Did you know that Netflix – the biggest online streaming service that produces and releases top movies and TV shows (you know, Stranger Things & Squid Game) owes its success to Big Data?
Their customer retention rate is 93%, the highest benchmark in the industry.
Surely, you’ve glimpsed the term “Big Data” thrown in some bits of news, articles, or even podcasts that are relevant to data science. But what is it in a layman’s term?
Well, first you need to understand that data is being produced every second you do anything on the internet, or even if you do nothing and just surf. This data gets accumulated at an unimaginable rate of zettabytes.
The latest estimates state that 328.77 million terabytes of data are created each day.
This chaos consists of extremely large, diverse, and complex collections of structured, unstructured, and semi-structured datasets that continue to grow exponentially. Big Data is chaos.
Yes, Big Data refers to these huge and complex data sets that are challenging to analyze by traditional data-processing applications.
Therefore, big organizations (such as Netflix, Amazon, etc.) collect them using processing applications with larger dataset capacities integrated with AI and ML to analyze, interpret, and mine data for valuable information and insights.
Now that we’ve explored the expansive world of big data and its pivotal role in shaping industries like entertainment, it’s essential to understand a fundamental distinction: the difference between data and information.
Let’s delve deeper into today’s raging topic, Data vs information.
What is data?
We all remember what we have read since elementary school, the classic “Data is a collection of raw facts and figures”. Well, that hasn’t changed as it is the core of ‘Data’ but there is more to it than just that.
Just as it is said in grounded theory methodology, “All is Data.”
Grounded theory is a valuable tool for researchers in fields like sociology, where they collect data first, analyze it, and then let it guide them to further data that needs to be collected.
With that data, they can refine the emerging theory rather than starting with a hypothesis and testing.
This concept emphasizes that everything a researcher encounters during the study can be considered data, not just the traditional interview transcripts or observation notes.
Even the field notes, interview recordings, participant artifacts, and even the researcher’s own observations and reactions, i.e. “all is data”
Likewise, data is not limited to individual facts or statistics, it can be anything from text, observations, symbols, images, codes, numbers, graphs, quantity, units, etc.
It is divided into two major types which are quantitative and qualitative data.
Value represented in numeric values such as the height & weight of a person, their age, income, expense, etc is quantitative data.
Whereas, Value not represented numerically, rather it is textual and descriptive, such as the name of the person, their gender, hair, eye color, etc is qualitative data.
Types of data for analysis
However, data generated on the web is not that simple. It is categorized into structured, semi-structured, and unstructured data.
Structured data features elements that are formatted, organized, and readily available for effective analysis. They reside in a relational database, possess rational keys, and are easily mappable into pre-designed fields. For example: XLS file.
Semi-structured data is partially organized, not in a formatted dataset or spreadsheet but it does have attributes that are easy to identify. It doesn’t exist in a relational database but it does have some organizational properties making it easier to analyze. For example: XML file.
Unstructured data on the other hand has data that aren’t organized in any particular format. They exist in free forms like text documents, photos, speech transcripts, web pages, blog posts, social media posts, and customer feedback.
It is scalable, flexible, and the most valuable asset for qualitative analysis. It is critical for sentiment analysis and allows you to gain the biggest competitive edge by uncovering trends and patterns in the market. For this, web data extraction is paramount.
Information from these data types can be a treasure to one and random noise to another. But keep an eye out if you’re a business owner as it most definitely is a treasure to you, given that you know how to leverage it.
Data Vs Information
Let’s look into how data and information differ from one another in different aspects.
“What is information then?” might be the next obvious question.
Information is a meaningful result that helps in decision-making after we analyze the collected, and organized data. It has context, relevance, and purpose.
It is the processed data used to take the next action. But, there is a catch to determining how authentic a piece of information actually is. We can compare it to accuracy, completeness, and timeliness.
Before jumping to conclusions you should double-check and decide whether the data where the information is coming from is accurate, complete, and relevant as per the context.
Difference in Data Vs Information
| Data | Information |
| Data is raw facts and figures that are yet to be processed and analyzed. | Information is processed and organized data with context, relevance, and purpose. |
| Purpose: It serves as the foundation for generating information. | Purpose: It serves in making business decisions, identifying problems/solutions, and gaining an understanding of a situation. |
| Volume: It exists in large volumes generated continuously from the web. | Volume: It is a condensed and synthesized form of the large volume of data into meaningful interpretations. |
| Context: Raw data lacks context and usually doesn’t have immediate relevance. | Context: Information on the other hand is contextualized and provides insights into the given situation or a problem. |
| Interpretation: It is stored with proper organization but to derive meaning, it requires interpretation and analysis. | Interpretation: It is already processed and interpreted, available for consumption and decision-making. |
| Significance: It is the most essential element (the raw material) that holds the potential to generate information to formulate business strategies. However, it does require processing and analysis to extract value. | Significance: It is immediately useful and actionable for decision-making or problem-solving. It conveys valuable insights after data analysis to support decision-making. |
| Example: 1. Number of traffic to a blog post in 3 months. 2. Average rating of a movie. 3. Price of a similar product on the competitor’s page. | Example: 1. Understanding what the people are searching for answers to. 2. Identifying reasons why the movie is liked/disliked by the audience. 3. Determine if the competitor is selling the product at a higher or lower margin than the market rate. |
Journey from Data to information
There exists quite a rigorous process of organization, analysis, and interpretation before data goes through metamorphosis to become information that is crucial for data-driven decision-making accelerating business growth.
Let’s go through the process step-by-step.
Collection of data
This is the beginning of extracting meaningful information. You can collect data from various sources on the web, surveys, social media, feedback forms, etc. For the easiest and hassle-free data extraction, reach out to Grepsr for fast, accurate, reliable, and real-time actionable data.
Organization
Then, you must organize the mostly unstructured raw data you’ve collected into a clean format. So this process involves data sorting, categorizing, transforming, aggregating, and removing redundancies & error values for further analysis.
Pre-processing
You know its serendipity when you realize the service (Grepsr) that provides you quality data also does everything else mentioned above with its robust and rigorous probabilistic QA framework.
That’s right, our QA process automatically detects any dataset abnormality like duplicate rows or missing values. Then, the seasoned team is right behind to fix those errors, they check and see whether each value in the data field matches the expected data type.
We ensure the accuracy and reliability of the delivered datasets. Thus, the data you receive on your end is ready to be integrated into analytics tools.
Processing and Analysis
Now that the data is organized, it’s time for it to undergo processing and analysis. This phase consists of applying multiple analytical techniques like statistical analysis, predictive analysis, AI integration, data mining, machine learning, and more to extract insights, trends, and patterns in the database.
Interpretation
As soon as you’re done with the analysis, you need to interpret it to derive meaning and relevant conclusions. This is when you can understand the insights in the context of the problem at hand.
Contextualization
Okay, so you have the meaningful information, what’s next? When the information is placed in the right context, only then it proves to be significant for business decisions. It is vital to apprehend how the valuable insights fit within the larger picture of the organization. Also, how they can contribute to achieving the specific goals.
Communication
Finally, now is the time for data storytelling where the insights in the form of visual interpretations narrate a story of itself.
You can effectively convey the information that needs to be communicated to the stakeholders in your presentation by creating dashboards, charts, and graphs.
In this way, you can transform raw data into actionable insights with the help of data analytics, data storytelling, and visualization.
How can businesses make the most of data and information?
You might be questioning, why is the difference between data vs information significant for businesses anyway?
Information gleaned from data can do wonders for your business if you know how to leverage it.
Data alone cannot provide much value until transformed, processed, and analyzed for insights. Businesses tend to have limited time and resources for manual tracking of competitor’s activity to gain a competitive edge.
But, with actionable information from data, they can properly monitor their competitor’s behavior and make informed decisions to position themselves better and outperform their rivals in the industry.
Having said that, you must keep in mind that the quality of the data you have in the database has the biggest impact on the overall procedure.
Manual extraction or the use of web-available tools compromises the primary characteristics of high-quality data, namely accuracy, reliability, completeness, relevance, and timeliness.
You can better opt for a real-time web scraping service like Grepsr that allows you to customize and specify your requirements while tailoring its solutions to fit your business needs.
Unlock limitless possibilities for data-driven growth, innovation, and success with Grepsr by your side!
Empower your team with unrivaled access to the most reliable, precise, and actionable web data available.
