After the emergence of Big Data as the new shiny thing for collecting data, we suddenly see businesses noting down every bit of information of a customer. It has kept on accumulating in hopes of a Minority Report kind of situation that we will predict the next step of the customers even before they think of it.
Is the data even useful; if it is dirty, improper, or overwhelmingly large for an analyst to make sense out of it? 80% of the time, an analyst works on cleaning dirty data. So, where is the time to derive insights to empower decision-makers?
There is a crucial difference between the data point and data insights.
Data points are the number, the information we capture as a result of a survey, polls, documentation, etc., this is usually in a standardized form. The format is consistent across platforms.
Analyzing this data information, we get an insight into what it means for our business. Without the people and tools to analyze information correctly, it doesn’t make sense to accumulate data. Our main goal as an analyst should be to interpret data, and rather than making reports with numbers, facts, and charts; we should be able to provide an insight that leads to a useful, actionable and attainable business goal.
What can you do to turn data into actionable insights-
- Measure the essential information for your business.
- Use a data visualization tool to put your point across, eg. Tableau.
- Align teams, stakeholders, business partners to understand their challenges and what they want to understand.
- Follow the best practices for developing the research plan.
- The clarity in making a hypothesis,
- Aligning team members,
- Reiterating the goal to keep everybody on the same page,
- Standardized data,
- Integration of data sources and different teams.
Businesses following best practices to understand and analyze data see substantial growth; they also understand the value of people who expertly understand and interpret it. Data without experts is just like raw ingredients without a cook; it will occupy space and rot.