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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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Data analysis tools

There are numerous tools that we can use to carry out data analysis. All of them are different and suit different needs of its users. We at Sightcorp integrate our solutions with some of the companies that provide data analysis tools and here we are going to talk about a few of them.

Tableau

Tableau works based on principle of PivotTable and PivotChart of Excel. This solutions helps you to better manage and organize the data within your system. It ensures that you use trusted and always up-to-date data during decision making process. The software has many application fields, from cataloging, searching to governance of Data Management Add-ons. This features are made in order to build trust in your personal data. The main objective of Tableau is to accelerate the adoption of self-service analytics and bring convenience to the end user. If you are looking to get to know more, visit Tableau data analytics website. 

The key points of Tableau

1.Trust for everyone

Tableau Data Management is focused on providing you with the full scale control and visibility over your data environment in order to bring trust and security in your data management system.

2.Data Availability

The software ensures that everyone is able to feel confidence in a fact that the right data is being used for the given analysis.

3.Integration with other Tableau services

If you need more control and management over your data, you can use an option of using data management solution from Tableau.

4.Scalable

With Tableau, you are able to automate the process of data analysis, ensuring that you are capable of analysing large quantities of data. Moreover, you also simultaneously facilitate the process of data analysis and management, as the solution is highly scalable and can be automated based on your preferences and needs.

Power BI

This data analysis solution was previously known as a plugin for Microsoft excel. Afterwards it became a separate tool. Power BI or also known as Microsoft Business Intelligence is a software that offers basic data analysis and management capabilities. it is quite similar to Excel Power Query. Through the use of Power BI, the users are able to create interactive data visualizations, various reports and dashboards that are highly customizable and user friendly. Moreover, the solution is capable of handling big data files and performs better in this field when compared to Excel. 

The main objective of Power Bi is to transform your data into highly visualized understandable information, so that you can focus on aspects that interest you the most. Organize the data, make sure that you stay up to date with trends and with relevant information. 

Power Bi is highly adaptive and capable of working with dozens of data types, such as typical excel files in CSV, but also Salesforce, Google Analytics, GitHub, Mailchimp and more. Moreover, the software is also capable of running R scripts, through the use of which you are able to pull in any data and use via R, directly importing it into Power Bi. One of the key advantages of the solution, lies in a fact that in its business model and data analysis capabilities.

At the current state, Power Bi has three licensing methods:

  1. Power Bi Free
  2. Power Bi Pro
  3. Power Bi Premium

As you might have guessed, the free version are not full and are limited to some extent, but also in the same time, the service portfolio that is offered by the free version is more than enough, when the program is used for personal use. Without a doubt, the data analysis that is offered by Power BI is powerful. The software allows to implement complicated and advanced data analytics that function similar to formulas in Excel.

The key points of Power BI

1.Understand your data easily

Transform raw inserted data into highly visualized extrapolation that will help you to understand the full picture at a glance. Moreover, quickly identify various trends and patterns, see the big picture and communicate that you gained in an understandable manner.

2.Identify risks and opportunities

Through enhanced data analysis you will be able to discover various opportunities that you can use in order to improve efficiency and moreover, identify possible risks before they can impact your enterprise. Create reusable, robust and live models, gain a deeper knowledge with live data as well as view all of you available data in a single view.

3.Enhance your decision making and make decisions based on reliable data rather than speculations

Share your data reports and dashboard. Make sure that everyone is on the page and your team is up-to-date and that all of the information is clear for everyone in your team. Through the use of Power BI you are able to predict different outcomes for any given preset option, as well as use various visuals to distill most important information. 

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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.