The Future of AI in Helping Businesses Manage Enterprise Data

In Blogfest-en by Baufest

There are many challenges when it comes to managing enterprise data. In this data-driven world, many businesses are grappling with issues of poor-quality data, security vulnerabilities, inefficiency, and slow data ingestion times.

Saturday 14 - May - 2022

So, how can we solve these common issues?

Answer: Applied Artificial Intelligence.

In this article, we will go over how AI has been improving how businesses manage their enterprise data and how that could help you in the future.

Security With Fraud Detection

With great data comes great responsibility. Oftentimes, you will have sensitive data you’ll need to protect to maintain client trust and business reputation. AI can help secure your data by flagging abnormal data as possible threats by cross-referencing from different databases. This puts less burden on cybersecurity teams that cannot detect all threats on their own.

Machine Learning models can be trained on historical data to detect pattern disruptions, thus minimizing security risks. Database anomaly detection tools like SolarWinds use machine learning to baseline normal data patterns so that when it comes across patterns outside the norm, it can automatically trigger a response. Tools like this allow security teams to manage all data across the enterprise more easily and with more trust.

Data Ingestion With Computer Vision and NLP

Computer Vision, a subset of the AI field, can be used to ingest data faster. With the influx of real-time data, the pressure is on for companies to ingest and utilize as much data as they can. For organizations that ingest digital text data, ingesting and categorizing may be easier. Still, others might need to ingest physical forms or digital images that require scanning, interpreting, and manual categorizing. This is where computer vision comes in.

Computer Vision was an integral player in analyzing the 2021 Argentina primary election telegrams when it was used to screen 130,000 telegrams to detect mistakes and missing data. A task that would have taken more time and labor if done by human workers. But with the help of machines, this task was done more efficiently and accurately.

Natural Language Processing (NLP) has also been used to automatically detect the context of incoming data and label them quickly to fasten the feedback of application development. NLP with the combination of ML has helped mobile apps analyze large amounts of customer reviews to give important feedback to developers and businesses to improve their services to meet their customer’s needs.

Risk Management

Another use of AI to manage data comes in determining risk. AI can be used to assess the risk of an action based on enterprise data, also called risk scoring. Credit scoring is an example of this, where AI uses data features other than credit scores to estimate a borrower’s risk for banks. By learning from data on past behaviors, purchases, repayments, types of items bought, and many other factors, a model can predict a borrower’s future risk.

From this angle, AI and ML help businesses mitigate risk and make the most of their data by collecting and analyzing data faster than any human can. It can also be used to detect early warning signs of risky operational behaviors based on internal data. Similar to fraud detection, AI’s ability to identify historical patterns in old data allows it to detect abnormal behaviors in current data, which can trigger an automatic warning that data engineers or analysts can see and dig into deeper.

Better Data Analytics, Better Business Decisions

AI can be used to analyze large amounts of data to discover new patterns or insights that humans may not think to look for. Machine Learning models like Kmeans and statistical models like Breush-Godfrey or Negative Binomial Distribution can be used in combination to identify influential features and build prediction models to convey different interpretations of data. And the better the data insights are, the more businesses can utilize them to make better decisions.

A prime example of this is Baufest’s case study with an international travel assistance company that was required to quantify a customer’s potential lifetime value so they could plan their marketing strategies effectively around it. Machine Learning was used to build prediction models to estimate customer value based on their current data.

Data-Driven Customer Behavior Predictions

Part of managing your data is being able to use it. Fast ingestion, fast processing, and fast usage are important. AI can be used to utilize your enterprise data for business needs, such as predicting your customers’ future behaviors that could greatly impact your business.

You can build ML prediction models to predict what a customer might buy and how many will remain with you during a big reorganization, or which customers will remain loyal to the brand for years to come. These insights can be used to understand what specific factors drive customer engagement and allow the business to lean into factors they might have overlooked before.

This also leads to greater insights into customer purchasing behavior for eCommerce businesses that use various ML algorithms like collaborative or content-based filtering to build recommendation engines to recommend products they have predicted customers would want to buy in the future. A great example of how AI can not only help manage your data or use it to grow your business.

Robotic Process Automation for Repetitive Tasks

Although technically not directly under the AI umbrella, Robotic Process Automation (RPA) is still a powerful ally to AI and, thus, worth mentioning. RPA is any software that mimics human actions. Unlike AI, it is not designed to “think” for itself. It is meant to record a task done by a human and then repeat that task exactly as seen. RPA allows workers to give repetitive, tedious tasks to machines which frees them to solve more complex problems.

RPA is so good at efficiency that it has helped companies like Claro, a telecommunications service company, to quickly update customers’ tax information and automate company initiatives.

Want to Learn More?

The spread of AI reaches all areas of data management and utilization. It increases operational efficiency, reduces risk, and supports business growth, and that is just a taste of what is possible for AI now and well into the future.

So the real question is, how will you apply these AI technologies to support your business endeavors?

To learn more about this topic, you can book a discovery session with our solution experts to see what tools you can use to manage your enterprise data based on your specific needs.