Big Data: How Valuable Is Your Marketing Data?

Advertisements

The issue of data volume obtains the greatest attention from those responsible for analyzing and compiling the huge amount of data compared to its accuracy and type.. What can be said that this interest is the most prominent feature in the work system in the space of big data.

However, this interest did not satisfy some companies with the presence of some technical errors in their marketing databases. It is striking that some statistics were recorded for a very large percentage of those gaps in the documents of one of the largest companies in the world.

Some of the pitfalls that were observed were highlighted as follows:

• Lack of sufficient knowledge of industrial information

• Lack of recorded information on revenues

• Not paying attention to employee records

• Neglecting to know the job titles of customers

Perhaps what was mentioned above makes us reconsider that theory with which we started our research and recall that it is more appropriate for everyone who deals with data to pay more attention to the quality and accuracy of the data than to the volume of data in order to seek to realize the desired goal and expand business activity.

This is reflected in several reasons, the most important of which are:

Attention to sales:

When those in charge of sales operations are armed with an abundance of accurate and correct data, then they can use their full potential and experience to acquire the largest possible number of active customers, and thus they have avoided as much as possible wasting time by searching for how to find solutions to the obstacles that hinder their progress and success, and this in turn applies to the employees of Marketing, as it is not acceptable for the salesperson to search for a customer’s number or mail and then discover that it is missing and not present in the contacts database. The attention to accuracy here avoids the work staff making such mistakes and they are able to divert their attention to convincing the largest possible number of customers to buy a product or service and thus do their part to the fullest.

According to the reports of marketing experts, email, mobile and search engine optimization are the elements that highlight the main role of big data in its impact on their marketing system.

Focus on the important points of the target group:

Based on what was mentioned, it can be said that sound and accurate data contributes to a major role in demonstrating the competence of marketing staff and providing what they have of experience and good judgment of matters according to the right track, such as conducting a quick study of the record of each customer so that they coordinate well-thought-out messages that suit the interests of the target customer.

Avoid wasting time and money:

The randomness of data coordination hinders the work of salespeople, as instead of investing time in the optimal organization of the marketing plan of preparing and sending promotional messages, they will have to search for a long time for ways to connect them to customers, so sound data is the way to avoid falling into a cycle of confusion and waste of time and everything It would hinder the workflow.

Good Sales Leadership Increases Profits:

The deep knowledge that results from good handling of clean data generates in the work staff sufficient experience and far-sightedness to deal with various types of commercial activities of all kinds, especially knowledge of transaction volumes, market requirements, good selection of projects with guaranteed profit, economic feasibility, forecasting sales operations, forecasting revenues, and so on.

We conclude from the above:

There is no point in the large amount of data if it does not enjoy regularity and coordination, then this huge amount of organized and clean data will form a mainstay for the company and its staff, and it is the main pillar for developing any business activity and achieving the required results with high efficiency.

Advertisements

ما مدى أهمية البيانات الضخمة في عملية التسويق الخاصة بك ؟

Advertisements

ينال موضوع حجم البيانات الحيز الأكبر من اهتمام القائمين على تحليل وتجميع الكم الهائل من البيانات مقارنة مع دقتها ونوعها .. ما يمكن القول بأن هذا الاهتمام يعتبر السمة الأبرز في منظومة العمل في فضاء البيانات الضخمة

إلا أن هذا الاهتمام لم يشفع لبعض الشركات بوجود بعض الأخطاء التقنية في قواعد بيانات التسويق لديها ومن الملفت للنظر تسجيل بعض الإحصائيات لنسبة كبيرة جداً من تلك الثغرات في وثائق إحدى كبرى الشركات في العالم

: وتم تسليط الضوء على بعض العثرات التي لوحظت وكانت على النحو الآتي

غياب المعرفة الكافية بالمعلومات الصناعية *

قلة المعلومات المسجلة عن الإيرادات *

عدم الاهتمام بسجلات الموظفين *

إهمال معرفة التسميات الوظيفية للعملاء *

ولعل ما ذكر آنفاً يجعلنا نعيد النظر في تلك النظرية التي استهلينا بها بحثنا ونسترجع القول بأن من الأجدر على كل من يتعامل مع البيانات أن يولي الاهتمام الأكبر لجودة ودقة البيانات لا على حجم البيانات للسعي وراء إدراك الغاية المرجوة وتوسيع النشاط التجاري

: ويتجسد ذلك في عدة أسباب أهمها

: الاهتمام بعمليات البيع

عندما يتسلح القائمون على عمليات البيع برصيد وافر من البيانات الدقيقة والصحيحة فعندها يمكنهم توظيف كامل إمكاناتهم وخبراتهم لاكتساب أكبر قدر ممكن من العملاء النشطين وبذلك يكونون قد تجنبوا قدر الإمكان إهدار الوقت جراء البحث عن كيفية إيجاد الحلول للعراقيل التي تعوق تقدمهم ونجاهم , وهذا ينطبق بدوره على موظفي التسويق إذ ليس من المقبول أن يقوم مندوب المبيعات بالبحث عن رقم أو بريد أحد العملاء ثم يكتشف أنه مفقود وغير موجود في قاعدة بيانات جهات الاتصال فالاهتمام بالدقة هنا تجنب كادر العمل الوقوع في هكذا أخطاء ويكون بمقدورهم صرف اهتمامهم إلى إقناع أكبر عدد ممكن من الزبائن بشراء منتج أو خدمة وبالتالي يقومون بدورهم على أكمل وجه

وحسب تقارير خبراء التسويق أن الرسائل عبر البريد الإلكتروني والجوال وتحسين محركات البحث هي العناصر التي تُبرِز الدور الرئيسي للبيانات الضخمة في تأثيرها على المنظومة التسويقية الخاصة بهم

: التركيز على النقاط المهمة للفئة المستهدفة

وبناءً على ما ذكر يمكن القول أن البيانات السليمة والدقيقة تسهم في دور كبير في إظهار الكفاءة التي يتمتع بها موظفو التسويق وتقديم ما يملكون من خبرة وحسن تقدير الأمور وفق مسارها الصحيح كقيامهم بدراسة سريعة لسجل كل عميل بحيث يقومون بتنسيق رسائل مدروسة تناسب اهتمامات العميل المستهدف 

: تجنب إهدار الوقت والمال

تتسبب العشوائية في تنسيق البيانات بإعاقة عمل مندوبي المبيعات إذ بدلاً من استثمار الوقت في التنظيم الأمثل لخطة التسويق المتمثلة بتجهيز رسائل الترويج وإرسالها سيتوجب عليهم البحث لفترة طويلة عن الطرق التي ستوصلهم بالعملاء , لذا فإن البيانات السليمة هي السبيل لتفادي الوقوع في دوامة الارتباك وهدر الوقت وكل ما من شأنه أن يعيق سير العمل

: القيادة الجيدة للمبيعات تؤدي إلى زيادة الأرباح

أن المعرفة العميقة التي يفرزها حسن التعامل مع البيانات النظيفة تولد لدى كادر العمل الخبرة الكافية والنظرة البعيدة للتعامل مع شتى أنواع الفعاليات التجارية على اختلاف أنواعها ولاسيما معرفة أحجام الصفقات ومتطلبات السوق وحسن اختيار المشاريع ذات الربح المضمون والجدوى الاقتصادية والتنبؤ بعمليات البيع وتوقع الإيرادات وما إلى ذلك

نستنتج مما ذكر : لا جدوى من كثرة البيانات وضخامتها إن لم تكن تتمتع بالانتظام والتنسيق عندها سيشكل ذلك الكم الهائل من البيانات المنظمة والنظيفة دعامة أساسية للشركة ولكادرها وهي الركيزة الرئيسية لتطوير أي نشاط تجاري وتحقيق النتائج المطلوبة بكفاءة عالية      

Advertisements

Big Data Analytics Tools: Talend

Ninth tool

Advertisements

A tool that makes data integration simplified and more effective, and it is free and open source of the ETL style, meaning that its function is to organize and coordinate raw, unstructured information and transform it into ready-made data for practical analysis. It has the ability to develop and manage its applications to the fullest extent, as it contains a central store of data with the ability to deal with metadata, making it the ideal tool for performing all analysis techniques with high efficiency and accuracy.

Advertisements

Talend : أدوات تحليل البيانات الضخمة

الأداة التاسعة

Advertisements

أداة تجعل تكامل البيانات مبسطاً وأكثر فاعلية

ETL وهي مجانية مفتوحة المصدر من نمط

بمعنى أن وظيفتها تنظيم وتنسيق المعلومات الأولية

غير المنظمة وتحويلها إلى بيانات مهيأة

لإجراء عمليات التحليل العملي

هذه العملية توفر للمستخدمين إمكانيات متعددة

مثل تكامل بيانات المؤسسات والشركات

بجودة عالية وخاصة الضخمة منها

ما يوفر لها تطوير تطبيقاتها وإدارتها

على أكمل وجه فاحتوائها على مخزن مركزي

للبيانات مع إمكانية التعامل مع البيانات الوصفية

يجعلها الأداة الأمثل للقيام بكافة تقنيات

التحليل بكفاءة عالية ودقة متناهية

Advertisements

Big Data Analytics Tools : Apache Cassandra

The eighth tool

Advertisements

It is a free and open source NoSQL database, and it is the ideal tool for analyzing big data with an expansive feature, which in turn works to avoid errors during the analysis process, thus obtaining accurate and more effective results.

The main features of this tool are summarized in the following points:

•  Its properties are somewhat similar to SQL, including the query language.

•  Provides a wide display area, especially for writing operations.

•  The ability to spread securely because it is not restricted to a central server.

•  Easy system of data.

 • The ability to replicate patterns and the flexibility of modification and coordination.

Advertisements

Apache Cassandra : أدوات تحليل البيانات الضخمة  

الأداة الثامنة

Advertisements

وهي عبارة عن قاعدة بيانات مفتوحة المصدر

NoSQL ومجانية من نمط

وهي الأداة الأمثل لتحليل البيانات الضخمة ذات الخاصية التوسعية والتي تعمل بدورها على تفادي حدوث خلل أثناء عملية التحليل وبالتالي الحصول على نتائج دقيقة وأكثر فاعلية

: تتلخص أبرز ميزات هذه الأداة بالنقاط التالية

بما فيها لغة الاستعلام SQL خصائصها تشبه إلى حد ما *

توفر مساحة عرض واسعة ولاسيما لعمليات الكتابة *

قابلية الانتشار الآمن نظراً لعدم تقييده بسيرفر مركزي *

منظومة سهلة من البيانات *

قابلية نسخ الأنماط المتماثلة ومرونة التعديل والتنسيق *

Advertisements

Big Data Analytics Tools : Xplenty

Seventh tool

Advertisements

This tool enables the processing of ETL solutions and various types of data as it is based on the processing of the basic database set. High security and flexibility of data transformation in addition to the fact that it contains a REST application programming panel. All these features and capabilities make Xplenty a platform that provides high efficiency and complete flexibility for big data analysts.

Advertisements

Xplenty : أدوات تحليل البيانات الضخمة

الأداة السابعة

Advertisements

ETL تتيح هذه الأداة معالجة حلول

ومختلف أنماط البيانات كونها ترتكز على

معالجة مجموعة قواعد البيانات الأساسية

كما وتوفر إمكانية التعامل مع البيانات المنظمة

وغير المنظمة من خلال توافقها مع الاتصال

بمصادر متنوعة كما هو الحال في

ومستودعات بيانات Amazon Redshift

وتقنيات سحابات التخزين SQL و NoSQL

كما وتتميز بمستوى عالي من الأمان

وبمرونة تحويل البيانات علاوة على أنها

REST تحوي لوحة برمجة تطبيقات

كل هذه الميزات والإمكانيات جعلت

منصة أتاحت كفاءة عالية Xplenty من

ومرونة تامة لمحللي البيانات الضخمة

Advertisements

Big Data Analytics Tools: Clickhouse

Sixth tool

Advertisements

It is considered one of the most important database control systems and it is an open source analysis tool designed to deal with columns from Yandex and by means of large coordinated data it allows its users to perform analytical queries within a short period of time.

It is one of the distinguished tools in dealing with big data and preferred by many analysts to work on all general analytical functions such as: Presto, Spark, Impala, and in general in dealing with databases represented by columns with the flexibility of controlling the master keys and procedures for deleting unnecessary data, as is the case in InfluxDB.

ClickHouse is based on its own SQL language and includes many graphical extensions such as high-format tasks, data models, interlaced data forms, URL-compatibility functions, probability algorithms, various mechanisms for working with dictionaries, formatting schemas formed from working on Apache Kafka, aggregation tasks, designing visualizations saved with their formatting, and many more the other.

Advertisements

: أدوات تحليل البيانات الضخمة

Clickhouse

الأداة السادسة

Advertisements

يعتبر من أهم أنظمة التحكم بقواعد البيانات

وهو أداة تحليل مفتوحة المصدر

Yandex مصممة للتعامل مع الأعمدة من

وبواسطة البيانات الضخمة المنسقة يتيح لمستخدميه

القيام باستعلامات تحليلية خلال فترة وجيزة

وهو من الأدوات المميزة في التعامل مع البيانات الضخمة

ويفضله الكثير من المحللين للعمل على كافة الوظائف التحليلية

Presto و Spark و Impala : العامة مثل

وإجمالاً في التعامل مع قواعد البيانات الممثلة

بالأعمدة مع مرونة التحكم بالمفاتيح الرئيسية وإجراءات حذف البيانات

InfluxDB غير الضرورية كما هو الحال في

المخصصة لها SQL على لغة ClickHouse تعتمد

فهي تتضمن العديد من اللواحق البيانية

كالمهام عالية التنسيق ونماذج البيانات وأشكال البيانات المتشابكة

URL ووظائف التوافق مع

وخوارزميات الاحتمالات وآليات متعددة

للتعامل مع القواميس وتنسيق المخططات المتشكلة

والمهام التجميعية Apache Kafka من العمل على

وتصميم التصورات المحفوظة مع تنسيقها

والعديد من المهام الأخرى

Advertisements

The Fourth And Fifth Tools

Big Data Analytics Tools

Apache AirflowApache Parquet

Advertisements

4. Apache Airflow

An effective tool in developing the analysis steps and making them more advanced, as Airflow is considered code in the Python language.

5. Apache Parquet

Apache Parquet is a dual-column, big data-architecture designed for Hadoop that allows it to represent compressed data by controlling new codes as they appear at the column level. Parquet is a popular environment for big data analysts and is used in Spark and Kafka and Hadoop.

Advertisements

الأداتين الرابعة والخامسة من أدوات تحليل البيانات الضخمة

Apache Airflow – Apache Parquet

Advertisements

4. Apache Airflow

أداة فعالة في تطوير خطوات التحليل وجعلها أكثر تقدماً

تعتبر كود في لغة بايثون Airflow إذ أن

5. Apache Parquet

هو نسق مزدوج مخصص للعمل على أعمدة حفظ

Hadoop البيانات الضخمة والمصمم للتعامل مع بيئة

الذي يتيح له تمثيل البيانات المضغوطة من خلال

التحكم بكودات جديدة فور ظهورها على مستوى العمود

بيئة متداولة بشكل كبير Parquet كما ويعتبر

من قبل محللي البيانات الضخمة

Hadoop و Kafka و Spark علاوة على استخدامه في

Advertisements

The Third Tool Of Big Data Analysis : Apache Spark

Advertisements

It is one of the open source tools that are highly efficient in analyzing big data due to its reliance on distributed computing technology in RAM, which speeds up the processing process and gives more accurate and effective results.

Spark is a suitable environment for many big data analysis professionals, especially for many giant companies such as eBay, Yahoo and Amazon due to the development of this tool for many functions used in analysis techniques such as iterative algorithms and data flow processing, as this tool mainly depends on Hadoop, the advanced system for MapReduce

Advertisements
Advertisements
Advertisements

Second Big Data Analytics Tool : Apache Superset

Advertisements

Superset is a data visualization technology that is done with the help of a group of other components. It is a suitable environment for designing control panels and confirming customer membership through OAuth, OpenID or LDAP. Its characteristics are consistent with most data sources designated to work on SQL program, and its features work in full compatibility with Apache ECharts.

Many giant companies such as Netflix, Airbnb, Twitter, Airbnb, and Lyft rely on Superset technology primarily to analyze their products due to its use in MediaWiki.

Advertisements
Advertisements
Advertisements

The First Tool From Big Data Analytics Tools Apache Hadoop

Advertisements

An integrated set of programs specialized in dealing with big data, application programming interfaces, and the techniques necessary to develop them, which are free and open source.

This tool consists of four sections:

  • YARN is a technology dedicated to handling data sets.
  • HDFS is a categorized file system that is built to run on standard devices.
  • A programming environment that enables other units to be compatible with HDFS.
  • MapReduce is an algorithmic pattern used for parallel computation provided by Google.

Advertisements
Advertisements
Advertisements

Introduction About Tools For Big Data

Advertisements

There are many tools dedicated to big data analysis on different software providers such as Microsoft, IBM and Oracle, and they are widely used by analysts of this type of data

Especially the open source programs that the largest companies rely on to analyze their products. There are also free tools such as Apache Hadoop, which are classified from the free Apache environment.

In the upcoming articles, we will discuss the big data analysis tools, each separately

Advertisements
Advertisements

Advertisements

Analyzing Large Amounts Of Data: 9 Proven Tools

Advertisements

Recently, with the advancement of science and technology, there have been many questions about techniques for dealing with big data, through which we can predict customer behavior, control resources, expand sales, thwart emergency conditions that hinder the progress of any business, and control fraud, in addition to making the daily transactions of many people more flexible and easy.

The term “big data” was given to a database that contains several rows or random data related to a topic, or techniques that deal with many inquiries at the same time.

Several years ago, the discussion of big data was not so important that even some data science professionals did not have a sufficient understanding of how to deal with the exact structure of this type of data.

Big data in its concept does not embody the data itself :

The concept of big data is not limited to the data itself, but goes beyond it to strategies related to dealing with that data, with another prevention, which is to find an effective mechanism to process a random set of information related to the activity of any government agency or commercial company, regardless of the amount of that information through which technicians and specialists can find The best organizational methods for converting that information into useful data conducive to overcoming all obstacles to the smooth functioning of that activity.

Moreover, according to the new concept of big data, it is considered the best way to get rid of the traditional pattern of effective relationships and transactions in the development of machine learning techniques and its branches, so that big data technicians and specialists receive greater attention and support compared to programming specialists and data scientists in general. Dealing with this large amount of data Data of all kinds leads to accurate and effective analysis and leads to following the right strategies in investing time and effort at the lowest costs to serve commercial or industrial activity or both for major international companies.

We will deal as a living model plan of a particular company planning to carry out advertising campaigns on a large scale or a company planning to evaluate its sales movement. The best option to implement these strategies that fall under the name of business intelligence is the use of big data as a model solution to implement these projects more effectively by using more accurate and professional techniques provided by this Type of data analysis.

This is done to deal with big data through several steps, and data preparation is one of the most important basics of the analysis process, which consumes the most time from the total integrated system for data analysis.

data collection :

The data is collected as a first stage by special tools from multiple sources and then stored on a file in its basic position without making any change in the properties because any change or transformation of information loses some of its features and thus reduces the efficiency of the analysis.

data selection

To explain the concept of data selection, we turn, in an illustrative example, to a promotional plan that is presented to customers for SIM products to be sold before the start of the school season, based on the analyzes of sales movement in the previous year. Based on previous analyzes without neglecting to rely on forecasting according to the surrounding developments and variables.

Here comes the role of data analysts in identifying the subgroups of the common data set, which are relied upon to find the best way to produce good results.

Clean the raw data:

This step includes filtering and processing unstructured, unformatted, or error-containing data, eliminating duplicates, if any, and analyzing them to take the form of useful and required information.

Data Enhancement and Integration:

Data is supplemented from local data sources or various other data sources (databases or information systems) and their aggregation is included when calculating new values ​​such that a game company collects and analyzes documents produced by games to gain insights into usage behavior and customer preferences so that it can produce plans to enhance the likelihood of opportunities Selling by developing new features that drive the growth of their business forward.

Data Format :

Sometimes it may require doing data formatting without modifying its values, such as sorting data with specific numbering and encoding, shortening long terms, and removing unnecessary punctuation marks in text cells.

Activate the role of forecasters

At this stage, the derived features are built and directed to work on the machine learning technology so that they are employed to raise the efficiency of the higher education algorithm and then deal with it by the forecasters.

Create an analytic model:

Since the model is a method of seeing data, this requires creating an analytical model to predict the required variable. For example, we can say that sorting is the collection of items with similar characteristics into subgroups according to certain criteria.

At this point, for the sake of clarity, we can sort customer groups based on the behavior of their customers: sports interests, vegetarians, etc. through tools designed for this purpose (such as IBM SPSS) via the built-in databases.

In practice, models that include machine learning characteristics are used to transfer the current analyzes to the future for the purpose of comparing them with reality and other samples. .

In general, this type of analysis requires analysts to devise a different method to apply it to the data because there is a state of chaos in the organization of the data resulting from not organizing and coordinating it well. Therefore, block analysis and machine learning are related to the variables created by the existing situation, so they invent a new method by writing more effective software codes. Contributes to bug fixing and rectification of errors.

As a last step in this analysis, it is possible to build control panels and charts due to the presence of a small number of data capable of graphic representation.

Advertisements
Advertisements
Advertisements