Key Points
- What to Look for in a Data Analysis Tool
- How to Integrate Data Analysis Tooling to Unlock Insights
- Assess your current analysis workflow for opportunities
- Work with development to ensure seamless integration
- Boost data analysis with generative AI
- Ensure you have a proper data governance strategy
- Looking Forward With Data Analysis Tools
The AI revolution is a topic of conversation across all different workplaces.
But as you may know, when it comes to data analysis, AI has already been making a critical impact.
With AI-driven tools, data analysts can reduce their manual workload while also gaining deeper, more detailed insights into everything from customer behavior to e-commerce purchases to marketing successes.
These types of tools can even help data analysts better predict and prepare for the future of their product, brand, and industry, allowing businesses to effectively forecast their trajectory.
With so many powerful data analysis tools on the market, what should you prioritize when investing in new tools for your stack?
Let’s take a look at what defines the top data analysis tools on the market, and how to integrate them into your workflow to upgrade your insights.
What to Look for in a Data Analysis Tool
AI or not, not all data analysis software is created equal.
Here are a few features to look for before making an investment in a new platform:
- Automatically processes data: AI-driven data analysis tools utilize machine learning to automatically collect, clean, organize, and analyze data. With this automation, you reduce the risk of human error, better maintain your data, and save your data analysts valuable time.
- Recognizes trends: Machine learning also recognizes patterns, changes, and strange occurrences in your data set in a way that’s much more challenging for humans to process. This information can help you pinpoint trends in your market, customer behavior, and more.
- Mitigates risks: Modern-day data analysis tools rely on predictive analytics to help recognize and handle risks before they become a strain on your company.
- Makes accurate predictions: As the name predictive analytics suggests, modern data analysis tools rely on current and historical data to help you foresee changes in your market and customers, allowing you to stay ahead of your competitors.
- Visualizes data: Today’s data analysis tools can track a great deal of information, which can be challenging for data analysts to process on their own. Ideally, your data analysis tools should have intuitive data visualizations, helping analysts to better understand the data sets.
- Scalable: If you’re in the startup phase, you might not yet be thinking ahead to the enterprise stage, but hopefully, it’s in your future. Your data analysis tools should be able to scale with you, as you’ll have to process much more data as you grow.
How to Integrate Data Analysis Tooling to Unlock Insights
1. Assess your current analysis workflow for opportunities
When deciding on a data analysis tool to integrate into your workflow, the first step is to ensure there’s a good opportunity.
Especially if you’re in an early growth stage, you don’t want to invest in too many tools, which can be a drain on your growing income. Invest in the tools that make the most sense with how your data analysis team operates.
Before making a purchase, speak with all those on your staff who will utilize this technology to ensure they believe that the tool would make sense with their current processes. Since they will be the primary users, they should be involved in the integration process.
They may have already used similar technology before, or done some research on tools themselves, so ensure that they have the room to vocalize ideas or potential issues.
2. Work with development to ensure seamless integration
Data analysis platforms work much more effectively if they’re tied into all the other systems you already use. When it comes to selecting a secure and integrable solution, You will likely need to look outside your data team to development.
And when the time comes to implement your chosen software, data analyst leadership will likely need to get involved in the sprint planning cycle to make sure the tool is effectively integrated and fully usable for the data team.
Once plugged in and the sprint review stage starts, select a few data analysts to test the new system and make sure everything is functioning perfectly.
3. Boost data analysis with generative AI
Generative AI takes data to produce new output. ChatGPT and its relative DALL-E are examples of generative AI that use a prompt to create something unique and new.
In the data analysis world, it’s this generative AI that is critical to some of the features we mentioned above, such as creating visualizations, summaries, and forecasts.
We’d recommend selecting analysis tooling that already incorporates generative capability. However, if this isn’t an option, you may need to take a cue from the last step and collaborate with your dev team on the best way to use APIs or other connectors to build a bridge between your data apps and modern genAI tooling.
4. Ensure you have a proper data governance strategy
When you collect data, you have a responsibility to your customers to protect their information.
Noncompliance with data privacy and security laws can not only result in hefty fines, but also a reduced trust in your company, which can hurt your brand for quite some time.
Beyond privacy, data governance can also help protect you from bad data.
According to a Gartner study, inaccurate or outdated data costs companies an average of $15 million per year in lost revenue. With a data governance strategy, you maintain the quality of your data through the use of tactics like a centralized data catalog, strict access controls, and the overseeing of the data by data stewards.
Looking Forward With Data Analysis Tools
When you ask data analysts to manually track and review data, you’re opening yourself up to potential human error, wasted time on repetitive tasks, and potentially squandering what you can do with your data.
With the help of data analysis tools, you can use machine learning and AI to recognize trends and patterns in your customer behavior and overall market, visualize your data for better comprehension, and prepare for economic fluctuations.
However, when you’re used to manually tracking data, integrating new data analysis tools can appear daunting. To make the process of integration even more seamless, it’s critical you learn how to successfully collaborate with your development team on iterative improvements.
You should also train your data team on the data governance policy you’ll utilize to maintain, protect, organize, and secure your customers’ information.
With these steps, you’ll create a data analysis strategy that unlocks insights for business development, marketing, sales, and beyond.
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Bio: Jacqueline Gualtieri is a writer and editor whose work has appeared in The Huffington Post, Insider, and The Slowdown. In addition to writing, she works as a digital media consultant and content marketer, driving online traffic for her clients. She’s always looking to advance her skillset and believes strongly in the early adoption of new technology.