In today’s world, modern organizations are prioritizing machine learning and data analytics. These priorities help businesses detect spending patterns, enabling companies to deliver “hyperpersonalized” products to customers online. Artificial intelligence (AI) is expected to flourish and grow within the next 10 years.
AI has the power to change the globe and could contribute up to $15 trillion to the global economy by the end of the decade. Even though many companies can see the benefit of deploying AI, few know exactly how they can get there and what is in store for the future.
This guide will explain the differences between data analytics and AI. Next, we’ll jump into how these concepts can be paired together. Data professionals can help your team make the most informed decisions by using their expertise in data analytics and AI.
Artificial intelligence and data analytics: Interconnected innovation
Artificial intelligence has been around for quite some time, but it’s only recently become more accessible to businesses, thanks in large part to technological advances and cloud-based computing. Many industries are finding value in harnessing the power of AI, but to make the most out of it, they have to understand the differences between AI and data analytics.
Artificial intelligence: In short, AI is a machine that makes assumptions. It can look at analytics and make intelligent assumptions. From there, it tests and learns from that testing, allowing it to improve continuously.
Although AI operates without human intervention, it is made possible by human engineers. AI is incredibly important because it can test and retest data faster than any human. Through testing, AI can learn to deliver micro target insights, which can drastically improve conversion rates and marketing return on investment (ROI).
Data analytics: Data analytics comes from tracking user data on websites and apps, CRM data analysis, website conversion rates, and advertising click-throughs. In particular, marketing managers have found data analytics to be a powerful tool to report results, search for a pattern, and improve their marketing approach.
Understanding these differences also gives you a better idea of where they intersect. Together, data analytics and AI automate, optimize, and catch value in ways that a human might overlook. Data analytics can be a great tool to identify areas to test through AI. By using data analytics first, you can help clarify the inputs introduced to the algorithm. Artificial intelligence can make quick assumptions off data, producing predictive analytics faster than humanly possible.
In the following sections, we’ll cover five of the many benefits of combining data analytics and AI:
- AI and data analytics can improve security
- AI can help anticipate outcomes
- Increase sales by predicting outcomes
- Cut costs with AI
- Structure data using AI
1. AI and data analytics can improve security
By analyzing real-time data, AI algorithms help detect abnormal activities and prevent a user’s data from being stolen or used in sketchy transactions. Machine-learning algorithms track a user’s behavior pattern and mark any deviation from this pattern as potential fraud. As a result, customer accounts are protected from potential issues.
In 2019, one of the most-watched technologies is blockchain. Blockchain is a secure platform that does not allow data to be modified or breached. Even though it is an industry leader, it requires complex interactions and is time-consuming. With data analytics and AI, blockchain operations can be enhanced, improving security and making it more accessible to other businesses.
Many industries can benefit from this technology. A few include:
- Finance
- Health care
- Government agencies
In addition, AI is used to protect customers’ data and financial integrity. Since the finance industry has started to use AI, it has helped protect customer data by investing more money into data-driven security systems. In this way, the industry pairs the information learned from data analytics and streamlines that data to the AI.
Around 85% of financial companies use AI. Here are more ways the finance industry is becoming safer through AI:
- Improving payment technology to help secure digital payments
- Autonomous AI-driven investment trading enables multiple transactions to occur at the same time
2. AI can help anticipate outcomes
By pairing artificial intelligence and data analytics, businesses can anticipate outcomes and modify their approach. Predictive AI technology that uses data analytics has already proven effective. The Kenso platform, which used AI software and data analytics, anticipated that the U.S. would leave the European Union during Brexit.
Business industries are also finding success by combining data analytics and machine learning. Here are two case studies:
- Fintech: Financial technology companies, otherwise known as fintech, are using AI to predict changes in the financial market based on historical data. They are using this data to speculate and adjust their approach in the market.
- Walmart: Walmart is using AI to anticipate which items are selling and struggling. The company can predict what items will be trending and highlight those items so that consumers shop at Walmart instead of their competitors.
3. Increase sales by predicting outcomes
As discussed, AI and data analytics can be paired to predict outcomes. By predicting outcomes, companies can adapt their approaches based on data analytics. In doing so, companies can increase their sales.
Sales are increased as marketing teams can:
- Automate tasks with AI
- Attract new customers through AI based on historical data
- Close more sales through targeted advertisements
AI helps to improve digital advertising, targeting advertisements to the individuals most likely to engage with it. This means that the right people will see the right ads. Ad development has gone beyond a creative endeavor and must target the correct audience to make the biggest impact. Ultimately, businesses that use AI for marketing can increase sales through targeted ads and personalization.
Nowadays, businesses need to be present on social media. AI can help generate content curated for the individuals using that platform. In doing so, marketers should potentially map out an end-to-end content strategy, which should rely mostly on AI instead of human labor.
4. Cut costs with AI
Even though it is often confused with AI, machine learning is a function of AI. It is the function that allows the AI system to learn from the information given to it. Using machine learning, AI, and data analytics help speed up business processes. This cuts down the overall time it takes to produce and sell a product, which ultimately reduces costs.
Where can you find machine learning?
- GPS navigation
- Facial recognition on your phone, tablet, or computer
- Search engine results
- Online customer support
- Smart speakers (Google Home, Alexa, etc.)
- Product recommendations
As you can see, many companies are finding ways to cut costs with AI. Erica, Bank of America’s virtual assistant, is an AI-fueled chatbot. It uses predictive analysis to transfer money between accounts, help you search the app, schedule meetings, and more. The more that users interact with Erica, the better help she can provide. Bank of America is continually improving its AI chatbot and increasing its capabilities based on data and additional requirements.
5. Structure data using AI
Analytics, although incredibly powerful, can become cumbersome when poorly organized. One of the greatest difficulties in the analytics industry is the difficulty of analyzing data sources. Preparing the data for analysis uses up 80% of the average analyst’s time, as they must manually clean data.
By automating the cleanup process of data, AI can greatly reduce the amount of time it takes to analyze data. Instead of tackling fragmented and disparate data, analysts can focus on the real tasks at hand, opening their schedules to analyze more data. The amount of time, energy, and money pumped into data analysis with AI is greatly reduced.
Put artificial intelligence to work for your team
Data analytics and AI are the future of many industries. The next step for many companies is finding the right team of individuals to analyze data and help implement AI.
Data visualization experts can help your business showcase important data tested through AI so that everyone can see and interpret this data. From there, marketing experts can use this data to understand trends. Instead of spending their time focused on targeting posts, AI will have already covered those individuals
