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In the data first age, businesses feel the need to hire more and more data scientists to handle data accurately. 0000005689 00000 n 0000002524 00000 n Data mining mainly focusses on identifying the problems in data quality, correcting it, which is commonly called data cleansing, and deploying algorithms in detecting and enriching poor data. 0000004938 00000 n <]>> 0000001532 00000 n 0000006459 00000 n l]qYwe7f2lSp0X% 'L zX +@ Bv 0 0000002374 00000 n Another essential facet is data anonymization which refers to safeguarding private and sensitive data by encrypting system that connects the stored data with an individual.

It can classify the data correctly and determine the accuracy of a big data set, which is the fundamental step in data analysis. 0000012718 00000 n Privacy guaranteed. 0000008087 00000 n 0000000893 00000 n xbbd`b``3 1 lC 0000005968 00000 n Errors in data is a common facet of data collection. The algorithms also have the power to measure the accuracy of data mining and data enrichment. Measuring data mining accuracy is significant after data collection. Let us discuss the different types of errors in data: Here is a time series representing before and after disruption with random noise: The noise removal is a complicated task, so data mining involves the usage of a robust algorithm to produce remarkable results. 545 0 obj <> endobj 0000006958 00000 n 0000002675 00000 n HO0W0IPlZ}{g/|/vy._=y\Vh=f c,+pux8Vi/%m}'){/@~ a>Pd(w'erBeM5RZKN``>0a#uwqfxh9 $I-Jm1wpN2Y*. But another, most considerable concern here in the foreground is the data quality.

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It can be due to various reasons like human errors, poor data collection, measurement error, and so on. 0000001980 00000 n %PDF-1.4 % The data quality and the accuracy of the resulting data can be measured statistically through the following ways: When machine learning algorithms potentially detect the data quality, the next step is data analysis and interpretation. An Agencys Handbook to Result-Based Partner Contracts, Importance of Business Agility in Outsourcing, Focus on Your Core Business for Profitable Growth, Building the Business Model of the Future. +x^ ;4i`@"J I,X&l6%lD!1@~=Acc.u.0 Enter your email address below to recieve updates each time we publish new content. 573 0 obj<>stream endstream endobj 572 0 obj<>/Size 545/Type/XRef>>stream 0000004332 00000 n However, businesses must consider hiring companies providing data mining services to get an accurate result from the experts. 0000004409 00000 n We had been talking about the quality of data all this while, which is inseparable from the discussion of data accuracy. Thus, data mining has immense potential to lead to accurate data analysis, provided the mining is reliable in turn. Poor or redundant data is as useful as scrap. So, businesses must indulge some considerable time in the business data mining and its accuracy to convert their strategies to reality. Today data mining and research are incumbent to every business, starting from discovering valid information and capturing data from the data lakes to modeling and prediction. But does it end here? The answer is it should not. 0000002340 00000 n

0000005725 00000 n 0000007389 00000 n A data enrichment method is the next big thing to think about so that it keeps the data updated from time to time. There can be missing values in the data, redundant and duplicate data. 0000003778 00000 n

0000009518 00000 n 0000000016 00000 n 0000003093 00000 n It can be used by companies to adhere to strict data privacy laws, protecting personally identifiable information (PII) through masking the private data attributes. %%EOF endstream endobj 546 0 obj<>/Metadata 67 0 R/PieceInfo<>>>/Pages 64 0 R/PageLayout/OneColumn/OCProperties<>/StructTreeRoot 69 0 R/Type/Catalog/LastModified(D:20100920152150)/PageLabels 62 0 R>> endobj 547 0 obj<>/PageElement<>>>/Name(HeaderFooter)/Type/OCG>> endobj 548 0 obj<>/Font<>/ProcSet[/PDF/Text]/Properties<>/ExtGState<>>>/Type/Page>> endobj 549 0 obj[550 0 R 551 0 R] endobj 550 0 obj<>/A 571 0 R/H/I/StructParent 1/Border[0 0 0]/Type/Annot>> endobj 551 0 obj<>/A 570 0 R/H/I/StructParent 2/Border[0 0 0]/Type/Annot>> endobj 552 0 obj<> endobj 553 0 obj<> endobj 554 0 obj<> endobj 555 0 obj<> endobj 556 0 obj<> endobj 557 0 obj<> endobj 558 0 obj<>stream Perhaps this is the reason why mining data cannot reap the benefits of addressing quality issues at the very sources. With big data being everywhere, there is a more significant concern about data and the techniques to control and monetize it. It summarizes the data in a way that is understandable to everyone and helps in drawing an inference around it based on the patterns being observed. Data anonymization helps in detecting security threats and sharing the data externally while making it useful and efficient for the users. Data mining techniques are created using machine learning algorithms tailored to the particular goals and objectives of businesses. Machine learning algorithms are savior.

We never share your info. startxref 0000008789 00000 n xref xb```b``b`e`~ @1V.,L }y?l3Eiw6m{cTutknvpSx-cHpmN.AhS5K+ifr=V@d3O03/8'}:T/34y+sHa

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