With a lot of data to work with, organizations have a clearly defined analytics process to get the most out of their data. Source: Shutterstock

With a lot of data to work with, organizations have a clearly defined analytics process to get the most out of their data. Source: Shutterstock

How to get most out your data in the age of data abundance

IN the digital age, businesses collect too much data which makes it difficult for them to decide what’s relevant when it comes to making an informed business decision.

As a result, they need to figure out if the data they collect answers all their questions, allows them to draw a reliable conclusion, and enables them to decide on a business strategy confidently.

To do all of that and more, businesses need better analytics solutions. With the right tools and streamlined processes, poring through mountains of data could be simplified.

Here are five steps to follow that will improve the data analysis process for any organization;

# 1 | Formulate well-defined questions

Firstly, businesses should start by asking the right questions, which are clear, well-defined and, measurable.

The questions should either present or eliminate potential options to solve the specific problem organizations seek to address using the data.

For example, the management of a logistics company that is experiencing competitive issues due to the rising cost of labor and other fixed costs could ask questions such as: Could the company adopt automation in its one of its sorting center to reduce labor cost while delivering the same work quality in a timely fashion?

# 2 | Knowing what to measure and how

The organization then will need to figure out what data will be required to answer the question.

In the process of answering that question, the organization may need to take on other sub-questions such as; Is the workflow is streamlined for optimal output? If not, what can be done to improve productivity?

Further, how a data is gathered is also vital to ensure a valid and credible analysis. Proper time frame (annual vs. quarterly), consistent units of measurement (currency) and other factors (time and output) has to be determined early on, before the data collection phase.

In the event of the logistics company, the number of staff during a specific shift should be monitored for a year including the number of packages being processed within their shift.

That way, peak season with a possible surge in workloads are also captured and accounted for, in the analysis.

# 3 | Collect data

Prior to collecting new data, the company should consider data that are already available on hand, that could be used in the analysis which could reduce redundancy and work. For example, at a logistics facility, data on the size of the workforce and number of packages processed at a specific timeframe should be readily available.

Proper storage systems with a standardized naming convention would enable more team members to participate without repeating each other’s work.

Beyond that, all data collection should be logged and in the event that data normalization is performed, a note must be made, to ensure a valid conclusion later on.

A shift supervisor could be tasked with collecting new detailed data using the standardized method at a sorting center in a logistics company

# 4 | Analyze data

At this stage, data could be plotted to find correlations, or deeper analytics could be applied to gain valuable insights to answer the initial questions.

In some cases, analyzing the data will yield the required answer to the initial questions, but more often than not, organizations may need to revise their questions and collect more data.

This preliminary analysis help focus the analysis to provide a viable answer to the primary question.

# 5 | Interpret results

It is important to keep in mind that a hypothesis could not ever be proven true but instead, only fail to be rejected, which means regardless of however solid the data may be, chance could always affect the results.

The interpretation of the data should be able to;

  • Answer the original question
  • Defend against objections to the proposed changes
  • Address all limitation to conclusions and other perspectives

Satisfying all the criteria above will yield a solid conclusion which could help make more informed business decisions.

In the hypothetical case of the logistics company, the management could reduce the human workforce, and automate the sorting center operations with robots, if it reduces labor cost and increases productivity, without compromising the quality. And they will have data to prove it.