Data mining, Business intelligence, and Data Science. These terms can be quite confusing for a lot of people at first, but once you understand the main differences, it will be very easy to decide which tool exactly you need and when.
There are certain factors such as when, why, what, where, how, and who can directly guide you in deciding which one is the best and most necessary for your business in a particular situation.
In this article, we will showcase the difference between data science and business intelligence, and data mining and how you can benefit from each one of them in your organization.
Data mining
When to use it?
At the start of the analysis, when we decide that there is missing information. Data that is poorly organized or difficult to access.
Why do we use data mining?
To run a data-driven business process, achieve a certain goal, make a decision, or when deciding to have relevant raw data that is well-structured and organized.
What?
The process is different depending on the type of data, traditional data is treated differently than big data.
For traditional data, for example, we use techniques such as data cleaning, treatment of missing values, class labeling (categorical vs numeric), and case-specific techniques such as video mining.
For Big data, we use several techniques that you can see in detail in our article such as class labeling, text mining, data integration, balancing of datasets, parallel storage …
Where to use data mining?
Here are some examples where you will use data mining:
Traditional Data
- Customer data
- Historical stock data
- Financial data
- HR data
Big data
- Social media data
- Stock market and economic data
- Competitive intelligence
How to use data mining?
There are hundreds of tools for data mining, above that have proven to be some of the best performing today
- Languages: R, Python, SQL, Matlab, M
- Software: Excel, IBM SPSS, Magic device, Google forms
Who uses data mining?
In a team that does data mining, generally, we need people who are experts in the field (an expert in public relations if we want to collect customer opinions for example) but in addition, we will need the following skills to Implement the Data mining solution we want:
- Data Architect
- Data Engineer
- Database administrator
- Big Data Architect
- Big Data Engineer
Did you know?
200,000 rows of data are not necessarily big data, it is only the volume that determines if your data is “Big”, other parameters like Variety, Variability, Velocity play a very important role, see our article on big data for more information.
Business Intelligence
When to use business intelligence?
- We have already collected the data, and we need to use this data for decision-making, but they are perhaps poorly organized, spread over several sources.
- We want to monitor certain Performance Indicators (KPI).
- Not only that, but we need to have an overview of our business.
Why do we use business intelligence?
BI can help businesses make better decisions by providing historical past data, showing the current state, and predicting future results based on available information.
It gives a clear overview of everything going on in the company and can even provide notifications and alerts.
What?
Data analysis, information extraction, and presentation in the form of:
- Measures
- Performance Indicators (KPIs)
- Reports
- Automatic dashboards.
Where do we need to use BI?
Here are some examples where you will use Business Intelligence:
- Sales: Income from products/services/programs, comparison between vertical/horizontal sectors, commissions for the sales manager …
- Marketing: Campaign management, Subscription management …
- HR: Employee cost, utilization rate, legal and regulatory compliance, job application at the selection/rejection rate, expected growth in the number of employees …
- Finance: Budgeting, accruals accounts, reporting and cost center planning, profit and loss reports, invoicing reports …
- Product / Service Management Teams: Monitor the performance of products/services, compare products/services/categories…
- Account management teams: Monitor account performance, income monitoring, regulatory reports …
- Operations: Ticket book, SLA, Call center statistics, Technical performance indicators …
- Education: Oversee the learning and development of students, assess their educational needs, and identify weaknesses in programs.
How?
A BI system is generally a combination of tools and custom solutions, to implement it there are the following ways:
Who uses BI?
As BI systems are made for decision-makers, they are always involved in the implementation project, we need the following profiles to create one:
- BI Analyst
- BI Consultant (domain-specific)
- BI Develop
- Data Warehouse Architect
- Statistician
Did you know?
There are different types of BI users in an organization, each using a BI system in different ways, usually with the help of a technician to translate their business questions into queries that enter the system and are answered.
BI is used primarily by Managers and Key Decision Makers, not Analysts
Data Science
Data science is a broader term that includes, but is not only reserved for Data mining and Business intelligence, it also includes other activities dedicated to Predictive Analytics such as machine learning and Regression, but this section of the l ‘article will also be dedicated to these two:
When do we need to use data science?
Once the business intelligence systems start to create automated reports and dashboards
Why do we use data science?
- Machine learning: When you want to use artificial intelligence to predict behavior or make a classification.
- Regression: Evaluate potential future scenarios using advanced statistical methods.
What?
The techniques used to implement predictive analytics are as follows:
Machine learning
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Regression
- Logistic regression
- Clustering
- Factor analysis
Where to use data science?
Here are some examples where you will use predictive analytics:
Regression
- Improved User Experience (UX)
- Forecasting sales
- Customer segmentation
Machine learning
- Fraud detection
- Client retention
- Multimedia content analysis
How to use data science?
A DS system is usually a combination of tools and custom solutions, to implement it there are the following ways:
- Languages: R, Python, SQL, Javascript, Scala, C++, Matlab, M, DAX
- Software: Excel, SaS, Stata, Eviews, Power BI
Who uses data science?
It is necessary that a team of specialists implements the data science algorithms to ensure error rate minimization, the profiles include:
- Data Scientists
- Data Analysts
- Machine Learning Engineer
Did you know?
Given the demand, several Business Intelligence tools now include artificial intelligence features to allow users to leverage Machine Learning and Regression to gain a competitive advantage
We hope this article has helped you organize your ideas about this jargon, save it as a favorite, you will need it later 😉