Many in the business community are asking, “What are the three distinct characteristics that distinguish big data from traditional data analysis?” This is due to rapidly increasing advances in computing, specifically with regards to artificial intelligence (AI). These technologies are being utilized by businesses in every sector of the global economy. Companies ranging from restaurants to financial services to oil companies to manufacturers are all realizing the incredible potential of AI.
Data mining and behavioral analytics are what distinguish the technologies from each other. Traditional databases collect and store historical information but present it in a relational form. Big data on the other hand collects and provides a richer and more diversified information that traditional databases cannot provide. The key difference between the two is the ability to identify unique units of related information. Specifically, this relates to the ability of a relational database to uniquely identify a customer, a product, a location, or even an action taken by a user in the web browsing experience.
Traditional relational databases allow for uniqueness. This allows users to only access that which they are familiar with and are likely to be able to recall. However, with big data there is a greater potential to uniquely identify multiple users within one system. For instance, say that you want to use a traditional database to gather information about your customers, then it could be very challenging for you to remember your customer’s name and where they live unless you took an initiative to collect their information themselves and store it in a relational database.
On the other hand, traditional intelligence gathering methods to collect information that is in its most basic form: numbers and facts. Big data however allows for much more granular and complex analysis of data. It also allows for real-time analytics. In doing so, it is able to provide a business owner with both unique insights into their market as well as information which can be immediately acted on. While some companies may opt to outsource their intelligence gathering needs, many more are taking advantage of the power of their own proprietary online repository.
Big data also allows for greater control and better aggregation. When it comes to the traditional data warehouse method of collecting and organizing large amounts of data, it often involves many different silos. Each silo potentially stores different types of data, which leads to a lack of uniformity in how the data is organized. Furthermore, there are typically several different ways in which the information can be accessed, which can cause delays in the ability to act on emerging trends or behaviors in your business and hinder your ability to make quick and appropriate changes to your business strategy.
With a data warehouse application, however, all of the information gathered within the silo is brought together in just the right way for optimal business value. This enables you to take advantage of the wide-ranging capabilities of all of the sources, which results in a more comprehensive view of your data and the ability to make the best decisions from this information quickly and effectively. This also helps you to create a more streamlined enterprise resource planning system by reducing the time and expense associated with navigating different sources of data and information to identify key relationships and opportunities. This is especially important for businesses that have a fairly large amount of data but a limited number of key relationships or places where they might exploit these opportunities. For instance, an enterprise resource planning application which searched the country’s ten largest databases to find the highest return on investment data for every city in the country would likely take much longer than if it had simply searched for, and aggregated, the most common data within each of the cities themselves.
Lastly, big data allows for a greater degree of analytical precision when formulating your business strategies and determining critical business steps. Traditional data warehouse applications typically have a greater degree of error because of the number of sources and disparate types of information which need to be analyzed and aggregated in order to create a meaningful and reliable result. The problem with relying on traditional data analysis techniques is that there are often human errors which can greatly skew the results of your analysis, making it impossible to reach a consensus regarding your critical strategic steps. In contrast, with a properly implemented data warehouse application, your analysts have the capacity to make the most accurate and reliable inferences using the data which is readily available to them.
The three distinct characteristics of big data are primarily due to its massive potential for leveraging the collective power of collective intelligence. These tools enable distributed agents to not only identify and extract key business opportunities from massive amounts of data, but also to process this data at scale, forming the basis of many of today’s advanced analytics and business strategy technologies. While the rapid advance of this technology has presented some challenges to the developers and organizations that desire to take advantage of it, organizations should recognize the advantages inherent in this rapidly advancing technology. Organizations that embrace this emerging data warehouse technology will begin to realize significant increases in productivity, while businesses that do not are likely to see their competitive position erode in a very short period of time.