By 2023, it seems as if the era of Skynet has finally begun for us, albeit a few decades later than in the Terminator movies.

AI is currently taking the world by storm, with many businesses already beginning to invest in it for the future. For example, Microsoft has developed their own predictive AI search engine, while CNET seems to be experimenting with it to generate journalistic articles like a production line in a factory.

While AI has come a long way and has become a major consideration in today’s automation, we shouldn’t forget just how integrated AI was already in business prior to its current popularity surge.

With the advent of technology in the mid-2000s, artificial intelligence and big data have become integral parts of our lives. Data and artificial intelligence have been used to gain keen insights into business growth by corporations, mainly social media companies, for several years now.

Due to a number of strict regulations on AI and data use, corporations are treading very carefully when it comes to disclosing their AI and data usage, until now AI has been a rather hush-hush subject.

In this article, we discuss the various ways businesses can leverage big data and AI to gain never-before-seen insights into their business and plan for the future based on these insights. Having said that, let’s begin with the basics: what is big data and how does AI work with it?

How does Big Data work?

Here’s what SAS experts have to say about big data:

The term “big data” refers to data that is so large, fast, or complex that it is difficult or impossible to process using traditional methods.

In the information age, there is a constant influx of data into the business, of all different natures. For example, customer data, financial data, market data, and so on and so on. It is gathered from a variety of sources, including social media, customer interactions, online surveys, and transactions.

In addition, traditional data analysis methods simply fail to cope with the sheer volume and speed of data coming in thanks to the Internet of things. We used to have enough data coming in that one analyst could initiate the process of data mining and find patterns in it, which then could be applied to further application. Today, however, this job cannot be performed by humans.

Big Data and Artificial Intelligence

The problem is solved completely by Artificial Intelligence (AI). By putting artificial intelligence algorithms on the job, whose advanced machine learning capabilities and ability to sift through and categorize large volumes of raw data, we can put the waterwheel of our business in this river of data and use it to fuel our business growth.

Insights into customer behavior, market trends, and other critical aspects of businesses have been gained by combining AI and Big Data. Advanced analytics tools allow businesses to analyze large datasets in order to identify patterns and correlates that can be used to improve business processes.

Big Data and Artificial Intelligence: Gaining Business Insights

Insights into customer behavior

Every interaction a customer has with your business, no matter how small, is valuable. As a result, you can use your customer base’s data to your advantage.

As an example, you can determine what kind of products sell well and what doesn’t by looking at the shopping patterns of your customer base. Furthermore, by looking at your customer’s biographical data such as age, gender, and spending habits, you can pinpoint your exact target audience, and tailor both your products and your customer experience to best suit your primary customer base.

For example, if you run a clothing brand, and see that most of your customers are female and your male customer base is significantly smaller. You can augment your business strategy to reduce the inventory you carry on male clothing, or hire more female sales staff to make a friendlier environment for your primary customer base. Furthermore, if you see that the average spending range of your customer base is for example $400, then you will adjust your pricing to be more appealing to your target demographic.

With advanced AI and machine learning-based techniques such as data mining and predictive modeling, you can gain a better understanding of your customer base and develop business strategies accordingly.

Insights into marketing

By analyzing customer data, businesses can identify the most effective marketing strategies, campaigns, and channels to reach their target audience.

As an example, if your company engages with customers through various social media channels like Facebook, Twitter, Instagram, and so on, you can combine all of that engagement data through processes such as data integration to get a single unified view of your customer demographics and preferences.

After that, you can segment your entire customer base based on various criteria, such as behavior, preferences, and other factors, to target specific groups with personalized marketing messages.

Insights into the financial world

The data mined and analyzed via data mining, data warehousing, and other data science methods can be put to amazing use in finance departments.

Data banks of the business can provide almost any information you need about your business, from its health to its prospects. On their own, this data is meaningless, but in the hands of competent bookkeeping accounting services usa and professionals who can interpret this data and tell you exactly how the business is doing and what steps need to be taken in the future, AI can be quite a powerful tool.

In addition, businesses can visualize their estimated financial future based on historical data by using predictive modeling and data visualization software.

Businesses can gain insights into market trends, investment opportunities, and potential areas of risk by analyzing large datasets.

Insights into business processes

Looking at your business’ workflow and identifying what isn’t a problem is very difficult. Especially in medium-to-large-sized businesses, where multiple departments all work in tandem, optimizing business processes can be a massive headache due to the interconnectedness of it all. AI and machine learning, however, make it very much more feasible.

A business analyst can determine where things are taking longer to happen, or what processes seem redundant or mishandled by observing your organization’s work patterns across the business. Suppose you’re looking at your supply chain and you see that your supply chain has faced regular setbacks in the past due to payments not going out on time. In that case, you can contact your AP staff and ask them what the issue has been for the delayed payments, and work your way up from there to find the root of the problem.

Through AI, you were able to consolidate and process your business data into meaningful information.

Governance of data

These insights are truly amazing, and they contribute greatly to business growth, but you should never forget the extremely sensitive nature of your company’s data.

This industry is incredibly heavily regulated. You can’t leverage any data however you want without the proper protocol and operation measures. Therefore, data governance must be enforced throughout the company.

In simple terms, data governance is the collection of policies, rules, and regulations that an organization enforces throughout their business for data security, access control, and proper handling of data.

Your data governance plan is where you decide what function gets access to what data, how that data can be modified and all interactions with that data be tracked, and how that data stays safe and in the hands of the business alone. Cyber security is important regardless of whether or not something goes wrong, so it’s important to follow best practices across the board. Expertise Accelerated publication titled “A Guide to Cyber Risk Management in Business Accounting”, while a post primarily about cyber safety in the accounting function, also goes in depth into cyber security best practices that can be applied all across the business. After all, the finance function always has the tightest data security measures in place, so why not use it as the standard for the rest of the business?

In conclusion

By leveraging AI and big data, businesses can access key business insights through data science and data analytics technology, which can spur business growth. You can use data analysis to make every business process better, whether you are in the marketing department, the finance department or the sales department. Moreover, you can even graphically forecast the business’ future based on historical trends by using predictive modeling and data visualization.

Remember that AI is not everything, and always use human input when using data insights. Computers are not flawless; they also cannot self-reflect and pick out issues logically. You always need input from humans when putting data insights to use in order to get the most out of it.