The late Kofi Annan once famously said, “Knowledge is power. Information is liberating. Education is the premise of progress, in every society, in every family.”
As the quote suggests, the key to the kingdom of modern civilization lies in gaining knowledge. This age of information technology is our future, and it is going to help humanity conquer frontiers that, for now, only exist in our collective imaginations.
Going along those lines, the recent growth spurt that has become synonymous with technological advancements has also given rise to a different mindset. That mindset, known as Data-Driven Decision Making (DDDM) in the business fraternity, quickly becomes the rule of law across nations and multinational corporations. It entails making decisions based on empirical research and factual data rather than being purely observational and experimental. So far, it has become an integral part of the medical, manufacturing, distribution, and transportation industries across the globe.
Also called data-based decision management, it involves a set of main data elements that prove efficient and effective for any data-related decision.
THE SCOPE
The process of gathering data is objective. But it goes beyond that, as this data is collected scientifically, analyzed, and then processed for decision making. Therefore, the insights gathered from the collected information act as a vital cog in the machine.
Manufacturing companies, specifically their higher management, find it useful to plan ahead for a faster production process. By utilizing online tools, software, and courses to train their employees, they can wield the power of data-based decision making and use it to their advantage.
Furthermore, DDDM allows corporations to use legacy information and predict what will happen in the near future. By managing risks related to false assumptions and internal biases, it reduces uncertainty in everyday decision-making. Big corporations often utilize this approach to analyze data, model diagnostics, and process higher outputs.
DDDM’s success is dependent on several factors, including the data collection method itself and the quality of the data collected. Thus, it is primarily a quantitative approach. In recent years, AI has played an increasingly important role in collecting, computing, and analyzing huge data sets.
ADVANTAGES
· Increased Transparency and Accountability
The outcome of any DDDM approach is usually increased transparency through improved teamwork and staff engagement. This approach helps a corporation in threat/risk management, internal and external, to enhance its overall performance. It is also a morale booster for hardworking employees who want to achieve a clear objective.
The accountability comes in when the data has been collected and processed for decision making, record keeping, and compliance. And so, the data has to be managed properly, information has to be prioritized, and goals have to be concrete for the overall outcomes to be measured accordingly.
· Improvement in Organizational Structure
One of the more common outcomes of DDDM is improvement in the structure it provides a big corporation. It enables a corporation to implement changes in increments, monitors important metrics and functions, and make any necessary changes to its structure based on DDDM.
It enhances the efficiency of an organization as the decisions made are reliant on facts and not on the knowledge or expertise of the higher management. As such, it aligns a corporation’s goals with a higher chance of scaling its business.
· The Analytical Approach
Entrepreneurs routinely use DDDM to mine data as it helps them save time in their business endeavors. A precise analytical structure plays a vital role in solving complex problem sets for big business, making DDDM an extremely powerful tool for performance and predictive insights.
Firms also use DDDM to compare and gauge themselves against the market by testing strategies and targeting their audience. This increases the speed of decision-making as real-time data reliably and efficiently predicts future patterns.
· Concise Feedback for Market Research
Reliable feedback is another outcome of DDDM as corporations base their future decisions on research and user reviews. It plays a vital role in formulating new and improved products and services and even coming up with advanced workplace initiatives.
Identifying valuable trends becomes easy and preemptive as DDDM analyzes historical data for better understanding and expectations. The clients are happy because their voices are being heard, and the corporation benefits from increased sales and brand recognition, thereby creating a truly symbiotic relationship.
· Reliability and Consistency
A DDDM driven organization will see consistency over the short and long haul as the workforce recognizes the implications of collecting and managing data. It builds on the core values of loyalty, engagement between different departments, and responsibility by having people interact with the data and each other.
· Cost Savings and Management
One of the most impactful initiatives to come out of DDDM is decreasing expenses using data analytics. According to a recent survey, more than 49% of the organizations that started this initiative reported increased value from their projects.
EXAMPLES OF DDDM
· Project Oxygen (GOOGLE)
Google prides itself on maintaining focus on “people analytics.” Project Oxygen has mined data worth more than 10,000 performance reviews and compared it with its employee retention rates and, in the process, perfected the art of employee retention.
Not only that, it monitored behavioral patterns of top managers to create training modules for the development of these skills and competencies.
· Starbucks and Real Estate
In 2008, Starbucks found itself in a bit of a hole as hundreds of its locations were closed-off permanently. The then-CEO, Howard Schultz, went ahead with DDDM to combat this problem for their future locations.
Starbucks now has a partner company that utilizes DDDM and location analytics to predict prime locations based on population data, the demographics, and traffic patterns. Starbucks uses this data to establish the likelihood of achieving success for a particular location before applying for a new investment.
· Amazon
The famous “recommended” feature by Amazon is a classic example of DDDM. It uses customer search data and purchase patterns for future purchases and recommendations. Instead of blindly recommending a product, Amazon utilizes data analytics, AI, and machine learning tools for its search engine. To put this into perspective, Amazon’s recommendation system is estimated to have contributed to 35% of its sales in 2017.
Conclusion
There are many different benefits of using DDDM but that does not mean it has to be an all-or-nothing approach. So start small, divide each goal, benchmark every achievement, document all data, and adjust accordingly. By doing so you will become more data-driven, organized, and thrive in any organization.
Since data is an integral part of every organization, it is essential to observe it in a scientific setting, in real-time, to gain preemptive insights. Having a research-based core, it aids management in implementing strategic policies for a company’s growth.
Furthermore, it improves transparency and accountability within an organization, among many other benefits. Therefore, companies seeking to scale in the near future should consider integrating DDDM into their training modules, processes, and functions. It is the key to future success!
