By now we know what data is, but now the question is: what is the purpose of collecting data? Data is useful for describing a physical or social phenomenon and to further answer questions about that phenomenon. For this reason, it is important to ensure that the data is not faulty, inaccurate, or incomplete; otherwise, the responses based on that data will also not be accurate or complete.
There are different categories of data, some of which are past performance data, experimental data, and benchmark data. Past performance data and experimental data are pretty self-explanatory. Benchmark data, on the other hand, is data that compares the characteristics of two different items or products to a standard measure. Data gets transformed into information, is processed further, and is then used for answering questions. It is apparent, therefore, that our next step is to achieve that transformation.
Data is collected and stored in several different forms depending on the content and its significance. For instance, if the data is about playoff basketball games, then it will be in a text and video format. Another example is the temperature recordings from all the cities of a country, collected and made accessible via different formats. The transformation from data to information involves collection, processing, and organization of data as shown in the following diagram:
The collected data needs some processing and organizing, which later may or may not have a structure, model, or a pattern. However, this process at least gives us an organized way of finding answers to questions about the data. The process could be a simple sorting based on the total points scored by basketball players or a sorting based on the names of the city and state.
The transformation from data to information could also be a little more than just sorting such as statistical modeling or a computational algorithm. It is this transformation from data to information that is really important and enables the data to be queried, accessed, and manipulated. In some cases, when there is a vast and divergent amount of data, the transformation may involve processing methods such as filtering, aggregating, applying correlation, scaling and normalizing, and classifying.
Data collection is a time-consuming process. So, businesses are looking for better ways to automate data capture. However, manual data collection is still prevalent for many processes. Data collection by automatic processes in modern times uses input devices such as sensors. For instance, underwater coral reefs are monitored via sensors; agriculture is another area where sensors are used in monitoring soil properties, controlling irrigation, and fertilization methods.
Another way to collect data automatically is by scanning documents and log files, which is a form of server-side data collection. Manual processes include data collection via web-based methods that get stored in the database, which can then be transformed into information. Nowadays, web-based collaborative environments are benefiting from improved communication and sharing of data.
Traditional visualization and visual analytic tools are typically designed for a single user interacting with a visualization application on a single machine. Extending these tools to include support for collaboration has clearly come a long way towards increasing the scope and applicability of visualizations in the real world.
Today, data is highly susceptible to noise and inconsistency due to its size and likely origin from multiple, heterogeneous sources and types. There are several data preprocessing techniques such as data cleaning, data integration, data reduction, and data transformation. Data cleaning can be applied to remove noise and correct inconsistencies in the data. Data integration merges and combines the data from multiple sources into a coherent format, mostly known as data warehouse. Data reduction can reduce data size by, for instance, merging, aggregating, and eliminating the redundant features. Data transformations may be applied where data is scaled to fall within a smaller range, thus improving the accuracy and efficiency in processing and visualizing them. The transformation cycle of data is shown in the following diagram:
Anomaly detection is the identification of unusual data that might not fall into an expected behavior or pattern in the collected data. Anomalies are also known as outliers or noise; for example in signal data, a particular signal that is unusual is considered noise, and in transaction data, an outlier is a fraudulent transaction. Accurate data collection is essential for maintaining the integrity of data. As much as the down side of anomalies, on the flip side, there is also a significant importance of outliers—specifically in cases where one would want to find fraudulent insurance claims, for instance.
Data processing is a significant step in the transformation process. It is imperative that the focus be on data quality. Some processing steps that help in preparing data for analyzing and understanding it better are dependency modeling and clustering. There are other processing techniques, but we will limit our discussion here with the two most popular processing methods.
Dependency modeling is the fundamental principle of modeling data to determine the nature and structure of the representation. This process searches for relationships between the data elements; for example, a department store might gather data on the purchasing habits of its customers. This process helps the department store deduce the information about frequent purchases.
Clustering is the task of discovering groups in the data that have, in some way or another, a "similar pattern", without using known structures in the data.
Database management systems allow users to store data in a structured format. However, the databases are too large to fit into memory. There are two ways of structuring data:
Storing large data in disks in a structured format like tables, trees, or graphs
Storing data in memory using data structure formats for faster access
A data structure comprises a set of different formats for structuring data to be able to store and access it. The general data structure types are arrays, files, tables, trees, lists, maps, and so on. Any data structure is designed to organize the data to suit a specific purpose so that it can be stored, accessed, and manipulated at runtime. A data structure may be selected or designed to store data for the purpose of working on it with various algorithms for faster access.
Data that is collected, processed, and organized to be stored efficiently is much easier to understand, which leads to information that can be better understood.
For readers who do not have access to organizational data, there are plenty of resources on the Internet with rich datasets from several different sources, such as:
http://grouplens.org (from the University of Minnesota)
http://weather-warehouse.com (weather data)
http://www.bjs.gov/developer/ncvs/ (Bureau of Justice Statistics)
http://census.ire.org/data/bulkdata.html (census data)
http://ww.pro-football-reference.com (football reference)
http://www.basketball-reference.com (basketball reference)
http://www.baseball-reference.com (baseball reference)
http://archive.ics.uci.edu/ml/datasets.html (machine learning)
http://archive.ics.uci.edu/ml/datasets/Heart+Disease (heart disease)
Information is quantifiable and measurable, it has a shape, and can be accessed, generated, stored, distributed, searched for, compressed and duplicated. It is quantifiable by the volume or amount of information.
Information transforms into knowledge by the application of discrete algorithms, and knowledge is expected to be more qualitative than information. In some problem domains, knowledge continues to go through an evolving cycle. This evolution happens particularly when the data changes in real time.
Knowledge is like the recipe that lets you make bread out of the information, in this case, the ingredients of flour and yeast. Another way to look at knowledge is as the combination of data and information, to which experience and expert opinion is added to aid decision making. Knowledge is not merely a result of filtering or algorithms.
What are the steps involved in this transformation, and how does the change happen? Naturally, it cannot happen by itself. Though the word information is subject to different interpretations based on the definition, we will explore it further within the context of computing.
A simple analogy to illustrate the difference between information and knowledge: course materials for a particular course provide you the necessary information about the concepts, and the teacher later helps the students to understand the concepts through discussions. This helps the students in gaining knowledge about the course. By a similar process, something needs to be done to transform information into knowledge. The following diagram shows the transformation from information to knowledge:
As illustrated in the figure, information when aggregated and run through some discrete algorithms, gets transformed into knowledge. The information needs to be aggregated to get broader knowledge. The knowledge obtained by this transformation helps in answering questions about the data or information such as which quarter did the company have maximum revenue from sales? How much has advertising driven the sales? Or, how many new products have been released this year?
In the traditional system, information is processed, and then analyzed to generate reports. Ever since the Internet came into existence, processed information is already and always available, and social media has emerged as a new way of conducting business.
Organizations have been using external data to gain insights via data analysis. For example, the measure of user sentiments from tweets by consumers via Twitter is used to follow the opinions about product brands. In some cases, there is a higher percentage of users giving a positive message on social media about a new product, say an iPhone or a tablet computer. The analytical tool can provide numerical evidence of that sentiment, and this is where data visualization plays a significant role.
Another example to illustrate this transformation, Netflix announced a competition in 2009 for the best collaborative filtering algorithm to predict user ratings for films, based on previous ratings. The winner of that competition used the pragmatic theory and achieved a 10.05 percent improvement in predicting user ratings, which increased the business value for Netflix.
Transforming knowledge into insight is achieved using collaboration and analytics as shown in the preceding diagram. Insight implies seeing the solution and realizing what needs to be done. Achieving data and information is easy and organizations have known methods to achieve that, but getting insight is very hard. Achieving insight requires new and creative thinking and the ability to connect the dots. In addition to applying creative thinking, data analysis and data visualization play a big role in achieving insight. Data visualization is considered both an art and a science.