Using Data-Driven Decision Making For A More Competitive Advantage

Decision Making

Ask any business and they will tell you that data is one of their most important assets. It is from this prime asset that they derive all their business decisions on marketing strategy, risk-related endeavors, product innovations and more.

A company, depending on its industry, can use a variety of methods to collect data. Special events like conferences and conventions present good opportunities to gather user data as well as network contacts. Surveys are also powerful data gathering tools, as long as questions are specifically structured to collect the most relevant information from customers. Some businesses make use of forms to acquire intelligence about their users as well as for lead generation. Public feedback gathering, or crowdsourcing, is also a popular source of data collection.

With this amount of information generated by a business, the most pressing issue remaining is how to “separate the wheat from the chaff.” Of this abundance of data, an organization’s IT team will need to pinpoint which is the most crucial for business purposes and which can be utilized to fulfill the organization’s objectives.

This is the main reason why data analytics is a vital part of the data gathering process. It is where data-driven decision-making (DDDM) is derived.

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DDDM has gained recognition as an optimal approach in business governance that places a premium on decision-making based on verifiable data. This is particularly true for businesses that value DDDM as a means of achieving a competitive advantage over rivals in the industry.

By running the huge amounts of data collected on a daily basis through data analytics, a business can make better decisions, optimize workflow and department functions, become more productive and gain added customer satisfaction and a larger market share.

Still, using a data-driven approach means that companies have to rely on the quality of the data collected and how well their data analytics infrastructure works. One downside to utilizing DDDM is that any errors occurring in data analysis and interpretation can result in some serious issues for the organization.

To avoid this, experts recommend these specific guidelines to optimize data gathering and analysis for the ideal approach to DDDM:

  1. Define the business objectives and pinpoint particular business metrics to focus on. In doing so, the IT team will be able to cull specific data targeted to meet these business objectives.
  2. Nurture talented individuals or teams within the company who are skilled in comprehending and analyzing collected data.
  3. Establish proper governance principles to set parameters for determining data accuracy, the procedures for analyzing the data collected, presenting data in a timely manner as well as security and privacy measures.

Thus, it is important to set up big data infrastructure and mechanisms to reap as much of the benefits of data analytics as possible. By using DDDM effectively, businesses will be able to react and respond to changing market conditions as they occur, understand and take action upon customer needs and drive revenue up.

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