Data analytics is definitely one of the most important elements of your company. Thanks to data analytics, you can spot new business opportunities, achieve improved operational efficiency, conduct more effective marketing campaigns, and provide customers with high-quality customer service. In this article, we are going to talk about how data analysis helps your company and how it can be tweaked with artificial intelligence. Let’s get right to it!
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Every company should be focused not just on making as much money as possible but also on making the most of the data it possesses. We live in a dynamic data-driven world and, today, data analytics is your shortest and most straightforward way to achieve every goal you set for your business. Data is everywhere, and we’re pretty sure your company gathers and processes more data than you’d imagine.
Let’s take a look at a few examples:
- Website data that can be accessed via Google Analytics tells you a lot about your customers, their interests, and behaviors. Furthermore, the same source shows you which one of your marketing channels is the most effective one.
- Social media data provides you with even more information concerning your customers’ interests and opinions about your products/services. You can use this knowledge to improve the way your company works and serves customers.
- Financial data provides you with vital information on your company’s performance and results. This way, you can assess the condition of your business and decide to implement necessary investments or corrective actions.
- Market data gives you priceless insight into market trends, fluctuations, and even potential business opportunities.
In fact, you can pick any business area. We bet that data plays a significant role in it. And that’s, shortly put, why you need data analysis. It’s a comprehensive service that allows you to make the most of the data you possess (or can possess) in your organization.
The role of data analytics
We could say that data analytics guides you through the intricacies of big data in your company. Given that your data is cleaned and organized, you’re on an open course to using it in a beneficial and effective way. Data preparation is one of the major prerequisites concerning data analytics, but that’s a story for a different time. In most instances, companies all over the world concentrate on data in the following areas:
- Customers, customer service
- Digital marketing campaigns
- Finances, investment, and cash flow
- Human resources and workforce management
- Operational costs and procedures
It all comes to one thing–better results. They can revolve around making more money, saving time or resources, improving management, enhancing customer service, etc. When it comes to data, your every action has to be purposeful.
Two approaches to data analysis
Data analytics can look differently, depending on your objectives and the data you process. In most instances, though, it’s all about analyzing appropriate data sources and looking for the information necessary to make a specific decision or to achieve a particular goal.
Data analytics can be done internally, with a team of data scientists and adequate software. It can also be outsourced to an external data analytics company just like Addepto. Both these solutions have pros and cons. If you decide to go with an in-house model, you have to be prepared for some serious investments and, frequently, adjustments as well. In the outsourcing model, it’s far easier. You just go to a selected company, discuss what needs to be done, and you let these experts do their job.
Different kinds of data analytics
To make matters even more complicated, we should mention that data analytics is not a uniform, universal solution. There are at least four different types of data analysis. The majority of them use artificial intelligence in order to improve the outcome, but we will talk about that in a minute.
Now, depending on your goal and current situation, you can decide to implement the following types of data analytics:
It’s the most basic form of data analysis. It’s all about finding the best option/scenario. If you’re facing a complex business decision, there are many variables to consider, and you want to find the best solution to your problem or challenge – go with prescriptive analytics. This service will also provide you with the necessary input to mitigate potential risks.
Here, your goal is to understand what happens within your company and market. Descriptive analytics is based on spotting and analyzing trends, customer behavior (and its changes), and other elements that have a significant influence on your company.
It’s by far the most complex version of data analytics. Predictive analytics is used to predict (hence the name) future outcomes or scenarios. Companies use predictive analytics to assess future sales levels, potential crises, and other critical changes that can adversely affect the entire organization.
It’s a back-looking form of data analytics. You can use it to understand why something happened in the past. Opt for diagnostic analytics if you’ve experienced some crisis or drop in the past and don’t fully understand why it happened. Surely, this service will provide you with vital insight concerning your past activity.
As you can see, data analysis can be used to analyze different critical areas of your business. However, due to the complex and voluminous nature of big data, exploring it without the support coming from artificial intelligence can be troublesome, to say the least. In many instances, it’s straight-up impossible. That’s why today, the vast majority of data analytics is fueled by AI-based algorithms. And that’s what we are going to talk about now.
How AI is used to improve data analysis
As you know from our other blog posts, artificial intelligence is all about doing jobs that in the past were reserved for humans. Thanks to its ability to learn and process gigabytes of data per second, AI has whatever it takes to improve the outcomes you receive from data analytics. Artificial intelligence makes data analytics more effective, more accurate, and, above all, faster.
When it comes to AI in data analytics, we have to mention three crucial technologies:
- Machine Learning: It’s a technology that allows intelligent algorithms to learn from data, especially in order to uncover trends and patterns and to improve the way these systems work.
- NLP: This technology supports understanding human language, primarily in the textual form, in order to obtain useful insights from different forms of texts, messages, and documents.
- Business intelligence: BI is a higher level of traditional data analytics. It’s all about making accurate and informed decisions, primarily when it comes to managing a whole company.
However, let’s be more specific. Now, we will analyze several aspects of how AI improves data analysis:
NLP supports text analysis
Natural language processing is a tremendous AI-related technology that allows you to analyze textual data and get more meaningful insights from it. Thanks to NLP, algorithms analyzing your texts can understand their content more thoroughly. Therefore, you’re no longer limited to getting quantifiable data like statistics and numbers. You can also get answers to qualitative questions, such as why and how something happened. NLP-fueled text analysis is frequently used in something called sentiment analysis. Companies use this solution to get more information on what their customers really think about their brand and products.
And since we’ve already mentioned sentiment analysis, let’s explain shortly how this technology works. You always start with a piece of information. It can be a social media post or comment published by one of your customers. Sentiment analysis algorithms scan this comment, analyze its content, and, thanks to NLP, provide you with accurate information, whether it’s a positive, negative or neutral opinion about your brand. And yes, someone might say: It’s nothing difficult to assess that. Why would I need an algorithm to help me with that? Large companies get thousands of comments each day. Analyzing all of them manually would be troublesome, to say the least. Instead, these algorithms can do that instantly and automatically, providing you with real-time data concerning your company’s image online.
Sentiment analysis algorithms use not just NLP but frequently machine learning as well, primarily in order to improve the quality of their assessments.
Granted, this facet of data analytics is similar to text analysis. Here, however, the goal is slightly different. Earlier, we tried to estimate the intent behind each piece of content. Here, we focus on the topic or theme behind each piece of content. Text classification algorithms can quickly and automatically assign various texts and messages received by your company to specific groups and categories. Suppose you’ve just received information about a new comment published on Twitter concerning your SaaS platform.
The user said, “Configuration was more difficult than I expected”. What should you do with such a message? Leave it on its own? Respond? Contact this user directly? Consider it a complaint or just a piece of neutral information? As humans, we can assess that quite quickly, especially when we know the company’s policy in such a situation. But again, if you had to analyze hundreds of such messages daily, it would be close to impossible in the long run. That’s why you need artificial intelligence for data analytics: In order to automate this process altogether.
In the same way, text classification algorithms can quickly transfer each message and each text to a specified department or team in your company so that nothing slips unnoticed. When it comes to your image and crisis management, it’s vital facilitation of work!
Thanks to technologies like OCR (optical character recognition), you can extract specific texts and keywords from your documents. This technology comes in handy when you have printed documents that you want to digitize. With OCR, you can scan them and almost instantly convert them to editable digital files. Text extraction comes in handy also when you need to find specific keywords or pieces of data in your documents.
As we’ve already mentioned, predictive analytics is based almost entirely on artificial intelligence. Today, predictions play a critical role in business. They help you make more informed decisions and plan the company’s development with confidence. Predictive analytics makes a case for itself also concerning various scenarios and outcomes. Below, we show you a couple of areas in which predictive analytics come in handy:
- HR: Here, we talk primarily about selecting the best candidates, assessing employee performance, and planning the most effective training programs
- Finances: You can use predictive analytics to estimate future sales levels and the financial condition of your company
- Logistics: Route optimization, reduction of transportation costs, but also drivers’ performance evaluation
- Customer service: Predictive analytics can help you in analyzing and predicting CLV (customer lifetime value), and customer churn levels
At this point, we ought to mention something called decision support systems. These are digital information systems that are designed to help you in preparing data for decision-making purposes. DSS often uses predictive analytics to help you estimate the consequences of various decisions, primarily on the general business level.
Predictive analytics plays a critical part in business intelligence as well. BI helps you manage and develop your company in a more informed and accurate way. This includes estimating the results of specific business decisions. Thanks to big data analytics supported with machine learning, you can use this technology to uncover patterns and deviations in data and use this knowledge for your company’s good.
How can chatbots come in handy when it comes to data analytics? These conversational AI tools are frequently backed with machine learning features, meaning they learn themselves from each conversation. You can analyze conversations your chatbot had with customers to improve your customer service and get more thorough knowledge of what your customers are usually interested in, what problems they have, and what questions they need to be answered.
With this input, you can adjust your other customer-service-related processes to enhance the overall quality of customer service in your organization.
Possibly, that’s one of the most interesting aspects of artificial intelligence for data analytics. Thanks to machine learning, AI-powered data analytics systems can work automatically, uncover hidden trends and patterns in data and analyze information coming to your company in real time. With this automation, your managers and employees can work more effectively and make more informed decisions, primarily because they have instant access to all the information they need at a specific time. We could even say that thanks to AI, data analytics becomes more accessible, even for less tech-savvy users.
Of course, you can automate more aspects of data analytics:
- Reporting and data visualization: Today, almost every decent BI platform comes with built-in reporting and visualization features. As a result, you can present data to your clients and stakeholders in a legible and straightforward way. Today, you don’t have to be a data scientist to understand the outcome of their work.
- Data collection: Usually, data in a company is dispersed over different files and sources. With AI, you can automate this process and thus, save a lot of time.
- Cleaning data: Cleaning and organizing unprepared data can also take a lot of time. Thankfully, modern AI-powered algorithms are capable of cleaning your data all on their own.
Data analysis tools
When it comes to artificial intelligence for data analytics, you have two options. You can opt for designing an entirely new system that’s fully adjusted to your needs and requirements. Or you can use a ready-made tool that will support the vast majority of tasks you set for it. If you decide to go with the second option, we invite you to take a look at these tools:
- Qlik Sense
- SAS BI
- Google Data Studio
The role of Addepto
We realize that this whole data analysis and artificial intelligence subject can be difficult for many companies, especially if so far you’ve never used either of these technologies. That’s why we’re here to help. Addepto is an AI and big data consulting company. Our role is to help our clients make the most of the data they possess.
We will gladly help you even if you’re not fully certain what can be achieved with your data and what for. Our role is to guide you through the entire process, from selecting the best solution to implementing it and maintaining it for future purposes. If you’d like to find out more about our work, just go to the contact section and feel free to drop us a line. We are at your service!