Data Insights

What are Data Insights?
Data insights refers to the deep understanding an individual or organisation gains from analysing information on a particular issue. This deep understanding helps organisations make better decisions than by relying on gut instinct.
Data = a collection of facts.
Analytics = organizing and examining data.
Insights = discovering patterns in data.

There’s also a linear aspect to these terms that differentiates them. Data is collected and organized, then analysis is performed, and insights are generated as follows:

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Data Insights Examples

Data insights will vary between industries and departments of organizations. Still, below are four key data insights examples which can apply to many teams.

Data insights that:

  1. Optimise processes to improve performance.
  2. Uncover new markets, products or services to add new sources of revenue.
  3. Better balance risk vs reward to reduce loss.
  4. Deepen the understanding of customers to increase loyalty and lifetime value.

Let’s use the last one, deeper customer understanding, to give a more specific data insights example…

How to Get Data Insights

The process to obtain actionable data insights typically involves defining objectives, collecting, integrating and managing the data, analysing the data to gain insights and then sharing these insights.

1) Define business objectives

Stakeholders initiate the process by clearly defining objectives such as improving production processes or determining which marketing campaigns are most effective, like in the example above.

2) Data collection

Ideally, systems have already been put in place to collect and store raw source data. If not, the organization needs to establish a systematic process to gather the data.

3) Data integration & management

Once collected, source data must be transformed into clean, analytics-ready information via data integration. This process includes data replication, ingestion and transformation to combine different types of data into standardised formats which are then stored in a repository such as a data lake or data warehouse.