Most businesses deal with data streams from multiple sources on a daily basis. This data forms the foundation for virtually every decision you make – or process you implement – for your business. Without inspecting this data, it’s almost impossible to make informed business decisions. So what use is collecting floods of data without the proper means to analyse it?
What is Data Analytics?
Before we jump into reviewing the various available data analytics tools, let’s first remind ourselves of what data analytics is and why analysis is a powerful process for all businesses.
In a nutshell, data analytics is the process of interpreting raw data so that one may discover relevant trends and draw actionable conclusions.
More specifically, for businesses, data analytics creates insights from single or multiple sources of raw data (various types of data can be used). This data is put through a cleaning process so that it’s ready to examine. Various methods of analysis are available to gain insights from data, depending on business requirements, and in order to make the most effective use of your data there are many available business data analytics tools. These tools are designed to help you bring in and collate your raw data, manage it, conduct data modelling and prepare it for analysis. We’ll review these tools later on in this article, and discuss the benefits and drawbacks of each.
What Are the Benefits of Using Data Analytics Tools?
There can only be positive outcomes to analysing business data. Regardless of whether your reports indicate a positive or negative result, analysing with the use of specific business analytics tools means you are better informed. You are therefore able to determine the most appropriate approach to address whatever you find.
However, to begin analysing data, you need it to be clean, readable and structured – this is where data analytics tools come in handy. Essentially, these tools do the following three things: 1) use a process called ETL (Extract, Transform, Load), 2) model your data, and 3) create data visualisations that are ready for analysis.
As many businesses collect and manage data from numerous data sources, ETL is the essential process which allows all of this data to be collated. The first step in this process is extracting the raw data from multiple sources – such as Microsoft Excel, Google Analytics or any existing database that you have – and storing them into a centralised data warehouse. The next step is transforming your data, also known as data cleansing. This usually involves filtering your data, applying rules so that it can be formatted into tables, sorting the data and even removing any unusable data. Data cleansing is a crucial part of the ETL process, as it ensures that all of the raw data has been reviewed and is ready to be analysed accurately. The third and final step is loading the recently cleansed data into a new location, such as a data warehouse. This is where your data analytics tool steps in to begin data modelling.
Data modelling is a process used in data analytics to define your data and its attributes, and analyse its relationship to other data types. It does this by producing a visual representation of your data and its connections to other data. Data modelling considers business requirements: for example, a data model acts as a visual guide for how your business should manage and use data to achieve your objectives. Data analysts are involved at this stage to audit these data models and pull out useful information in line with the business requirements.
Ultimately, using business data analytics tools will help you make sense of your data so that you can make better-informed decisions about various aspects of your business via the in-depth analysis you’ll have conducted. For example, analysis of sales and marketing data can help you discover new customer trends, and this knowledge is invaluable if you are an ecommerce business. Having a detailed understanding of your customers, you are now in a position to react directly and accurately to them.
Aside from marketing decisions, having all of your business data – whether that is from CRM systems, analytics tools or internal databases – stored and organised can improve your business from a financial perspective, and potentially highlight opportunities to improve the quality of the work your business produces.
Recommended Business Data Analytics Tools
As mentioned earlier, data analytics tools are invaluable when it comes to managing and reviewing data. Many are built as all-in-one-tools, meaning they are capable of bringing in data, loading your data, performing ETL, carrying out data modelling, and creating data visualisations. Some data analytics tools also have built-in connectors ready for integration with data sources (such as Microsoft Excel, Microsoft Sharepoint, Google Analytics 4 and Azure) making your life that little bit easier as an analyst!
Below we’ve listed some of the best data analytics tools out there and explained their main features, benefits and possible drawbacks.
Power Bi is a fast growing data visualisation tool in the world of data analytics. Created by Microsoft, it allows you to connect to various data sources such as Microsoft Excel or Google Analytics. Like most tools, it is able to transform that data, and then visualise it. Power Bi also has many built-in functions to analyse the data. Alternatively, you can use DAX, which is the underlying language that enables the user to create new formulas and information. As Power Bi is a Microsoft product, it can integrate with other Microsoft tools such as Power Automate, which allows the user to create a large range of automations using the underlying data. A simple example of this could be email automations that are dependent on specific dates.
Power Bi is a very versatile tool which can be utilised by beginners. However, there is a learning curve to obtain expertise when using it.
Tableau is the most established and popular tool around. It can also connect to data and transform that data and then visualise it. It provides us with a variety of practical features and functions that enable us to analyse raw data using visualisations. We can then take useful information from it. It can handle very large data sets and has very fast performance, hence its popularity amongst the larger organisations. Tableau also contains a vast library of visuals to select from that have many configuration options. As a result, it has a reputation for being able to produce visually-stunning dashboards.
However, Tableau is quite pricey in comparison to its competitors and its capabilities mean that it’s suited to larger companies. It is also not considered beginner friendly and will require an initial learning period, but once you have overcome those barriers you will have a powerful data analytics tool at your disposal.
Qlikview is another data visualisation/business intelligence tool with strengths that lie in its ability to load data efficiently and quickly. It has excellent ETL capabilities, although data loading is dependent on the RAM available in the machine being used. It also has numerous functions to be able to drill into data in different ways, as well as creating pivot tables.
Qlikview excels in allowing the user to go in-depth into their data, but its user interface severely lags behind its competitors. Knowing the language “SQL” is also a requirement if you plan on querying your data and getting the most out of Qlikview.
Whilst it is another very strong option which enables the end user to drill deep into their data, it lacks the versatility of its competition.
Google Analytics 4
Although not exclusively a data analytics tool, Google Analytics 4 (GA4) is a step closer to being one (when compared to its predecessor, Universal Analytics). Like the tools mentioned above, GA4 also provides opportunities to create interactive dashboards and custom visualisations. It uses machine learning to gain predictive insights into user behaviour on both websites and applications, allowing you to create more accurate marketing campaigns targeting specific audiences.
However, Google Analytics 4 focuses more on web analytics data than business data, so if you wish to solely gather, analyse and report on web data – such as website visitors, sales and page views – GA4 is a great data visualisation tool to use. But if your data analysis needs run beyond websites (e.g internal business data) then tools like PowerBi, Tableau and Qlikview are your preferable choices.