The term big data is making big-time in the business industry. The growing amount of data being generated by large business organizations can lead to a huge volume of data streaming from multiple sources. To regulate this “Big Data” and make sense of what this data is suggesting about the business is the biggest challenge of this time. Many Business intelligence softwares and data reporting and analytic software have been developed and marketed to organize, format, and analyze this big data and generate meaningful insights to tap into the world of information and data relations that are derivable from big data.
Evolution in data science has led to various tools and techniques to understand and formulate the big data to derive actionable insights that can help data analysts ask questions about business and find out answers in the data trends that can be identified by performing analytics on big data. Data science encompasses many areas of expertise to refine, analyze and derive required information from data through careful analysis and study.
What is data science?
Data science is a growing discipline that helps data analysts maintain and make sense of big data. A data scientist works with big data and helps to aggregate it from multiple data sources. The data coming from these sources can be structured or unstructured. This data is assembled in a compatible format and readied for analysis to derive meaningful visualizations and reports that can make predictions about datasets that can help management to take timely decisions for improvement in business strategies and increase in revenue.
Data Science includes skills from mathematics and statistics, programming, and SQL, as well as knowledge of trending areas like Machine learning and Artificial Intelligence. It applies tools and techniques to understand data by performing analytical functions, data cleansing, and predictive analysis of big data.
“A data scientist is someone who can obtain, scrub, explore, model, and interpret data, blending hacking, statistics, and machine learning. Data scientists not only are adept at working with data but appreciate data itself as a first-class product.” – Hillary Mason, founder, Fast Forward Labs.
How can data science be used to interpret big data?
Big data is usually characterized by a large volume of data coming from multiple data sources and is often not easily contained by a single database management system. The data may have incompatibility and some data may be structured and some unstructured. Data science tools and techniques are used to regulate and perform analysis of big data. The data would first need to be refined and stored in a compatible format to identify relations and trends in the data set.
Interpreting big data can be a daunting task and data scientist tries to simplify this task by applying skills from data mining, text analytics, and machine learning to get meaningful insights from big data that might be previously inaccessible or incomprehensible. These techniques present data in a format where data analytics and reporting can be performed and business questions can be answered by observing previous years’ data and reaching conclusions based on this data.
Some key features of Data science tools that play an important role in making the data scientists’ task easier are enumerated below. These features encompass a wide range of fields that come under the umbrella of Data Science.
Data visualizations are meaningful interpretations of big data in the form of visuals. These visualizations make it easier for data scientists to identify business trends and clear doubts and questions related to business strategies and operations. Business management can understand which strategies are working and which are not and take actions accordingly to improve business performance in their niche. Softwares like Tableau can help users create powerful visuals to derive meaning from big data for performing business analytics.
One important way for performing data analytics and reporting using big data is to create data reports and charts. These reports can have different layouts that present data from a different angle to help data scientists make predictions and answer questions related to business queries for improving statistics about products and services. Ad hoc reporting tools like dotnet report builder makes users’ task easier by providing arrange of report layouts that they can use to present the data set in different levels of detail.
Compatibility of data
Big data is not very consistent and compatible as it is coming across the organization from multiple data sources. One big task of Data scientists is to aggregate and organize this combination of structured and unstructured data in such a way that it is compatible and easily accessible for data analysts to perform their tasks easily without any irregularity and discrepancy.
Identifying data trends
The main value of big data lies in its ability to give a picture of previous data in such a way that data analysts can identify the data trends and patterns that are useful in identifying common mistakes in business operations and strategies. Once, the impact of these faulty strategies is visible through the data trends management can make amends to these strategies and change the trends to move in a positive direction. Data Science tools like DataRobots can be used to identify data trends that show the demand for a product in a specific area geographically. This can help plan marketing strategies and reach new markets for business products.
Contextualize the data
Contextualization of data means adding related data together to add meaning to the data and identify the context in which it is presented. Adding context to big data presents a picture that is easily decipherable by Data scientists. Big data is so large in volume that often it is hard to make meaning of it without contextualization and creates a challenge for data scientists that leads to many complications related to interpreting big data. Adding contextual information in this data will help data scientists to present it in a more meaningful way and they can relate with other data and derive insights to answer business questions.
Predictive analysis is a very important data science tool that allows data scientists to analyze data from previous years to identify patterns that can help them make predictions for the future. The historical data forms the basis of these predictive outcomes and can be very helpful to analyze the impact of a decision on the business operations before implementing it. Usually, business management asks data scientists to perform predictive analysis for any strategic changes that they want to implement to find out the effect it would have in the future. Predictive analysis uses a combination of data assessment, predictive modeling, and machine learning techniques to reach its goal and generate outcomes for a set of questions.
“I think you can have a ridiculously enormous and complex data set, but if you have the right tools and methodology, then it’s not a problem.” – Aaron Koblin, entrepreneur in data and digital technologies.
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