Data Management with AI: Making Big Data Manageable

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On a global scale, people are estimated to generate 463 exabytes (one exabyte = one billion gigabytes) of data each day by 2025. To put that in context, at the start of 2020, approximately 44 zettabytes of data (one zettabyte = one trillion gigabytes) existed overall. If companies tried to shift through this data on their own, it would be an unmanageable task. But artificial intelligence (AI) makes it possible because AI models can work much faster than humans, and they don’t require breaks.

Managing vast amounts of data with AI

How AI is Transforming Business Intelligence

While humans can do most of what AI can in business intelligence, the main benefits of using AI are speed, consistency, and accuracy. “In all areas, AI enhances what humans can do alone by automating time-consuming, repetitive tasks and making sure those tasks are executed with consistency,” says David P. Mariani, Founder and CTO of AtScale.

Marani breaks this down further by explaining the ways AI supports humans in a couple of different categories of data management. “In the area of data preparation, AI can automate the matching, tagging, joining, and annotating of data,” he explains “AI can also automate data quality checks and make recommendations for improving data integrity. For business intelligence, AI can uncover hidden trends and surface insights that an ordinary human might not see. By automating segmentation and clustering and highlighting key drivers, AI can lead [humans] to insights faster.”

However, humans also have limitations that make AI better-suited for data management. AI models can work around the clock without breaks or sleep, and their processing times are consistent. Mike O’Malley, SVP of SenecaGlobal gives an example of this. “AI algorithms can analyze data sets that would take humans years or longer. Take, for example, the human genome project. Scientists started the project in 1990 and finished in 2003. It took thirteen years to create a remarkable advancement in the science of fighting genetic disease.  Data scientists using AI can now repeat that process in 24 hours.”

O’Malley also explains that managing data at scale likely wouldn’t be possible without AI. “Humans can repeat processes but to successfully scale processes requires more highly skilled trained humans, usually data scientists, which are globally in short supply.” 

The shortage of these skilled employees combined with the burnout many workers are facing in the wake of COVID-19 means expert data scientists are harder to find than ever before. While AI can’t replace these positions, it can lessen the burden on them or allow companies to gain some insights without them.

Structuring Unstructured Data

Machine learning programs like natural language processing, text analysis, and sentiment analysis take the qualitative nature of unstructured data and make it quantitative. These models crawl the text from customer reviews and social media posts and provide insights into the different types of feedback a business is getting. 

For example, AI might take a customer review that says, “The new reporting feature kept crashing at first, but customer support was very helpful and responded to me quickly,” and turn it into the following row in a table:

Mentions Customer Support? Sentiment Mentions reporting? Sentiment
Yes Positive Yes Negative

This kind of structured data is helpful after rolling out a new feature. Companies can take qualitative data from surveys or reviews asking about the new feature and find out how many people liked or disliked it. Then, they can dive into a smaller portion of unstructured data to determine what they need to fix.

Also read: Steps to Improving Your Data Architecture

More business intelligence vendors are building AI into their tools because they see the benefits that it can provide. Here are a few business intelligence tools that include artificial intelligence.


AtScale uses AI in data preparation, data science, and business intelligence to provide valuable insights without human input. According to Marani, AtScale can use “AI to create aggregate tables to accelerate performance based on end-user query behavior. This is how AtScale delivers speed of thought queries against billions of rows of data.” Live connections provide real-time access to data, giving businesses the most relevant and recent information to base forecasts and decisions on. Companies can run “what-if” analyses and use the drag-and-drop builder to create new visualization models.


Qlik uses powerful AI models to build accessible data visualizations. The interactive charts and tables automatically update as data changes or more is added. Natural language processing simplifies the querying process and helps users find what they’re looking for faster. The cloud-based platform also builds attractive and easy-to-read reports and allows companies to schedule them for automatic delivery, perfect for agencies. The mobile app is responsive and even provides offline analysis, so users can make decisions on the go.


ThoughtSpot is built with AI to deliver personalized insights for businesses. Open APIs allow developers to connect the platform with other SaaS apps for real-time data visibility. Some actions do developer work, but users with any level of expertise can use the platform to gain actionable insights. Developers can use the low-code platform to build interactive data applications, while non-technical people can use ThoughtSpot to answer queries on their own.

Actionable Insights with AI

Artificial intelligence analyzes data at scale to provide actionable insights from both structured and unstructured data. It can work faster than humans and doesn’t need breaks, providing quicker results and forecasts. If you want to make sure you’re getting the most from the data you have, you should consider using business intelligence tools that include artificial intelligence. 

Read next: Best BI Tools 2021: Business Intelligence Software

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