Understand the differences between business analysis and business intelligence in one article.
Business intelligence and business analytics are so similar yet so different. This article explores the differences between them and how they complement each other. Is there a difference between business analytics (BA) and business intelligence (BI)? Yes. Otherwise, I wouldn't have spent over 3,000 words writing this article, because I could say without hesitation: No!
To be honest, some people use business analytics and business intelligence interchangeably. Business analytics and business intelligence serve different purposes in an organization. In this article, I'll explore their differences, as well as their respective goals, components, and tools. Finally, I'll discuss how these differences complement each other and work together to achieve the same goal.
I. Business Analytics and Business Intelligence: Definitions
Business analysts collect and analyze historical and current data to predict future trends. It is rooted in statistics, and its goal is to identify patterns in data, predict future trends, and support business decisions that can anticipate these trends.
BI analysts also analyze historical and current data to assist with current business operations. They do this by reporting past and current performance metrics.
Business Analytics and Business Intelligence: Goals
An overview of the goals of BA and BI is shown in the following figure.
I'll now introduce each goal in more detail.
1. Goals of Business Analytics
Let's first outline the goals of BA, focusing on predicting future trends.
Focus on Predicting Future Trends
Business analytics is primarily forward-looking. It does use historical and real-time data, but its purpose is to predict future trends and outcomes. This is mainly achieved through the use of predictive data analytics, and sometimes even prescriptive data analytics.
Trends can refer to any part of an organization's business. Therefore, business analytics can be used to predict changes in sales, costs, inventory, stock prices, interest rates, and foreign exchange rates, inventory level requirements, demand for new products or improvements to existing products, the success of marketing campaigns, and staffing needs; these are just a few examples.
In short, BA turns a business from reactive to proactive.
Identify Opportunities
All these predicted future trends are not an end in themselves. The goal is to discover opportunities for the company's growth. These opportunities may include untapped markets and customer segments, as well as improvements to products and services, marketing and pricing strategies, reducing customer churn, optimizing the supply chain or sales channels, identifying seasonal trends, and innovation.
Optimize Business Processes through Data Analysis
Business analytics can also help improve the efficiency and effectiveness of business processes. Using data and conducting analysis can identify inefficiencies and bottlenecks. Additionally, analysis can provide suggestions for improvements in content and methods.
Optimizing business processes is one way for a company to reduce costs, increase sales, or both. For example, solving problems that cause production delays means the company will increase production and deliver more products to customers more quickly.
The same applies to inventory management, optimizing delivery schedules and routes, labor, sales channels, quality control, or any other business process.
Optimization can even include the full automation of certain processes.
2. Goals of Business Intelligence
The three main goals of business intelligence are historical data analysis, reporting, and visualization.
Emphasize Historical Data Analysis
The main type of data analysis used in BI is descriptive data analysis. This is because BI aims to analyze historical data and describe the company's performance over a period of time. By extracting trends, patterns, and anomalies from historical data, a company can learn from its mistakes and have a clearer understanding of what it did right or wrong.
Unlike business analytics, business intelligence is not proactive. Therefore, if a company only engages in BI, it can only respond reactively.
Some examples of BI data analysis are as follows:
Sales performance analysis - Identify sales peaks, troughs, and their patterns
Customer retention analysis - Calculate the customer retention rate and discover factors that affect customer loyalty
Financial reporting - Create financial reports to evaluate current performance metrics and compare them with previous periods
Inventory turnover analysis - Calculate the turnover rate and optimize inventory levels
Marketing campaign effectiveness - Analyze marketing campaign metrics to improve future marketing campaigns
Employee performance evaluation - Analyze productivity metrics to identify high-performing and underperforming employees
Supply chain efficiency - Analyze suppliers' delivery times and costs
Product quality assessment - Calculate product defect rates and common problems to improve product quality
Customer support metrics - Analyze customer support data to evaluate the quality level of customer support and areas that need improvement
Website traffic analysis - Analyze historical website traffic data to understand user behavior and evaluate the success of online content
Reporting
The main tool used by BI to present its historical data analysis is reporting. BI tools are used to create reports that outline business operations in a systematic and easy-to-understand manner. Decision-makers can have a comprehensive understanding of the performance of the company or a specific department and make decisions regarding the required improvements.
Visualization Provides Insights into Past Performance and Current Trends
One of the most effective ways to report BI insights is through data visualization. Complex data sets, numerous tables, and numbers take a lot of time to understand their meaning. But with visualization, everything becomes much easier!
For example, take a look at this chart.
No explanation needed! Just a glance shows that May and December are the months with the highest sales, and something went seriously wrong in September. But what was the problem? Is it necessary to investigate further?
The CEO says no, it's all clear. He knows that this chart usually follows the seasonal changes in the company's sales. The only exception is that decline. However, yes, the CEO knows that September is the month when the entire sales department resigns due to overwork, so it's no wonder that sales plummeted.
With just one chart, you've explained everything.
Visualizing data in this way makes it easier to understand trends, correlations, and outliers, which are difficult to reveal through tabular reports.
Many times, we combine multiple charts to create a dashboard. In this way, we can not only show a part of the chart but also the whole. For example, we can not only display the sales chart but also show key performance indicators (KPIs) for a comprehensive insight into the overall performance or certain areas.
BI reports can be interactive, making it convenient for users to operate. For example, before implementing a decision, multiple "what-if" scenarios can be explored to simulate the impact of the decision.
II. Business Analytics and Business Intelligence: Key Components
Business analytics has to do something extraordinary: use historical data to extract insights and predict the future. This "magic" is reflected in the key components of business analytics.
A. Key Components of Business Analytics
BA Key Component #1: Prediction Models
Prediction models are a technique that uses historical data to build statistical models to predict future trends. Wow, predicting the future. Isn't that exactly the goal of business analytics (BA)? No wonder prediction models are its cornerstone.
Typically, prediction modeling includes five steps.
1. Data Collection and Preparation
The quality of prediction models depends on the quality and quantity of data - the more and better the data, the better. Data comes from various sources, such as databases, CRM systems, IoT devices, social media, and public data sets.
Since data quality and consistency are crucial, the data must be cleaned and prepared for subsequent stages. The data needs to fill in missing values, standardize formats, correct data, and remove duplicates. Additionally, data transformation is required through methods such as data normalization and scaling, encoding categorical variables, and creating new derived features.
2. Feature Selection
The second step in prediction modeling is feature selection. In this step, you must determine the variables that have the greatest impact on the model's ability to predict results. It's important to have enough of these variables. However, it's also equally important to remove variables that don't contribute to the model and only reduce its performance.
Additionally, at this stage, you need to perform feature engineering, which, as the name suggests, is to create new features from existing data. What's the purpose of doing this? To improve the model's predictive ability.
3. Model Building
This stage involves selecting appropriate algorithms and using them to train the model. What does training a model entail? Generally, it means letting the algorithm freely process the data and allowing it to learn the patterns and relationships between the model features and the target variable.
4. Model Validation
The purpose of model validation is to evaluate its ability to predict future results.
Typically, you'll split the data into a training data set and a validation data set. Then, you'll use performance metrics - Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), Precision (PR), Recall (RE), and Area Under the Curve (AUC) - to evaluate the model's prediction performance.
Then, you'll perform cross-validation to ensure that the model can generalize well to different subsets of data; you'll train and validate the model multiple times on different data splits.
5. Model Deployment
The final stage is to deploy the model into production, that is, into the actual environment of the company's business processes.
Typically, you won't just leave it there; the model needs to be monitored, fine-tuned, and sometimes even updated and recalibrated to ensure its high-quality performance.
BA Key Component #2: Machine Learning Algorithms
Machine learning is such a large and important field that it forms the pillar of prediction models and is worth discussing as an independent BA component.
Machine learning algorithms enable the model to learn from historical data. Broadly speaking, these algorithms belong to three types of machine learning.
1. Supervised Machine Learning Algorithms
Put simply, supervised learning means that the algorithm learns from labeled data, which means that each training example is matched with an output label. The goal is for the model to learn from these examples and then make accurate predictions on new data.
Here is an overview of some common supervised learning algorithms and how to use them.
2. Unsupervised Machine Learning Algorithms
Unlike supervised learning, unsupervised machine learning algorithms learn based on unlabeled data and try to discover hidden patterns in the data. In other words, the results are not predefined.
The most common such algorithms are presented here.
3. Reinforcement Machine Learning Algorithms
If you associate the word "reinforcement" with dog training, you're right. Training a reinforcement algorithm is a bit like training your dog. Just as your dog interacts with the world and receives negative or positive reinforcement (e.g., treats), so does the algorithm. If the model's prediction is labeled as the desired result, the model gets a reward. If the result is not ideal, it gets punished.
You can find an overview of the algorithm below.
Other Advanced Machine Learning Techniques in Business Analytics
In addition to these three main types of machine learning, some other techniques are also relatively commonly used in BA.
1. Text Analysis: Use Natural Language Processing (NLP) to extract information from text data, such as sentiment analysis, to draw conclusions about customers' opinions of products from their comments on social media.
2. Simulation Modeling: Simulation modeling involves creating a virtual version of the company's system and testing different scenarios. It allows risks to be evaluated and the results of different decisions to be tested in a controlled environment.
3. Prescriptive Analysis: It suggests the steps required to achieve the desired results by using optimization and simulation techniques.
BA Key Component #3: Data Mining
Data mining is a set of methods for deeply exploring large data sets and discovering patterns, correlations, and relationships that are helpful for decision-making.
One commonly used technique in data mining is association rule learning, which can identify interesting relationships between variables in a data set. This technique is often used in market basket analysis, with the goal of understanding which products are often purchased together.
Classification and clustering are also commonly used techniques in data mining; you should have learned about them in machine learning. Classification categorizes items based on their attributes. This method is very useful in spam detection, image recognition, and medical diagnosis. On the other hand, clustering groups data points based on their features. A typical use of clustering is customer segmentation, image segmentation, and anomaly detection.
Some other techniques worth mentioning in data mining include regression analysis (predicting the results of continuous variables), text mining (using NLP to extract information from text), and dimensionality reduction (reducing the number of variables considered by taking the main variables)
B. Key Components of Business Intelligence
Since the main goal of BI revolves around historical reporting of performance, it's no wonder that the three main BI components support this goal.
