Talk To us

Data structure – how to ensure a positive contribution to the business?

14 June 2022
Article by Beatriz Ribeiro, consultant in Industrial Engineering and Management at INEGI.

We live in an age of information, and the success of any business therefore depends on the organization and quality of the information at its disposal. But watch out! Quantity is not synonymous with quality.

Our experience in consulting projects in Industrial Engineering and Management allows us to say that a company that holds a huge amount of data does not necessarily (probably!) hold its equivalent in qualitative terms. But then, how to ensure a positive contribution to the business?

Above all, it is important to know that the quality of the data is, only and only, determined by the results extracted from its analysis. Only when this analysis becomes a solid basis for making strategic decisions can we say that there is quality. Quality that will translate into gains for the business - eventual cost and time reductions, the successful launch of new products and services, among other potential results.

Data management tools must live up to business goals

Edward Curry1 , a data science specialist at the National University of Ireland, clarifies that there are five distinct steps to take into account when processing data:
  1. Acquisition – collection, in real time, of the data that will be filtered, submitted to cleaning and, finally, structured.
  2. Analysis – treatment and modeling of data, allowing its homogenization, proper reading and extraction of results.
  3. Curation – maintenance of data, attesting to both its quality and its suitability for the purposes it should serve.
  4. Storage – organization and analysis with a view to data conservation.
  5. Use – use itself, focused on the business strategy and as a support for decisions that are adjacent to it.

Each of these phases is of equal importance for the success of the final solution, so only an adequate IT tool - that is, with a capacity proportional to the company's daily needs - will support a thoughtful assessment of the distribution of work and supply needs, production, storage, shipping and distribution.

Thus, firstly, the needs and objectives to be achieved must be measured, and then the design of the processes involved in the supply chain must be carried out and, finally, the market must be consulted in order to weigh the most appropriate support solution.

Effective data management adds value in various business areas

On the other hand, the author Doug Laney2 breaks big data into five main characteristics, which have been called the Five Vs of big data:
  • Volume – referring to the huge amount of data generated every second and its tendency to increase.
  • Speed ​​– alluding to the high speed with which data is generated and with which the same data becomes outdated. That is why, the faster the processing, the greater the probability of designing solutions based on useful data.
  • Variety – related to the fact that the data collected come from a great diversity of sources, which makes their heterogeneity understandable, implying the need to speed up their processing, as well as to develop a way to homogenize them.
  • Veracity – regarding the existence of up-to-date and out-of-date data in the high volume of data collected. Being able to distinguish one from the other is the key to making decisions based on true facts and that do not condemn the business.
  • Value – points out that not all circulating data add value to the company, so, in addition to analyzing the veracity of the data, it is also crucial to understand its importance and usefulness in the business universe.

We can therefore conclude that understanding and translating data are tasks to be completed in practically real time. It is therefore essential to provide the necessary processing and organization capacity through tools that match the business goals.

In this way, the correct reading and organization of data will add value to the business on several fronts and in several business areas - whether they are related to cost reduction, process improvement or even with the analysis of new business opportunities and/or . On the other hand, data duplication, inconsistency, validity, inadequacy and even an inadequate data analysis methodology can be the basis of improper and ineffective management that can gradually doom the future of your business.



Curry, E. (2017). The Big Data Value Chain: Definitions, Concepts, and Theoretical Approaches. New Horizons for a Data- Driven Economy. (pp.29-37). Disponível em:

2 Patgiri, R.; Ahmed, A. (2016). Big Data: The V’s of the Game Changer Paradigm. 18th IEEE High Performance Computing and Communications. Disponível em:, 2016.

Related Pages

Consulting | Industrial Engineering and Management

Cookie Policy

This site uses cookies. When browsing the site, you are consenting its use.   Learn more

I understood