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Add value to your data: a luxury or a need?


Jorge Puentes

Business Analytics
January 18, 2022

In 2006, Clive Humby coinedthe famous phrase "data is the new oil". However, it was not until2017 when an article by The Economist, entitled "The world's most valuableresource is no longer oil, but data", made this famous phrase catch theattention of companies, academics, and various data-enthusiasts around theworld [1]. But what makes data so important as to be compared to oil? Let'sfirst clear up a couple of points in this comparison. The first is that, unlikeoil, data is not a finite resource. And secondly, data is reusable and itsprocessing does not generate a significant negative impact on the environment.Given the above, this comparison is established to make an analogy to what oilrepresented for humanity in terms of technological and economic advances sometime ago.

In this sense, data is like oil, it is a source of power. Those who control thedata (Amazon, Alibaba, Google, Meta, etc.) are establishing themselves asowners of this digital universe, just as the pioneers of oil did about acentury ago.

Now let's think about the following numbers: between 2010 and 2020, the volumeof data created, copied, and consumed globally increased 32 times. Going from 2zettabytes in 2010 to 64.2 zettabytes in 2020, and with projections of reaching181 zettabytes by 2025 [2]. Due to this exponential growth, companies from allindustries have found it necessary to take full advantage of this data in orderto generate competitive advantages that allow them to survive and grow in anenvironment where tools such as Business Intelligence (BI) and Data Scienceplay a key role.

BI and Data Science are presented as two essential tools for companies to turntheir data into valuable and timely knowledge. Although these two tools worktogether, BI focuses on the past and the present, providing a retrospectivelook at what happened and facilitating decision-making in the present. On theother hand, Data Science focuses on the future, and encompasses a series ofbranches such as Machine Learning, which seeks to create predictive modelscapable of automating multiple processes through computer vision techniques,time series forecasting models, recommendation systems, virtualassistants, among many other applications [3] [4].

The foregoing allows companies to properly manage and use their data, allowingthem to accelerate innovation and development processes, reduce costs, makefaster and more reliable decision-making, shorten delivery times, and increasecustomer satisfaction. Likewise, it is important to point out that thesebenefits are not limited exclusively to large companies. It does not matter thelevel of maturity that your company has in relation to these technologies, ifit is an SME or a multinational company, or if you still store all your data inExcel files or you have it in the cloud. We can all make use of thesetechnologies and create tailor-made solutions that allow us to take fulladvantage of all that data currently stored and waiting to be converted intoknowledge and tangible benefits.

Have you already started adding value to your data? At Data Value we want toget to know you andhelp you get the most out of each of your bits. Schedule nowa free meeting with us by clicking on the Book a meeting button. Stay tuned,see you in the next entry!

[1] The Economist, «The world’s most valuable resource is no longer oil, butdata,» 2017. [En línea].Available:

https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data.[2] Statista, «Volume of data/information created,captured, copied, and consumed worldwide from
2010 to 2025,» 2021. [En línea]. Available:
https://www.statista.com/statistics/871513/worldwide-data-created/.[3] X. Zongben, T. Niansheng,X. Chen y C. Xueqi, «Data science: connotation, methods,technologies, and development,» Data Science and Management, vol. 1, no 1, pp.32-37, 2021.
[4] N. Yanfang, Y. Limeng, Y. Jie, B. Mengqi y S. C.B, «Organizational businessintelligence and
decision making using big data analytics,» Information Processing &Management, vol. 58, no
6, p. 102725, 2021.

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