Michael Wood, an accomplished, results-driven marketing executive with >25 years’ c-level experience shares his insights about the importance of the connection between data and information on decision-making. Read on to see what he has to say about understanding the cause-and-effect relationships in your data.
Data comprises raw observations and little or no context. A dataset is ultimately only a body of quantities and text that may or may not be significant. Information comprises data with context, processed data, value-added data, and organised data which may or may not be meaningful, and lead to knowledge and ultimately wisdom.
The bridge between data and information is therefore critical to decision-making. If you have inappropriate data, you have nothing, and collecting more inappropriate data will not remedy the situation. However, even if you have appropriate or relevant data, if the said data is not processed or organised meaningfully, your conclusions will probably be erroneous.
The nature of the assumptions and hypotheses you use when processing and organising your data will have an immense influence on the deductions, interpretations, and inferences ‘extracted’ from the data, i.e., the ‘information’ you extract.
To illustrate the point, let’s use the age-old practice of joining the dots as an analogy. First, you need to collect appropriate dots and then ensure that you connect these dots according to an insightful cause-and-effect framework that informs the relationship between the dots. Then you can make deductions, interpretations, and inferences; i.e., extract what the joining of the dots is ‘telling’ you. Without such a framework, e.g., a numbering system, it is possible to arrive at different outcomes by connecting the same dots differently, as shown below. It is therefore also possible to group the same dataset in different ways, extract different permutations of information, and arrive at different, often conflicting, conclusions.
A well-documented example of this is the Rosetta Stone. Until the ‘rediscovery’ of the Rosetta Stone by Napoleon’s army in July 1799, Egyptian hieroglyphs were essentially just indecipherable inscriptions for most scholars, i.e., raw data. As an insightful cause-and-effect framework, the Rosetta Stone facilitated meaningful deciphering, i.e., turned data into information.
Understanding the cause-and-effect relationships in your data and being able to separate leading and lagging indicators will go a long way to enabling the extraction of meaningful information.
Authored by Michael Wood (not a SAFREA member)
Michael is an accomplished, results-driven marketing executive with >25 years’ c-level experience including owning an agency for 15 years. Michael has deep B2B experience in communication, positioning, content, branding, and strategy across several verticals. This insight, and a balanced and foundational post-graduate education complement, allows Michael to add value through campaign design, and strong messaging across multiple mediums. Simply put, he tells the right stories, to the right people, using the appropriate channels, at the right time. Michael has a keen interest in the convergence of technology.
Proofread and copyedited by Delilah Nosworthy (SAFREA member)
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