When discussing data quality, we need to understand exactly what we mean by the word data. Often, the words information and data are used interchangeably, yet they are not the same thing.

Data is, or are (depending on your knowledge of Latin), fundamental to business intelligence. But how do we recognise data as data – and why is bad data such a pernicious entity?

First Things First: Data vs Information

There’s a really simple way to understand the difference between data and information. When we understand the primary function of the item we are looking at, we quickly see the distinction between the two.

Here’s a simple way to tell one from the other:

  • Computers need data. Humans need information.
  • Data is a building block. Information gives meaning and context.

In essence, data is raw. It has not been shaped, processed or interpreted. It is a series of 1s and zeros that humans would not be able to read (and nor would they want to). It is disorganised and unfriendly.

Once data has been processed and turned into information, it becomes palatable to human readers. It takes on context and structure. It becomes useful for businesses to make decisions, and it forms the basis of progress.

Understanding Data

Data refers to raw, unprocessed facts and figures.

It is the basic form of information and can be quantitative or qualitative.

Data on its own might not make much sense or have direct meaning. For instance, numbers, measurements, or observations collected through surveys, experiments, or real-time tracking are all examples of data.

Understanding Information

Information, on the other hand, is data that has been processed, organized, or structured in a way that is meaningful and useful. Information typically provides context to the data, making it interpretable and valuable.

For example, a set of data points can be analyzed and turned into a report that offers insights or conclusions.

Specific Differences Between Data and Information

  1. Nature: Data is raw and unprocessed, while information is processed and structured.
  2. Meaning: Data often doesn’t carry meaning without context, but information is data interpreted to derive meaning.
  3. Form: Data can be in the form of numbers, symbols, or unorganized facts. Information is usually in the form of organized data, like reports or analyses.
  4. Utility: Data on its own may not be useful; however, information, being an interpreted form of data, is useful and can guide decision-making.

Examples in Practice

  • Data: A company’s sales figures for each month.
  • Information: An analysis of the sales figures showing trends, patterns, and seasonal variations.

While the bigger picture is slightly more complex, this gets us part way towards understanding what data means.

The Bigger Picture

When we look at the relationship between data and information, we can establish a larger chain. This is the DIKW Pyramid.

Why DIKW? It stands for Data, Information, Knowledge, Wisdom, and describes the hierarchy between all four.

The DIKW Pyramid describes the acquisition of data, its processing, retention and interpretation, and it’s as applicable to businesses as it is to the human brain.

What is the Difference Between Data and Information?
The DIKW Pyramid shows that raw data evolves to become an understanding of a concept.

To see the DIKW Pyramid in action, consider the following example.

  • Data: I have one item. The data displays a 1, not a zero.
  • Information: It’s a tomato. Now, we understand the item and its characteristics.
  • Knowledge: A tomato is a fruit. We can identify patterns in the information and apply them to the item.
  • Wisdom: Tomato is never added to a fruit salad. There is an underlying, commonly understood principle that governs the item’s purpose.

Leveraging Data and Information in Business

  1. Data-Driven Decision Making: Businesses can use data and information to make informed decisions, reducing risks and increasing the chances of success.
  2. Market Analysis: By analyzing market data, businesses can identify customer preferences, market trends, and areas for expansion.
  3. Improving Operations: Data can help in optimizing operations, reducing costs, and improving efficiency.
  4. Customer Insights: Information gathered from customer data can help in personalizing services and products, enhancing customer satisfaction.
  5. Innovation: Insights from data can lead to innovation in products and services, keeping businesses competitive.

Data Quality: The Building Block

In this article, we have truly put data in context. We now understand its position as the foundation. It is the base of a pyramid; the beginning of a continuum.

To recap, data is the raw, uncollected facts that are usually unorganized, while information is data that has been processed and given meaning. Businesses leverage both by converting data into information for informed decision-making and strategic planning.

If data is flawed, the DIKW Pyramid breaks down.

The information we derive from the data is not accurate. We cannot make reliable judgments or develop reliable knowledge from the information. And that knowledge simply cannot become wisdom, since cracks will appear as soon as it is tested.

Bad data costs time and effort, gives false impressions, results in poor forecasts and devalues everything else in the continuum.

Data quality software addresses problems with data to avoid these kinds of problems. It ensures that data processing results in reliable information that improves response and retention. This information unlocks the potential of marketing campaigns, increases sales, improves accuracy and adds value.

That’s why data quality is so vital to us all.

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