn more below.
What Exactly Is Meant by “Big Data”?
Before diving further into big data analytics, let’s explain what we mean by “big data.” Big data roughly defines itself in the name. It’s data that is either too big in size for traditional databases or is of the type of data that doesn’t fit in traditional databases. Big data is high volume and needs big data solutions to process. The wide variety of sources and the methods of big data capture means more advanced solutions are required if you want to make the most of big data. That’s where big data analytics enters the picture.
In a nutshell, big data analytics collects data, processes it, cleans it, and then analyzes it. Let’s take a quick look at these steps:
Collecting data: Big data is collected from every source related to your organization. Big data is able to handle structured data, unstructured data, data from the cloud, data from your apps, data from your marketing, and much more. Once your data is collected, it will need to be processed.
Processing data: After collection, data will be organized and processed so the data becomes accessible to the analytics tools and the data can be retrieved with queries. Once the data is processed, it also needs to be cleaned.
Cleaning data: Cleaning your data ensures that the format is consistent, that duplicates are eliminated, and that the data is high quality and accurate. Clean and healthy datasets are key if you want to get the best analytics and insights possible.
Analyzing data: Once the data has been collected, processed, and cleaned, then it’s ready for big data analysis. Big data analytics tools can use this data to generate insights and reports to drive important data-driven decisions and to create data-driven processes.
Let’s take a look at how companies can use big data analytics in real-world applications.
Examples of How Companies Use Big Data Analytics
Companies use big data analytics in a variety of ways. Primarily, big data should empower a company to find opportunities, make data-driven decisions, and increase profitability. When big data is used well, companies can improve in just about every way. When you’re able to find trends and patterns in your data, you can make strategic adjustments to benefit your bottom line while also providing a better experience for your customers.
Check out some examples of big-data analytics in action.
Product Recommendation Engines for eCommerce
Big data analytics can help eCommerce companies build out and refine product recommendation engines for their online shops. As you take in the information for what customers are buying, your analytics can recognize buying trends and patterns on both macro and individual levels. Plugging these insights into your product recommendation engine can create a more tailored experience for your customers and drive sales. It can help your team create the best eCommerce site possible and pull in as many conversions as possible.
Dynamic Pricing for Hotel Reservations
Hotels and hotel aggregation websites can use big data analytics for dynamic pricing. As you take in historical and real-time data for reservations, you can better understand the demand for a hotel and how the room rates should be priced in relation to this demand. Instead of just relying on typical season, shoulder season, and off-season pricing, you can fine-tune pricing at a more granular level. This maximizes profits for the hotels and makes sure the prices of the rooms aren’t being set too low when demand is high. It also prevents rooms from being overpriced and driving away potential customers during times of lower demand.
Listener Experience Optimization for Music Streaming Platforms
Music streaming platforms can create a much more personalized experience for users when they use big data analytics. By taking in large sets of user listening data, these services can find correlations between which artists, songs, or genres a user might enjoy based on what similar users are listening to. This allows the services to make curated suggestions or playlists that are tailored to the users, helping them discover new artists and enjoy their streaming experience more. The more accurate the algorithm is, the better the chance users will be loyal to the platform.
Analyzing Consumer Demand for Franchise Expansion Strategy
Expanding a business or bringing a franchise into a new territory is a huge decision. If it’s something that a company wants to do, it’ll need data to back up the decision. With big data analytics, a company can analyze consumer demand for expansion and find out if new franchise locations would be a worthwhile and profitable endeavor. Big data can also help companies identify new expansion opportunities they might not have considered. Big data is all about helping companies make smart, data-driven decisions, but it’s also incredibly useful for finding opportunities that you might have missed otherwise.
Narrator Provides the Tools to Enable Big Data for Your Business
If you’re looking to enable big data in your business, Narrator’s platform can help. Narrator is a self-service analytics platform that puts the power of data in the hands of all your employees. Typically, your employees rely on your data team to generate reports and insights. With Narrator, they can make their own queries and get these reports in minutes.
Data teams will benefit from a simpler, more robust data modeling layer that’s easier to maintain. It allows data engineers to quickly model anything in the warehouse and seamlessly bridge disparate data sources with robust identity resolution.
If you’re interested in learning more about how Narrator’s Data Platform can help your business, please contact us today.