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Tuesday 27 June 2017
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Application of Data Science Versus Big Data

It’s almost impossible to overstate the role of data in today’s economy. We normally utilize the digital version of our world by the tools and actions we employ in our daily activities. The data we capture is still waiting to be used. Many industries consider data to be the secret to improving developing competitive strategy.

There are two different terms; both used to connect the potential of data. These are Data Science and Big Data. Many confuse these terms to be almost the same, but they fundamentally play different roles in assisting your organization to collect relevant data.

People don’t clearly understand these two terms. Data Science and Big Data help organizations to focus on their marketing interests. Data Science works on capturing the unseen patterns of complex systems and transforms them into working applications.

Big Data, on the other hand, looks to collects and manage larger volumes of different data to assist large sensor networks and large-scale web applications.

The two terms have the potential to collect valuable data; however, only one statement can summarize the difference between the terms.

Collection does not mean discovering

This looks obvious and the truth in it is always overlooked when companies rush to equip themselves with complex data technologies. Most companies always believe that the value of their data continues to increase as they continue to collect more and more data.

This means the companies continue investing in data collection tools rather than methods to approach already collected data. Therefore, data collection is prioritized, leaving organizations with a collection of big tools and a small amount of knowledge on how to convert the collected data.

Collecting data without the necessary analytical tools

The industrial revolution brought the ability to convert raw materials into valuable products, more efficiently and at scale. However, the focus on scaling was not based on the collection of more material. It was coming up with tools that scaled and mechanized the expertise in mechanizing.

With this, came the need to understand the craft since to operate, maintain and innovate at a scale relied on one’s deep understanding of the process used to convert raw materials into products that help to shed more light on the issues surrounding the always changing demands on the market.

The expertise of converting data is called Data Science. The reason why science is used to convert raw data into valuable data is that raw data is never useful in its raw form. Raw data has a lot of irrelevant information and misleading patterns.

It requires us to study the properties and the discovery of a working model that can identify the model we are looking for to convert raw data into useful information.

Having such a model in place, despite the other factors means that an organization is on the right tracks to discovering and innovation Thus, the model can help businesses to know what to look for.

Conversion should scale before collection

It’s impossible for businesses to invest in data collection without a team or tools to convert the data into a valuable resource. Industries would consider this a very bad investment. Trying to use data collection tools to gather information and end up without any meaningful information adds little strategic benefit.

Mostly, we normally study the methods employed by large companies to understand their operations which assist them to remain competitive in the market. However, this is an unfortunate aspect of big data.

Most of these companies people look up to rarely have any challenges faced by other companies. This means that their applications are designed mainly support large scale applications, thus enabling them to face every bit of their challenge.

These companies rely on the designing of these applications for their daily operations and meeting the daily demand of output. However, these applications help the businesses to discover and convert the collected data into expensive models that help them analyze the factors behind how their markets operate. Competing using data means the ability for organizations to explain and predict its dynamic environment.




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