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. Big data mining is another concept that employs collective data mining or extraction techniques that are performed on huge volumes of data.
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To help you understand the various business data processes, it is important to know the differences between business intelligence vs. big data vs. data mining. We’ve outlined the definitions of each, and detailed how they relate and compare to each other.
Article Roadmap:
- What Is Business Intelligence?
- How Is Big Data Defined?
- Data Mining: What Is It?
- BI vs. Big Data
- BI vs. Data Mining
- Big Data vs. Data Mining
- In Short
What Is Business Intelligence?
Business intelligence is a discipline that 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 tools track trends and illustrate how those trends will continue as time goes on.
Business intelligence visualizations created in Exago BI.
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. For example:
- How does the new HR software impact 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 collected as 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 digging through large data sets to identify relevant or pertinent information. However, decision-makers need access to smaller, more specific pieces of data as well. Businesses use data mining to identify specific data that may help their companies make better leadership and management decisions.
Data mining finds solutions to problems. For example, analyzing data sources can uncover useful insights that help hiring teams conduct thorough background analysis while recruiting. Digging deeper into data highlights information that may not be obvious from standard observation.
Information overload can make data analysts believe they’re ignoring key points that could enhance business performance. Data mining experts unearth huge information repositories to identify points of interest.
Various software packages and analytical tools make data mining possible. The process is manual or automated. Data mining allows individual workers to send specific queries for information to archives and databases so that they can obtain targeted results.
BI vs. Big Data
Business intelligence is the collection of systems and products 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 or type of data, while others use the term in reference to specific approaches to analytics.
So, how do big data and business intelligence 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 mining.
Of course, there is some overlap involved in the use of comprehensive business intelligence systems that handle large datasets. 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.
The objective of business intelligence is to help businesses make data-driven decisions. BI helps deliver robust reports by extracting information from different data sources to gauge business performance and processes. The purpose of big data is to ingest, process and analyze huge datasets, both structured and unstructured, to boost business outcomes.
Big data analytics from Alteryx.
Another significant difference between big data and business intelligence is the use of components. BI uses operational systems, ERP software and data warehouses to store data, while big data uses Hadoop, Spark, Hive, R server and more.
BI vs. Data Mining
As previously stated, business intelligence is the methods and tools organizations use 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 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.
Business intelligence and data mining differ in core aspects, including purpose, volume and results. The purpose of BI is to convert raw data into useful information for executives and stakeholders. It tracks and presents key performance metrics on reports and dashboards to facilitate robust, data-driven decisions.
On the other hand, the primary objective of data mining is to explore and analyze data to uncover solutions to specific business problems. It leverages complex algorithms and computational intelligence to detect trends and patterns.
Data mining algorithms process datasets from specific departments, customer segments or competitors. Digging deeper into datasets lets them find answers to specific business problems. On the contrary, business intelligence processes enterprise-wide data to deduce business performance.
Since data mining focuses on resolving complex business problems, the end result is a statistical data model that looks for patterns and relationships within similar datasets. However, business intelligence produces charts, graphs, dashboards and reports.
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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.
By some definitions, big data includes 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.
When discussing big data vs. data mining, big data refers to huge amounts of data in a structured, semi-structured and unstructured form. Data mining involves digging deeper into huge datasets to identify trends and patterns to make strategic decisions.
Big data mining involves the collective data mining or extraction methods performed on huge datasets or big data. Big data mining extracts patterns or trends from data to make robust decisions. It leverages data searching, extraction, refinement and comparison algorithms.
Ideally, the goal of data mining is classification or prediction. When classifying, the goal is to sort information into groups. For example, a marketer might classify respondents based on positive or negative responses to a particular advertising campaign.
Prediction algorithms predict the value of a continuous variable. For instance, a marketer might be interested in determining the number of people who will respond ‘yes’ to a marketing campaign.
In Short
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 concepts differ, BI, big data, data mining and big 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, data mining and big data mining? Leave a comment below.
