Much like any major industry, tools drive business intelligence. Without them, organizations will have a way harder time gathering data and presenting it for analysis and overall insight acquisition. What’s great about most of these tools is that they’re often comprehensive platforms, meaning they tend to cover all the bases, so to speak. 

Also, there’s no shortage of software that can seamlessly adapt its solutions to various major industries like healthcare, manufacturing, IT, and banking, to cite a few. We’ve listed down the ones that we highly recommend in 2021. These were shortlisted based on the relative popularity of the platform, customer feedback, and their ability to stand out from the competition. 

Incidentally, should you need any customization done on any of your BI endeavors, know that we at Techmango would gladly take up the project for you. More or less, we utilize most of these tools for the said purpose. 

Top 8 Business Intelligence Tools

 

Tableau

Tableau (or Tableau Desktop) caters mostly to mid-sized and larger enterprises. What makes it great is that it’s able to smoothly tailor its solutions based on the requirements of a specific industry. There’s no shortage of organizations in the manufacturing, healthcare, HR, sales, and banking industries that can vouch for its software. 

BI trends continue to point towards artificial intelligence, and part of what makes Tableau unique is that it harnesses that technology’s potential through natural language processing. This way, their Business Intelligence software actually casts aside the conventional way of making data queries. Imagine being able to describe instead what you want to look at. 

Additionally, the platform makes do without coding and makes great use of visual storytelling when collecting data, besides performing the standard forecasts and data calculations. 

Sisense

Many love the sheer versatility of Sisense’s APIs, especially if analytics is required to be embedded into specific products, web apps, and other custom tools. It’s as easy to use as it’s adaptable, too, which is a bonus most users would gladly take. What ultimately makes it stand out is its use of embedded BI, which is another rising trend in 2021, and also AI and machine learning. 

A lot of companies in marketing, IT, HR, customer service, and finance prefer Sisense for its versatility alone, and the fact that it uses a fully web-based client only seals the deal for a lot of companies in the industries mentioned. 

Microsoft Power BI

Microsoft users can continue to rely on Microsoft Power BI for data analytics either locally or on the cloud. What’s unique about this platform is that it is largely graphics-driven, allowing users to discover data and perform subsequent analytics visually rather than textually. 

The app also provides plenty of leeways for collaboration through its interactive and dynamic dashboard, while smoothly integrating other tools like Teams and Microsoft Office365. This makes publishing reports easier, and the icing on the cake is that it supports annotation of the said reports, too. It’s for these reasons why it’s preferred by more sizable companies. 

Informatica

Informatica easily stands out with the kind of ETL system it offers. The graphic user interface is just as good as most of the ones mentioned here — if not better. Another aspect we love about it is that it works well with Oracle and MySQL. 

Everything is organized, especially when it comes to the tools that you will be used for real-time data load and bulk data load. There’s a corresponding single tool for each load. The same can be said for sales management and web services. While its licenses are comparatively more expensive than most, besides that it could take a while to train your team members with it, many users can’t get enough of the said features as well as the relatively fast ETL transaction.

Amazon Redshift

SQL queries, as much as possible, should be processed quickly, and this is pretty much a guarantee with this data warehouse. Not to mention that its overall interface is very user-friendly, too. Also, organizing your data doesn’t take a backseat since table data can be sorted according to the said queries. 

The columnar way in which Amazon Redshift presents its database practically ensures quicker processing compared to most databases. The icing on the case is that Redshift connects with numerous other tools, to the point that essential steps like reporting can be entirely automated. 

Python

As far as business intelligence and Python interact, most of it is centered on descriptive analytics. Data analytics and business analytics aren’t so much just about data gathering, after all, as understanding trends. And what better way to evaluate the data you’ve acquired than when through description? 

Hence, the term descriptive analytics, for which Python opens up plenty of routes for making deeper explorations of available data. Python also relies on machine learning to perform predictive analytics and could even help businesses in the final decision-making step through prescriptive analytics. 

Apache Spark

Apache Spark has been a mainstay in data analytics and faster big data processing. Many use it as a reliable memory data engine for streaming data, SQL workloads, and, more importantly, analytics. It works in great synergy with Hadoop file systems, too, which makes it ideal for large-scale data analytics. 

It’s versatile, to say the least, and can be used on the entire spectrum of analytics being conducted by any company. A lot love its rich APIs, which can make transforming data less of a hassle. What’s equally neat is that it has efficient interoperability with Python and SQL. 

MySQL

MySQL is considered one of the most popular databases in the world exactly because it lays the foundation for every other favored BI tool mentioned here, not least of which are Power BI, Sisense, and Tableau. It even partners well with good, old Microsoft Excel, and what data analyst hasn’t relied on this to make graphs and charts in seconds?

When it comes to reports and dashboards pertaining to data, MySQL reporting is versatile enough to focus on either of two or come up with solutions to support both. MySQL, in short, continues to unlock most of the conveniences companies, big and small alike, enjoy, and for this, it definitely deserves a spot here. 

Conclusion

By and large, it’s just about learning what BI tools you need to use. Once you have a good idea about your options and already have a clear choice, it’s equally crucial to make the most of the said tool. This takes knowing intimately its features and capitalizing on the unique advantages it provides to your enterprise, assuming it’s the most suitable tool for it. 

 

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