Data is a buzzword in the business environment today and rightly so. With the advent of technology, billions of consumers are spending more and more time online and more businesses are taking the digital route, creating quintillions of data every year. As technology and communication continue to rapidly develop and revolutionize the way the world works, we are ushering in an era where each individual is going to generate 1.7 megabytes of data per second.
Big data vs Data Science
There is a humongous volume of data, both structured and unstructured, available to businesses which can be leveraged to their advantage. This is big data. Big data cannot be analyzed using traditional applications and is managed using different tools and sources. This is most widely used in telecommunication, financial services, e-commerce and retail.
Data Science is a combination of tools, techniques, algorithms, processes and systems that are used to clean, prepare and align data and thereafter, extract information and insights from unstructured data. Using such meaningful insights and information, data scientists build further algorithms and products to meet the needs of their clients to fulfil their business objectives. Though data science is not a new field by any means, it is only with the increase in the volume of data that data science is being increasingly embraced by businesses. This is important in fields like digital advertisements, internet ads and search recommendation. There is a heavy demand for marketing professionals with specialization in the field of data and has accordingly caused a spurt in the number of certification courses in marketing management, big data, analytics and data science.
Importance of the integration of big data and data science
Big data and data science are not one and the same and it would be a big mistake if they are thought to be the same and the same approaches used. They are different and need different approaches. However, they can be especially useful if integrated and used in tandem with each other, based on the requirements of the business. It would also a mistake to completely ignore one because you are using the other.
If you take a closer look at the two fields and their applications by businesses across sectors, you can find that they are most widely used in the marketing sphere, in digital marketing to be specific. While the content of each business’ digital marketing efforts is different, digital marketing strategies are not too different. It would be imprudent of the businesses to invest in huge sums of money on just creating an online presence if it cannot leverage it to improve its ROI. Big data tools can be used to clean up and structure raw data and analyze it while data science algorithms can be used to unearth hidden patterns, trends and correlations from seemingly unrelated data. Through these findings, businesses can hyper-personalize and customize the marketing efforts to the needs of the customers, creating AHA moments.
If you are interested in this fast-growing field, you should obtain a certificate in marketing management and give yourself a competitive edge.
