This era belongs to technology and in this rapid innovative society, Data Science has a lot of importance. Data is not only helping companies perform better but also reducing the risks of uncalculated experiments. Zazz is the one of the top data science development company in New York. Our compilation will surely help you make an informed decision.
When to implement big data?
If a company feels the need to implement big data in its processes and units, it immediately associates the solutions with online models and high volumes of data processing, but this is not entirely true. Many organizations perceive the urgency of incorporating big data into their processes just because they are on the trend, but in a hurried way, if there is no mapping, there is a risk of misinterpreting data.
Brands are excited to believe that with the implementation of big data, they will have access to user databases. However, the reality today is that companies like Google, and in general social networks, only deliver information grouped with algorithms that are often unknown.
When you look at the term big data in broad strokes, it is understood as the ability to store and process, at extraordinary speeds, large volumes of data. In this first point, the concept generates emotion because you think of companies like Google or Facebook, which comply with that theory. However, in this regard it is worth asking if our business core really needs to capture so much information at such high speeds, or if it was already able to capture a minimum amount of information, integrate it and process it at admissible speeds. .
The most important question would be whether we have already managed to analyze it and turn it into valuable information for decision making. So, before getting excited about this concept of all the information and quickly, let’s first analyze the needs of the company and what it has done before with the available data.
The invitation is to ask ourselves why we want to implement big data and that the scope of the project and the investment in it be sized.
Metrics are another type of data that tell how the performance of the brand’s digital assets is: a portal, an App or a social network. It details how many people enter the site, how many click on a content, measurements that allow us to know how the efficiency of our actions is, what we should improve, where we are making mistakes to correct them and reveal opportunities for online strategy.
Metrics allow us to make comparisons and analyze user behavior and the effectiveness of our digital strategy. If these measurements are not made, we will not know what we are getting right and what is failing.
Use the data to know your users
Companies should look for new ways to take advantage of the information that users generate in their transit through a website, to understand their behavior in order to design more personalized digital strategies.
Learn about two data analysis alternatives that give us a deeper understanding of what happens with users on the internet, but to make them easy to do and apply in the best way, it is necessary to have an adequate information structure.
Imagine we ran out of eggs for breakfast the next day; this is why we must move to the nearest market to acquire a new quantity of the product. However, at the time of purchase, there is a high probability that, in addition to the eggs, we decide to bring cheese, chocolate, butter and bread, which are usual companions of the egg during the first meal of the day.
Well, this relationship between this set of products refers to what in economics is known as complementary products, that is, when one is acquired, it is common that more are acquired.
Other examples may be swimsuit and sunscreen, as well as milk and cereal. That is why a good strategy to increase sales is to offer the complementary product when the user acquires one of the two, that is, to offer the milk when the cereal is bought.
This applies in various fields of economics, and not only in physical products but also in digital products (educational, musical content, etc.) or a combination of both modalities.
Advantages of Data Science
The implementation of Data Science decreases subjectivity in decision making, these will not be based on personal beliefs, assumptions or tastes but on real and interpretive data, which are analyzed at the exact moment.
The numbers are made and have no discussion, with that, we stop talking about generalities. Many times, brands are clear who their customers are, for example, that the majority are women between 25 and 35 years old. But what about the rest of the public? Having the great mass characterized does not imply neglecting the others. Precisely, it is about understanding the entire universe of users, that each one has a different need, so we try to look for microniches and microsegments, which allows us to treat and improve them.
The experience of data, in the end, is to understand that the interaction of a person X is different from that of a Y, the idea is that, that they communicate differently and that the brands treat them differently: that allows the data.
Using Data Science is to stay ahead of the competition and offer a better user experience, without making decisions from subjectivity and without shooting the air, on the contrary, you can be assertive and have a better strategy.
Data science will always be necessary, but under a conscious methodology, understanding what the need is, knowing what we want to solve, and from that, determining how big or small is the deployment of big data, metrics or any source around the Data Science.