how data mining works
data mining involves exploring and analyzing large blocks of information to glean meaningful patterns and trends it is used in credit risk management fraud detection and spam filtering it also is a market research tool that helps reveal the sentiment or opinions of a given group of people the data mining process breaks down into four steps data warehousing and mining softwaredata mining programs analyze relationships and patterns in data based on user requests it organizes information into classes for example a restaurant may want to use data mining to determine which specials it should offer and on what days the data can be organized into classes based on when customers visit and what they order in other cases data miners find clusters of information based on logical relationships or look at associations and sequential patterns to draw conclusions about trends in consumer behavior warehousing is an important aspect of data mining warehousing is the centralization of an organization s data into one database or program it allows the organization to spin off segments of data for specific users to analyze and use depending on their needs cloud data warehouse solutions use the space and power of a cloud provider to store data this allows smaller companies to leverage digital solutions for storage security and analytics data mining techniquesdata mining uses algorithms and various other techniques to convert large collections of data into useful output the most popular types of data mining techniques include association rules classification clustering decision trees k nearest neighbor neural networks and predictive analysis the data mining processto be most effective data analysts generally follow a certain flow of tasks along the data mining process without this structure an analyst may encounter an issue in the middle of their analysis that could have easily been prevented had they prepared for it earlier the data mining process is usually broken into the following steps before any data is touched extracted cleaned or analyzed it is important to understand the underlying entity and the project at hand what are the goals the company is trying to achieve by mining data what is their current business situation what are the findings of a swot analysis before looking at any data the mining process starts by understanding what will define success at the end of the process once the business problem has been clearly defined it s time to start thinking about data this includes what sources are available how they will be secured and stored how the information will be gathered and what the final outcome or analysis may look like this step also includes determining the limits of the data storage security and collection and assesses how these constraints will affect the data mining process data is gathered uploaded extracted or calculated it is then cleaned standardized scrubbed for outliers assessed for mistakes and checked for reasonableness during this stage of data mining the data may also be checked for size as an oversized collection of information may unnecessarily slow computations and analysis with a clean data set in hand it s time to crunch the numbers data scientists use the types of data mining above to search for relationships trends associations or sequential patterns the data may also be fed into predictive models to assess how previous bits of information may translate into future outcomes the data centered aspect of data mining concludes by assessing the findings of the data model or models the outcomes from the analysis may be aggregated interpreted and presented to decision makers that have largely been excluded from the data mining process to this point in this step organizations can choose to make decisions based on the findings the data mining process concludes with management taking steps in response to the findings of the analysis the company may decide the information was not strong enough or the findings were not relevant or the company may strategically pivot based on findings in either case management reviews the ultimate impacts of the business and recreates future data mining loops by identifying new business problems or opportunities different data mining processing models will have different steps though the general process is usually pretty similar for example the knowledge discovery databases model has nine steps the crisp dm model has six steps and the semma process model has five steps 1applications of data miningin today s age of information almost any department industry sector or company can make use of data mining 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 and what products were sold using this information the shop can strategically craft its product line once the coffeehouse 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 the findings of data mining 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 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 whether funds are being mismanaged human resources departments often have a wide range of data available for processing including data on retention promotions salary ranges company benefits use 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 new hires customer satisfaction may be caused or destroyed by many events or interactions imagine a company that ships goods a customer may be dissatisfied with shipping times shipping quality or communications the same customer may be frustrated with long telephone wait times or slow e mail responses data mining gathers operational information about customer interactions and summarizes the findings to pinpoint weak points and highlight what the company is doing right advantages and disadvantages of data miningit drives profitability and efficiencyit can be applied to any type of data and business problemit can reveal hidden information and trendsit is complexresults and benefits are not guaranteedit can be expensiveeven large companies or government agencies have challenges with data mining consider the fda s white paper on data mining that outlines the challenges of bad information duplicate data underreporting or overreporting 2data mining and social mediaone of the most lucrative applications of data mining has been undertaken by social media companies platforms like facebook tiktok instagram and x formerly twitter gather reams of data about their users based on their online activities that data can be used to make inferences about their preferences advertisers can target their messages to the people who appear to be most likely to respond positively data mining on social media has become a big point of contention with several investigative reports and expos s showing just how intrusive mining users data can be at the heart of the issue is that users may agree to the terms and conditions of the sites not realizing how their personal information is being collected or to whom their information is being sold examples of data miningdata mining can be used for good or it can be used illicitly here is an example of both ebay collects countless bits of information every day from sellers and buyers the company uses data mining to attribute relationships between products assess desired price ranges analyze prior purchase patterns and form product categories 3ebay outlines the recommendation process as a cautionary example of data mining is the facebook cambridge analytica data scandal during the 2010s the british consulting firm cambridge analytica ltd collected personal data from millions of facebook users this information was later analyzed for use in the 2016 presidential campaigns of ted cruz and donald trump it is suspected that cambridge analytica interfered with other notable events such as the brexit referendum 4in light of this inappropriate data mining and misuse of user data facebook agreed to pay 100 million for misleading investors about its uses of consumer data the securities and exchange commission claimed facebook discovered the misuse in 2015 but did not correct its disclosures for more than two years 5