Data Mining for Sustainable Data ManagementRashi DesaiBlockedUnblockFollowFollowingJun 26In the rapidly expanding technological world of today, when smartphones, tablets, PCs have become an inseparable part of the human life, it is the quintessential philosophy that the power of information and data is realized.
Today, as we live in the ‘information age’, the data volumes are exploding; more data has been created in the past two years than in the entire previous history of the human race.
The enormous potential that lies behind data analytics and data mining, the future has to imbibe the implementation of data mining techniques for sustainable data management.
Data is growing faster than ever before and by the year 2020, about 1.
7 megabytes of new information will be created every second for every human being on the planet.
However, at the current moment, less than 0.
5% of all data is ever analyzed and used.
Data Mining is at the intersection point of:Data MiningData mining, an umbrella term in the data science field, is the process of sorting large data sets to identify patterns within and establish inter-relationships to solve problems through data analysis.
Data mining is the discovery of interesting, unexpected or valuable structures in large datasets.
Statistics: the numeric study of data relationshipsArtificial intelligence: human-like intelligence displayed by software and/or machinesMachine learning: algorithms that can learn from data to make predictions for future trendsThe Need for Data MiningData mining captures the large sets of data in order to identify the insights and visions of that data.
In the current scenario, the demand for the data industry is rapidly expanding.
It is exceptionally important not to miss out that we analyze the data and convert it into meaningful information.
By 2020, with over 4.
4 zettabytes of present data in our accumulated digital universe will grow to around 44 zettabytes or 44 trillion gigabytes.
The sustainable management of this data will be of extreme importance.
Each data set is of great importance to its analysis can foresee the trends in business, sales prediction, predict costs, etc.
Importance of Data MiningUnstructured data alone makes up 90 percent of the digital universe.
But more information does not necessarily mean more knowledge.
Data mining allows to:Shift through all the chaotic and repetitive noise in your dataUnderstand what is relevant and then make good use of that information to assess likely outcomesAccelerate the pace of making informed decisionsAs data mining technology keeps evolving to keep pace with the limitless data available, the data needs sustainable governance.
Sustainable Data ManagementData governance is about how better control and management of data enables strategy, improves outcomes and reduces risk.
It can be recognized as meeting the needs of the present without compromising the ability of future generations to meet their own needs.
Successful data management organizations create institutional awareness and knowledge regarding the value, utility, and relevance of their data at all levels.
Data mining is a cornerstone of data analytics, helping to develop the models that can uncover connections within millions of records.
Data Mining for Sustainable Data ManagementData mining, as a composite discipline, represents a variety of methods or techniques used in different analytic capabilities that address a gamut of organizational needs, ask different types of questions and use varying levels of human input or rules to arrive at a decision.
Following the procedure as:Requirement gathering: Data mining project starts with the requirement gathering and understanding.
The requirement scope is defined with the business perspective.
Once, the scope is defined we move to the next phaseData exploration: Here, the data is gathered, evaluated and explored according to the requirement of the project.
Understand the problems, challenges and convert them to metadata.
In this step, data mining statistics are used to identify and convert data patternsData preparations: Convert the data into meaningful information for the modeling step.
The ETL process — extract, transform and load can be used herein this step.
They are also responsible for creating new data attributes.
Here, various tools are used to present data in a structural format without changing the meaning of data setsModeling: The best tools are put in place for this step as this plays a vital role in the complete processing of data.
All modeling methods are applied to filter the data in an appropriate manner.
Modeling and evaluation are correlated steps and are followed at the same time to check the parameters.
Once the final modeling is done the final outcome is quality provenEvaluation: This is the filtering process after successful modeling.
If the outcome is not satisfied then it is transferred to the model again.
Upon the final outcome, the requirement is checked again so that no point is missed unanalyzed.
mining experts judge the complete result at the end.
Deployment: This is the final stage of the complete process.
The data to vendors in the form of spreadsheets or graphsAfter the data is sorted, the different techniques that are employed for the final data representation modeling can be classified as:Descriptive ModelingPredictive ModelingPrescriptive ModelingA.
Descriptive Modeling:It will uncover and focus on the shared similarities or groupings in historical data to determine the reasons behind success or failure.
Example: Categorizing customers by product preferences or sentiment.
Predictive Modeling:This modeling goes deeper to classify events in the future or estimate unknown outcomes — for example, using credit scoring to determine an individual’s likelihood of repaying a loan.
Predictive modeling also helps uncover insights for things like customer churn, campaign response or credit defaultsC.
Prescriptive Modeling:With the growth in unstructured data from the web, comment fields, books, email, PDFs, audio and other text sources, the adoption of text mining as a related discipline to data mining has grown significantly.
There rises an immediate need to successfully parse, filter and transform the thus obtained unstructured data in order to include it in predictive models for improved prediction accuracy.
Prescriptive modeling looks at internal and external variables and constraints to recommend one or more courses of action.
Data mining should not be looked on as a separate, standalone entity because pre-processing (data preparation, data exploration) and post-processing (model validation, scoring, model performance monitoring) are equally essential in the process.
Fields of ApplicationData mining services for sustainable data management can be used for the following functions:Research and surveysInformation collectionCustomer opinionsData scanningExtraction of informationPre-processing of dataWeb dataCompetitor analysisNewsOnline researchUpdating dataSales PredictionBusiness TrendsCustomer SegmentationUsing data mining for sustainable data management will:Reduce the consequences of unmanaged data growth with sustainable practicesLower data storage costs with unified data protectionDecrease the risk of data loss or theft with global data visibilityEnsure regulatory compliance with comprehensive enterprise data governanceEmbrace a cleaner digital environment for a cleaner natural oneCONCLUSIONData mining is a rapidly growing industry in this technology trend world.
Everyone requires the data to be managed in an appropriate manner and up in the right approach in order to obtain useful and accurate information.
Data Mining is here NOW and we need to dig down and reach the mine!I hope this read helps!.Happy reading!.Cheers!.. More details