Big Data

Big Data and AI: A partnership for success

by
Jon Reilly
,
February 24, 2022

While good economic growth is generally pegged at a steady 2 to 4 percent, machine learning is growing at an astonishing 39.4% compound annual growth rate. Meanwhile, data creation and replication will experience a 23% CAGR

The confluence of these two exponential phenomena - big data and AI - is driving businesses forward and powering organizational transformation. Forward-looking businesses are already taking advantage of the potential of big data and AI to create new products, services, and customer experiences. While data has always been around in some form or another, it’s only recently that we’ve been able to harness its power in a way that allows us to extract value and insights at scale and in real-time.

This article explores how businesses can embrace big data and AI to create value for their customers and shareholders. We'll also explore how no-code AI platforms are making it easier for everyone to join in on the AI revolution.

What are AI and Big Data?

AI is a process that helps computers make better decisions. It is a collection of technologies that allow machines to learn and work on their own. In some cases, AI can even outperform humans in specific tasks. 

There are different types of AI, but some common applications include facial recognition, natural language processing, and predictive analytics, which are virtually always done with machine learning. These technologies are used to make sense of large data sets, identify patterns, and predict outcomes. 

AI has the potential to improve efficiency and decision-making in a number of industries. For example, it can help retailers personalize product recommendations, airlines optimize flight schedules, and banks prevent fraud. 

Big data is so large the traditional approaches of storing it, managing it and analyzing it with statistical methods are no longer feasible. Traditional data analytics proved suitable for smaller amounts of data, but big data analytics is needed for large amounts of data, including the use of AI systems. With AI, we can use computer-assisted techniques to help find patterns and insights in such data.

The 5 Vs of Big Data

The so-called 5 Vs of big data (volume, velocity, variety, veracity, and value) create unique challenges for data management and analytics. Let's highlight each "V" and how AI addresses it.

Volume: AI can help us analyze data that is too large to process manually. With Akkio, there’s no limit to volume, as you can upload or import as much data as you have. This is a vital element for AI success amidst an ongoing “data explosion.” Today’s massive amounts of data call for AI and machine learning.

Velocity: Data is coming at us faster than ever before. The growth of the internet of things (IoT) is generating huge volumes of data that need to be processed in real time. For example, Statista projections highlight that there may be over 25 billion IoT connected devices worldwide by 2030. Machine learning can help us keep up with this fast-paced data flow by using real-time data analysis and AI algorithms.

Variety: Data is becoming more diverse as we move to a world of more unstructured data. This includes data from sources like social media, images, and videos. AI can help us make sense of this unstructured data by using a combination of machine learning technologies.

Veracity: Data is only valuable if it is accurate and trustworthy. In order to ensure the veracity of big data, we need to use robust data management and analytics techniques. AI can help us validate data sets, identify errors and fraudulent activity, and correct inconsistencies.

Value: The potential value of big data is vast. With the help of AI, we can unlock this value by extracting insights and creating predictive models. For example, machine learning can be used to find patterns in data that can be used to improve decision-making.

The Insights Big Data and AI Give

There is a lot of buzz around big data and AI working together, but what can they actually achieve? Combining big data and artificial intelligence can create a powerful toolset that allows businesses to get insights they never would have otherwise.

As we've highlighted, big data is made up of large volumes of data that can be used to gain insights into customer behavior, trends, and more. And since AI can help make sense of all of that data by analyzing it and drawing conclusions, when these two technologies are combined, businesses can gain an understanding of what is going to happen and how they can improve their products and services.

Some ways that big data and AI can work together include: 

  • Human Resources
  • Financial Services
  • Sales and Marketing
  • Customer Service
  • Product Optimization

To better understand the value of big data and AI technologies working together, let's take a closer look at these three examples in a bit more detail. 

Human Resources

Amidst "the Great Resignation," HR teams have an unprecedented opportunity to collect and analyze data to better understand why employees are leaving their jobs. By analyzing employee exit data with the help of big data and AI, HR professionals can identify patterns in behavior that indicate which employees are more likely to leave and the reasons behind their departure. Armed with this knowledge, companies can work to improve their policies and procedures to retain talented workers.

For instance, if it is found that a high number of employees are leaving because of poor job satisfaction, the company could work to improve their work/life balance or increase their benefits package. Alternatively, if it is found that a large number of employees are leaving because of a lack of opportunity for growth, the company could invest in employee development programs.

While many HR teams are facing incredible difficulties in hiring and retaining good talent, the use of big data and AI provides a unique opportunity to gain valuable insights into why employees are leaving and how they can be retained, ultimately improving the company's bottom line.

Financial Services

In the financial services industry, big data and AI are being used to great effect in a number of ways. For example, machine learning is being used to streamline the process of reviewing loan applications. By analyzing a customer's credit score, repayment history and other data points, machine learning algorithms can quickly determine whether or not to approve a loan. This process used to be done by human underwriters, but with the help of big data and AI, it can now be done more quickly and efficiently.

Another way that big data and AI are being used together in the financial services industry is to predict future costs and revenue. By analyzing past data on customer spending habits, companies can get a good idea of how much revenue they can expect to bring in over the next few months or years. Similarly, by analyzing data on past fraudulent events, financial services firms can accurately detect fraud in real-time.

For instance, a company could use AI to predict that a new product will be a success and increase their marketing budget for that product. By using big data and AI together, financial services companies can make more informed decisions about their business, which ultimately leads to more successful outcomes.

Sales and Marketing

Big data and AI are also being used together to improve sales and marketing efforts. For example, businesses can use AI to score and rank leads. This is done by analyzing data on customer behavior, such as their website browsing history or the pages they have viewed on a company's website. By doing this, businesses can determine which leads are more likely to convert into customers and focus their efforts on those leads.

In addition, big data can be used to optimize content for SEO purposes. By understanding what content is most popular among customers, businesses can produce more of that content, which will then help to boost their website's ranking in search engine results pages.

Additionally, businesses can use big data to reduce cart abandonment and churn. For example, if it is found that a large number of customers are abandoning their carts on the checkout page, businesses can use big data to identify the reasons behind this behavior. Once the reasons have been identified, businesses can take steps to address them, such as by displaying relevant product recommendations or simplifying the checkout process.

By using big data and AI together, businesses can achieve better outcomes in their sales and marketing efforts. By understanding what customers want and how they behave, businesses can produce content that is more relevant and effective, which ultimately leads to more sales.

These use cases merely scratch the surface of what’s possible, as deep learning and neural networks are highly versatile technologies that can learn from many types of data, which is why today’s digital transformation initiatives focus on AI-enabled automation of decision-making processes across departments.

From healthcare to robotics to the facial recognition in smartphones, AI is truly all around us.

Customer Service

"The customer is king" is a phrase that has never been more true than it is today. In the age of the Internet, customers have more power than ever before and they are not afraid to use it. If they are not happy with a product or service, they will quickly take to social media to voice their displeasure.

The saying "the customer is always right" may not be 100% true, but it is important to remember that no matter how right or wrong the customer is, the support of your customers is the only thing that keeps your business afloat.

In fact, the adage goes one step further in Japan: "The customer is God." This saying may sound a bit exaggerated, but it underscores the importance of providing excellent customer service.

In today's world, excellent customer service is not a luxury; it is a necessity. With the help of big data and AI, businesses can provide their customers with the best possible experience by quickly resolving their issues and managing their expectations.

For example, by analyzing customer data, businesses can route customer support requests to the appropriate agent. Additionally, by analyzing ticket resolution times, businesses can identify areas where they need to improve their process or add more resources.

Ultimately, the goal of customer service is to keep the customer happy. By using big data and AI together, businesses can ensure that they are meeting this goal and providing their customers with the best possible experience.

Product optimization

The product is the bread and butter of any business. Therefore, it is essential that businesses constantly strive to improve their products and keep them up-to-date with the latest technology.

Big data and AI can be used together to improve the product development process. For example, by analyzing customer feedback, businesses can quickly identify areas where their products need improvement. Additionally, by analyzing data on customer usage, businesses can detect potential problems before they arise.

Further, big data and AI can be used to create better cost models for products. By understanding the cost of materials and labor, as well as the potential revenue from selling a product, businesses can make more informed decisions about pricing their products.

Simply put, the goal of using big data and AI to improve the product is to increase sales and boost the bottom line. By understanding what customers want and need, businesses can develop products that meet those needs and create a competitive advantage for themselves in the marketplace.

How Can You Apply AI and Big Data to Your Business?

The ubiquity of data and the advance of artificial intelligence technology means that businesses can now harness these technologies to improve performance. Akkio is a company at the forefront of using AI to automate the process of building predictive models. Akkio’s platform allows users with no coding experience to build and deploy models using a simple point-and-click interface.

Akkio’s process is 100X faster than legacy AutoML tools, while offering free model training and enabling high accuracy predictions. Business teams from a wide range of industries are already using Akkio to improve predictive modeling and decision-making. Some of these cases are highlighted below.

HR teams, for instance, can use predictive modeling to assess the risk of an employee leaving the company. By analyzing past data on employee turnover, HR teams can use Akkio to develop models that can predict which employees are most likely to leave. Armed with this information, companies can take preventative measures to keep high-risk employees from leaving.

Retailers can use Akkio to improve inventory management. Akkio can be used to analyze past sales data in order to identify trends and forecast future demand for products. This information can then be used by retailers to make informed decisions about inventory levels and product stocking.

Customer support teams can use Akkio to improve the customer experience. By analyzing past data on customer complaints, support teams can use Akkio to develop models that can predict which customers are most likely to complain. Armed with this information, companies can put in place measures to prevent or mitigate customer complaints.

All of this can be done in clicks, as Akkio handles the heavy-lifting in the background, from data integration and preparation to model tuning and deployment. For more examples of how businesses are using Akkio to apply big data and AI, please see our applications page.

How to Use Akkio to Implement AI in My Business?

Historically, businesses would need to hire computer science experts and data scientists to handle everything from data preparation and data engineering, using tools like SQL, with human intervention at every step of the process.

With Akkio, it’s a simple three-step process.

First, connect data. Akkio has connectors for a variety of data sources, including Excel, Hubspot, Salesforce, and more. Once data is connected, it is automatically cleaned and prepared for use in training a machine learning model.

Next, click the column you want to automatically build a model. This process only takes moments and does not require any coding or expertise in machine learning.

Finally, click deploy to send real-time model outputs in any setting. Akkio can be directly deployed in your business tools, from Google Sheets to big data tools like Snowflake, as well as through thousands of applications with a Zapier integration. More technically-savvy folk can use the Akkio API to deploy Akkio models in any setting they choose. Users can also train on new data at any time.

Conclusion

In a world where businesses are collecting more data than ever before, it's essential to use all of that data to create insights that give you a competitive edge. And with the help of AI, you can do just that.

AI can help you make sense of all the data you're collecting, so that you can see patterns and insights that would be impossible to find manually. And with those insights, you can take actions that will improve your business's performance.

While legacy AI tools require a certain level of data science expertise, Akkio is designed for business users. It's easy to use, so you can quickly build models for sales and marketing, HR, finance, legal, operations, and more.

Akkio offers a free trial so you can try it out for yourself. You'll be able to see how AI can help you get insights that will give you a competitive edge.

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