Data has to be dealt with by every business. Successful management and analysis of data can determine the future of an organization. But it needs a fair amount of initial investments and it is not very easy to understand the true data needs of a company. The large scale businesses that have put data to great use to get ahead of their competitors have inspired businesses of all scales to build data insight strategies but success is not always imminent. A very basic and usual shortcoming in a company’s data strategy is failing to identify the distinction between big data and data science. These can be called two radically different sides of the same coin that need distinctive treatment.
The conceptual difference
Data science deals with everything related to data cleansing, preparation and analysis. Data science builds the models that move data from raw to relevant. It adds value to the great amounts of collected data.
Big data on the other hand refers to the humongous amount of data that is collected and managed through various sources and needs to be dealt with in order to draw important insights.
The different fields
Both Big data and data science have individual roles in various fields.
Data science holds direct relevance with
- Digital advertisements
- Internet searches
- Search recommendations
Big data is more important in fields such as
- Financial services
- Tele communication
We can theoretically segregate big data and data science in respect to generalized concepts and for certain fields. This generalization can be fatal for new enterprises that are only just forming a data strategy and getting ready to make fresh investments on it. Big data and data science need different approaches but they work best when they are integrated and used as big data science.
Big data science – The integration of two giants
According to an article by Forbes we are moving toward an age when every human being on the planet will create 1.7 megabytes of data every second. We live in a connected world; we add data to the web with our every activity. Companies are desperate to leverage the data from all available sources to improve conversion rates, product design and customer experience. The consumer base is expanding and so is the number of peers for each company. The only way of survival is staying ahead of time and to do that the big data and data science industries are growing at an unconceivable pace. This is a time when every mistake can cost you a fortune.
It is important that you know your requirements and understand the measure that need to be taken in order to fulfill those requirements. Messing up between big data and data science is the worst mistake that one can commit but using one and staying completely away from the other is just as bad.
You need to integrate the two giants. Make them work together.
Use data science algorithms to identify and analyze the relevant datasets.
Use big data tools to streamline huge amount of data and analyze it.
Together they are stronger
The amount of resources and time assigned to each field will depend on the scale, market and consumer base of the business. To be honest no business today has a completely unique model. A manufacturing unit may well use the same digital marketing policies as a service based software network. Toward the beginning of the article there is a section that explains the different roles of big data and data science; a closer scrutiny should tell you that none works without the other.
How many retailing chains can you find, which do not use search engine optimization and digital advertisements? Not too many, I’m sure. The whole generation is looking at the mobile and the tablet screens, popping up there is the most plausible way to make an impression on their mind. But you cannot just invest a huge amount of money and appear before a large number of audiences whenever they open a certain web page; it is not only unaffordable but unreasonable as well. Data Science comes into play to tell you which set of individuals to target at what time. Data science algorithms help you to optimize every detail of a digital ad in accordance with the people it is addressed to. More importantly it identifies the right set of people for you. This is just a small example of the numerous things that data science can do for you. You cannot really leave out machine learning while talking about all these. Though it is a different topic altogether but when integrated with data science it can do wonders for you – say predict the object your customer will wish to buy next.
Big data science has changed many fortunes. It is the most potent weapon that a business of any scale can use to earn a competitive edge. IT, Finance, Healthcare, Manufacturing, and Military, all the industries are becoming data centric creating hundreds of thousands jobs around the globe. There was never a better time to be a part of the world of big data science.