Market Watch

How will AI/machine learning impact data management?

Using data to inform decision making is hardly a new concept, but as more and more businesses try to centre their practices around the newest revelations and insights, it’s clear that data-centric decision making is becoming a top priority. That’s why the newest technologies based on data are revolutionising the market across so many industries. The biggest player helping businesses achieve data-driven transformation is artificial intelligence (AI), the most well-known branch of which is machine learning.

Of course, artificial intelligence and machine learning have been around for a long time; the birth of the concepts pre-date the internet. What is new, and the reason AI and machine learning are so popular right now is that these technologies are more user-friendly and affordable than ever before. Modern businesses of all sizes now have a legitimate option of investing in AI and machine learning to manage their data, streamline processes and drive efficiency. Data management consultants are now regularly asked by businesses what AI systems are available, and how they can be used to benefit data management.

Greater Efficiency

As businesses grow, so does the data they manage. Whether it’s customer sales data or staff records, every business accumulates huge amounts of data that needs to be processed, stored and managed on a daily basis. Unlocking underlying trends in this data could mean the difference between a company skyrocketing and stagnation. 

But a business’ data is only as good as what manages it. Companies often struggle to make sure their data management systems can keep up with the growing tide of data they process. Overloaded systems are a drain on resources and can impact performance, and often require manual input from skilled staff to correct. As a company grows, the demand for staff hours and expertise only increases, as do the associated costs. Managing data can quickly become a very expensive task, reducing the number of resources a company can put into growth and expansion.

Artificial intelligence takes the training wheels off of data management software. AI systems can help to automate simple repeated systems without them being overloaded. Then, by learning from experience running thousands of processes at once, AI data management systems can prioritise tasks based on how much time and resources they’ll take to complete. This provides a more predictable and stable system that takes some of the risk out of data management, allowing businesses to redirect their energy away from database management and towards growth.

More Accuracy

Ask anyone who works with data and they’ll tell you attention to detail is critical in data management. One mistake can throw off a process or jeopardize a database, possibly resulting in hours of lost work. But dotting every I and crossing every T takes time, and while it may save time in the long run, it’s still something that can slow down the data insight process. The bottom line is, manual data management can achieve great results and deliver unique insights, but it also depends on hours of painstaking work, even from the most efficient data professionals.

A database managed by machine learning systems has the freedom to manage time differently, allowing for greater accuracy in a fraction of the time. Many hundreds and thousands of processes can be executed much faster than any manual data process could work. And, by learning from swathes of data, pinpointing errors becomes more efficient every day, creating more confidence in results and allowing faster more accurate insights.

Empowered Analysts

To the vast majority of people outside of the data management industry, data is really complicated. What data is, how it is used and what it can do for businesses is a mystery to most people who work at a company, even if that company depends on data to function. This is why there has traditionally been a rift between those who know how to manage data and those who don’t. When those who don’t want to use data-driven processes to generate insights into operations and potential new methods, they rely entirely on data management experts to deliver that information.

What AI and machine learning systems do is even out the playing field? When the system itself does a lot of the heavy lifting when it comes to data processing, there is less reliance on data management professionals. The learning curve is now flattened out for those who know nothing about data (except that it is crucial to business strategy planning). Of course, this doesn’t mean data management professionals are obsolete: how data is interpreted, analysed and displayed is still an important skill that every business can benefit from. But this democratization of data analysis can let more stakeholders in on the insight process, providing decision-makers with more tools to pursue growth and development.

Increase Data Access

Operational data can hold so many crucial insights that it’s important to give as many key stakeholders access. The trouble is, with manual data management processes, providing access to data and preparing it in a way that’s comprehensible can be a hugely time-intensive process. Part of the problem is that every business process generates data, so knowing which strands to pull together to understand where a certain methodology can be improved requires creativity, diligence, and a lot of trial and error.

Machine learning can do a lot of the grunt work when it comes to access and preparation. Just as AI-driven systems can make data management more accessible to those without data management expertise, they can also help to organise various streams of data into more intelligible, actionable categories. AI systems can analyse various business processes at the same time, discovering how they interact and identifying areas for streamlining or improvement. Plus, the more mundane tasks of maintaining accessible systems can be passed onto machine learning systems, freeing up some time for database administrators to focus on finding solutions to problems, rather than the problems themselves.

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