Data-Driven Approach- Mastering the Definition & Process
Businesses have always explored ways to use and gain value from large quantities of data. While some organizations use the data in combination with other factors when they make a business decision, others let the information direct them. This is called having a data-driven approach in business. Understanding what this means can help companies use data to develop more successful business practices.
Most organizations acknowledge that data analytics is a powerful tool when it comes to making important business decisions. However, surveys show that 58% of businesses still use gut feeling to base roughly 50% of their business decisions. In fact, just one-third of organizations use their data to forecast for demand and discover new sales opportunities.
Therefore, taking the steps to become more data-driven can give organizations the necessary competitive advantage in a saturated market.
What Does It Mean to Be Data-Driven?
Organizations that are data-driven use quantitative information to make their decisions rather than relying purely on intuition and gut feeling. This data-driven approach involves collecting and cleansing datasets, then analyzing the information for patterns that can be used to forecast future trends.
When it comes to using data in business, it is important to be clear about the difference between data-driven decisions and data-informed decisions. Being data-informed means that the extracted information is only one factor that goes into making a business decision and can also take industry experience and expert opinions into consideration.
On the other hand, being data-driven means that the data is the core ingredient for business decisions, only leaving room for cold, hard facts. Being data-driven can empower a company to make better decisions about their products and services based on consumer preferences and sales data.
5 Steps to Creating a Data-Driven Approach
Creating a data-driven approach in regards to decision-making doesn't need to be difficult. Organizations simply need to have the right strategy for their objectives. Here are 5 general steps that can help companies get started.
1. Understand the Objectives
The first step towards a more data-driven approach is to understand the business, as well as the industry, and determine what the biggest challenges are for both. It is essential that this understanding goes beyond a cursive look at the company or competitors. Businesses should conduct a thorough examination and market research to develop a comprehensive understanding.
Once this has been achieved, determine the biggest questions the business faces. How do these challenges impact organizational goals? The key is to develop specific questions that can be answered through data acquisition.
Mastering this first step will help to prevent any wasted time and resources in the data gathering and analyzing process.
2. Collect the Data
Once a clear understanding of the problems and questions has been obtained, the next step is to determine where and how the data will be collected. There are many different methods that organizations can use to collect information. These can be from online customer surveys, social media insights, internal databases, interviews, focus groups, case studies, and more.
The questions that need to be answered may be the biggest factor in determining the ideal data collection process. Organizations using data from different sources may find it difficult to discover common variables among the data sets. Therefore, businesses should plan for additional time to overcome these potential challenges.
For companies utilizing forecasting software, this step is automatically completed by the systems. By integrating the point of sale system, accounting tools, and other relevant software solutions, businesses can perpetually collect valuable business data.
3. Organize and Clean the Data
Once data has been collected, it will need to be cleaned and organized. That is, before any information can be extracted, businesses should remove or adjust any incomplete or inaccurate information from the raw data.
This is the most time-consuming step within the data analysis process but may also be one of the most important as it ensures data accuracy. Analysts will generally spend 80% of their time on cleansing the information, while the actual analysis process only takes up roughly 20% of their time.
To conduct a proper data analysis, businesses can begin by creating tables where the data can be gathered in a readable way. It is also helpful to create an overarching data dictionary, a table that catalogs data variables and extracts what the information means in the current business context.
4. Conduct the Analysis
Once the data sets have been cleaned and organized, the process of analyzing begins. This step is usually handled by a data analyst who uses statistical models to identify key information. However, the calculations can also be automated using forecasting software, which will produce detailed reports and demand projections in seconds.
Data needs to be tested at this stage and the analyst will seek to answer the questions derived from the first step of the data-driven approach. At this stage, a decision about how the information will be presented also needs to be made. There are 3 ways that data findings can be prepared for presentation-
- Descriptive - This presentation technique will focus solely on the quantitative facts with no inference.
- Inferential - This involves factual data along with some inference of what it means with regards to the organization.
- Predictive - This includes the inference derived from the data set facts and also includes business advice for the organization.
Being aware of how the information will be presented can help businesses stay organized during this stage of the process.
5. Form a Conclusion
The final step in the data-driven approach involves considering what the findings reveal in regard to the business questions that were defined at the beginning of the process. Some organizations may find that the data challenges their assumptions about their objectives or their industries.
To fully embrace the data-driven approach, companies may need to abandon firmly held beliefs if they lack the data evidence. By becoming data-driven, businesses will be able to make more informed choices about their operations and become empowered to strategically plan for financial growth.