We live in a world run by big data. The TV shows we watch, social media we follow, news we read, emails we receive — even the optimized routes we take to work — are all dictated by big data analytics.
Consumers have grown accustomed to tailored marketing campaigns, and expect to see new features and products that appeal specifically to them on a regular basis. Companies must constantly monitor customers’ changing behavior and preferences in order to gain attention for their marketing messages and products.
So how do leading companies predict what customers want so accurately? The key is to employ a combination of data mining and business intelligence (BI). While people may use BI and data mining interchangeably, the meaning of each term is quite different.
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What is business intelligence?
Companies have an enormous influx of data coming from their customer base. Every previous purchase, social media interaction, and search engine query is a clue into what a consumer may buy next. Besides the need to store this gross amount of data, businesses must be able to make sense of it. This is where business intelligence shines.
Business intelligence (BI) is a collection of applications and techniques used to transform data into actionable information. BI involves enterprise-level data analysis that pinpoints areas for operational improvement and external expansion. In addition, business intelligence can incorporate data visualization, which further facilitates strategic business decisions.
Aside from internal data analysis, companies can use BI on third party databases to obtain insights about rivals or potential business partners. Ultimately, companies use business intelligence to make decisions that better serve and target customers while simultaneously increasing cost savings.
What is data mining?
Data mining is a branch of data science that searches through vast datasets, mining for nuggets of wisdom. Data mining exposes patterns in massive datasets that can provide valuable business intelligence.
There are several data mining methods, including classification, clustering, and association. Classification divides large datasets into specific categories. This is most effective in marketing, allowing companies to publish different ads in different domains, ensuring that the right ads target customers who would respond most favorably.
Clustering takes classification to a new level, detecting small anomalies or similarities that humans cannot observe. As such, clustering can uncover ways to make targeted marketing, operational efficiency, and product innovation even more powerful. Association uncovers the relationships between variables over time. By tracking and analyzing customer activity, businesses can begin to predict future behavior.
Business intelligence vs. data mining
Business intelligence and data mining differ in a few core ways. Namely, in purpose, volume, and results.
The purpose of business intelligence is to convert data into useful information for executives. Business intelligence tracks key performance indicators and presents data in a way that encourages data-driven decisions. By contrast, data mining is geared towards exploring data and finding solutions to particular business issues. Data mining has the computational intelligence and algorithms to detect patterns that are interpreted and presented to management via business intelligence.
In that same vein, data mining is most optimal for processing datasets concentrated on a particular department, customer segment, or competitor(s). By analyzing these smaller datasets, data mining can reveal hidden answers to specific business questions. Unlike the specificity of data mining, business intelligence processes dimensional or relational databases in order to deduce how an enterprise is performing on the whole.
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Since data mining is more oriented towards getting data into a usable format and resolving unique business problems, the results of data mining are unique datasets. Conversely business intelligence results are presented in charts, graphs, dashboards, and reports. Displaying BI results is vital to influence data-driven decisions.
Lastly, data mining and business intelligence differ in their focus. Studying patterns through data mining helps companies develop new KPIs for business intelligence. Business intelligence is therefore focused on showing progress towards data mining-defined KPIs. Broad metrics like total revenue, total customer support tickets, and ARR over time paint a holistic picture of company performance and give stakeholders the confidence to make significant decisions.
Exploring and formatting data to find answers to business problems
Interpreting and presenting data to stakeholders to inform data-driven decisions
Processes small, specific datasets for focused analysis
Processes relational databases to track enterprise-level metrics
Unique datasets in a usable data format
Dashboards, graphs, charts, reports
Identifying new KPIs
Demonstrating KPI progress
How data mining and business intelligence work together
While the definitions of business intelligence and data mining are different, the two processes work best when used in tandem.
Data mining can be seen as the precursor to business intelligence. Upon collection, data is often raw and unstructured, making it challenging to draw conclusions. Data mining decodes these complex datasets, and delivers a cleaner version for the business intelligence team to derive insights.
In addition, data mining can delve into smaller datasets. This allows businesses to identify the root cause of a specific trend, and use business intelligence to suggest methods for capitalizing on it. Analysts utilize data mining to gather specific information in the format they need, and then follow up with business intelligence tools to determine and present why the information is important.
In other words, companies use data mining to gain an understanding of the “what” in order to answer for business intelligence to answer the “how” and “why”. Businesses that invest in both BI and data mining tools can perform, test, and interpret sophisticated analyses quickly. Consequently, data mining and business intelligence beget more streamlined processes and increased financial yield.
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The cloud and the future of data mining for BI
Unsurprisingly, the demand for data mining and business intelligence is rising with the omnipresence of big data and the cloud. As the number of data companies grows, on-premises solutions become obsolete. Not only do on-premises solutions struggle to store immense datasets, they are unable to lay the groundwork for accurate, efficient, and speedy data mining and, in turn, business intelligence.
Cloud solutions, on the other hand, can host tremendously large datasets. What is more, cloud platforms typically have connectors to a multitude of data mining and business intelligence tools. The cloud also enables stakeholders to source the information they need almost instantaneously.
Instead of waiting hours — or even days — for reports to run, data mining specialists can set up data pipelines that feed directly into BI tools. With self-service access, stakeholders can log into the BI tool and run a report in minutes. While this already sounds revolutionary to us, businesses are starting to push the data mining and business intelligence envelope even further.
Companies are developing machine learning programs, diving into artificial intelligence, and investing in deep learning with cloud-based data lakes. Clearly, companies need to keep up with the ever-changing customer landscape, and require the capacity and tools necessary to decipher the meaning behind their data. So long as customers continue to use the internet, mobile applications, and social media, data mining and business intelligence will continue to evolve.
Getting started with data mining for business intelligence
Organizations with both BI and data mining tools have the unique ability to test hypotheses, make swift, data-driven choices, and interpret the outcomes of those decisions. As a comprehensive suite of self-service apps that connects to over 900 sources primed for data mining, Talend Data Fabric enables companies to establish a trusted end-to-end data mining to BI process.
Talend Data Fabric captures data lineage, automatically documents metadata to ensure business intelligence integrity, improves collaboration, and empowers business stakeholders to make their own data-driven decisions.Businesses with a robust cycle of data mining and business intelligence are in a league of their own. Get ahead of the game by exploring Talend Data Fabric today.