Data Analytics Tools and Their Application to the Analytics Process
There are so many data analytics tools available itâ€™s hard to know what their applications are and how they help data analysts. Itâ€™s easy to feel overwhelmed, even if youâ€™re technically literate and an active participant in the analytics community. However, when it comes down to it, most of these data analytics tools exist to facilitate one or more areas of the analytic process itself. In other words, if you break down the process of data analytics, each step of that process has a variety of tools associated with it that can help with the task at hand in some way. Here are a few examples of some of the data analytics tools that exist and how they apply to the analytics process.
Open Source Tools
Open source tools exist to help managers and project directors handle data-related tasks without having to spend a lot of money or go through years of training in order to do it. Some of these tools help to transform or clean data, others feel more like spreadsheet applications, and still more offer multiple functionalities.
Data integration programs also exist which help enterprises and large-scale organizations carry out cloud storage, data management, and Big Data tasks, just to name a few. Accessibility and the facilitation of real-time decision making are key features of many of these programs, and their open-source nature means that theyâ€™re beneficial to the bottom line.
Open-source programs also tend to have large communities surrounding them full of experts and helpful users who are happy to assist those who might not understand all the functionalities of a particular product.
Since open-source describes accessibility and not functionality, any of the types of data analytics tools weâ€™ll discuss in this article could, and are likely to, have open-source options. While most of the powerful features youâ€™ll need in order to carry out large-scale analytics can only be found in paid versions, open-source tools can provide a low-risk entry point for those seeking more information about what some of these tools can do, and some of them are quite capable of handling almost any task you throw at it.
Data Visualization Tools
Data visualization tools help users create graphical representations of the data they contain. Itâ€™s very important that anyone who uses and presents data has a data visualization tool in their arsenal. While there are plenty of data analytics tools that can help you find, clean, and manipulate data, no toolbox is complete without a way to visualize it. Some of these tools help you create dashboards that have interactive components so that people who arenâ€™t technically literate can still find, use, and benefit from the data your organization has. Others can help you create heat maps, tables, and charts so you can give powerful presentations with clear and convincing images that tell the story of your data. No matter what kind of data you have, how you analyzed it, or who youâ€™re presenting it to, you must be able to seal the deal with high-quality graphics that are accurate, persuasive, and perfectly matched to the technical literacy of those youâ€™re trying to influence or educate.
Data Extraction Tools
Another critical component of your collection of data analytics tools is a data extraction tool. Web-scraping functionality, especially at an enterprise level, is very important. Some data extraction tools even allow you to create stand-alone agents that you can market royalty free. Other tools are more visual which can be a benefit to those who donâ€™t have as much experience in the technical sphere.
There may be times that you need to extract mass amounts of information from sites in bulk. In these situations, most people donâ€™t want to spend hours coding that task, even if theyâ€™re able to, so these data extraction tools that focus on visual interactivity can help with that. You might also need a web-scraping tool that offers you various types of information, such as price listings, statistics, historical data, and reviews, and some of these tools are built with that purpose in mind.
Regardless of which tool you use or how you use it, if you intend to work with data and transition your organization to a data-focused entity, youâ€™re going to need a data extraction tool at some point. Thereâ€™s no way that you could manually scrape the web the way that these tools can, and even if you could you would utilize so many human resources that it would be cost-prohibitive. Thatâ€™s why data extraction tools exist.
Anybody whoâ€™s in business needs to keep their finger on the pulse of their customer base. Whether itâ€™s patients using a medication you sell, members at a gym you operate, or people who see you for medical care at your private practice, if youâ€™re in the business of healthcare administration in any capacity, you need to know what those you serve think of how youâ€™re doing. But how can you do that? You donâ€™t have hours a day to go through millions of social media pages and forums or months to conduct in-depth surveys. Thatâ€™s exactly why sentiment tools were created.
Some tools focus more on tweets, comments, texts, and other sentiment-related media from clients and patients. These data analytics tools will then determine, based on the data they collect, what an organization can do to help improve sentiment among their users, patients, or customers. Itâ€™s important to be able to spot trends, identify patterns, and determine areas in which you could improve your offerings.
Since most customer bases exist on social media and in the comments sections of blog posts, thatâ€™s where most sentiment tools focus their attention. However, more advanced tools also utilize a combination of statistical and linguistic rules to identify a spectrum of sentiments and can rank various words based on their positivity, neutrality, or negativity. They can define multi-level taxonomies that pull a sentiment summary from larger documents based on an analysis of the linguistic rules used within it. Whether or not you need that level of analysis, it can be fascinating to learn just how sophisticated some of these data analytics tools truly are.
Data Wrangling Tools
Data wrangling is the process of taming, or cleaning, data that exists in its raw form. When data analysts â€œwrangleâ€ data theyâ€™re looking for a variety of things. For example, data might be incomplete, in which case it serves very little use. Other sources of data might be corrupt, incorrect, outdated, or improperly formatted. All of these errors can drastically shift the overall analysis of the dataset as a whole, especially when working with Big Data, which is common in healthcare data analytics.
Thatâ€™s why having a data wrangling tool is so important. Instead of having to physically comb through inordinate amounts of data, an analyst can simply code or program a data wrangling tool to filter and either save or eliminate data sources that have certain characteristics.
This process can even run in the background on some applications, leaving the analyst free to conduct other tasks, such as creating algorithms, while the data wrangling tool tames the beast of Big Data in the background. Some manual checking for accuracy might still need to take place, but it will be substantially less than it would have been without the help of the data wrangling tool.
You might not find yourself in a position where you need data analytics tools in every single category mentioned above, but itâ€™s nearly impossible to conduct analytics without having at least a few. Every application that exists seeks to help analysts do their jobs faster, more accurately, and more automatically so that the areas of the analytics process that require human direction donâ€™t have to wait so long to be completed. Itâ€™s worth checking out the available offerings in each area mentioned above just to see if they might be helpful to your organization. Whatâ€™s more, there are multi-disciplinary tools that can help you with several of the above-mentioned tasks at once. Do some research and see how you might be able to facilitate more efficient data analytics by bringing one or a few of these tools on board.