BI tools are thought of as the “secret sauce” for enhancement in businesses small and large. But which are the best and what can you expect when using them, especially with the new AI-flavored ones?
The growing influence of AI-driven BI tools
In the past, basic analytics and visualizations were all you could find in most business intelligence (BI) tools. Now, though, Artificial Intelligence (AI) is making these same resources smarter than ever before.
For example, algorithms run through huge datasets faster than any human could, uncovering trends or abnormalities almost instantly.
Here’s what I mean:
- Automated analytics — AI continuously scans data in real time, alerting users to significant changes or anomalies, which is invaluable in industries like finance and healthcare.
- Enhanced data visualization — AI generates context-aware visualizations, automatically selecting the most relevant chart or graph based on the data being analyzed.
- Personalized user experience — AI algorithms learn from individual user behavior, customizing the BI tool’s interface and reports to suit each of the user’s needs.
- Predictive and prescriptive insights — AI-driven BI tools can forecast future trends based on historical data and suggest actions to optimize outcomes.
Five BI tools worth trying out
While most modern BI tools are incorporating AI-based features and pushing the field further, there are some essential tools you need to check out.
Tableau is a highly rated business intelligence and data visualization tool that provides businesses with the means to access, interpret, and share valuable insights.
It’s well known for its powerful features which make it an essential part of industries such as finance or healthcare. Plus they tend to offer quite comprehensive online support resources so even if your project gets complex there’ll always be help just around the corner.
One of Tableau’s shining stars is “Ask Data,” which applies Natural Language Processing (NLP) to allow users to query data using ordinary language, just as you would ask your professional data analyst friend.
For example, a person can ask, “What were the sales last quarter?” and Tableau will produce an appropriate visualization based on the existing info, rendering intricate data analysis more open and vibrant.
2.Power BI (Microsoft)
Power BI is a collection of business analytics tools developed by Microsoft. The suite facilitates smooth combinations with other Microsoft services or products, making it an obvious pick for organizations already invested in the Microsoft universe.
Using artificial intelligence, Power BI creates visuals that automatically recognize patterns in data, making predictive analytics more accessible for business users. With its tools, everyone in your team can build machine learning models within the service and with no specialized knowledge right. This can help your organization gain better insights and make smarter decisions based on trend forecasting.
QlikView takes an associative approach which allows people to investigate their datasets freely without having to query them according to strict relationships. This way they’re able to spot correlations between multiple factors quickly and easily, empowering informed decision-making by offering the understanding of how different elements connect with one another.It’s like having a data scientist built directly into the software.
In my tour of QlikView, I found their cognitive engine to be particularly notable. There were times when it was able to suggest analyses that perfectly fit what I had in mind without me explicitly telling it so. It was almost as if the app understood my expectations based on past interactions with me.
The feature has been very effective in pushing me towards more profound knowledge and prompting me to ask more incisive questions about my data.
I’ve used many BI tools before but none compares to Sisense for its complete coverage of all aspects of managing your data.
I dig the fact that Sisense is designed to make complex data from multiple sources simple, so it’s easy for everyone in an organization to understand. One of its coolest features though has got to be anomaly detection — this tech is designed to automatically identify unusual patterns that do not conform to expected behavior. It’s particularly useful for identifying errors or significant fluctuations in datasets, which indicate a potential issue or an opportunity for further digging.
Number 5 on my list would have to be ThoughtSpot. One thing I like here is its relational search capability.
My experience with ThoughtSpot has helped make data exploration as easy and user-friendly as using Google. Its AI-driven analytics engine (SpotIQ) allows people to enter natural language queries which are then presented in the form of interactive charts and graphs.
When it comes to getting deeper insights out of your questions, like asking about sales trends for the last quarter, Spot IQ takes it one step further by showing you related patterns such as seasonal spikes or how a recent marketing campaign impacted that trend.
Flashy trend: AI tools may have been viewed initially with some angst, fearing they might eventually take over human insight from data analyst jobs, but this isn’t true! AI can detect certain correlations between figures yet only humans can provide real understanding since we give things context. In reality, these two elements should always work hand in hand: complementing each other perfectly so neither could replace the other on their own.
Things to watch out for – The overpromise of complete automation
In the world of AI-driven BI tools, automation is a buzzword that often gets stretched beyond its actual capabilities. Here are what I think are key points to watch out for and ways to avoid getting entangled in the buzzword rabbit hole:
- Misleading metrics — AI flags data spikes as positive trends, but human insight is needed to discern the context. For example, a surge in website traffic might be due to negative publicity, not a successful campaign.
- Lack of nuance — AI algorithms process data based on predefined criteria. They miss subtleties that a human analyst, with industry knowledge and contextual understanding, would catch.
- Dependency risk — Over-reliance on automation leads to a lack of in-house expertise, leaving a company vulnerable if the system encounters an issue that requires human intervention.
BI AI-driven tools – Is a Terminator-like business future near?
In the busy intersection of AI and BI tools, it’s essential to separate the wheat from the chaff. AI-driven BI tools, when used effectively, can be a powerful asset for any business, enabling data-driven decisions at an unprecedented scale.
However, they aren’t a silver bullet.
They should complement, not replace, human expertise and should be chosen based on the specific needs of the business, not the flashiness of the features. The list of BI tools I’ve presented here are the perfect starting point for your BI strategy. You should definitely check them out and see whether they are what you need or if you look beyond these to find the perfect BI fit.