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.

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Tableau
Hive
scala
Python

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

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.
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Blogs and Success Stories

Outliers in data and how to detect themData Science & AnalyticsTech Bites
March 23, 2023

Outliers in data and how to detect them

Outliers: how do they happen and why are they important? Outliers are extreme values of the data. They are record values that significantly differ from the rest of the data. Outliers can vary in different values, abnormally low or abnormally high. In a set of data, there may be more…
Amar Aladzuz 0
Love10
Comparing different plotting systems in RData Science & AnalyticsTech Bites
February 24, 2023

Comparing different plotting systems in R

Data visualization is the step in the data analysis process where, if done right, “ gives you answers to questions you didn’t know you had” (Ben Schneiderman). Alongside keeping our understanding of trends and patterns, the visual language that should effectively communicate the insights generated through the analysis is probably…
Mahir Hrnjic 0
Love9
Social Media AnalyticsBlogData Science & Analytics
February 9, 2023

Social Media Analytics

We all use social media. Facebook, Instagram, YouTube, LinkedIn & co. have been part of our lives for a long time now. We subscribe, we post, we like, we comment, we share, and we click - without keeping track of the data we generate.  But don’t worry, some people do keep…
Nadja Hota 0
Love8
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Let's collaborate

Whether you’re looking for a partner or just want to chat, drop us a message below.