Harnessing information from data is the cornerstone of any sound business strategy. Still, it’s astonishing how many people confuse data vs information. This is partly due to the fact these two concepts are interwoven. They are both integral components of the modern marketing equation.
Yet, there is a fine line to follow here. The juxtaposition of data vs information is a nice way to approach the issue, as it emphasizes their differences. And there are a couple of those to keep in mind. Data comes in the form of unsystematic facts and figures. Most of it is of no value for businesses. It’s just a bunch of scattered and unorganized symbols, numbers, and files. On the other hand, information is not easy to come by. To acquire it, you first have to collect and process data. Then, you anchor data in the business context and attach meaning to it.
That is where research, data management, and aggregation come into play. They pave the way for reliable judgments and killer campaigns. As you can see, you need to cover plenty of ground. So, let’s get right into it and banish ambiguity and confusion.
Two Sides of the Coin
Data and information are used interchangeably in a modern business lexicon.
However, there is a clear distinction here. In its most rudimentary form, data is a series of 1s and 0s that only computers can make sense of. It can also appear as a string of random words and numbers. Or it’s a statement, audio recording, text, email address, conversation, a picture, etc. A standard classification recognizes two main types of data: primary and secondary. The former can be quantitative and qualitative. The latter is either internal or external data.
Understanding Data Types
To delve deeper, let’s explore the characteristics and examples of these data types:
Qualitative Data: This refers to non-numerical information that describes qualities or characteristics. For example, the texture of the skin or the color of the eyes offers insights that are qualitative in nature. Such data is often used to understand perceptions or behaviors that aren’t easily reduced to numbers.
Quantitative Data: In contrast, quantitative data is numerical and answers questions like “how much” or “how many.” This type of data is essential for statistical analysis and often forms the backbone of scientific research, enabling precise measurements and comparisons.
Data Sources
When considering the source, data can be:
Primary Data: This is original data collected first-hand for a specific purpose. It can be both qualitative, like interviews, or quantitative, like surveys.
Secondary Data: This refers to data that has already been collected and published by others, such as reports or studies. Secondary data can be sourced internally within an organization or externally from third-party sources.
By understanding these categories and their nuances, one can effectively gather, analyze, and apply data to a variety of contexts, ensuring that information truly serves its intended purpose.
Characteristics of Data
Data comprises raw and unorganized facts, symbols, figures, and descriptions. Below are key characteristics that define data:
Raw and Unprocessed: Initially, data exists in its most basic form, lacking structure or meaning. It’s akin to scattered puzzle pieces waiting to be assembled.
Various Formats: Data can be textual, numerical, visual, or auditory. This versatility allows it to be collected from diverse sources such as surveys, sensors, and online interactions.
Quantifiable: Often expressed in bits and bytes, data serves as the foundational unit of computer storage and processing. Its size can impact how quickly and efficiently data can be managed.
Neutrality: Data itself does not have intrinsic meaning or intent. It requires analysis and context to transform into actionable insights.
Consistency and Granularity: Data sets can vary in consistency—how uniform the data points are—and granularity—the level of detail within the data. These aspects determine its suitability for specific analyses.
In sum, data’s role is to provide the raw inputs necessary for transforming into information through processes like sorting, organizing, and contextualizing. This transformation turns meaningless fragments into valuable insights.
Face Value and Beyond
Getting to the level of wisdom is the primary goal of making the right calls and solving customers’ problems. But let’s not get ahead of ourselves. Information is derived from data, not the other way around. If data is flawed, the whole structure falls like a house of cards. Furthermore, data is input for computer systems, while information represents the output humans handle and evaluate. Information is data imbued with meaning, data we format. It’s never a single data point but a string of data points melded together.
Many different bits and pieces of information have to be put into context, organized, and processed. For example, the string 01012020 represents data. At first, it seems like a random string. A better look at it reveals it’s a date, not just any date. It’s the upcoming New Year’s Day. This information can be helpful because it relates to other data points. Most importantly, we know the shopping activity skyrockets around this date. This realization allows us to prepare email marketing campaigns and drive more sales in this period.
Date of birth is a similar example. When tied to a person, it becomes information for launching targeted event-based marketing. Namely, you can remind customers you care about them by sending a personalized birthday card. Adding more data and processing lets you customize the message even further.
Understanding Valid Information and Its Verification Process
Valid information is information that can be trusted for its accuracy and reliability. It serves its intended purpose effectively because its authenticity has been confirmed.
Key Characteristics of Valid Information:
Reliable: Consistently trustworthy and dependable.
Accurate: Free of errors and precisely conveys facts or data.
Relevant: Pertains directly to the subject or context at hand.
Comprehensive: Provides a full picture, leaving little room for ambiguity.
Verification Process
Ensuring information is valid requires a structured verification process. Here’s how it typically unfolds:
Source Evaluation: Check the credentials and reputation of the source. Use only reliable sources such as academic journals, established news outlets, or governmental reports.
Cross-Referencing: Compare information with other reputable sources to confirm consistency. Identify common conclusions across various sources to enhance reliability.
Fact-Checking: Use specialized fact-checking services like Snopes or FactCheck.org. Identify discrepancies in data that may point to errors or bias.
Review and Revision: Regular updates to information where necessary, ensuring it reflects the latest data or developments. Peer review processes often add another layer of scrutiny.
By adhering to these steps, one can ascertain the validity of any information, ensuring it is credible and valuable for making informed decisions or conducting detailed research.
For Good Measure
Every business generates a whole lot of data by default. This takes place across a variety of channels, systems, and touchpoints. But, organizations don’t capture and store all this data automatically. There must be agreed standards and guidelines for carrying out this process selectively. Insufficient data is a tremendous waste of time and money. It leads to false assumptions, poor forecasts, and strategic missteps. The consequences can be nothing short of business-sinking. In other words, we must interpret data to inform our digital strategies carefully. This improves the reliability of data and powers market research. We can justify the claims and conclusions we make in day-to-day business life.
The good news is that nowadays, computers do the heavy lifting. They employ algorithms, scripts, and artificial intelligence (AI) to turn data into information. Eventually, this process breeds invaluable business intelligence. Alas, the problem is this process is seldom as straightforward. You have to go in-depth with analysis and prioritize a whole matrix of data points. Number crunching is just the tip of the iceberg. It just tells us that something exists. What we have to do then is understand its characteristics and role.
From Theory to Practice
Let’s give another example. A number of your website visitors originating from different countries is raw data. It just tells us how many people are coming and from where. Comparing the size of groups over time and analyzing fluctuations provides information. But, it’s not clear how that information adds value to the businesses. Should the website cater more to shrinking audiences from certain countries? Maybe it would be better to reach out to a completely new demographic? The answers to these questions are not cut-and-dried. That is to say, data can be misleading and ambiguous. But, this issue doesn’t stem from data itself. It’s always related to faults like collecting incomplete data or failing to put it in the proper context. Businesses of all shapes and sizes are prone to meddling with inaccurate or irrelevant data.
What data you should draw depends on a specific business case. That is to say on your industry, goals, growth stage, and other factors that give a sense of purpose and direction. For instance, let’s say you notice your website advanced in Google ranks. That’s great, but to what effort do we attribute this improved ranking? You need to gather new sets of data that explain who clicked when and where. You may find out some pages perform better than others. Once you examine the whole visitor base and your digital real estate, you can evaluate and fine-tune your SEO strategy. This may involve directing more attention to poorly-optimized segments with high bounce rates.
Know Better to Do Better
The purpose of data-driven marketing is to uncover patterns and trends in consumer behavior. These are the key insights that lay the groundwork for smart strategic planning and execution. The basic premise is simple. A bulk of data emerges from customer interaction and engagement. You conduct thorough research keeping a close eye on historic records of purchases. While gathering such data, you need to separate the good form the bad data. Ideally, pertinent data equals unprocessed facts or numbers that hold shards of information. They let you accurately predict future behavior.
To generate insights relevant to the target audience, establish clear measurement standards. Make sure to utilize data quality software and other cutting-edge solutions. Automate processes without losing a human touch. Finally, format and showcase data in a way that is easy to understand. You can use visual tools such as data trees, tabular data, and data graphs. This should make it easier to get a buy-in from the company’s leadership.
All these efforts are likely to pay dividends.
Ascending to the level of wisdom enables you to deploy retargeting, dynamic advertising, optimized paid search, and other tactics. You are in a position to understand the underlying principles that govern market trends. This allows you to develop products and shape multi-channel user experience (UX) better. It’s a clear win-win.
Data vs Information: Dichotomy and Unity
Data and information are different stages in the same evolution chain. Understanding the crucial distinction and connection between them is paramount. At the same time, grasping data vs information concept is just the first step on a long journey. We still cannot afford to sit back and let computers do all the work. We have to fill the gap between data, information, and insights (business intelligence)
First off, you need to establish goals and criteria for data selection. Here, make educated decisions that account for your business requirements and expectations. Set up a standardized system for filtering, refining, and structuring data. Factor in demographics and other indicators that define your target audience. Try to better understand customers, their wants, needs, and preferences.
Set the foundations for data-driven marketing, which boosts customer engagement, retention, and satisfaction. Contact us if you struggle to meet these goals. We’ll help you tailor messages to resonate with consumers and gain a powerful competitive edge.