Big data is simply data in big volumes. We use the term big data to refer to information that is so massive in volume and so complex that traditional methods fail to handle it. Making sense of such “Big Data” requires special tools and techniques and funnily enough, the tools and techniques employed to analyze, visualize, and utilize big data are also included in the umbrella term Big Data.
When we first started creating massive amounts of data, most businesses did not have the infrastructure or resources to store and handle such data. Years have passed, and big data has formed the core of everything from distributed file systems to quantum computing. Every online venture from a streaming channel to a video game is putting big data to use. We’ll learn how.
What is the importance of Big Data?
A company might be sitting on a humongous pile of structured and unstructured data. This data could bring benefits like strategic insights and deep understanding of customer sentiments, but only if the company could make sense of it. This is where big data analytics come into play.
With the right tools at hand, a company can draw actionable insights, predictions, and even prescriptions from big data. What does that mean?
Let us say, you run an online gaming website which gets a footfall of 5000 people every day. They play your game and communicate with their friends on an in-game forum. If you have access to the in-game chat and comments, you’d be sitting on a treasure trove of information.
You can employ a natural language processing engine to scrape valuable data from the audio and textual chats. Then, with the use of machine learning algorithms you can analyze the sentiments of the players to understand what would make them happier and engage them better.
In fact, you can learn from mistakes made by other gaming platforms to improve your own by using big-data analytics.
Big data analysis helps your decision making. It ensures you are predicting the future based on data and not on instinct.
Big data across industries
In the age of cloud-computing, you cannot name one industry that has a digital wing and doesn’t make use of big data.
Take the healthcare industry, for instance. Insurance companies analyze data to reform premiums and detect fraudulent claims whereas patient data produced by medical equipment are used to improve the equipment as well as the treatment methodology.
Similarly, the entertainment industry uses big data analytics to a) recommend perfect shows to audiences b) produce shows that are more likely to do well in a certain socio-economic scenario.
The retail and e-commerce industry uses big data to place discounts, recommend products, analyze estimated arrival times, and manage inventory.
The logistics industry takes advantage of big data analytics to analyze optimum routes, prevent breakdowns, and maximize throughout.
As I was saying, you’d be hard-pressed to find an industry that doesn’t use big data. Even government bodies depend on big data for decision making.
Use of big data in poker game sites
We’ve already discussed how a gaming site might use big data, but that was just one use case. In this section we’ll discuss some more use cases of big data specifically in poker sites. Most of these use cases apply regardless of the type of poker game.
Player behavior analysis
Poker sites collect data on players to answer questions like what types of games they prefer? What are their betting patterns? Etc. This information helps the site understand the player at a personal level. This allows them to tailor promotions, game offerings, discounts and features to create a personalized experience.
Big data analytics can help site admins detect play-patterns that indicate cheating or collusion among players. Data-driven algorithms can flag suspicious activities so that the poker site’s security team can investigate and remediate the issue before the company loses money.
Poker sites often host large tournaments with thousands of players. Big data becomes essential in terms of managing logistics. It allows sites to optimize starting times, adjust prize structures, and predict participation levels.
While handling high volume of players and transactions, big data helps poker sites optimize server loads to ensure smooth gameplay even during peak times.
Sites can determine the most potent causes of player churn by analyzing their activity data. This allows the poker-game hosts to build retention strategies.
By showing real-time data, statistics, history, and odds calculations poker sites can make live games more exciting and engaging.
All of these aside, big data has use cases in marketing, UI design, and overall UX design. One could say that live poker games with thousands of players would be almost impossible to host without the involvement of big data.