Business Intelligence Network, Baghdad (2026)

Name: Business Intelligence Network
Address: Baghdad, IQ
Telephone: +9647726004400

11/02/2021

Data profiling steps—an efficient process for data profiling
Ralph Kimball, a father of data warehouse architecture, suggests a four-step process for data profiling:
1. Use data profiling at project start to discover if data is suitable for analysis—and make a “go / no go” decision on the project.
2. Identify and correct data quality issues in source data, even before starting to move it into target database.
3. Identify data quality issues that can be corrected by Extract-Transform-Load (ETL), while data is moved from source to target. Data profiling can uncover if additional manual processing is needed.
4. Identify unanticipated business rules, hierarchical structures and foreign key / private key relationships, use them to fine-tune the ETL process.

10/02/2021

Kaggle Day Meet Up - Iraq
On 9th February 2021
Sponsored by Business Intelligence Network – BIN

06/02/2021

Types of data profiling
There are three main types of data profiling:
Structure discovery
Validating that data is consistent and formatted correctly, and performing mathematical checks on the data (e.g. sum, minimum or maximum). Structure discovery helps understand how well data is structured—for example, what percentage of phone numbers do not have the correct number of digits.
Content discovery
Looking into individual data records to discover errors. Content discovery identifies which specific rows in a table contain problems, and which systemic issues occur in the data (for example, phone numbers with no area code).
Relationship discovery
Discovering how parts of the data are interrelated. For example, key relationships between database tables, references between cells or tables in a spreadsheet. Understanding relationships is crucial to reusing data; related data sources should be united into one or imported in a way that preserves important relationships.

02/02/2021

Data warehouse: a foundation for business intelligence

A data warehouse is a repository that stores current and historical data from disparate sources. It’s a key component of a data analytics architecture that creates an environment for decision support, analytics, business intelligence, and data mining.
A data warehouse holds data from multiple sources, including internal databases and SaaS platforms. After the data has been loaded, it can be cleansed, transformed, catalogued, and checked for quality before it’s used for analytics dashboards, reporting, machine learning, or anything else.
Historically, businesses used ETL tools to pipe data into expensive on-premises data warehouse systems. Due to the limited capacity of these expensive systems, business users needed to perform as much prep work as possible before loading data into the system. Today, however, cloud-based data warehouses — including Amazon Redshift, Microsoft Azure SQL Data Warehouse, Google BigQuery, and Snowflake — offer flexible infrastructure whose processing and storage capacity can quickly scale based on an organization’s data needs. More and more organizations are opting to skip preload transformations in favor of running transformations at query time — a process referred to as ELT. This lets business users transform raw data within a data warehouse at any time for any particular use case.
Data warehouses vs. data lakes vs. data marts
Although a data warehouse is an effective and useful way to store data for business analytics, it’s best suited for structured data defined by a schema.
By contrast, a data lake can hold both structured and unstructured data, so in addition to sources defined by schemas, it can hold raw data such as log files, internet clickstream records, images, or social media posts.
A data mart is similar to a data warehouse, but holds data for one specific department or line of business, such as sales or finance. A data warehouse can feed data to a data mart, or a data mart can feed a data warehouse.
Data warehouses, data lakes, and data marts perform different duties. Businesses may use all three for different purposes.

30/01/2021

Why do you need ETL?
There are many reasons for adopting ETL in the organization:
• It helps companies to analyze their business data for taking critical business decisions.
• Transactional databases cannot answer complex business questions that can be answered by ETL.
• A Data Warehouse provides a common data repository
• ETL provides a method of moving the data from various sources into a data warehouse.
• As data sources change, the Data Warehouse will automatically update.
• Well-designed and documented ETL system is almost essential to the success of a Data Warehouse project.
• Allow verification of data transformation, aggregation and calculations rules.
• ETL process allows sample data comparison between the source and the target system.
• ETL process can perform complex transformations and requires the extra area to store the data.
• ETL helps to Migrate data into a Data Warehouse. Convert to the various formats and types to adhere to one consistent system.
• ETL is a predefined process for accessing and manipulating source data into the target database.
• ETL offers deep historical context for the business.
• It helps to improve productivity because it codifies and reuses without a need for technical skills.

25/01/2021

ETL transformation types
Regardless of where in the process transformation takes place, it’s an important step in the analytic workflow. Transformations prepare the data for analysis. Here are some of the most common types:
• Basic transformations:
• Cleaning: Mapping NULL to 0 or "Male" to "M" and "Female" to "F," date format consistency, etc.
• Deduplication: Identifying and removing duplicate records
• Format revision: Character set conversion, unit of measurement conversion, date/time conversion, etc.
• Key restructuring: Establishing key relationships across tables
• Advanced transformations:
• Derivation Applying business rules to your data that derive new calculated values from existing data – for example, creating a revenue metric that subtracts taxes
• Filtering: Selecting only certain rows and/or columns
• Joining: Linking data from multiple sources – for example, adding ad spend data across multiple platforms, such as Google Adwords and Facebook Ads
• Splitting: Splitting a single column into multiple columns
• Data validation: Simple or complex data validation – for example, if the first three columns in a row are empty then reject the row from processing
• Summarization: Values are summarized to obtain total figures which are calculated and stored at multiple levels as business metrics – for example, adding up all purchases a customer has made to build a customer lifetime value (CLV) metric
• Aggregation: Data elements are aggregated from multiple data sources and databases
• Integration: Give each unique data element one standard name with one standard definition. Data integration reconciles different data names and values for the same data element.

23/01/2021

يسر كل من منظمة Kaggle البولندية و بالتعاون من LogiclAI دعوتك لحضور أول ملتقى من سلسلة لقاءات "Kaggle Days Iraq" التي سيعقد في (9 شباط 2021 في العاشرة صباحا بتوقيت بغداد ) في الجامعة المستنصرية - كلية العلوم و بالتعاون مع أمازون العراق , (AWS IQ) مختبر انترنت الاشياء (IoT LAB) ,منصة ذكاء الأعمال (MeinTech , (BIN و منصة بيت التسويق (HOM)
ما هو Kaggle Days Meetups ؟
هي سلسلة من المؤتمرات التي تقيمها منظمة Kaggle بالتعاون مع Logical AI و التي تهدف لخلق مجتمع يضم كل من الكاجلرز و الأشخاص المهتمين بعلوم البيانات.
Kaggle Days Meetup is coming to Iraq
Kaggle organization and LogiclAI are pleased to invite you to attend their First meetup of “Kaggle Days Iraq “meetups series which will be held on (9th Feb. 2021 10AM BGH) at Al-Mustansiriya University - College of Science In cooperation with AWS IQ, IoT LAB, Business Intelligence Network, Maintech and House of Marketing.
What are Kaggle Days Meetups?
Kaggle Days Meetups are a series of events all over the world, created by Kaggle and LogicAI, that aim to gather Kagglers and people interested in Data Science.
For more information please to visit our website: https://kaggledays.com/meetups/
For registration please to fill this form:
https://docs.google.com/forms/d/e/1FAIpQLSfyyLrncbxKGfmEDB83m2BCX1jd2UKRJTrkbr3kMQ6cxSp8Lw/viewform?fbclid=IwAR2yrVDc9HThKAx0-Phmp0zjML3rBv4_DDuDKiK8wi-7syUts6QyHTvbNEk

19/01/2021

How Can Augmented Analytics Add Value to Business Intelligence?
There are numerous Benefits to Augmented Analytics approach to business intelligence and, with the right self-serve analytics and data discovery tool, the average enterprise can expand these benefits and add even more value. Here are just a few of the ways in which self-serve augmented analytics can add value to business intelligence.

• A self-serve augmented analytics solution allows analysts, data scientists and internal IT staff to focus on critical projects and to provide support for strategic issues, freeing them from focusing on the day-to-day analytical needs of the business community.
• Augmented analytics empowers business users and provides critical information that will allow them to focus on goals, provide objective metrics and enable data sharing to advance the interests of the business.
• Augmented Analytics provides complex, sophisticated techniques and tools in an easy-to-use interface to bring together data from disparate data sources and allow for confident, accurate decisions.
• Augmented analytics provides immediate, objective results and improves ROI and TCO.
• This approach to analytics ensures accurate business forecasting and predictions and provides metrics to ensure that appropriate decisions are made and that the business takes appropriate action regarding products and services, pricing, competition and other crucial business factors.
• Simplified augmented analytics tools improve user adoption, data sharing, the advancement of data popularity, the integration of social BI within the organization and data and metrics literacy.

14/01/2021

What is data profiling?
Data profiling is the process of reviewing source data, understanding structure, content and interrelationships, and identifying potential for data projects.
Data profiling is a crucial part of:
• Data warehouse and business intelligence (DW/BI) projects—data profiling can uncover data quality issues in data sources, and what needs to be corrected in ETL.
• Data conversion and migration projects—data profiling can identify data quality issues, which you can handle in scripts and data integration tools copying data from source to target. It can also uncover new requirements for the target system.
• Source system data quality projects—data profiling can highlight data which suffers from serious or numerous quality issues, and the source of the issues (e.g. user inputs, errors in interfaces, data corruption).
Data profiling involves:
• Collecting descriptive statistics like min, max, count and sum.
• Collecting data types, length and recurring patterns.
• Tagging data with keywords, descriptions or categories.
• Performing data quality assessment, risk of performing joins on the data.
• Discovering metadata and assessing its accuracy.
• Identifying distributions, key candidates, foreign-key candidates, functional dependencies, embedded value dependencies, and performing inter-table analysis.

10/01/2021

how to distinguish between business analyst, BI, or other related roles?
Truth be told, the industry does not have a standard definition of a data scientist. You have probably heard Jokes like “a data scientist is a data analyst living in Silicon Valley”.
Traditional BI/Reporting Professional:
The BI professional generates reports from structured data using SQL and some kind of reporting services (SSRS for example) and sends the data back to management. Management asks more questions based on the data that was sent, and the cycle continues. Insights about the data are most likely not included in the reports. A person in this role will be experienced mostly in database-related skills.
Data Analyst:
In addition to doing what the BI professional does, a data analyst will also keep other factors like seasonality, segmentation, and visualization in mind. What if certain trends in shopping behavior are tied to seasonality? What if the trends are different across gender, demographics, geography, or product category? A data analyst will slice and dice the data to understand and annotate the report. Aside from database skills, a data analyst will have an understanding of some of the common visualization tools.
Business Analyst:
A business analyst possesses the skills of a BI professional and the data analyst, plus they have domain knowledge and an understanding of the business. A business analyst may also have some basic skills in forecasting.
Data Mining or Big Data Engineer:
A data miner does the job of the data analyst, possibly from unstructured data if needed, plus possesses MapReduce and other big data skills. An understanding of common issues in running jobs on large scale data and debugging of MapReduce jobs is needed.

Statistician (a traditional One):
A statistician pulls data from a database or obtains it from any of the roles mentioned above and performs statistical analysis. This person ensures the quality of data and correctness of the conclusions by using standard practices like choosing the right sample size, confidence level, level of significance, type of test, and so on.
In the past, statisticians did not traditionally come from a computer science background, needed for writing code to implement statistical models. The situation has changed, Stat students now graduating with strong programming skills and decent foundation skills in CS. This enables them to perform the tasks that previous statisticians were not trained for traditionally.

25/12/2020

The Purposes Metadata Serves
Metadata serves a variety of purposes, with resource discovery one of the most common. Here, it can be compared to effective cataloging, which includes identifying resources, defining them by criteria, bringing similar resources together and distinguishing among those that are dissimilar.

It also is an effective means of organizing electronic resources, which is an important use given the growth in Web-based resources. Typically, links to resources have been organized as lists and built as static webpages, with the names and resources hardcoded in HTML. A more efficient practice, however, is to use metadata to build these pages. For Web purposes, the information can be extracted and reformatted through use of software tools.

Another use of metadata is as a means of facilitating interoperability and integrating resources. Using metadata to describe resources enables its understanding by humans as well as machines. This permits the most effective levels of interoperability, or how data is exchanged among many systems with disparate operating platforms, data structures and interfaces. In turn, it facilitates resource searches across the network.

Metadata also facilitates digital identification via standard numbers that uniquely identify the resource the metadata defines. Along these lines, another practice is to combine metadata so that it acts as a set of identifying data that differentiate objects or resources, supporting validation needs.

Finally, metadata is an important way to protect resources and their future accessibility. It’s a critical concern given the fragility of digital information and its susceptibility to corruption or alteration. For archiving and preservation purposes, it takes metadata elements that track the object’s lineage, and describe its physical characteristics and behavior so it can be replicated on technologies in the future.

05/12/2020

دورة مهارات المبيعات الفعالة
تقيم شركة BHT للتدريب وتطوير الكوادر البشرية بالتعاون مع منصة تطوير الاعمال BDC و ذكاء الاعمال BIN دورة تدريبية (اون لاين) في مهارات المبيعات الفعالة ولمدة 5 ايام من 2/1/2021 الى 6/1/2021 الساعة الخامسة عصرا ولساعتين في اليوم

تهدف هذه الدورة الى تطوير الطاقات الشبابية وجعلهم قادرين اتقان واحترتف مهارات المبيعات التي اصبحت ضرورية في اي نشاط تجاري

محاور الدورة
مقدمة في مهارات المبيعات
العلاقات في المبيعات
تخطيط المبيعات الفعالة
قنوات المبيعات
تصميم قمع المبيعات
جذب والحفاظ على الزبائن
التواصل مع الزبائن
التفاوض في المبيعات
الاقناع في المبيعات
عروض البيع الاحترافية

مخرجات الدورة
فهم ادارة المبيعات
احتراف طرق المبيعات
تصميم قمع المبيعات لزيادة الايرادات

يحصل المشاركون في الدورة على شهادة مشاركة صادرة من شركة بيت الحكمة للتدريب
الدورة من خلال منصة Zoom
سعر الاشتراك: 75 دولار

استمارة التسجيل ادناه :
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Business Intelligence Network

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