Lately, there have been tremendous shifts in the business technology landscape. Advances in cloud technology and mobile applications have enabled businesses and IT users to interact in entirely new ways. One of the most rapidly growing technologies in this sphere is business intelligence, and associated concepts such as big data and data mining.
To help you understand the various business data processes towards leveraging business intelligence tools, it is important to know the differences between big data vs data mining vs business intelligence. We’ve outlined the definitions of each, and detailed how they relate and compare to each other.
What Is Business Intelligence?
Business intelligence encompasses data analysis with the intent of uncovering trends, patterns and insights. Findings based on data provide accurate, astute views of your company’s processes and the results those processes are yielding. Beyond standard metrics such as financial measures, in-depth business intelligence reveals the impact of current practices on employee performance, overall company satisfaction, conversions, media reach and a number of other factors.
In addition to presenting information on the present state of your organization, the utilization of business intelligence can forecast future performance. Through the analysis of past and current data, robust BI systems track trends and illustrate how those trends will continue as time goes on.
Business intelligence encompasses more than observation. BI moves beyond analysis when action is taken based on the findings. Having the ability to see the real, quantifiable results of policy and the impact on the future of your business is a powerful decision-making tool.
How Is Big Data Defined?
The term big data can be defined simply as large data sets that outgrow simple databases and data handling architectures. For example, data that cannot be easily handled in Excel spreadsheets may be referred to as big data.
Big data involves the process of storing, processing and visualizing data. It is essential to find the right tools for creating the best environment to successfully obtain valuable insights from your data.
Setting up an effective big data environment involves utilizing infrastructural technologies that process, store and facilitate data analysis. Data warehouses, modeling language programs and OLAP cubes are just some examples. Today, businesses often use more than one infrastructural deployment to manage various aspects of their data.
Big data often provides companies with answers to the questions they did not know they wanted to ask: How has the new HR software impacted employee performance? How do recent customer reviews relate to sales? Analyzing big data sources illuminates the relationships between all facets of your business.
Therefore, there is inherent usefulness to the information being collected in big data. Businesses must set relevant objectives and parameters in place to glean valuable insights from big data.
Data Mining: What Is It?
Data mining relates to the process of going through large sets of data to identify relevant or pertinent information. However, decision-makers need access to smaller, more specific pieces of data as well. Businesses use data mining for business intelligence and to identify specific data that may help their companies make better leadership and management decisions.
Data mining is the process of finding answers to issues you did not know you were looking for beforehand. For example, exploring new data sources may lead to the discovery of causes for financial shortcomings, underperforming employees and more. Quantifiable data illuminates information that may not be obvious from standard observation.
Information overload leads many data analysts to believe they may be overlooking key points that can help their companies perform better. Data mining experts sift through large data sets to identify trends and patterns.
Various software packages and analytical tools can be used for data mining. The process can be automated or done manually. Data mining allows individual workers to send specific queries for information to archives and databases so that they can obtain targeted results.
Business Intelligence vs Big Data
Business intelligence is the collection of systems and products that have been implemented in various business practices, but not the information derived from the systems and products.
On the other hand, big data has come to mean various things to different people. When comparing big data vs business intelligence, some people use the term big data when referring to the size of data, while others use the term in reference to specific approaches to analytics.
So, how do business intelligence and big data relate and compare? Big data can provide information outside of a company’s own data sources, serving as an expansive resource. Therefore, it is a component of business intelligence, offering a comprehensive view into your processes. Big data often constitutes the information which will lead to business intelligence insights.
Again, big data exists within business intelligence. This means the two differ in the amount and type of data they include. As business intelligence is an umbrella term, the data that is considered a part of BI is much more all-inclusive than what falls under big data. Business intelligence covers all data, from sales reports hosted in Excel spreadsheets to large online databases. Big data, on the other hand, consists of only those large data sets.
The tools involved in the processes of big data and business intelligence differ as well. Base-level business intelligence software has the ability to process standard data sources, but may not be equipped to manage big data. Other more advanced systems are specifically designed for big data processing.
Of course, in the big data vs BI discussion, there is some overlap involved in the use of comprehensive business intelligence systems that are made to handle large sets of data. Most business intelligence software vendors offer tiered cost models which increase functionality depending on the price. Big data capabilities may also be offered as an add-on to a BI software system. And that’s BI vs big data.
BI vs Data Mining
As previously stated, business intelligence is defined as the methods and tools used by organizations to glean analytical findings from data. It also consists of how companies can gain information from big data and data mining. This means business intelligence is not confined to technology — it includes the business processes and data analysis procedures that facilitate the collection of big data.
Data mining falls under the umbrella term of “business intelligence,” and can be considered a form of BI. Data mining can be considered a function of BI, used to collect relevant information and gain insights. Moreover, business intelligence could also be thought of as the result of data mining. As stated, business intelligence involves using data to acquire insights. Data mining business intelligence is the collection of necessary data, which will eventually lead to answers through in-depth analysis.
The link between data mining and business intelligence can be thought of as a cause-and-effect relationship. Data mining searches for the “what” (relevant data sets) and business intelligence processes uncover the “how” and “why” (insights). Analysts utilize data mining to find the information they need and use business intelligence to determine why it is important.
Big Data vs Data Mining
Big data and data mining differ as two separate concepts that describe interactions with expansive data sources. Of course, big data and data mining are still related and fall under the realm of business intelligence. While the definition of big data does vary, it generally is referred to as an item or concept, while data mining is considered more of an action. For example, data mining may, in some cases, involve sifting through big data sources.
Big data does, by some definitions, include the action of processing large data sets. Conversely, data mining is more about collecting and identifying data. Data mining will usually be the step before accessing big data, or the action needed to access a big data source. These two components of business intelligence work in tandem to determine the best data sets to provide answers to your organization’s questions. Following the processes involved in data mining vs big data, analysts can begin evaluating data and eventually offer suggestions for business procedure improvements based on their findings.
The practices of business intelligence are not a step-by-step operation. It’s not as simple as mine for data, complete the big data function of processing the information and perform business intelligence analysis. Analyzing data with the intent of using that data to influence business decisions is an ongoing, interconnected process. During analysis, you may find you need new data or that your current approach is not successful. Necessary adjustments made to your business intelligence plans along the way will ensure accurate, truly insightful analysis.
Additionally, as one purpose of business intelligence is to deliver real-time insight, this will be a continuing project. Your company will need to be constantly collecting and investigating data to achieve the most up-to-date information portraits possible.
Business intelligence, big data and data mining are three different concepts that exist in the same sphere. Business intelligence can be considered the overarching category in which these concepts exist, as it can be simply defined as data-based analysis of business practices. Big data is mined and analyzed, resulting in the gain of business intelligence. While these three concepts differ, BI, big data and data mining all work together to serve the purpose of providing data-driven insights. They are tools which can lead to a greater understanding of your business, and ultimately more streamlined processes which increase productivity and financial yield.
How does your organization use BI, big data and data mining? Leave a comment below.