We make
Data Analytics
look easy.

Samra Džubur

Data Analyst Manager @Atlantbh

Meet Samra and other Atlanters>
SERVICES

Data Analytics

We help you turn raw data, delivered in various formats and knowledge domains, into meaningful insights. While the process is highly automated, we consistently include an individual analyst’s perspective, which takes into account your specific business requirements and goals, ensuring we provide you with the best possible solutions based on your data set. Subsequently, we are able to deliver an array of statistical overviews, data quality issue detections and recommendations for data-driven decision making.

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Technology stack

We specialize in a wide range of technological solutions relating to Data Analytics to tackle any data set. When necessary, we can also provide Data Science solutions.

What we do

We offer a comprehensive kit of Data Analytics services to convert your data into actionable insights. Whether you have historical or real-time data, traditional Big Data, we can accommodate your business needs and offer transparency and flexibility throughout the process.

  • Data preprocessing
  • Data visualization
  • Issue detection
  • Pattern recognition
  • Data comparison
  • Statistical reports
  • Data quality reports
  • Recommendation delivery
  • Development of custom analyses
  • Predictive analysis

How we do it

Data is a numbers game and we make sure the figures always add up.

1

Defining Analysis Goals

Communication with our clients is essential when defining analysis goals. Just like our conclusions are used in the client's decision-making process for business needs, the client's feedback is used in our decision-making process when choosing an analysis approach. There are many perspectives to a single problem, we use the client's specific requirements and input to help us choose the adequate perspective.
2

Defining Analysis Steps

After defining the goal of the analysis, we proceed with creating precise steps for answering the analysis questions. However, we do not implement them before checking their logic flow with other teammates.
3

Improving the Analysis Process

While there are many perspectives on a single problem, there are also many problems that have not been identified earlier. This is a typical use case for when we need to improve our current analyses or create new ones. The first step is to create a Proof of Concept (POC), which we present to our clients and, of course, consult with the whole team during the process.

Blogs and Success Stories

mlBlogData Science & Analytics
April 9, 2024

Comprehensive Guide: Creating an ML-Based Text Classification Model

In the previous blog, we first defined the problem of customer support ticket classification that Atlantbh had the opportunity to solve. After presenting the business goal, we briefly described the proposed approach and obtained results. This blog post serves as a follow-up. We aim to delve into individual steps of…
BlogData Science & Analytics
April 9, 2024

Harnessing the Potential of NLP: Effortless Experience & Efficient Customer Service

As businesses strive to stay ahead in today’s dynamic market, harnessing the potential of Artificial Intelligence (AI) has become a strategic imperative rather than a choice.  Natural Language Processing (NLP), one of the branches of AI, encompasses a powerful set of techniques with a wide range of applications, especially in…
DataOpsBlogData Science & Analytics
November 20, 2023

DataOps: The Data Analytics (R)evolution

In the global, increasingly growing IT industry, we hear the term ‘DevOps’ almost daily. But how often do you encounter ‘DataOps’ related topics? We all use some of its practices, but it still seems relatively unfamiliar to many. In a world where data is the core of almost everything we…

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