Driving Digital Strategy – Framework Towards Becoming Digital – Part 2

Digital Solutions in Business: Distinguish Data Science and Analysis from Machine Learning (ML) and Artificial Intelligence (AI)

Not all solutions come from the same type of technology, and in this article, you will learn how to distinguish AI from machine learning and data science. Making this distinction is important for you to understand how different technologies can be used, and what you can expect from them. While AI, ML, and DA are three different domains, they are still connected. However, each has different uses.

Now, let’s explain what each of the technologies represents:

Data Science and Data Analytics

Data science is a study field that focuses on creating systems and processes for obtaining data and interpreting their meaning. Data scientists apply different principles, algorithms, applications, and tools to gather and interpret clusters of data. With the extraordinary amount of data that is currently being generated and stored, data scientists also work on data warehousing and modelling. Perhaps, the most important piece of information for you is that data science applications in business are commonly used for achieving company goals and guiding business processes. Here’s what data science can do for you:

• Business intelligence. Data analysis apps can gather information, analyze trends and patterns, and then generate reports so that you can draw conclusions. However, you will still need a business intelligence expert to help you fully understand the trends and patterns that were identified in the report—and there is plenty of responsibility for you to decide on the course of action.

• Prescriptive analysis. Have you ever wondered how data collecting and processing fits into the big picture of your business? You probably have a vague idea that it does, but without a proper plan and goal setting, you don’t really know how much time and resources to allocate for data processing. Plus, you don’t know what type of information to focus on and what to deduce based on them.

Prescriptive data analytics shows the most effective actions to take to get the most out of your data, and more importantly, how to organize data analytics to fit your business goals. With this, you get the right guidance that makes data processing productive and purposeful for your company. You can spend all the time in the world informed about the age groups of your consumers, their shopping habits, and places they visited in your online or offline store. But, if you don’t know how to use this information, your time won’t be well spent.

• Predictive causal analytics. Do you want someone to tell you what are the possible outcomes of your business decisions? Do you want to be able to make a future forecast that focuses on your business, and is based on consumer data? A data analyst can give you an effective predictive business model that shows whether your plans and ideas have a chance of bringing success. The more you invest in predictive analytics, the greater the chances of profiting from changes and improvements you make in your company.

From designing a new product line to getting guidance for your marketing plan, predictive analytics shows you what will work and what won’t. Aside from helping you make smart business decisions, this type of analysis gives you material to write reports and back up your claims when applying for loans or pitching for projects. Let’s say you work with contractors, and you have a potentially lucrative deal coming your way. But you know that the potential client is also browsing other service providers. Data analytics can help you demonstrate the stability of your business and services, and it can also give you the information to show that your other clients benefited from your work.

As you can see, there’s a lot of power and potential in data analytics, but it doesn’t come easily. The science itself and the experts who practice it navigate numerous advanced technologies, like Hadoop, Python, and SQL. Using cutting-edge methods and technologies gives data experts the possibility to extract meaning from your data, which is done with the use of distributed architecture, data visualization, and statistical analysis.

Machine Learning

Now, let’s make another important distinction. What is machine learning (ML) compared to data science (DS)? At first glance, you can conclude that machine learning is the science of making machines that can learn automatically so that a large portion of data work is taken off the staff’s plate. It is a branch of AI, and it designs algorithms that allow software and machines to operate independently, without having to have someone constantly monitoring them. On the other hand, data science is very narrowly focused on working on data. Match the two, and you get a hybrid that is capable of independently collecting data and generating predictive reports (e.g., accessibility compliance scanning and reporting on violations), or independently collecting data to create predictive reports that put your investments on the right track.

The difference in combining ML with DS, as opposed to working only with DS, is that well-made software can create trustworthy predictions and forecasts without requiring hands-on staff work. Instead of having a team of people working on collecting data and writing reports full-time, you can use their time more wisely and just bring them in to check and verify that the information from the reports is accurate, and, of course, give their take on predictions.

Combined with ML, data science can give an automated solution for predictive reporting and give optimal guidance for future actions. Moreover, ML can be used for unsupervised learning without pre-set parameters and generate data reports that are based on newly discovered patterns. One of the most popular algorithms used for this is called clustering.

Artificial Intelligence vs. ML & DA

You already know what artificial intelligence is, but how does it compare with data analysis (DA) and machine learning? In essence, AI incorporates both data analysis and machine learning to replicate human reasoning and intelligence (Das, et al., 2015). The goal of AI research is to not only achieve—among others—the previously mentioned actions, but also to be able to do so independently, without motoring, and to be capable of independent troubleshooting and self-correction. Hypothetically, the more experience an AI machine has, the more it learns from those experiences and improves itself independently for better future performance. For this, AI machines use natural language processing and deep learning.

While streamlining production and workflow and providing dependable data processing services is neither the focus of AI nor its final limit, there are more benefits and some limitations to what this technology can do. Here’s a clarification:

•  Simple and easy automation. AI-based software lets you automate high-volume, repetitive tasks through systems with frequent applications. If you look at your company, what are the low-priority, tedious tasks that take up a lot of work hours and staff labour, but still don’t make a significant contribution to company growth? Paperwork is the first thing that comes to mind, with checking for legal compliance, cleaning, transportation, packing, and many other followings closely. All of these are just some of the tasks that consume a lot of time for your workforce but don’t particularly affect profitability. Imagine being able to drop those duties onto robots and software, and then assign all your staff with high-impact tasks. You can easily see how your business would benefit from that.

•  Product updates. If you’re working with or producing tech-related products, AI can add “smart” features to your products to improve their practicality, utility, and value. Let’s say a company produces a robot vacuum cleaner. Depending on how willing you are to work with AI, you could add a virtual assistant feature that would recognize different users’ voices, follow their commands, and even learn from their requests to predict what sort of work they like.

Imagine this scenario. If a child would give a voice command to a vacuum cleaner, the AI would know to go straight into their room and vacuum. Or, if another adult did it, the AI would know at what time, which rooms, and at what pace to work on. This is only an imaginative example, but you can see how the manufacturer could give their product a wide range of features, and even install different ones into different lines for different prices. The same can be applied to platforms, software, or any other digital product that needs to cater well to the individual needs of its users.

• Progressive learning. Hypothetically, AI algorithms can be trained to classify, predict, and perform different actions and desired action patterns that lead to an outcome that is preset. This trait enables the AI technologies to self-improve over time, without needing a team of experts to detect, plan and install the necessary changes and updates.