How do organizations get from data to decisions?

The Role of Business Intelligence and Decision Support Systems in Digital Businesses In today's digital world, almost everything has become data.

From the clicks we make in an online store to banking transactions, user behavior in applications and even the times we scroll a page.

But I want to get to the point, does having more data mean better decisions?

Of course not, but let's dig a little deeper.

Many organizations have a huge amount of data but still make the wrong decisions. The reason is simple:

Data alone is not worth much; what is important is turning that data into insight and making the right decisions.

In this article, I want to see very simply and step by step:

• How data becomes information.
• How information becomes knowledge.
• And finally, how organizations reach decisions using business intelligence (BI) and decision support systems (DSS)

What is the difference between data, information and knowledge?

Before we get into intelligent systems, we need to clarify a basic concept. These three words are often used interchangeably:

• Data
• Information
• Knowledge

While each of these three words has a specific and different meaning and concept.

Data

Data is raw facts. Numbers, records or events that have not yet been analyzed.

For example: 10,000 site visits, 350 orders, average user time: 2 minutes. It's just plain data.

Information

When data is analyzed and categorized, it becomes information.

For example: the highest sales are from 9 to 11 pm, mobile users make 60% of purchases, the conversion rate of Instagram users is higher than Google. Here, data has been transformed into understandable patterns.

Knowledge

When information is transformed into understanding and insight that can be used for decision-making, we call it knowledge.

For example: it is better to run an advertising campaign at night, mobile site design should be a priority, investing in Instagram has a higher return.

At this stage, the organization can make a decision.

The path from data to decision

In many digital organizations, this path is approximately as follows:

Data → Data Warehouse → Business Intelligence → DSS → Decision

Step 1: Collect Data

In every modern organization, data is collected from various systems. For example: sales system, website, application, CRM system, ERP system, social networks.

This data is usually stored in operational systems, which are called OLTP (Online Transaction Processing). These systems are designed to perform daily operations, not for analysis.

Step 2: Data Warehouse

The main problem is that data is scattered across different systems. For better analysis, organizations collect data in a central repository called a Data Warehouse. Before transferring data to this warehouse, a process called ETL is usually performed:

• Extract or extract data
• Transform or clean and convert data
• Load or load into the data warehouse

The goal of this step is to make the data integrated, clean, and analyzable.

Stage 3: Business Intelligence

In this stage, BI tools come in. Business Intelligence helps the organization analyze the data in the Data Warehouse and display it in an understandable way.

Examples of BI outputs:

Management dashboards, analytical reports, performance charts, trend analysis

Tools such as: Power BI, Tableau, Looker, Qlik

But the important point is this: BI usually answers the question "What happened?"

For example: How much were the sales this month? Which product was the best-selling? Which channel did users enter the site from?

Step 4: Decision Support Systems (DSS)

This is where Decision Support Systems come into play. DSS are systems that help managers make the best possible decisions.

These systems are usually a combination of the Data analytics, statistical models, algorithms, decision rules (BRMS).

In fact, DSS tries to answer more complex questions:

• What happens if we reduce the price by 10%?
• Which product should we include in the advertising campaign?
• Which customers are more likely to buy?

Difference between BI and DSS

These two concepts are often confused. But they have an important difference.

Business Intelligence

• Analyzing past data
• Displaying information
• Helping to understand the situation

Decision Support Systems

• Scenario analysis
• Decision suggestion
• Helping to choose the best option

In simple terms:

BI helps us understand what happened. DSS helps us decide what to do

Suppose an online store has the following data:

User behavior on the site, best-selling products, customer purchase history

BI can show: Which products sell the most, What time do customers buy the most.

But DSS can suggest: Which product to display on the front page, Which customers to give discount codes, When to run an advertising campaign.

In fact, many of the product recommendation systems we see on sites like Amazon or Netflix are a type of decision support system.

Why do many organizations fail to make good decisions despite having a lot of data?

One common problem is that organizations: have data, have dashboards, have reports but do not have a clear decision-making system.

Common reasons: Low data quality, lack of system integration, lack of data-driven culture, cognitive biases of managers.

For this reason, simply having BI tools does not mean that the organization is intelligent.

At the time of writing this article, I am doing my master's degree in e-commerce and I have found it interesting that many companies think that having BI dashboards has transformed them into a data-driven organization. While the real value is created when data enters the decision-making process.

Simply put, data is valuable when it can change the future behavior of the organization.