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What’s the difference between business analytics and business intelligence, and what’s the role of artificial intelligence and machine learning in data analytics?
This article will discuss how to leverage data analytics, including artificial intelligence (AI) and machine learning (ML), to create successful business strategies. But before we dive in, let’s discuss the importance of information for your business because the end goal of data analytics is discovering helpful information that can drive new opportunities.
Knowledge is power.
It’s an adage that predates our modern technology by several hundred years. And while it certainly still rings true, there is a better way of phrasing this more accurately.
Information is power.
Information is indeed the currency of our day, and until recently, most people had no idea how valuable information is. More specifically, consumers only recently began to understand the value of their information.
Much to the contrary, one young company that didn’t exist 25 years ago understood the value of information and began collecting as much as possible. Of course, this company is now the tech giant: Google. Google now has firm control over web browsing and offers one of the most powerful marketing tools available. Google’s control over the web is so powerful that trying to build a meaningful online presence without it is entirely futile.
Why is this relevant?
Because Google is the perfect example of what a business can do when it takes data and information seriously when making business decisions. Google is now synonymous with both web browsing and marketing. So now, let’s get down to business.
An Overview of Data Analytics
Business intelligence and business analytics. Both these terms get thrown around so much that they almost seem like buzzwords with no meaning. While many might use the terms interchangeably, business analytics and business intelligence refer to different aspects of the larger sphere of analysis known as data analytics.
What is Business Analytics?
Think of this sphere of analysis as looking into the past. Business analytics refers to the practice of organizing and interpreting the data from a company’s history to understand historical trends and get a better picture of what the company is facing right now. It’s important to note that these tools don’t tell you what to do or the best route to take.
Instead, they’re descriptive tools that provide you a snapshot of what worked, what didn’t work, and what is currently taking place based on the company’s past and present data. These tools are ideal for identifying any deficiencies or hold-ups in your existing operations. When used correctly, your managers can use the insights gleaned from business analytics tools to make operational changes on the fly.
What is Business Intelligence?
In contrast to business analytics, business intelligence can be thought of as looking to the future. The goal of business intelligence is to provide businesses with predictions and insights regarding future trends and events. Companies can then use this information to adjust their strategies and set themselves up for future growth and success.
How is all of this done? This sphere of data analytics is done through a process called “predictive analysis.” Predictive analysis focuses heavily on artificial intelligence and machine learning to analyze and interpret a company’s data.
What’s the Role of Artificial Intelligence and Machine Learning in Data Analytics?
Every day, businesses and consumers are collectively creating tens of quintillions of bytes of data. Even though your business only accounts for a fraction of that collective amount, sifting through data is an arduous task. With the sheer volume of data being created, we’re all going to reach a point where human analysts simply can’t keep up. That’s where AI and machine learning step in.
AI technologies are in just about everything these days, and they are some of the most promising technologies on the market. Everything from your music recommendations to your social media feeds, and even your online search results are all generated by AI. Even modern cybersecurity tools are utilizing AI to identify anomalous activities. If you haven’t implemented them already, they deserve a place in your data analytics strategy.
Machine learning is a version of artificial intelligence that teaches your applications to become more accurate at predicting certain behaviors using past data. For example, when machine learning is used in conjunction with your enterprise resource planning (ERP) solution, it uses your past and current data to predict future trends. This AI solution can analyze and interpret large amounts of data faster than any human and can even organize data into insights that are understandable for those without the vast technical experience of a data analyst. In this way, AI is capable of democratizing data.
The Impact of Data Analytics
Let’s be frank. If you’re not taking advantage of your data by implementing business intelligence and analytics tools, you’re letting your competitors gain the upper hand. There are three critical benefits that advanced data analytics can provide your company: increased revenue, increased efficiency, and decreased operations cost.
- If we’re honest, nobody would be adopting these tools if there wasn’t some money to be made. Data analytics is still a relatively new field, and we’re only starting to see the potential for revenue gains. According to research from McKinsey & Company, when companies first adopted tools for business analytics, they saw a 6% increase in revenue.After those companies had invested in data analytics for a five-year period, that number increased to 9%. McKinsey & Company also stated that the average investment in these technologies was 0.6% of the corporations’ revenues. That’s an incredible return for such a small investment.
- Implementing tools for data analytics can have extreme impacts on your company’s overall efficiency. For instance, you’ll gain insight into consumer trends and customer feedback. This allows you to adjust your offerings to meet their needs and address any issues with your current products and services.Another way to enhance your efficiency with data analytics is by connecting your departments. These tools can centralize your data and ensure that every department is working with the same information. The relationship between Marketing and Sales is a prime example. If your data isn’t centralized, likely, your marketing department doesn’t always have access to the latest sales insights. Without that critical information, you could end up wasting money pushing products that aren’t in demand.
- When you invest in an effective data analytics strategy, you’ll be able to identify spending abnormalities quickly. When you can clearly see where money is spent, you can identify and stop any inefficient or frivolous spending.
Making the Most of Your Analytics: Moving Data to a Centralized Repository
For more than 30 years, data warehousing has been a standard practice for companies looking to leverage their data to drive new opportunities. These warehouses acted as repositories for the myriad of data being collected across various sources. While they were a definite improvement from previous methods (or lack thereof), the rigidity of data warehousing severely limits the flexibility and scope of your analytics strategy.
When you take the amount of time and money needed to set up and maintain a data warehouse, it becomes increasingly evident that a more modern solution is necessary.
Modernizing Your Analysis Strategy with Data Lakes
Data lakes have quickly become a must-have among top companies that are focused on seizing growth opportunities. Data lakes leverage the improvements of cloud infrastructures to create a repository capable of synthesizing data from across your organization—essentially eliminating data silos. With your data out of silos, you can improve your data analytics strategies and gain more impactful insights.
Why?
Because when your data is siloed, your analytics tools can’t get a complete picture of what is taking place in your company. If you implement artificial intelligence and machine learning tools into a data warehouse, they can only provide you with fragmented insights based on the incomplete picture of your business operations. However, when all your data is centralized in a data lake, those tools can analyze your business in its entirety and provide you with more accurate insights and projections.
Also, unlike their predecessors, data lakes offer you a low total cost of ownership (TCO). Traditional on-premise data warehouses require significant capital expenditures to set up and maintain. Not only do you have to purchase servers, storage, security, and other infrastructure components, but you also have to factor in the cost of maintenance, energy consumption, and end-of-life disposal, among other variables. It would help if you also too consider that the average lifespan of these devices is around five years. After that, you’ll need to replace the obsolete technology. Depending on how complex your data warehouse is, it can cost you hundreds of thousands or even millions of dollars to set up and implement when all the costs are considered.
You won’t have to worry about making large capital expenditures to set up and maintain your data infrastructure by opting to use a data lake. Instead, you lower your TCO by paying a third party to store the data for you on the cloud. For instance, each gigabyte of storage on Amazon Simple Storage Service (S3) only costs $0.25.
Data lakes also provide additional flexibility; instead of being forced to design your schema before implementation, you can handle that on the back end at the time of analysis.
These two examples only scratch the surface of the benefits that data lakes can provide for your business. Even if you don’t have an experienced data scientist on staff, you can take advantage of the benefits of data lakes by partnering with a managed service provider (MSP).
A Competitive Advantage
Syntax is ready to help you gain the competitive advantages that business analytics can offer. With over 20 years of business analytics experience—our team has the knowledge and expertise to help you overcome data silos by synthesizing the various sources across your organization into a single centralized data lake. With all your data in one place, Syntax can help you implement artificial intelligence and machine learning tools—such as Amazon Forecast—to gain better insights and predictions.
No matter how large or small your business is, and regardless of how many data repositories you utilize, Syntax Business Analytics Services can help you migrate your data to an AWS data lake and implement efficient business analytics tools. If you’re ready to take your data to the next level, contact us today to discuss how we can help you evolve from business analytics to business intelligence.