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.
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.
- 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.
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.
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.