How to Make Your Data Useful

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Data contextualization is a term used to describe putting data in a context that is meaningful for the user. When the presented data is meaningful, users can draw insight quickly for decision-making.

The massive volume of data captured in business systems offers a vault of business intelligence. However, more data is not necessarily better. You need to invest time and effort to contextualize them. There are several steps to contextualize data.

  1. Know your question

Determine the question you want to address. You won’t know what data to extract when you aren’t clear on the problem you want to tackle.

A good place to start might be an operational scenario that requires attention. For example, a manufacturer wants to understand the source of product defects.

Once you’ve identified the question, the next step is to determine your data need.

  1. Specify data elements

With the question stated, determine specific data elements you need to evaluate the situation. Consider what, how, when, where, and frequency of the situation occurring.

For the product defect problem, the manufacturer would want to gather data on:

    • product model
    • what the defect is about
    • how the product was used when the flaw was revealed
    • the negative effects
    • purchase date and location

The first three pieces of data would be used to diagnose cause. The negative effects provide a sense of significance. Purchase date and location would be used to identify associated production information.

Clarity on data elements helps to hone in on the minimum information required to do a proper assessment. It also makes it easier to determine where to find the data.

  1. Identify best data source(s)

Businesses generally use multiple applications that might not be integrated. Duplicate and inconsistent data across applications are common challenges.

To determine where the best source of data is, work with the teams who use the applications. Inquire about completeness of the data captured and use consistency.

For the manufacturer, defect-related data are captured in the customer support application. It would need to cross reference that data with the production data archived in the manufacturing ERP.

  1. Understand limitations

As you explore the best data source(s), capture gaps and anomalies identified. These limitations would serve to guide adjustments and assumptions to be incorporated in the information presented.

Depending on the limitations, you might choose to use a subset of the data in the interim while devoting effort to improve data gathering.

This step is critical in data contextualization especially when available data are not as complete as you would like.  Reliable data offers a higher level of confidence with the information presented. Data limitations deserve attention as you shift toward a data-driven culture.

  1. Present data in an intuitive manner

The final step is to present data in a readily consumable format. The simpler the format, the easier it is for the user.

What is the best way to communicate the essence of the data? Would it be meaningful to look at the absolute numbers, relative percentages, changes over time, comparison across categories or geographic areas?

Revisit the question defined in step 1. If the data presented doesn’t lead to actionable information, you would need to refine steps 1 to 4.

Be aware that how you present the data makes a difference in the interpretation. The scale of a graph can skew the perception on significance. The grouping of data points can camouflage an underlying problem.

Data don’t lie but their presentation could mislead. Data don’t lie but their presentation could mislead. Share on X

Data are numbers (or words) with little meaning without contextualization. In order to distil intelligence from data, you need to go through the process to capture the right data, amalgamate and segregate data in proper context for consumption.

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