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Data Analysis Software

Everything about Data Analysis Software


What is data analysis software?

Data analysis software is a computer-based program that allows users to systematically apply statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. Furthermore, data analysis refers to all the ways that data can be reviewed and analyzed to form some sort of finding or conclusion. It involves asking questions about what happened, what is happening, and what will happen.

Data analysis software programs are widely used in business to enable organizations to make more-informed business decisions. The analytics data can help increase revenues, improve operational efficiency, optimize marketing campaigns and customer service efforts, respond more quickly to emerging market trends and gain an edge over competitors. Leveraging business analytics for success is dependent on data quality, skilled data analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.

How data analysis software works?

There are many software programs available on the market for data analytics. They provide a lot of services embedded in them. A good data analysis software, also known as an analytics tool, should be able to perform five key tasks. These are explained below:

1. Dashboard
Dashboards provide a real-time overview of key performance indicators (KPIs) in a visual format that is easily shareable. Some data analysis tools allow users the ability to create their own dashboards, based on workflow and objectives so that they can get a clearer picture of specific business operations.

2. Dataset creation
For a business forecast to be reliable, the quantity of data that is collected needs to be of quality. Software for data analysis should, therefore, allow users to scrub, aggregate and split data as needed.

3. Interactive exploration
Considering today’s technological advancements, static pie charts and line graphs are old fashioned. Analytic tools that provide interactive exploration of data provide eye-catching ways of visualizing trends, such as heat maps and time motion views.

4. Sharing
Sharing functionalities are important for collaboration as they allow business leaders, teams, or departments to work together more efficiently. When everyone is seeing the same data sets, it’s easier to interpret the information in the same way.

5. Ease of use
A good analytics tool should be fairly easy to operate. While usability will vary depending on how robust and technical your platform is, the interface should be intuitive enough for trained staff to use with minimal support.

Benefits of Data Analysis Software

Data analysis tools help identify, interpret and predict trends and patterns that affect the business. The benefits of these tools, therefore, are essentially the benefits of business intelligence. These vary depending on the individual case.

Data analysis software can help, for example:Clarify the correlation between new marketing initiatives and improved sales

  • Better predict customers’ needs by analyzing past purchases and browsing habits
  • Improve internal workflows and suggest solutions to common bottlenecks


By extracting meaningful insights from the data that is collected, a business can be in a better position to understand what it will take for it to increase profitability. The great advantage of using software for the purpose of understanding what makes a business run smoothly is that it’s often more reliable—and less time-consuming—than manual data coding methods.

Data analysis software use cases

Some business applications for data analysis software include the following:

Customer analytics

Customer analytics includes analyzing customer demographics, behaviors and characteristics to develop models for customer segmentation, predicting churn and making next-best-offer recommendations to help with customer retention.

Sales and marketing analytics

  • This involves identifying opportunities to improve how customer-facing applications make direct recommendations to the customer. An example is identifying opportunities for cross-selling and up-selling, decreasing abandoned shopping carts and generally improving the accuracy of integrated recommendation engines.
  • Sales and marketing analytics also show the performance of marketing processes and campaigns and can give recommendations on how to adjust and optimize that performance.


In-store retail analytics

  • Retail analytics offer insights into factors such as in-store traffic counting and retail conversion rates. This analytics data gives retailers a real-time look at what customers do when they visit a store. This allows for a deeper understanding of the customer, helping retail managers make informed business decisions.


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Technical Specifications

The table below shows how FaceMatch SDK performs on the Labelled Faces in the Wild (LFW) dataset:

FPRTPRThreshold (Inverse of distance)
0.10.99900 ±0.002130.55448
0.010.99667 ±0.005370.59791
0.0010.99367 ±0.006050.62989

FPR = False Positive Rate
TPR = True Positive Rate

These results are an indication only and are based on the specific dataset Labelled Faces in the Wild. Customers can expect similar performance, with possible variations due to hardware and the availability of annotated data.