BI and Data Mining
So many words were said regarding BI, so many articles written about data mining. But still, it is a hot topic. So let us repeat and provide you with renewed and complete info about BI and data mining. Here we go.
Let’s touch upon BI first. Business Intelligence is a set of IT technologies for collecting, storing and analyzing data. It allows providing users with reliable analytics in a convenient format, based on which you can make effective solutions for managing the company's business processes.
All user levels, from employees to founders, have flexible access to the management reporting they need, without IT specialists help.
There are following major BI platforms:
- ETL tools: programs that allow you to load data into DWH from various accounting systems.
- DWH Storage: a complete SQL database for preparing and storing data for analytics.
- OLAP-cubes: a technology that allows you to make any reports in real time (1-5 seconds) and carry out a complete data analysis.
- Client applications: as a rule, users deal with Microsoft Excel PivotTables connected to OLAP cubes for detailed data analysis and building dynamic reports. For surface analysis and visualization of key indicators, WEB-applications are also used, which should support access to reports from any device: computer, tablet or phone.
Introduction to data mining
The volumes of data we have today are so impressive that we simply cannot afford to analyze them on our own. The need for such an analysis is quite obvious, because the raw data contain knowledge that can be used in making decisions. To conduct automatic data analysis, Data Mining is used.
Data Mining is a technology for revealing hidden relationships within large databases. Many companies have accumulated important business information over the years, hoping that it will help them in making decisions. And, it would seem, extracting the facts from the database — let’s say, to find out that on a particular day and time the customer ordered goods X in store 123 — is not so difficult. However, what is needed here are not facts themselves, but knowledge that, for example, stores 123 and 130 sell goods X 30% more than other points.
In general, the more specific the information, the more it is useful for decision making. Thus, Data Mining is the process of discovering this kind of useful business knowledge.
The algorithms used in Data Mining require a lot of computation. Previously, this was a deterrent to the wide practical application of Data Mining, however, today's growth in the performance of modern processors has removed the severity of this problem. Now you can conduct a qualitative analysis of hundreds of thousands and millions of records for an acceptable time.
Tasks solved using Data Mining methods:
- Classification: the assignment of objects (observations, events) to one of the previously known classes.
- Regression: including forecasting tasks. Establishing the dependence of continuous output on input variables.
- Clustering: a grouping of objects based on data (properties) that describe the essence of these objects. The objects inside the cluster must be similar to each other and different from the objects included in other clusters. The more similar the objects within the cluster and the more differences between the clusters, the more accurate the clustering.
- Association: the identification of patterns between related events. An example of such a pattern is a rule indicating that event Y follows from event X. Such rules are called associative. For the first time, this problem was proposed to find typical patterns of purchases made in supermarkets; therefore, sometimes it is also called market basket analysis.
- Sequential patterns: establishing patterns between time-related events, i.e. detecting the dependency that if event X occurs, then after a specified time, event Y will occur.
- Deviation analysis: identifying the most uncharacteristic patterns.
Areas of use
The scope of Data Mining is unlimited: it is everywhere where the data is used. But mostly, Data Mining methods today related to commercial enterprises deploying projects based on information data warehouses. Basically, data mining is useful for:
Retailers collect detailed information about each individual purchase using store-branded credit cards and computerized control systems. Here are some typical tasks that can be solved with Data Mining in the retail industry:
- shopping basket analysis
- the study of temporary patterns
- the creation of predictive models
Data Mining technology is used in banking to solve the following common tasks:
- credit card fraud detection
- customer segmentation
- forecasting clients changes
In the field of telecommunications, Data Mining methods help companies more aggressively promote their marketing and pricing programs to retain existing customers and attract new ones. It includes:
- analysis of records of detailed call characteristics
- identification of customer loyalty
Insurance companies have accumulated large amounts of data over the years. Here is an extensive field of activity for Data Mining methods:
- fraud detection
- risk analysis
Correlation with BI
The first thing you need to remember here: BI and data mining are not the same. However, the correlation between them is obvious.
However, business intelligence is more complex and big term. BI is how companies can get information from big data and data mining. It is not limited to technology - it includes business processes and data analysis procedures that facilitate the collection of big data.
As BI is an umbrella term, data mining can be considered as a form of BI, its function used to collect relevant information and obtain information. Moreover, business intelligence can also be seen as the result of data mining. As already mentioned, business analytics involves the use of data to obtain information. Data Mining is the collection of necessary data that will ultimately lead to answers through in-depth analysis.
The line between data mining and business intelligence can be seen as a causal relationship. Analysts use data mining to find the information they need, and use business intelligence to determine why it matters.
But for those who need more fact, we can provide a visual comparison of two technologies:
|Analysis style||BI only reflects past data at different scales. There is no intelligence in the system itself, but professionals can interpret the information for better decision making.||The data mining technique relies on computational intelligence to identify important business factors on a small scale. This method implies that management professionals work closely with data analysts.|
|Results||Dashboards, consolidated views of the KPIs in graphics, diagrams and charts.||Reports promoting strategic decision making with useful recommendations.|
|Data volume||Large datasets.||Small datasets.|
|Focus||Helps in monitoring KPI’s factors: price, value, profit, total cost.||Identifies data patterns, creating new KPI for BI.|
Summing all up we remember of course to answer your top questions. So let’s go from words to actions and overview the topic once again:
What are the top data miming and BI tools?
The most relevant question now. Of course, after realizing all the benefits you can get implementing business intelligence and data mining in your business, you need to know which tools to choose. We are glad to help you with this choice.
Business Intelligence tools:
- DOMO: a business management cloud package that integrates with multiple data sources, including spreadsheets, databases, social networks, and any existing cloud or on-premises software solution. It is suitable for companies of any size, from small to large, compatible with Windows or Mac platforms, iPad tablets, and also works on mobile devices.
- Sisense: a business intelligence class solution that simplifies the analysis of complex data by offering an end-to-end solution for combining and visualizing Big Data and arbitrary disparate data arrays. Sisense supports the full BI cycle, from data collection and preparation to comprehensive analysis and visualization with extensive functional development and management
- Ranet OLAP: a ready-to-use solution for business data analysis. Ranet OLAP provides components for reporting, forecasting and analyzing the data from different perspectives. These OLAP tools provide end-users with wide options for ad-hoc data analysis and reporting. You also can try it for free as Ranet OLAP offers demo and free 30-day trial.
- QlickView: a self-service business intelligence platform for all corporate business users. Using it, you can analyze data and use the results to support solutions. QlikView program gives you the opportunity to ask yourself questions and answer them, to independently follow the path of knowledge.
Data mining tools:
- Hadoop: a group of software tools aimed to assist in the progress of computer problem-solving processes. It delivers a software framework processing Big Data and distributing its storage. The modules of Hadoop are developed with an idea that hardware breakdowns are commonplaces which should be managed by the framework.
- Spark: an open source framework for implementing distributed processing of unstructured and weakly structured data, part of the ecosystem of Hadoop projects. Spark uses specialized primitives for recursive processing in random access memory, which makes it possible to obtain significant gains in speed for some classes of problems, in particular, the possibility of multiple access to user data loaded into memory makes the library attractive for machine learning algorithms.
- Weka: a free data analysis and machine learning software written in Java at the University of Waikato. Weka is a set of visualization tools and algorithms for data mining and solving forecasting problems, along with a graphical user interface for accessing them. The tool allows you to perform data analysis tasks such as preprocessing, feature selection, clustering, classification, regression analysis and visualization of results.
Which areas of use does the data mining include?
Mostly related to commercial enterprises deploying projects based on information data warehouses. Basically, data mining is useful for:
What is the difference between BI and Data miming?
First of all, BI is how companies can get information from big data and data mining. It is not limited to technology - it includes business processes and data analysis procedures that facilitate the collection of big data.
BI is an umbrella term, and data mining can be considered as a form of BI. It’s function used to collect relevant information and obtain information. Moreover, business intelligence can also be seen as the result of data mining.
What is the role of data mining in BI?
BI technology includes data visualization, tools for building dashboards, KPI systems. In addition, these tools generate results that are ultimately used to gain a competitive advantage over competitors, improve and increase the efficiency of business operations, and increase survivability and risk management.
Data mining tools provide better customer relationship management as it is identifying real habits and diverse models. You should use a business intelligence strategy to apply knowledge to maximize the benefits of the company.
Thus, Business Intelligence acts as a strategic factor for business, providing insider information to respond to business problems: access to new markets, financial control, cost optimization, production planning, analysis of customer profiles, profitability. Here's how data mining is used to create business intelligence.
Business intelligence and data mining are two different concepts that exist in the same field. Business intelligence can be seen as a comprehensive category in which the concept of data mining exists, because it can simply be defined as an analysis of business practices based on data. Although the two concepts are different, BI and data mining work together to provide data understanding. These are tools that can help you better understand your business and ultimately simplify processes that increase productivity and financial profitability.