Data analytics software can help you gain insights from your data, improve your decision-making, and optimize your business performance. But with so many options available, how can you choose the right one for your needs? Here are some research skills that can help you find the best data analytics software for your business.
Before you start looking for data analytics software, you need to have a clear idea of what you want to achieve with it. What are the main questions you want to answer with your data? What are the key metrics you want to track and improve? What are the specific features and functions you need from the software? By defining your goals, you can narrow down your options and focus on the ones that match your requirements.
Set some targets for what you want to get out of the software. Make a checklist and assess different available softwares against the checklist and choose your best fit.
Once you have a list of potential data analytics software, you need to compare them based on various criteria. Some of the common factors to consider are: the price, the ease of use, the scalability, the compatibility, the security, the support, and the reviews. You can use online tools, such as comparison websites, feature matrices, or user ratings, to help you evaluate different options. You can also request demos, trials, or case studies from the vendors to see how the software works in practice.
An active user base is one of the most important factors. When you're learning a new program, chances are that someone has already had the same hangups, and you need to be able to rely on that forum being easy to find. Some pricier options also have formalized online help files that include some of the rationale behind different approaches which can be accessed without a license. Other factors are whether your collaborators use the same program and whether your employer will pay for it.
Before you make a final decision, you need to test the software yourself and see how it fits your needs. You can use your own data or sample data sets to run some analyses and see the results. You can also ask for feedback from your team members or other stakeholders who will use the software. You should pay attention to how easy it is to access, upload, manipulate, visualize, and share data with the software. You should also check how fast, reliable, and accurate the software is.
é importante solicitar testes de aderência ao fornecedor e realizar Benchmark com parceiros. Entender como as organiza??es que já possuem o software s?o atendidas, qual o nível de satisfa??o e os problemas que foram encontrados e superados s?o assuntos que devem ser explorados pela empresa contratante. A aquisi??o de um software pode se tornar um grande problema para a empresa caso n?o sejam observadas quest?es como modalidade de atendimento do fornecedor, SLAs, qual o planejamento para evolu??o da aplica??o e escalabilidade.
Nearly every reputable software offers some type of trial. Use these to learn everything 'the brochures' don't show. How's the navigation? Are the tools in intuitive places? Do the tools offer the solutions you are looking for? Even things that you might think about like saving files and exporting file types. And definitely check your legacy data and how it imports. New software should not mean redoing data beyond mild formatting.
While free trials are always ideal, don’t overlook programs with money-back guarantees. Some companies require you to pay to play!
After you test the software, you need to evaluate the results and see how they align with your goals. You can use some criteria, such as the quality, the relevance, the usefulness, and the impact of the results, to measure the effectiveness of the software. You can also compare the results from different software and see which one provides the best insights and value for your business. You should also consider the feedback from your team members or other stakeholders and see how satisfied they are with the software.
Many software programs have a bunch of overlapping functionality. One big consideration is how easy it is to get something like regression output out of the program's default format and into something more palatable for the intended audience. Consider not only how easy it is to get results/images/graphs/figures but also how easy those items are to manipulate to display to others.
Finally, you need to review your choice and make sure it is the right one for your business. You can ask yourself some questions, such as: Does the software meet your goals and expectations? Does the software fit your budget and resources? Does the software have a good reputation and support? Does the software have any drawbacks or limitations? By reviewing your choice, you can confirm that you have made a well-informed and rational decision.
I use a combination of free and licensed software (Stata, R, MikTex, and Python). I paid for computing speed because of large sample sizes (100K-10M or so) common in my work, but that doesn't apply for everyone. For most folks, Excel probably gets the job done (small samples of a few hundred rows). R typically requires more knowledge of computer science and linear programming for advanced statistics, but it's pretty flexible for graphics. Stata is pretty easy to pick up once you know the basics.