[Data Science]

Data Science Development Services

Data Science Development

Data science reveals trends and generates information that companies can use to make better decisions and create more innovative products and services. Data is the foundation of innovation, but its value comes from the information that scientists can extract and then use from them. Since modern technology has allowed the creation and storage of increasing amounts of information, the volume of data has increased. It is estimated that 90% of the data in the world was created in the last two years. The large amount of data collected and stored by these technologies can generate transformative benefits. That's where data science comes into action.


Our Services

The companies realized that if there was no integrated platform, the data science work was inefficient, insecure and difficult to scale. A good platform alleviates many of the challenges of implementing data science and helps companies convert their data into information faster and more efficiently. Zazz is one of the leading Data Science Development Company and our services include:

Our approach

Technology Stack for Big Data Development

Our scientific approach to data analysis allows us to use the best tools for your business. We know how to structure, visualize and analyze your data so that everyone in your business can understand. Zazz is a data science services company that offers services based on Artificial Intelligence and Machine Learning technologies to build more valuable solutions for our clients. We use all the contemporary tools and technology stack to build and offer a data science solution that is modern, state of the art and future proof for at least a decade.

Big Data Development

What is data science?

It is the collection of various knowledge or skills around to understand and solve a problem. This science is responsible for collecting information, unifying it and making use of it to solve hypotheses.

This is achieved with statistical support, for example, around the user's understanding, applying segmentation methodologies such as RFM or machine learning algorithms, which anticipate the future of the behavior that users will have. There are algorithms for every need that is found, it is a very academic topic, supported in mathematics and statistics.

There is another component of Systems Engineering to automate processes and ensure that information and results arrive at the time they are needed and it is not a waste of a work team that grinds and grinds data so that the results are late, when they are not I can take any action about it.

The Data Science collects, for example, a user investigation, databases or information that is found on the Internet on sites of the Dane, the Chamber of Commerce and Super societies, among others.

This is a trend, but it is not about delivering the results based on statistics, with relationships or formulas, but talking about a reality from the data and the processing we give to solve a need.

How fast do I need to obtain the information?

Many times, people confuse speed with online models. This makes the solution more complex in development (time) and more expensive in tool. The concept of speed must be associated with how much time we have gained in the automation of processes.

These types of projects must have clear objectives and needs to be resolved from the beginning. If a process (report) previously took five days to obtain the data (information extraction, construction of results and presentation), but with the big data model the time was reduced to two hours, we can say that we comply with the item of speed in the project.

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 such as 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.

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 micronichos 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.

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