How Data Mining can help Organizations as well as Startups?

Modern businesses/startups have the ability to gather information on customers, products, manufacturing lines, employees, and storefronts. These random pieces of information may not tell a story, but the use of data mining techniques, applications, and tools can help in joining pieces of information to drive value. The ultimate goal of the data mining process is to compile data, analyze the results, and execute operational strategies based on data mining results.

Today we are going to discuss how data can be converted into insights that will help you to take better decision or develop an effective marketing strategy. Before going into all that lets first understand what is Data Mining?

?? The Data Mining Process:

Data Mining is a process of sorting through large data sets to identify patterns and relationships that can help in solving business problems through data analysis.

By using data mining techniques, organizations/startups can learn more about their customers to develop effective marketing strategy, increase sales and decrease costs.

Data mining techniques and tools enable enterprises to predict future trends and make more-informed business decisions.

Data mining is used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data, businesses can learn more about their customers to develop more effective marketing strategies, increase sales and decrease costs.

?? How Data Mining works:

Data mining is typically done by data scientists and other skilled BI and analytics professionals.

Its core elements include machine learning and statistical analysis, along with data management tasks done to prepare data for analysis.

The use of machine learning algorithms and artificial intelligence (AI) tools has automated more of the process and made it easier to mine massive data sets, such as customer databases, transaction records and log files from web servers, mobile apps and sensors.

?? Stages of Data Mining:

The data mining process can be broken down into these four primary stages:

1. ?? Data gathering: Relevant data for an analytics application is identified and assembled. The data may be located in different source systems, a data warehouse or a data lake, an increasingly common repository in big data environments that contain a mix of structured and unstructured data. External data sources may also be used. Wherever the data comes from, a data scientist often moves it to a data lake for the remaining steps in the process.
2. ?? Data preparation: This stage includes a set of steps to get the data ready to be mined. It starts with data exploration, profiling and pre-processing, followed by data cleansing work to fix errors and other data quality issues. Data transformation is also done to make data sets consistent, unless a data scientist is looking to analyze unfiltered raw data for a particular application.
3. ?? Mining the Data: Once the data is prepared, a data scientist chooses the appropriate data mining technique and then implements one or more algorithms to do the mining. In machine learning applications, the algorithms typically must be trained on sample data sets to look for the information being sought before they're run against the full set of data.
4. ?? Data Analysis and Interpretation: The data mining results are used to create analytical models that can help drive decision-making and other business actions. The data scientist or another member of a data science team also must communicate the findings to business executives and users, often through data visualization and the use of data storytelling techniques.

?? Types of Data Mining:

Data mining uses algorithms and various techniques to convert large collections of data into useful output.

?? Pattern recognition is a common data mining use case that's enabled by multiple techniques.

Pattern Recognition process:

?? Anomaly detection aims to identify outlier values in data sets. below is the example of semantic anomaly detection.

There are some popular data mining techniques that we will discuss below

1. ?? Association rule mining: In data mining, association rules are if-then statements that identify relationships between data elements. Support and confidence criteria are used to assess the relationships -- support measures how frequently the related elements appear in a data set, while confidence reflects the number of times an if-then statement is accurate.
2. ?? Classification: This technique uses predefined classes to assign to objects. These classes describe characteristics of items or represent what the data points have in common with each. This data mining technique allows the underlying data to be more neatly categorized and summarized across similar features or product lines. Decision trees, Naive Bayes classifiers, k-nearest neighbor and logistic regression are some examples of classification methods.
3. ?? Clustering: In this case, data elements that share particular characteristics are grouped together into clusters as part of data mining applications. Examples include k-means clustering, hierarchical clustering and Gaussian mixture models.
4. ?? Regression: This is another way to find relationships in data sets, by calculating predicted data values based on a set of variables. Linear regression and multivariate regression are examples. Decision trees and some other classification methods can be used to do regressions, too.
5. ?? Sequence and path analysis: Data can also be mined to look for patterns in which a particular set of events or values leads to later ones.
6. ?? Neural networks: A neural network is a set of algorithms that simulates the activity of the human brain. Neural networks are particularly useful in complex pattern recognition applications involving deep learning, a more advanced offshoot of machine learning.

?? How Data Mining helps Organizations/Startups?

Now we will discuss several use cases on how startups/organizations use these data mining techniques to improve their productivity and acquire/retain customers.

1. ?? More effective sales: The ultimate goal of a company is to make money, and data mining encourages smarter, more efficient use of capital to drive revenue growth. Consider the point-of-sale register at your favorite local coffee shop. For every sale, that coffeehouse collects the time a purchase was made, what products were sold together, and what baked goods are most popular. Using this information, the shop can strategically craft its product line.
2. ?? More effective Marketing: Once the coffeehouse above knows its ideal line-up, it's time to implement the changes. However, to make its marketing efforts more effective, the store can use data mining to understand where its clients see ads, what demographics to target, where to place digital ads, and what marketing strategies most resonate with customers. This includes aligning marketing campaigns, promotional offers, cross-sell offers, and programs to findings of data mining.
3. ?? Improved supply chain management: Organizations can spot market trends and forecast product demand more accurately, enabling them to better manage inventories of goods and supplies. Supply chain managers can also use information from data mining to optimize warehousing, distribution and other logistics operations.
4. ?? Better customer service: Thanks to data mining, companies can identify potential customer service issues more promptly and give contact center agents up-to-date information to use in calls and online chats with customers. Imagine a company that ships goods. A customer may become unhappy with ship time, shipping quality, or communication on shipment expectations. That same customer may become frustrated with long telephone wait times or slow e-mail responses. Data mining gathers operational information about customer interactions and summarizes findings to determine weak points as well as highlights of what the company is doing right.
5. ?? Increased production uptime: Mining operational data from sensors on manufacturing machines and other industrial equipment supports predictive maintenance applications to identify potential problems before they occur, helping to avoid unscheduled downtime. For companies that produce their own goods, data mining plays an integral part in analyzing how much each raw material costs, what materials are being used most efficiently, how time is spent along the manufacturing process, and what bottlenecks negatively impact the process. Data mining helps ensure the flow of goods is uninterrupted and least costly.
6. ?? Stronger risk management: Risk managers and business executives can better assess financial, legal, cybersecurity and other risks to a company and develop plans for managing them.
7. ?? Lower costs: Data mining helps drive cost savings through operational efficiencies in business processes and reduced redundancy and waste in corporate spending.
8. ?? Fraud Detection: The heart of data mining is finding patterns, trends, and correlations that link data points together. Therefore, a company can use data mining to identify outliers or correlations that should not exist. For example, a company may analyze its cash flow and find a reoccurring transaction to an unknown account. If this is unexpected, the company may wish to investigate should funds be potentially mismanaged.
9. ?? Human Resources: Human resources often has a wide range of data available for processing including data on retention, promotions, salary ranges, company benefits and utilization of those benefits, and employee satisfaction surveys. Data mining can correlate this data to get a better understanding of why employees leave and what entices recruits to join.