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Introduction to Data-Driven Decision Making

Time and time again, the digital industry raises the importance of optimum usage of ‘big data’, leveraged on the wealth of digital insights made easily available with the power of business intelligence. By setting the right reporting tools into place and understanding how to measure your data accurately, ‘big data’ becomes the ultimate solution to drive your business forward.

On that note, organisations have been utilising data-driven decision making (DDDM) to collect data based on measurable goals or KPIs. In return, these insights would be used to analyse patterns and verify facts, thus developing strategies and tactical activities that would benefit your business in various aspects than decisions made ‘in the dark’. However, you can only generate genuine value from your data if you base it off your aims.

Previously in older versions, data-driven decision making was an all-in-one task that would analyse insights but would take too long to generate and would eventually delay decision-making procedures. DDDM has come a long way since those days, as business intelligence software today continuously progress towards better development and democratization. Users no longer require advanced knowledge and expertise to analyse and extract insights from their data, a change that has substantially reduced the required manpower in IT support to produce reports, trends, visualizations, and insights.

These developments eventually led to the evolution of data science: a specialty that filters through large amounts of collected, raw data for intelligent data-driven business decisions derived from two distinct types of data that are critical for data-driven business decisions, i.e qualitative and quantitative.

Qualitative analysis focuses on data that’s not defined by numbers or metrics as it is based on observation rather than measurement. Hence, data collected should be coded to ensure that items are grouped methodically and intelligently. Quantitative analysis, however, is derived from measurable data, such as numbers and statistics. Therefore, it is ideal that we analyse both qualitative and quantitative data to make holistic data-driven business decisions.

Data in business decisions enables companies to retrieve the best actionable and useful insights to create new business opportunities, generate more revenue, predict future trends, and optimise current operational efforts. With DDDM, your organisation would grow and evolve, making market adaptation breeze to remain relevant. As the digital world is in a constant state of flux, organisations like yours need to leverage data to make more informed and powerful data-driven business decisions as these decisions can either make or break companies.

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