A 2022 prediction says that each user would create 1.7 megabytes of new data every second. Within a year, there would be 44 trillion gigabytes of data accumulated in the world. Through the right data analysis tools, data can be utilized by businesses for decision making, optimizing business performances, studying customer trends, and delivering better products and services. There are many tools to assist the data-driven decision-making process, and choosing the right tool is a challenge for data scientists and data analysts. In this article, we will discuss the top 5 data analysis tools and techniques and some basic steps of the data analysis process. 

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Konstanz Information Miner or most commonly known as KNIME is a free and open-source data analytics, reporting, and integration platform built for analytics on a GUI-based workflow.  The first version of KNIME Analytics Platform was released in July 2006 with a mission to make data analytics available and affordable to every data scientist in the world. 

KNIME provides the following two software:

Companies such as Siemens, Novartis, Deutsche Telekom, Continental use KNime to make sense of their data and leverage meaningful insights.

The development began in 2001 with the name YALE and in 2007, the name of the software was changed to RapidMiner. It is a powerful integrated data science platform developed by the same company that performs predictive analysis and other advanced analytics like data mining, text analytics, machine learning, and visual analytics without any programming. 

RapidMiner can incorporate with any data source types, including Access, Excel, Microsoft SQL, Tera data, etc. RapidMiner provides all the technology users need to integrate, clean, and transform data before they run predictive analytics and statistical models. Users can perform nearly all of this through a simple graphical interface. 

RapidMiner can also be extended using R and Python scripts, and numerous third-party plugins are available through the company’s marketplace. However, the product is heavily optimized for its graphical interface so that analysts can prepare data and run models on their own.

Companies such as BMW, Hewlett Packard Enterprise, EZCater, Sanofi use RapidMiner for their Data Processing and Machine Learning models.

R and Python are open-source languages and are used extensively in data sciences. 

R Language is used for machine learning algorithms, linear regression, time series, statistical inference, etc. It was designed by Ross Ihaka and Robert Gentleman in 1993. It has a steep learning curve and needs some amount of working knowledge of coding. However, it is a great language when it comes to syntax and consistency.

Python is a widely-used general-purpose, high-level programming language. It was created by Guido van Rossum in 1991 and further developed by the Python Software Foundation. It is a powerful Data Analysis tool and has a great set of friendly libraries for any aspect of scientific computing. Its library Pandas was built over NumPy, which is one of the earliest libraries in Python for data science.

Companies like Facebook, Google, Twitter & Uber generally use R for behavior analysis, data visualization, semantic clustering, advertising effectiveness, and economic forecasting. Top Companies that use Python for data analysis are Spotify, Netflix, NASA, Google and CERN, and many more. 

Power BI is yet another powerful business analytics solution by Microsoft. It was originally conceived by Thierry D’Hers and Amir Netz. Initially named Project Crescent, it was later unveiled by Microsoft in 2013 as Power BI for Office 365. was first released to the general public in 2015.

Power BI comes in three versions – Desktop, Pro, and Premium. The desktop version is free for users; however, Pro and Premium are priced versions. It allows you to bring your data to life with live dashboards and reports. You can visualize your data connected to many data sources and share the outcomes across your organization.

Power BI integrates with other tools, including Microsoft Excel, so you can get up to speed quickly and work seamlessly with your existing solutions. The top companies using Power BI are Nestle, Tenneco, Ecolab, and more.

Apache Spark started as a research project at the UC Berkeley AMPLab in 2009 and was open-sourced in early 2010. It is 100% open-source, hosted at the vendor-independent Apache Software Foundation and a wide range of developers contribute to its development.

Spark Is an integrated analytics engine for Big Data processing designed for developers, researchers, and data scientists. It is a high-performance tool and works well for batch and streaming data. Learning Spark is easy, and you can use it interactively from the Scala, Python, R, and SQL shells too.

Spark can run on any platform such as Hadoop, Apache Mesos, standalone, or in the cloud. It can access diverse data sources. Uber, Slack, Shopify, and many other companies use Apache Spark for data analytics.

What Is The Data Analysis Process and Relevant Techniques?

Clustering can be explained as grouping the elements of a data set based upon their similar attributes where consequently each group is different from the other. Since there is no target variable when clustering, the method is often used to find hidden patterns in the data. The approach is also used to provide additional context to a trend or dataset.

Cluster analysis is an exploratory technique that seeks to identify structures within a dataset. The goal of cluster analysis is to sort different data points into groups or clusters that are internally homogeneous and externally heterogeneous. This means that data points within a cluster are similar to each other and dissimilar to data points in another cluster. Clustering is used to gain insight into how data is distributed in a given dataset, or as a preprocessing step for other algorithms.

For instance, if we look at it from a business perspective, in a perfect world, marketers would be able to analyze each customer separately and give them the best-personalized service. But let’s face it, with a large customer base, it is impossible to do that. That is where clustering comes in. By grouping customers into clusters based on demographics, purchasing behaviors, monetary value, or any other factor that might be relevant for your company, you will be able to immediately optimize your efforts and give your customers the best experience based on their needs.

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A cohort is a group of people who share a common characteristic during a given period. For example, students who enrolled at university in 2020 may be referred to as the 2020 cohort. Customers who purchased something from your online store via the app in December may also be considered a cohort.

The cohort analysis method uses historical data to examine and compare the characteristics of different segments. By using this methodology, it’s possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group. As a result, it helps you understand the impact of your campaigns on specific groups of customers. 

To understand, imagine you send an email campaign encouraging customers to sign up to your site. For this, you create two versions of the campaign with different designs, CTAs, and ad content. Later on, you can use cohort analysis to track the performance of the campaign for a longer time and understand which type of content is driving your customers to sign up, repurchase, or engage in other ways. 

Regression analysis is used to estimate the relationship between a set of variables. It uses historical data to understand how a dependent variable’s value is affected when one or more independent variables change or stay the same. By understanding each variable’s relationship and how they developed in the past, you can anticipate possible outcomes and make better decisions in the future.

There are many different types of regression analysis, and the model you use depends on the type of data you have for the dependent variable. For instance, you work for an e-commerce company and you want to examine the relationship between (a) how much money is spent on social media marketing and (b) sales revenue. In this case, sales revenue is your dependent variable. It is the factor you are most interested in predicting and boosting. Social media spend is your independent variable; you want to determine if it has an impact on sales and, ultimately, whether it’s worth increasing, decreasing, or keeping the same. 

Using regression analysis, you’d be able to see if there’s a relationship between the two variables. A positive correlation would imply that the more you spend on social media marketing, the more sales revenue you make. No correlation at all might suggest that social media marketing has no bearing on your sales. Understanding the relationship between these two variables would help you to make informed decisions about the social media budget going forward.

As its name suggests, the time series analysis is used to analyze a set of data points collected over a specified period. It also allows researchers to understand if variables changed during the duration of the study, how the different variables are dependent, and how did it reach the result. 

In a business context, this method is used to understand the causes of different trends and patterns to extract valuable insights. Another way of using this method is with the help of time series forecasting. Powered by predictive technologies, businesses can analyze various data sets over a duration and forecast different future events. 

When conducting time series analysis, the main patterns you’ll be looking out for in your data are:

A great example that puts time series analysis into perspective is seasonality effects on sales. By using time series forecasting to analyze sales data of a specific product over time, you can understand if sales rise in a specific period. You might see a peak in swimwear sales in summer around the same time every year. These insights allow you to predict demand and prepare production accordingly.

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When you think of data, your mind probably automatically goes to numbers and spreadsheets. Many companies overlook the value of qualitative data, but in reality, there are untold insights to be gained from what people write and say about you. So how do you go about analyzing textual data?

One highly useful qualitative technique is sentiment analysis. It belongs to the broader category of text analysis. With sentiment analysis, the goal is to interpret and classify the emotions conveyed within textual data. From a business perspective, this allows you to ascertain how your customers feel about various aspects of your brand, product, or service. 

Each with a slightly different focus, there are several different types of sentiment analysis models:

In a nutshell, sentiment analysis uses various Natural Language Processing (NLP) systems and algorithms that are trained to associate certain inputs with specific outputs. For example, the input “annoying” would be recognized and tagged as “negative”. Sentiment analysis is crucial to understanding how your customers feel about you and your products, for identifying areas for improvement, and even for averting PR disasters in real-time.

What is Data Analysis: Methods, Process and Types Explained

Analyzing the data progressively helps you stay organized. Here is a rundown of the 5 essential steps of data analysis:

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