Data-Driven Decision Making: Top 9 Best Practices
The phrase data-driven decision making – certainly popular in the field of data analytics – may seem redundant. After all, nearly everything is driven by Big Data or we wouldn’t have petabytes of databases in public and private data centers around the world.
So what exactly does it mean to be “data-driven?” It’s quite straight-forward. Data-driven decision making (DDDM) is the process of making organizational decisions based on actual data analytics rather than intuition, anecdote, or observation.
Business intelligence (BI), another popular data term, is entirely data-driven decision making. Using enterprise data analytics applications like TeraData or Microsoft Power BI, IT managers and business people process data, extract facts, figures, and patterns from that data, and make decisions based on the cold, hard facts, not gut feelings.
DDDM is the art and science of using facts, metrics, and other data to guide strategic business decisions to meet your company’s goals and objectives. Done right, DDDM helps you make better business decisions and spot strategic opportunities.
So how do you do DDDM right? You start with making it the norm. Your organization needs to make data-driven decision-making standard operating procedure. Sure there is room for gut instinct but first and foremost you need a culture of analytics. That’s why analytics has become so predominant in technology. As data has exploded, so has the opportunity for insight from that data, whether it’s through business intelligence, Big Data, data warehouses or data lakes.
Data mining results in general fall into two distinctive types: qualitative analysis and quantitative analysis, and both are equally valuable to making a data driven decision.
Quantitative data analysis is what DDDM is all about. It is measured analysis that focuses on numbers and statistics and other elements such as median and standard deviation. Qualitative analysis focuses on data that isn’t defined by numbers or metrics, such as images, videos, and social media.
Qualitative data analysis is observational while quantitative is factual. Both qualitative and quantitative data should be analyzed to make smarter data driven business decisions.
The good news is that employees across the board can participate in DDDM. While some of the more arcane data science disciplines belong to data scientists with advanced degrees, there are plenty of DDDM-related business applications for mere mortals, starting with Microsoft Excel.
Beyond apps, companies have to develop data skills through practice and application through best practices and business models with security and governance to watch over things. To effectively utilize data, professionals must take several steps:
If you don’t know your destination, how can you get there? That should be the first step in any DDDM scenario: ask yourself what are you trying to solve. Identify and understand your goals thoroughly. You need to do this before you begin collecting data so you know what data to collect and not to collect.
To get the most out of your data, companies should define their objectives before beginning their analysis. As Sun Tzu said in The Art of War, “Victorious warriors win first and then go to war, while defeated warriors go to war first and then seek to win.” Set a strategy to avoid falling into traps through Key Performance Indicators (KPIs) as measures of success or failure.
