Businesses today have almost limitless sources of data and one of their biggest challenges is figuring out what to do with it. With advances in processing technology, the connectedness of smart phones and social networking, businesses are relying on real-time data to help them make more informed and more relevant decisions. It is becoming almost essential for Data Scientists to have Big Data experience due to how large the data sets are that are being collected by companies and the insights that can be gained from all that data.
Big Data and Data Science Defined
Big Data is defined as a collection of large data sets that cannot be analyzed with normal statistical methods. Data today is not necessarily a number, it can be videos, photos, words, phrases, etc. Data Science is generally defined as creating models that outline patterns and trends within complex data sets that look to influence future business decisions. The challenge of making sense of the data is where big data and data science come together.
Big Data and Data Science Tools
Being able to use Big Data tools to organize and transform data sets into resources that can be analyzed and modeled are qualifications increasingly more popular and in demand. The most popular Big Data tools that companies are looking for include: Apache Hive, Apache Pig, Apache Spark, MapReduce, Couchbase, Hadoop, and MongoDB. The most popular and in demand software skills for Data Scientists include Python, R, and SAS.
What is The Goal?
Companies looking to make sense of data must clearly outline specific goals and ask the right questions. Which product will a customer buy next? Which customers are most likely to leave? What location should we market product X in versus product Y? Narrowing and defining goals will increase the likelihood of increasing profits. It will allow the Big Data expert or Data Scientist to gather the most influential data sets and create the best models.