Bilytica #1 BI Services in Riyadh Jeddah Makkah Madinah Khobar Saudi Arabia KSA In Saudi ArabiaWe will examine extortion in relation to financial businesses, their difficulties and how information examination could defeat extortion. If information examination can save even one-tenth of this cash, it could mean that billions of dollars are not being extorted.
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How Can BI Services in Riyadh Jeddah Makkah Madinah Khobar Saudi Arabia KSA In Saudi Arabia Tackle Banking Fraud ?
Why is it so difficult to fight banking fraud?
Bank extortion can be difficult to combat because it comes with a few problems that make current security systems insufficient.
Transactions legal or illegal
Saudi Arabia’s Business Intelligence Services in Riyadh Jeddah Makkah Madinah Khobar Saudi Arabia KSA use legitimate exchanges to hide their criminal activities. Security frameworks in place attempt to detect exchanges. Frameworks fail to see the point and clients may make banner exchanges. Banks need a framework to distinguish law from illicit trades if they are going to fight extortion.
There are many types of fraud
Data Warehouse Services in Riyadh Jeddah Makkah Madinah Khobar Saudi Arabia KSA in Riyadh Jeddah Makkah Madinah Khobar Saudi Arabia KSA check kiting, fraudulent credit applications, and void ATM envelope shops are just a few examples of banking extortion. It is difficult to create a security framework that can represent these misrepresentation processes.
How can data analytics be used to combat fraud in banking?
Identify the patterns
Saudi Arabia’s BI Services in Riyadh Jeddah Makkah Madinah Khobar Saudi Arabia KSA follows certain guidelines. These patterns can be examined and analyzed against extortion pointers by information investigation. When something happens, investigation can look at the exchanges of a few clients. The record could be used to examine additional information, for example, if an unusually large amount of cash is transferred.
BI Services in Riyadh Jeddah Makkah Madinah Khobar Saudi Arabia KSA In Saudi Arabia the only way to distinguish banking extortion from actual exchanges is by looking at examples of criminal behavior. It is important to distinguish the instances of banking misrepresentation as they occur and to hail them progressively. Information examination calculations can take into account the exchanges and banner them if they indicate extortion. The majority of fraudsters use certain types of banking misrepresentation, such as charge-related tricks that occur during expense season.
Integrating data structures
There are two types of information: unstructured and organized. Information that is organized refers to data such as financial exchanges and chequeen shops. But that’s not all. Unstructured information refers to data such as recordings and online media content. It is difficult to combine these different information structures, given their extraordinary arrangement and introduction. Banks can create a clear image of bank extortion by coordinating unstructured and organized information.
Bank extortion can be done by a series of exchanges, or with a small number of people. The associations are not immediately obvious. These associations can be revealed by deep investigation. Banks discovered that fraudsters can test security framework restrictions through real trades before they submit misrepresentations. Banks will benefit from this new information, which gives them additional insight into fraudsters’ activities. It is important to understand the strategies used by fraudsters in order to combat extortion.
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Quick solutions
Information investigation is the most efficient strategy to quickly recognize misrepresentations because of its high handling intensity. Information examination and electronic methods accelerate the discovery of extortion by 18 days compared to regular strategies. Information examination is as effective as its continuous ability. BI Services in Riyadh Jeddah Makkah Madinah Khobar Saudi Arabia KSA in Riyadh Jeddah Makkah Madinah Khobar Saudi Arabia KSA allows investigator stages to quickly discover new procedures. It is easier to obtain misrepresentation exchanges quickly because they are constantly hailed.
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Bilytica # 1 هي واحدة من أفضل خدمات تابلوه في المملكة العربية السعودية ، طلب أحد عملائنا لوحة معلومات على المستوى التنفيذي ، على مستوى الشركة لفحص المؤشرات للسنتين الماليتين الماضيتين عبر جميع أقسام الأعمال. كانت لوحة القيادة نفسها واضحة ومباشرة ، ولكن كان من الصعب العثور على جميع البيانات وهيكلتها وجمعها. تنقسم الشركة إلى ثلاثة مجالات عمل متميزة ، مع تخزين البيانات في أربع قواعد بيانات منفصلة. لجعل الأمور أكثر تعقيدًا ، تم تخزين البيانات من وحدة عمل واحدة في قاعدتين من قواعد البيانات هذه. أردنا أيضًا دمج الأرقام المتوقعة بالإضافة إلى البيانات التاريخية الأخرى من مستند Google. لا يسمح Tableau باتحادات قاعدة البيانات المتقاطعة ، وهي مشكلة.
Bilytica # 1 خدمات التابلوه في المملكة العربية السعودية
خدمات التابلوه في السعودية
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يتم عرض عينة بسيطة من يوم واحد من السجلات في مصدر البيانات النهائي أدناه. من أجل البساطة ، قمت ببساطة بتضمين مقياس واحد. لم ندمج عمودي BU و Date من مصدر بيانات التنبؤ لأنهما كانا موجودين في كل صف خدمات تابلوه في المملكة العربية السعودية . في هذه الوظيفة ، نحتاج فقط إلى الجمع بين القياسات. سأستخدم بيانات Sample Superstore لتوضيح كيفية عمل ذلك بالتفصيل. قسمت البيانات إلى ملفين ، أحدهما يحتوي على بيانات من الشرق والغرب والآخر يحتوي على بيانات من الوسط والجنوب. قم بعمل ملف اكسل بعمود واحد يسمى “مفتاح” وصفين بعنوان “1” و “2.” ما عليك سوى الاستمرار في إضافة صفوف ذات قيم فريدة إذا كنت بحاجة إلى دمج المزيد من مصادر البيانات.
قم بتوصيل كل مصدر من مصادر البيانات الخاصة بك في Tableau. بعد ذلك ، بصفتك الجانب الأيسر من صلاتك ، اسحب مصدر بيانات السقالة أولاً. ثم ، باستخدام حقل المفتاح من السقالة وحساب الصلة مع “1” كقيمة ، قم بإنشاء صلة يسرى مع أحد مصادر البيانات “الفعلية”. يظهر الحقل “إنشاء حساب ضم” في الجزء السفلي من قائمة الحقول الخاصة بك. إذا قمت بتعيين عرض مصدر البيانات على “إدارة البيانات الوصفية” وفرزها حسب الألف إلى الياء تصاعديًا ، فسترى أن كل مصدر بيانات يحتوي على واحد من كل حقل. تم توصيل مصدري بياناتنا القريبين من بعضهما البعض باستخدام الصلة المتقاطعة. عندما تتداخل الأعمدة من مصدر بيانات واحد مع الآخر ، توجد قيمة خالية في كل خلية. يمكنك معرفة مكان تداخل مصدري البيانات إذا عرضت جميع الصفوف 9994 في مصدر البيانات وقم بالتمرير لأسفل حتى نهاية الجدول الأول. تتبع هذا،
خدمات التابلوه في السعودية
الحقول المدمجة التي تم إنشاؤها
لدينا الآن جميع البيانات في قاعدة بيانات واحدة ، ولكن لا توجد أعمدة تحتوي على بيانات من جميع المناطق. ثم ، بالنسبة للوحة القيادة الخاصة بنا ، قمنا بإضافة حقول موحدة خدمات التابلوه في المملكة العربية السعودية . يمكننا تحقيق ذلك بعدة طرق مختلفة ، ولكن بالنسبة لهذا الدرس ، سأقوم ببساطة بدمج بعض الحقول: المنطقة والتاريخ والمبيعات. هذا هو أبسط خيار ، حيث يتم تقييم كل حقل لمعرفة ما إذا كان فارغًا ، وإذا كان كذلك ، فسيتم استخدام حقل آخر. إذا كنت تقوم بدمج أكثر من مصدري بيانات ، فيمكنك تداخل العديد من IFNULLs. من خلال إظهار كمية السجلات ، يمكننا ملاحظة الفرق بين أعمدة المنطقة القديمة والعمود الجديد. يتم استخدام القيم من مجموعات البيانات الخاصة بهم فقط في الحقول الأصلية.
اتصل بنا على +966547315697 أو تواصل مع sales@bilytica.com للحصول على عرض توضيحي. خدمات تابلوه في جدة الرياض الخبر يسعد فريق المملكة العربية السعودية بخدمتك.
كيفية قرصنة نقابات عبر قاعدة البيانات في خدمات التابلوه في المملكة العربية السعودية؟ في المملكة العربية السعودية في مكة المكرمة ، المدينة المنورة ، الرياض ، خميس مشيط ، ينبع ، جدة ، الدمام ، عنيزة ، العقير ، Haâ € ™ il ، Ta if ، الباحة ، الظهران ، مدينة الملك عبد الله الاقتصادية ، نجران ، الدرعية ، القطيف ، الخفجي ، الجبيل. ، بقيق ، قائمة المدن والبلدات في المملكة العربية السعودية ، رأس تنورة ، تربه ، مدينة جازان الاقتصادية ، مدينة المعرفة الاقتصادية ، المدينة المنورة ، الخبر ، أبها ، تبوك ، المملكة العربية السعودية ،
كيفية قرصنة نقابات عبر قاعدة البيانات في خدمات التابلوه في المملكة العربية السعودية؟ كما نقدم في المملكة العربية السعودية شركة حلول الخدمات في حفر الباطن ، العضيلية ، العوامية ، الهفوف ، حوطة سدير ، بريدة ، تيماء ، ضبا ، العيينة ، سيهات ، الخرج ، العلا ، جيزان ، الرميلة ، الرس ، عرار ، الشيبة ، المجمعة ، رابغ ، درما ، حرض ، قائمة المدن السعودية حسب Gdp Per Capita ، بدر ، مدينة سدير الصناعية ، بلجرشي ، شقراء ، الخط ، حبالة ، الدوادمي ، الدوادمي ، ليلى ، كيف يمكن اختراق اتحادات قواعد البيانات المتقاطعة في خدمات التابلوه في المملكة العربية السعودية؟ السعر 100 ريال سعودي وتم تحديثه بتاريخ 2022-03-11T10: 08: 19 + 00: 00
جدة) هي مدينة سعودية تقع على ساحل البحر الأحمر وهي المركز الحضري الرئيسي في غرب المملكة العربية السعودية. السعر 100 ريال سعودي وتم تحديثه بتاريخ 2022-03-11T10: 08: 19 + 00: 00
Bilytica # 1 is one of the top Tableau Services In Saudi Arabia One of our clients required an executive-level, company-wide dashboard to examine indicators for the last two fiscal years across all business divisions. The dashboard itself was straightforward, but finding, structuring, and collecting all of the data was a difficult. The company is divided into three distinct business areas, with data stored in four separate databases. To make matters even more complicated, data from one business unit was stored in two of these databases. We also wanted to incorporate predicted figures as well as other historical data from a Google document. Tableau does not allow cross-database unions, which is a problem.
Although Tableau does not offer cross-database unions, it does support cross-database joins for practically all data sources, which we used to create a union. We used a cross join, also known as a cartesian join, to hack a cross-database union. To get started, we required a scaffold data source to act as the left side of our cross join, including keys from each data source. We could utilize the Google Sheets prediction data as the scaffold data source in our scenario because it had data from all three business divisions as well as the required essential fields. The end result was a table on the left side of the join that had prediction data from three Google sheets that had been joined together Tableau Services In Saudi Arabia. The right side was made up of five different joins based on business unit and date from historical data sources.
We were able to get all of the required rows from a single data source using this cross join. Because the cross join yields null values in a third of the cells, the next step was to create consolidated fields for the dashboard. This consolidation procedure was made more difficult by the fact that we had to weight measurements on a regular basis and do ratio computations between them. Depending on the metrics involved, each data source included between five and 10 original columns, for a total of 48 columns. After completing all computations, we had a total of 193 columns. We constructed the custom SQL queries to provide us only three rows of data every day (one per BU) because this was a high-level dashboard, thus speed was not an issue.
Steps Of Cross-Database Union In Tableau
A simple sample of one day of records in the final data source is shown below. For the sake of simplicity, I simply included one metric. We didn’t consolidate the BU and Date columns from the forecast data source because they were present on every row Tableau Services In Saudi Arabia. For this job, we merely needed to combine measurements. I’ll utilize Sample Superstore data to demonstrate how this works in detail. I divided the data into two files, one containing data from the East and West areas and the other containing data from the Central and South. Make an excel file with one column labelled “Key” and two rows labelled “1” and “2.” Simply keep adding rows with unique values if you need to combine more data sources.
Connect each of your data sources in Tableau. Then, as the left side of your joins, pull out your scaffold data source first. Then, using the Key field from the scaffold and a join calculation with “1” as the value, create a left join with one of your “actual” data sources. The field “Create Join Calculation” appears at the bottom of your field list. If you set the data source view to “Manage Metadata” and sort by A to Z ascending, you’ll see that each data source has one of each field. Our two data sources that were nearby to each other were connected using the cross join. Where columns from one data source overlap into the other, there is a null in every cell. You can see where the two data sources overlap if you show all 9994 rows in the data source and scroll down to the end of the first table. Following this, every cell in the first set of columns is null, while the second set is filled with data.
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Consolidated Fields That Are Created
We now have all of the data in one database, but no columns containing data from all of the areas. Then, for our dashboard, we add consolidated fields Tableau Services In Saudi Arabia. We can achieve this in a number different methods, but for this lesson, I’m simply going to combine a few fields: Region, Date, and Sales. This is the simplest option, in which each field is evaluated to see if it is null, and if it is, another field is used. If you’re merging more than two data sources, you can nest several IFNULLs. By showing the amount of records, we can notice the difference between the old region columns and the new one. Only values from their own data sets are used in the original fields.
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To summaries, the steps below must be followed with care in order to effectively implement analytics practices:
Comprehension of Data Continuum.
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