Data vs Information: The Biggest Difference You Never Thought About

 

Harnessing information from data is the cornerstone of any sound business strategy. Still, it’s astonishing how many people confuse data with 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. You cannot use any of these kinds of data as a basis for business reasoning and planning. It holds little to no business value as it is. Yet, it plays a vital role in a grander scheme of things.

 

To put things in perspective, let’s use A DIKW pyramid model.

It has four segments:

  • Wisdom
  • Knowledge
  • Information
  • Data

 

Data forms the bottom of the pyramid. It is the building block of information. To be more precise, it becomes information when contextualized in a proper setting. Wisdom is in knowing whether certain information is valuable and why. On the other hand, knowledge refers to collected and categorized information about something or someone.

 

Face Value and Beyond

Getting to the level of wisdom is the primary goal. From this pyramid’s top, we can observe data in a new light. We are able to make the right calls and solve customers’ problem. But, let’s not get ahead of ourselves. Information is derived from data, not the other way around. If data is flawed to begin with, the whole structure falls down 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. Taking a better look at it reveals it’s a date and not just any date. It’s actually the upcoming New Year’s Day. This can be a useful piece of information 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 ahead of time 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.

 

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 have to be agreed to standards and guidelines for carrying out this process selectively. Bad data is a tremendous waste of time and money. Even worse, it leads to false assumptions, poor forecasts, and strategic missteps. The consequences can be nothing short of business-sinking. In other words, we have to carefully interpret data to inform our digital strategies. This improves the reliability of data and powers market research. We are able to 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.

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