Which analytics systems should you choose for your business?
Analytics systems help businesses collect, structure, and analyze data. This saves specialists time and companies money. We explain in detail the types of systems and how to choose them.
Why does a business need analytics systems?
Data collection and analysis are essential for the efficient operation of any business. Information helps make informed decisions, reduce costs, and increase revenue. However, many companies face challenges processing large volumes of data. Business intelligence (BI) systems help solve this problem. Let’s take a closer look at how BI systems help businesses.
● Automated information processing. Systems handle information processing and report preparation. Previously, it was necessary to manually collect data from various sources, combine it, select the required data, and output it into a single document. Now, a BI system can do this work.
● Efficiency. Previously, generating a report could take weeks; now it can be done instantly. For example, the system can display a company’s financial results in seconds.
● Building business development models and scenarios. A BI system can calculate company indicators, analyze them, and generate hypotheses.
For example, in retail, food companies use consumer behavior analysis to personalize offers to customers. In banking, it is used to manage loan portfolios and protect against fraud. Banks analyze borrowers’ credit histories, calculate the probability of loan default, and thereby prevent potential losses.
Another important area of analytics is pricing. A business analytics system can determine the optimal price for a product or service, taking into account various factors: competition, seasonality, demand elasticity, and other parameters.
Main types of analytics systems
There are four main types of analytics systems.
- Descriptive analytics is a holistic view of the current state of affairs. The primary goal of descriptive systems is to describe a past event. Such systems answer the question, “What happened?” or “What events occurred?”
Consider standard website traffic statistics: sales for a specific period or order volume trends. The user receives simple information about past events, but no explanations of the causes, conclusions, or next steps are offered. - Diagnostic analytics investigates the causes of events, looking for connections between factors and results. It answers the question, “Why did this happen?” For example, diagnostic analytics helps identify the factors behind a sharp decline in sales: decreased demand and product quality, price changes, competitor actions, or external circumstances. Users of diagnostic analytics receive a detailed picture of events, the exact cause, and conclusions that help prevent the recurrence of negative events or replicate positive ones. A drawback of the diagnostic approach is its exclusive focus on the past.
- Predictive analytics builds forecasts based on historical data and statistics. It answers the question, “What will happen next?” Using machine learning and statistical analysis, predictive systems can forecast probable events, consumer behavior, market fluctuations, and much more. A typical application scenario is forecasting sales volumes or anticipating loan defaults. The advantage of predictive systems is their ability to prevent undesirable consequences and prepare responses in advance.
- Prescriptive analytics suggests the optimal solution to a problem or suggests next steps. It answers the question “What to do next?” These systems recommend actions to users to achieve a goal.
Examples of prescriptive analytics include a product recommendation system for online stores or a system for selecting the optimal maintenance schedule for industrial equipment.
All analytics systems are most often used in tandem. For example, a single system might combine predictive and prescriptive analytics, or descriptive and diagnostic. We’ll discuss how to choose the right system for your business below.
Popular analytics systems on the market
Criteria for choosing an analytics system for business
When choosing the right analytics system, you need to consider everything from the company’s size to the ability to integrate with CRM. Let’s discuss this in more detail.
- Goal setting. It’s important to understand why a business needs an analytics system and what tasks it should accomplish.
For example, the goal might be to increase sales and boost customer loyalty. In this case, turnover, average purchases, and repeat purchase rate are important metrics.
- Company size. Small businesses will benefit from a lightweight and affordable cloud solution, such as Yandex DataLens. Larger organizations will need a scalable system capable of processing large volumes of data, such as Looker Studio.
- Integration with other resources. Consider the ability of the selected system to integrate with other systems, such as CRM and ERP. Choose a system that supports the maximum number of protocols and data transfer standards (JDBC/ODBC, REST API, etc.).
- Budget and feasibility. Determine your financial limits. Small companies typically focus on inexpensive cloud solutions with basic analytics functionality. Large businesses are more focused on system capabilities than cost.
Additionally, use demo versions before purchasing to ensure sufficient functionality. Review reviews and implementation cases of similar systems in your niche. Choose providers who can support your company through all stages of system implementation. We’ll cover these steps below.
How to start implementing an analytics system in your business
Implementing a BI system can take months or even years. The process consists of six stages:
1. Data collection. Users specify their needs: reports, dashboards, metrics, and update frequency.
2. Data source search. The team determines where the BI system receives data: CRM, ERP, etc. The data is verified for accuracy and configured if necessary.
3. System selection. The BI analyst selects a ready-made solution or initiates the development of a custom system.
4. System configuration. The data warehouse is configured, integration with sources is performed, and the interface is created.
5. System testing. The data is verified for accuracy. Testers or users can perform testing.
6. User training. After system implementation, the integrator conducts staff training. Users can choose the training format: in person or through instructions, videos, or webinars.
