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Big Data in e-commerce

August 7, 2020
August 7, 2020 Njla772

Any company today knows that information is a competitive advantage, in other words: "information is power", and now more than ever. However, the amount of data generated daily makes it difficult to process it using traditional tools. For this reason, a large number of organizations have chosen to use certain Big Data in your business, since it allows you to process and store such information in real time without being overwhelmed by your systems, which would cause the loss of a large amount of information.

The Big Data is a set of software, hardware and other tools in charge of collecting large data sets, manipulating, processing and analyzing them, practically in real time. Thanks to this, companies can get to know their customers and their purchasing patterns, discover how their skills and the sector in which they operate work, etc.

Why is the use of data so important?

For every minute that a user spends on the Internet, thousands of data are generated such as purchases, visits to shops and products, tastes, etc. The generation of this large amount of information has been seen as a great opportunity in recent years, so if we are able to collect it, group it and analyze it we can change and improve business strategies, user experiences in your online store, recommend similar products, spy on the prices of other companies, etc.

Why apply Big Data in e-commerce?

If you apply the Big Data in companies can mean a remarkable increase in their potential, e-commerce businesses are no less, since they have been very benefited by the use of Big Data. Online stores generate a large amount of information and a good analysis of this helps significantly to modify the business strategy.

Among its elders advantages is found:

  • More organized databases: It is true that e-commerce companies generate a large amount of information but, first of all, it is essential to have well-structured databases to be able to collect and store data from multiple sources.
  • Decision-making is based on data: Once you have the information stored and well structured it's much easier to be able to analyze the data. Later, after this analysis, it is possible to obtain certain conclusions to finally make decisions based on data and not on assumptions or past experiences.
  • Greater control of the stock: There is a great amount of tools in charge of managing the stock like Inflowwhich has the capacity to keep the stock updated and to organize all the agents of the logistic process. In addition, by means of a predictive analytics it is possible to predict the demand for certain products.
  • Personalization according to the user: The electronic commerce allows to analyze those products that have been visited by a user, being able this way to offer these products or similar the next times that you enter the web.
  • Price revitalization: Online consumers typically search for and compare the same product among different vendors to find the one with the most features. Therefore, the Big Data allows you to analyse the prices of your competitors, making it possible to establish a dynamic pricing policy.

Retos del Big Data en un e-commerce

  • Quickly access information to make better decisions based on data.
  • Handle large volumes of data: Every day the amount of information we have is greater than the day before. Whether it is product data, orders, customer databases that are becoming larger and more complicated to manage, etc. That is why we must be aware of the amount of data we are working with and have under control all those inconveniences that could be generated in our business and those of the customers if the information became impossible to store and process. Therefore, not only should we use Big Data  when the data has already become untreatable but we must anticipate it and get into this world as soon as possible, because in the long run it will be beneficial for the company and will save you problems in the future.
  • Customizations according to the client, such as offering discounts, products, offers, etc.
  • Collect, store and organize information from multiple data sources in real time for later analysis and decision making.

5 errores típicos a la hora de aplicar Big Data

  • Estrategia poco clara. Aunque el término Big Data esté actualmente en boca de todos, hay que tener bastantes conocimientos para aplicarlo en nuestro negocio. Y es que una gran cantidad de empresas se lanzan a aplicar Big Data sin realizar una planificación mínima, tanto de trabajo como de objetivos.
  • Personas poco formadas. Como se comentaba anteriormente, el término Big Data requiere de un gran conocimiento para poder aplicarlo de forma eficaz. Este es un error bastante común en las empresas ya que encargan un proyecto de Big Data a empleados sin formación o con muy poca experiencia. Esto se debe normalmente a que los máximos responsables no saben exactamente de la complejidad de este sector y creen erróneamente que cualquier persona con unos conocimientos mínimos puede llevar a cabo un proyecto de Big Data.
  • Demasiada prisa para comenzar. Antes de empezar con un proyecto tan complejo debemos disponer de una base sólida, es decir, datos de buena calidad. Debemos asegurarnos de poseer buenos conjuntos de datos ya que de nada sirve tener un proyecto impecable de Big Data si los datos sobre los que se sustenta el mismo son erróneos, faltan bastantes datos, etc.
  • Pensar que el Big Data es la solución a todos los problemas. A pesar de que hay opciones muy avanzadas, el Big Data necesita complementarse con Business Analytics, Inteligencia Artificial, Business Intelligence, etc. Por lo que, creer que el Big Data es la solución a todos los problemas de datos de un negocio es tener una noción muy limitada.
  • Utilizar grandes conjuntos de datos innecesariamente. Muchas veces no nos paramos a pensar para qué queremos esos datos, cómo los vamos a utilizar y cuál es su utilidad. Por mayor cantidad de datos, el proyecto no será de mayor calidad, al contrario. Disponer de grandes conjuntos de datos da lugar en una gran cantidad de ocasiones a un ruido muy elevado, por lo que debemos cuestionarnos seriamente cuál es nuestro objetivo y seleccionar exclusivamente aquellos datos que nos ayuden a lograrlo.
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