Data-Driven Decision Making: Using Data To Fuel Growth

Last Updated: April 6, 2021

With the exponential growth of data, organizations are consistently looking for the best possible ways to capitalize on it to drive business decisions. Regardless of size, sector, or type, every company is trying to have a data strategy for competitive advantage. Be it Sales, Service, Human Resources, or any other department, companies are expanding data-driven efforts to boost sales, deliver personalized customer experiences, nurture relationships, and improve their products.

What is Data-Driven Decision Making?

Gone are the days when businesses relied solely on intuition and gut feel. While “gut-feeling” can definitely prove useful in some cases, when a business is at stake, trusting inner voice blindly can lead to disastrous results. This is where Data-Driven Decision Making (DDDM) comes into the picture. 

Data-Driven Decision Making or “DDDM” is the process of making informed decisions and business strategies through deep analysis of available data. Essentially it involves data collection cleansing, mining historical data, data analytics to make better decisions in the future. However, with the development of data science and powerful business intelligence tools, this process has been made simpler.

Benefits of Data-Driven Decision Making

1. Informed strategies 

One of the most explicit benefits of establishing data-driven decision-making methodology is the confidence it brings in implementing and testing business strategies. It also dramatically increases the speed of decision making when the strategies are based on data and facts.

2. Continuous Improvement

Data-based decision-makers are constantly aiming for improvements. Organizations that strive to measure performance and business outcomes are always looking to perfect their operations, processes, and new products continuously. For example – In the Healthcare and Logistics industry, using Big Data analytics can bring light to new opportunities for streamlining resource flows and production that can ensure continuous improvement in a shorter time-frame.

3. Greater Transparency and Accountability

Another key benefit of using DDDM is that it leads to improves transparency and accountability for teams within the organization. This greatly improves teamwork and staff engagement. When fostering a data-driven culture at the workplace, the onus of visible company’s growth is placed on each team member who works towards a shared goal, making everyone a stakeholder. This helps nurtures relationships, improves collaboration, easier communication, and team cohesion tighter.

5 Steps to get started on Data-Driven Decision Making

1. Set meaningful goals

The first step in becoming more data-driven is making a conscious decision to be more analytical and setting goals. Establishing realistic KPIs (Key Performance Indicators) and metrics can set your organization up for success. Identifying the business questions that you want to answer to achieve your organizational goals will not only make the next steps more structured but also reduce wasting resources.

For example – if you are looking to improve customer service initiatives through the IVR system in the next quarter, a KPI could be tied to the amount of inbound ticket inflow through that channel.

2. Collect data 

Once you have the necessary goals set, the next step is to collect the right data. Be prepared to collect raw data from multiple sources and once you have an idea of the breadth of data sources, start the process of cleaning and data preparation. Some sources you can find a goldmine of data are:

  • Customer Relationship Management software
  • Helpdesk Ticketing System
  • Workforce Management System
  • Surveys and Customer Feedback system
  • Website and Social Media Analytics
  • Excel and CSV files

For our example above, the best data for customer interaction would come from a helpdesk ticketing system and surveys. Finding common variables among each dataset be a tedious task,  so investing in a self-service unified business intelligence platform can come handy.

3. Analyze Data

Once you have the data you need, the next steps are to take a look at the historical data and try to identify patterns or trends. Using a powerful Business Intelligence system can make the extraction of meaningful insights and analytical reports a more seamless experience. Visual elements like charts, graphs, can help teams more effectively see and understand trends and outliers. You can compare data over the last few quarters to see what call wait times look like or how many abandoned calls have affected the CSAT score.

4. Draw Conclusions and Strategize

Visualizing your data is crucial to DDDM as it brings life to your findings. Using a stacked bar chart for comparative analysis or a Map chart data visualization to see what areas of the country you get most tickets from can help you find trends that can help answer to longer wait times. Through these analytical reports, you can create a stronger and data-backed plan of action to put your decision into practice. It is also vital to foster an environment for collaboration, hence sharing your reports and dashboards is imperative.

5. Measure Success and Evolve

The process of being data-driven doesn’t end at only drawing conclusions. Great leaders learn from their initiatives and efforts. They measure both successes and failures. With continuous data analysis and repeating the above steps, you can use data to create new and better KPIs or set higher benchmarks for existing ones.

Bottom Line

With the goal to successfully move towards digital transformation, companies need to do their due diligence and make data their best friend. If embraced by everyone in an organization, real-time data becomes a critical enterprise asset. After all, nothing is more persuasive than hard facts when it comes to the decision-making process. Getting started on the Data-driven decision-making journey can seem daunting at first but with a great team and system, It could be the technique you need to fuel growth.