September 7, 2020

5 Data Analysis Software You Need to Know About

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Data analysis software provides businesses with an easy way to transform information into insights. The powerful tool can pull data from multiple sources together to give a full view of business events. 

With the proliferation of data analysis software, it has become more challenging to choose the right tool than it is for the tool to perform the work. So, we’ve broken down the pros and cons of popular data analysis software in the market for your consideration. 

What is Data Analysis?

Before we jump into the tools that can help your business thrive, let’s define exactly what data analysis entails. Data analysis involves processing, inspecting, cleaning, transforming and modelling data. All of these actions are performed with the same end goal of providing useful and accurate insights from data which can help businesses make better decisions. Data analysis can be carried out by connecting many data systems, generating a real-time analytical report, while also enabling trend analysis and modelling.

Data analysis covers both quantitative and qualitative research. Quantitative analysis is information that can be quantified numerically and involves understanding insights. Both work together to transform massive amounts of data into digestible bits of knowledge. 

The Types of Data Analysis Software 

Data analysis software encompasses the entire process of taking data and making it useful. This involves statistics and analytics. Before data can be used, it must be inspected, cleaned and validated. Then, it’s applied to a model, analysed and transformed into a visual representation for humans to be able to better understand and attribute in their use cases. 

As such, data analysis software like SolveXia must be masterfully designed to cover all the bases of data analysis. Software tools consist of these capabilities: 

  • Data acquisition: The ability to move data from sources to the tool via import. Acquiring the data is the first step to be able to analyse it through the use of query builder. The query builder will then automatically match the necessary data and save the end-user valuable time. 
  • Data clean up: You wouldn’t ever want to draw insights from misinformation. This is why data clean up is essential. The software will make it easier to clean and unify data so that analysis is accurate. For example, it can consolidate categories from various sources into one tab. The software can easily spot duplicates and highlight gaps in data. 
  • Data visualisation: Some patterns and trends can be discovered through data analysis. The data analysis tool makes it easy to explore and understand data through graphical displays, reports and dashboards. 
  • Basic data analysis: Software tools incorporate statistical analysis and modelling tools so that you can generate useful information from the raw data. You can toy with the summaries to create your columns of interest and automatically view graphs according to your specification of what you’re trying to measure. 


Data Analytics Methods

As alluded to before, data analytics stems from two methods, namely, qualitative and quantitative analysis. Here’s how you can glean information via each technique: 

  • Qualitative: This is information that is informed by understanding insights that are not numerical. For example, it can be gained from interviews and observations. 
  • Quantitative: As several heavy methods, quantitative data is more mathematical and scientific by nature. It can be obtained via surveys and experiments. 

Data Analytics Process

No matter what kind of data analysis software you choose, the process remains standardised in the sense that it covers the following necessary steps. 

  • Define Your Needs: Before you analyse any data, you want to specify what questions you’re looking to answer. The need typically originates from a business challenge or problem. Once you know what you’re looking to solve, you can define what type of information you need and how you will get the data in the first place. 
  • Collect Data: Here, you will start to collect the data you need to answer the question. Data collection can come from many sources. Primary sources are internal sources and can come from marketing automation tools and CRM tools. These sources may have information about sales, customers, finances, plus more. Secondary, or external sources, can be structured or unstructured data that can be gained from social media, interviews, open sources and more. 
  • Verify Data Quality: When you pull together all your data from various sources, your data analysis software and the team can ensure that the data is clean and useful. Not all information is created equally, so it’s necessary to remove duplicate data and anything inconsistent which can skew the analysis. This is the step that most data analysts spend the majority of their time in—as such, having a data software tool to help can significantly maximise productivity and allow your team to focus on analysis rather than data cleaning. 
  • Define the model: Here’s where you define the model which you’ll apply to the data. Different types of analysis can be used. 
  • Training model: You will apply a training dataset to train the model. This is a crucial step to determine the efficacy of the model on your data. The output values will not be incorporated into the algorithm. Instead, you’ll gather predictions based on the trained model from the inputs of the test dataset. Then, you’ll compare them to the withheld output values of the test set. This way, you can measure the performance for the model on the test dataset and estimate if the algorithm is trained to make predictions on unknown data. 
  • Run the model: Once you verify the model is well-tuned for the task at hand, you can run the model iteratively and repetitively for any dataset. You will leverage past data to predict the future through data analysis software. 

Data Analysis vs Data Mining vs Data Modeling

When it comes to data analysis, you’ll run into a lot of technical terms. It’s useful to understand their nuances and purposes when looking for the right analysis software to use for your business. Take a look:

  • Data analysis: This is what’s performed to answer specific questions. It consists of a variety of techniques, much like business analytics does. 
  • Data mining: Data mining is about finding patterns in data. It applies computational and mathematical algorithms to existing data to generate new data. 
  • Data modelling: Data modelling is how you manage your data. It consists of methodologies. For data modelling to work, data analysis must first be run. 

What Does Data Analysis Software Do?

Put, data analysis software does the heavy lifting and automates the process of transforming data into useful insights. More technically, data software performs the following:

  • Provides the necessary tools to conduct qualitative and quantitative analysis 
  • Applies statistical and analytical capabilities for decision-making 
  • Processes and transforms information to analyse correlations between datasets 
  • Maximises your team’s efficiency and allows your human resources to focus on high-level tasks

5 Data Analysis Software Tools 

You’re probably here because you are looking for a data analysis software tool that fits your business needs. While there are plenty on the market, here’s a quick overview of five well-known tools, along with their pros and cons. 

SolveXia: 

SolveXia is a powerful human analytical automation software. It allows for teams to standardise and automate business processes and run models for analysis with the click of a button. The tool provides you with an out-of-the-box library of drag and drops options to automate workflows. It also pulls data from multiple datasets, including legacy systems and stores it in a centralised and secure location using bank-grade security on the cloud. 

You can also easily set up scheduled reports that will automatically be sent to the necessary stakeholders. Furthermore, you can access real-time data through dashboards. As such, you can create access controls and allow teams to utilise the software according to their needs to glean insights and make rapid and smart business decisions. It’s also great for reconciliations and is built for finance to deliver more high-value insights across the business and free up their time from manual low-value tasks.

Microsoft BI: 

Microsoft BI is a robust tool that incorporates data science and visualisation. On the upside, you can start using it for free to create dashboards, reports and datasets. Once you need a team to access the same data, then you can choose on-premise or cloud hosting. The fees are generally low cost for the number of capabilities it offers. Making visualisations from data is user-friendly, and there is an integration with Excel. You can also customise data visualisation as desired. However, there are some limitations to formulas, and it can quickly become hard to work with when you’re incorporating your coding. 

Tableau: 

Tableau is regarded as a leader in data visualisation. Their system provides organisations with easy-to-read dashboards of insights which is much easier to use than a spreadsheet. The system can handle massive amounts of data with no issue and can incorporate other coding languages if need be. On the downsides, Tableau doesn’t offer a simple way to refresh reports automatically or to schedule them. Additionally, you can’t customise the formatting of data fields, and you’ll need another tool to clean the data before Tableau can be used for visualisation. 

Hubspot: 

As one of the most widely-known marketing automation tools, Hubspot can help your business monitor website traffic and drive conversions through its analysis capabilities. Before visitors buy on your site, the tool can spot trends and provide you with necessary insights to maximise conversions. Most users state that the software is easy to use and does a great job at tracking KPIs and automating workflows. On the downside, many features can cause overwhelm, and the price point isn’t exactly cheap for small businesses. 

Oracle: 

Some of the most widely held views about Oracle is that it is powerful and safe. Oracle’s database is secure and compliant through encryption. It’s easy to scale for large datasets, and the organisation provides good technical support. It works in the cloud so user access can be gained from any location. However, licenses are expensive and unstructured data has been reported to be hard to use. 

The Bottom Line 

Data analysis software has become an integral part of organisations in every industry. With the sheer amount of growing data, it’s only useful when it can be collected, stored, transformed and analysed to make quality business decisions. Data analysis software helps your team conduct analysis and visualise data in easy-to-read reports and dashboards, making your data worthy. 

When choosing between data analysis software tools like SolveXia, its important to fully understand your requirements, needs, and pain points and make sure the tool meets all of these

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