Data Driven Approach | 4 mins read

Becoming Data Driven- An Approach for Beginners

becoming data driven an approach for beginners
Lauren Christiansen

By Lauren Christiansen

Data collection, or facts and statistics collected for analysis, has changed dramatically over time. Between social media platforms, in house customer databases, websites, and business intelligence tools, there continues to be a number of ways to find data. Collected information from this wide variety of sources can be utilized to make data-driven decisions, or decisions based on evidence rather than instinct.

Data-driven organizations are making decisions void of bias, and utilizing data to answer specific business questions. Read ahead on how to become data-driven, and why it's important to stay competitive.

How to Make Data-Driven Decisions

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When choices are based on hard facts and definitive evidence, it helps to validate that choice. Here are some guidelines when making data-driven decisions-

1. Know the Mission
Identifying and understanding the various problems a particular industry faces (economic conditions, competition, poor internal customer service, etc.) can help equip the company to know what type of data to collect and what to look for within that data.

2. Identify Data Sources
Choose which data sources will be utilized. Make sure that the sources address the initial problem(s) or set of circumstances that were identified from the Mission. Data can be extracted from different internal databases, such as Customer Relationship Management systems (a database that holds customer information and purchase activity), company websites, or even social media platforms.

3. Clean and Organize Data
Make sure that a proper Extract, Transform, Load (ETL) tool is utilized to cleanse and upload data into an analytics system. ETLs gather data from various sources, change it into a single readable format, and then put it into one place that users can access. ETL tools also remove any duplicated data or data that inadvertently shares the same information as another data set.

If the company does not utilize an ETL tool, it's important to go through each set of data that relates to the particular set of issues that need to be addressed and check for inconsistencies or errors.

4. Perform Statistical Analysis
First, the data needs to be properly analyzed to determine trends. There are several different ways to perform statistical analysis. Some of the most popular techniques include-

  • Linear Regression A linear approach to modeling that shows the relationship between a response and an explanatory variable. For example, a linear regression model can be utilized to quantify the impacts of a bad economy on customer purchasing patterns
  • Decision Trees A tree-like model that shows decisions and their consequences, including resource costs or chance event outcomes. For example, a decision tree model could demonstrate the possible positive/negative outcomes of opening up a new franchise

5. Draw Conclusions
Consider what new information was gleaned from the collection of data, and whether or not it properly answered the problem/circumstance. The conclusions drawn will help the organization make more informed business decisions about that particular topic/set of circumstances in the future.

Examples of a Data-Driven Approach

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The following are illustrative examples of utilizing a data-driven-approach-

1. Strategy
A data-driven strategy is one that utilizes hard evidence to support a business decision rather than relying on intuition. For example, an owner may want to fire a sales employee who he believes is not meeting the sales call monthly quota. However, the owner wants to make sure that he has evidence to support this suspicion, so he looks at collected data to see how many cold calls the employee made over a 6-month period. The owner confirms from the call log that his employee is, in fact, not hitting his numbers. The owner can fire the employee based on findings from hard evidence, as opposed to instinct alone.

2. Automation
Data can be utilized to automate and streamline business processes. For example, banking sites allow customers to apply for credit cards online. The customer inputs their basic information (name, address, social security number), and the banking site automatically retrieves their FICO score. The software program automatically determines whether the customer is qualified or not.

3. Performance Management
Performance management is the process of setting standards for employee performance and then measuring their performance against these standards. Many companies utilize data to track how well an employee is doing, across different metrics.

For example, business intelligence tools can be utilized to track key performance metrics or a measurable set of values that demonstrates how effectively a company is performing. A sales manager may utilize BI tools to monitor the sales leads of a new employee over the span of a few weeks to make sure the employee is on the right path towards hitting KPIs.

4. Marketing
Customer data can be sourced from internal databases that hold customer information, such as Customer Relationship Management systems, activity on websites, online surveys, or social media platforms. A data management platform, or a platform that connects and organizes real-time data from online sources, can be utilized to collect and track this data. The data is then utilized to learn about their target audience's characteristics and purchasing habits. Then, the company can create a marketing campaign geared towards that target audience.