Big data has been a buzz word amongst CIOs and CTOs for the past few years, but most people in an organization aren't entirely sure what it entails. We've gathered the 5 most frequently asked questions about big data.
What is Big Data?
Big data is substantially complex. It cannot be effectively measured using any preexisting systems or processes. It involves gathering hundreds of thousands of terabytes of information, compiling all the information into sections, and analyzing it for patterns, inconsistencies, or faults.
Industry analyst and influential IT figure, Doug Laney, provides an explanation to help you understand big data as the 3Vs:
- Volume: The amount of data in which you are trying to access. This includes transaction-based data or online shopping, sensor and machine-to-machine data, data from social media accounts, and much more.
- Variety: The different types of data that can be used to compile information. Types of data such as spreadsheets, databases, text files, images, video, music, and more are the variety sources.
- Velocity: The speed in which the data is captured. Consider the fact that there are 300 million photos posted to Facebook each day, 5 new profiles created each minute, and over 293,000 status updates are posted every sixty seconds. Being able to weed through all of the data and only select the important pieces -- quickly -- is velocity.
Big data can come from a variety of sources, ranging from customer data, social media data, or the Internet of Things (such as GPS navigation, cars, home automation, and more). It‘s capable of producing information about just about every aspect of your customer's life in the form of a data trail, and if analyzed correctly, can lead to a better understanding of their lifestyle and purchasing habits. This is how big data started to change the way businesses run.
How Will Big Data Help My Company?
As companies grow and become more data-driven, these insights will become more important to not only business strategies but operational efficiency. Big data gives companies the opportunity to collect much more in-depth market and customer intelligence, giving them the information, they need to provide much more in-depth analytics of their target audience.
Everyday business transactions will benefit from big data. For example, insurance companies can detect a potentially fraudulent claim by analyzing the claim to verify if it can be automatically reviewed and approved or flagged for review by a specialist.
Needless to say, if your business has a website, a social media presence, or even accepts credit cards, it collects data on its customers. The data collected can range from location, the name of the customer, where they found your website, or what they did on it. Because of this, companies need to have a strategy set in place to record this information and analyze it to improve their services.
Sophisticated analytical tools generate more questions. At some point, you need to get some measurable results, and that's when you realize you're heading in the right direction."
Fabio Luzzi, VP of Advanced Analytics and Data Science, Viacom.
Do I Have to use Hadoop?
Absolutely not! While Hadoop may be a free open source architecture, it isn't without its costs. To use Hadoop, often you will need to purchase expensive hardware, hire experienced analysts and developers, and keep up with maintenance. This can run you upwards of $1 million just for the initial implementation, plus costs adding to it as time goes on. It can get a bit scary. If you aren't able (or willing) to shell out some serious dough for some serious information, we have provided a list of a few Hadoop alternates:
NoSQL
Businesses are preferring NoSQL despite having SQL. NoSQL, or Not Only Structured Query Language, is an alternate database to Hadoop that is known for its ease of development and use. NoSQL systems are called "Not only SQL" to emphasize that they are capable of supporting SQL-like query languages. Because of its flexibility, businesses can store data without having to modify it to fit their structure or tabular relations.
Apache Spark
Apache Spark is an open source cluster computer framework, originally developed by the University of California. The Apache Software Foundation took it over in 2013 and has maintained it since. Spark provides a programming interface for entire data clusters. Ready to learn more about Apache Spark? Check it out here.
HPCC
High-Performance Computing Cluster (abbreviated as HPCC) Systems--also known as DAS (Data Analytics Supercomputer) --is an open source, data-intensive computing system platform developed by LexisNexis Risk Solutions. It offers ECL, an easy-to-learn and consistent programming language, which was specifically designed for big data processing. Learn more about HPCC Systems here.
Do I Need to Hire a Data Scientist?
Often a tricky question, the choice of hiring a data scientist for your company largely depends on your company's situation. While the demand for data scientists has grown significantly in the past 4 years, they aren't for everyone. Many companies will only need a Data Architect or Analyst.
To help you define your needs and decide which route is best for you, Better Buys has created a handy quiz. It's simple - simply answer their questions with "yes" or "no," and your results will appear with a recommendation! To start the quiz, click here.
Is Big Data Really the Future?
59 percent of executives say Big Data at their company would be improved through the use of AI according to PwC”
Asking if big data is the future is a bit moot, as it is very much so here and now. Many corporations are already leveraging big data and all its benefits. The question should instead be, "how relevant will big data be in the future?" The answer? Critical.
As referenced earlier in this post, big data is becoming more of a necessity in business versus an idea. It will continue to fundamentally change the way that companies view their customers, their competitors, and their business.
To be aware of what makes big data the future, go through the top takeaways from CAMPIT'S intelligence, analytics and big data conference
