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<Img
img="/uploads/Blog_1_320fd9f520.png"
alt="Why You Need Data Modeling"
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Business cannot exist without data. Companies plan, analyze and solve their problems using data. Information is collected from everywhere at each stage of the organization's activities. Modeling is needed to make the most of the collected data.

## What is data modeling

Data modeling is a method of compiling and structuring information. You don't need special technical skills to do data modeling. All you need is logic and understanding of business processes.

## What are the benefits of modeling

Here are some of the challenges data modeling can help you solve in business:

- Organizes business processes;
- Provides storage, systematization of information and make it easier to find the information you need;
- Sorts extra data and fills in gaps.

Proper organization of data ultimately leads to effective management decisions.

## How to model data

Start by building a conceptual model, then create a logical model based on it, and finally a physical one. 

### Conceptual data modeling

This is a visual representation of business processes and the relationship between them. This stage does not require software; it is enough to draw a model by hand.

### Logical data modeling

You create a conceptual model in the first step, and then you can convert it into a logical data model. To do this, add key and non-key attributes to the entities. And also you can show the relationships between primary and foreign keys. The result is an independent model that is compatible with all databases.

### Physical data modeling

The entities are transformed into tables; the attributes are transformed into columns. The names of the table and columns within it are adapted for compatibility with databases.

For ordinary users, the physical model is difficult to understand. Use the logical concept for dialogue with users, and use the physical one for in-depth analysis.

## Data Modeling Approaches

### Hierarchical data model

The simplest model was presented by IBM in 1996. In the approach, much attention is paid to the hierarchy based on the principle of a tree structure. The «parent» entity is located at the top, «children» goes down from it.

[**Information Management System**](https://www.ibm.com/ibm/history/ibm100/us/en/icons/ibmims/) is great for taking inventory. In a clothing store, at the top of the model is «clothing», its «children» can be «male» and «female». The decomposition separates women's wardrobe items (tops, dresses, skirts) from men's wardrobe items (trousers, shirts, jackets).

<Img
img="/uploads/hierarchical_data_model_a6083d81ed.png"
alt="Hierarchical data model"
/>

### Relational data model

This model was invented by Edgar Codd in 1970. Edgar, an IBM employee and computer scientist, based the model on an intuitive way to create tables. The data is organized in columns and rows, each cell is in its place. This method is still used effectively in the field of enterprise computing. Specialists use this method in the field of enterprise computing. Using relational models, it is convenient to track the number of goods in stock, store information about customers and conduct transactions in e-commerce.

### ER data models

This model stands for «entity-relationship». Within the model, entities are connected using relationships.

For example, when a user places an order in an online store, the order is assigned a unique code. This is an entity. Further, many events can happen with this order: the client can add or remove items, change the quantity, or even make a return. All these actions are the relationships to the entity.

### OODM

The Object Oriented Data Model (OODM) maps any complex scenarios. A closer look highlights the obvious and not so obvious business problems. OODM has proven to be great for funnel analysis. A potential client acts as an object with its own attributes (name, email, phone, etc.). As long as the lead has not [**turned into a client**](MQL-and-SQL), there are two objects: a «potential client» and a «client» itself. The «client» has all the same attributes as the «potential client», but with additions: ID, addresses and payment details.

OODM is designed to build relationships around a real-world object. When specialists build such a model, they rely not only on logic and mathematics, but also on the actual situation.

### Dimensional data model

When there is too much information, it is divided into data of the first, second and subsequent orders. In such a structure, it is very difficult to quickly find the required solution. Dimensional models are used to optimize databases and make it easier to find the information you need.

<Img
img="/uploads/dimesion_data_model_fa8c1d26c3.png"
alt="Dimensional data model"
/>

It is recommended to use OLAP (online analytical processing) for trend analysis, sales forecasting, financial reporting, etc.

## How to build an efficient data model

1. **Set a clear, measurable goal.** The goal should work for the result. Imagine what effect you want to achieve through model building.
2. **Strive for simplicity.** This advice applies to both the initial stage of working with the model and subsequent ones. At the very beginning, do not try to make the model complex and confusing. Get maximum clarity. In the future, you will fill it with the necessary information without making it difficult to understand.
3. **Organize your data based on facts, dimensions, filters, and order.** Let's say a business owner has over a hundred stores. The task is to find the point that is the most effective in terms of sales of product N over the past year. "Fact" is the sum of sales of each specific store. "Dimensions" are the product N that the owner is interested in, and the location of the store. "Filter" sets the time interval (year), and "order" determines the Top 10 stores according to the specified parameters.
4. **Avoid mistakes.** This is especially important when it comes to a large-scale project. If you skip a mistake to the next stage, there is a chance that at the very end of the project you will have to return to the roots. To avoid such situations, double-check.

## Data modeling tools

There is a wide range of data modeling tools on the market. In this post, we list a few of them. When choosing a tool, rely on the needs of your own business and pay attention to the following points:

- How clear is the organization of work with large amounts of data?
- Is there a visualization functionality?
- Can this tool streamline data mapping through the enterprise?

Here are some options:

- [**Erwin Data Modeler**](https://www.erwin.com/products/erwin-data-modeler/) is a tool from Quest. Suitable for designing and visualizing data and compiling detailed reports. 
- [**Enterprise Architect**](https://sparxsystems.com/) does a great job of visualizing business processes. 
- [**ER/Studio**](https://www.idera.com/products/er-studio/enterprise-data-modeling) is a service for working with complex data; you can build dimensional and relational models in it.
- [**Altcraft Platform**](/en) is a marketing automation platform that combines data from various sources into a single profile. Analytics works in real time, visualization is achieved through charts and reports. The advantage of the tool is the speed of processing large amounts of information.

<Img
img="/uploads/summary_report_d793cf8f9d.png"
alt="Data modeling in Altcraft Platform"
src="Data modeling in Altcraft Platform"
/>



In this post, we talk about what data modeling is, why it is important, and what tools to choose for data modeling.

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Most of your data will come from your business processes and software system. Modeling is used to maximize value from the data.

Data Modeling: What It Is and What Tools to Choose

Why You Need Data Modeling

<Img 
img="/uploads/data_science_9a8b2aae50.jpg"
alt="Data Science"
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In a second, Google processes [**over 99 thousand**](https://www.internetlivestats.com/one-second/#google-band) search queries, and thousands of photos are posted on Instagram. The amount of data on the Internet is skyrocketing. Being a resource for business and marketing, data is everywhere. Data science allows one to process and analyse data. It also helps to deal with business issues in retail, industry, banking and other fields. 

When data science first emerged in the 70s of the last century, it studied the data life cycle. In the 2010s, computers could process a wealth of information. At that time, data science grew in importance and began to be included in the programmes of the world's universities.
 
Nowadays, data science helps business and drives innovation. In this piece, you will learn what data science is and how to apply it in business and marketing.  

## What is Data Science

<Note>
Data science is a field of knowledge that studies and analyses data and looks for patterns to make decisions in practice. 
</Note>

Actually, data science is not exactly a science. Data is not a subject of data science, but a multidisciplinary resource (business analytics, mathematics, statistics, programming, strategic planning, system analysis) for gaining knowledge. Data science is closely associated with [**artificial intelligence**](https://www.britannica.com/technology/artificial-intelligence), [**machine learning**](https://www.sas.com/ru_ru/insights/analytics/machine-learning.html), [**big data**](https://www.oracle.com/big-data/what-is-big-data/) and [**deep learning**](https://www.ibm.com/cloud/learn/deep-learning).

<Img 
img="/uploads/data_science_i_tekhnologii_e7a204d00e.jpg"
alt="Data science and data technology"
src="Data science and data technology"
/>
 
Data science receives data from the Internet, smartphones and other smart devices, special sensors. Other sources are corporate log files, archives, transactions history. Software is the primary tool for data scientists. It processes, structures, analyses data and visualises it in a format that is understandable to the end user.
 
Data science looks into the future — it gives forecasts based on data. It predicts demand for goods, user behaviour in the application, disease progression, climate disasters and so on. 

## How does Data Science Work

Data science processes data for analysis: organises, aggregates and arranges it for non-professional users. Data scientists extract information from arrays of processed data to find patterns, build hypotheses and model a picture of the future. 
 
Stages of data science:

<Table>
| **Stage** | **Description** |
| ------ | ------ |
| **Perspective** | Setting a goal and a problem to be solved. What do we want from data? | 
| **Data collection and processing** | Searching and extracting raw data from available sources. |
| **Data preparation** | Data storage and cleaning. Blank fields from databases and irrelevant information are removed. Raw data is transformed into a form that can be analysed further. |
| **Data processing and modelling** | Clustering, modelling and classification, searching for patterns. This stage reveals what happened in the past (descriptive analytics) and which scenario is possible in the future (predictive analytics). Methods of statistics, intelligent data analysis and machine learning "look" into the future. At this stage, modelling is applied — «what if» and «if... then what». |
| **Data analysis** | The following types of analysis are used: regression, exploratory, confirmatory, predictive, confirmatory qualitative, etc. |
| **Data formatting** | The data is presented to the end user as reports and visualisation: charts, graphs and so on. |
| **Decision-making** | Business decisions are made based on the data. |
</Table>
 
## Why does Business Need Data Science

Data science answers specific business questions by testing hypotheses and ideas. Data science:

- takes into account the amount of information that a person cannot process to make decisions;
- excludes subjective opinion in decision-making.

An unbiased answer to a business question protects from errors and misconceptions. The main thing is not the authority of the head, but facts. 
 
**Data science in business also:**

- Studies the [**target audience**](creating-a-customer-profile). A company that knows the customer perfectly well offers the right products and outperforms its competitors. 
- Forecasts trends (insights for new services and products). 

## How Data Science is Applied in Business

### Industry 

Data science forecasts demand and helps to optimise supply chains. It also reduces the risks of surplus goods when demand is low and shortages when orders are large. On the basis of data, production efficiently allocates resources, controls expenses and revenues and finds vulnerabilities — the causes of defects. Predictive analytics show when equipment will fail due to wear and tear and indicate when to carry out repairs to prevent this. 
 
### Logistics and transport

Optimisation of supply chains: reduction of delivery time, search for optimal routes and reduction of operating costs. As in industry, review of data forecasts demand for services of transport companies. Data science optimises warehouse space and reduces the risk of errors in storing and moving goods. 

In transport, data science forecasts the volume of passenger traffic and vehicle occupancy and ensures safety — predicts breakdowns and accidents. 

*Example. Russian Railways has implemented data science technologies to predict dangerous failures of railway facilities. The algorithms simulate situations with more than 50 factors, assess the condition of railway tracks, the technical condition of trains and indicate areas for urgent repairs to avoid accidents and train wrecks.*
 
### Retail 

In retail, data science provides:

- forecasts of demand for product groups;
- stock control;
- insight into trends and audience needs based on social media data and information about competitors;
- custom-tailored offers: which product to offer as an alternative or for additional sale;
- price forecasts.
 
### Mobile operators 

Telecom sets a task for data science to personalise services and products. Data highlights what is important for the customer, how to optimise service packages. Mobile operators also use data to determine where it is better to place their sales points and cell towers.

Telecommunication companies have their own data science departments that create digital solutions not only for internal business needs. 

*Example. MTS offers customers a service for developing tourism that analyses 5,000+ metrics for audience segmentation.*

### Banking

In banks, data scientists examine data to determine the credit capacity of customers. Loans not repaid on time are a risk for banks. Data science forecasts how the customer will behave depending on the market situation, assesses the reliability of the borrower. Data helps banks to personalise services. Algorithms identify the user's interests by activity and offer services that customers themselves were not looking for. 

An important task for data science is to search for the actions of scammers. In the case of large transactions, the system may suspend the transaction and ask for confirmation from the customer. Atypical purchases, several accounts with similar data will be under suspicion.  

### Real estate 

In this field, data research is not limited to offering suitable property options to the buyer. Data science analyses the documentation of objects: buildings, landing plots. Data forecasts the demand for real estate, the price in a given area, and investors decide which properties are worth investing in. 

Data speeds up rental accommodation in services. *Example. Airbnb [**analyses**](https://medium.com/airbnb-engineering/location-relevance-at-airbnb-12c004247b07#.vtj3t52mm) information from users: options they viewed and which ones they eventually chose. Data science forecasts the likelihood of booking in certain areas of the city.*

<Img 
img="/uploads/karta_s_dannymi_veroyatnosti_223d2938c9.jpg"
alt="A map with the probability of booking"
src="A map with the probability of booking in San Francisco according to Airbnb data"
/>
 
## How Data Science is Used in Marketing

### Optimisation of platforms 

Data science will show how users behave on different Internet platforms: websites, applications, social networks. Based on the data, the company understands how to simplify the purchase process and which elements to add to make the product more appealing to the customer. Which features to use to improve service. Data science indicates successful and ineffective content on the company's resources: which materials are read to the end and which are just scrolled through.
 
### Audience research and personalisation

Data science studies customers in detail: interests, preferred communication channels, online and offline behaviour. Knowledge of the audience enhances the personalisation of offers: the company knows where the customer responds best to ads, which ads they respond to, how to organise [**cross-sales and additional sales**](marketing-automation-for-insurance-companies). 
 
*Example. [**Facebook**](https://data-flair.training/blogs/data-science-at-facebook/) stores a huge amount of user data. It guarantees the accuracy of targeted advertising. Everyone on Facebook can set up what ads they want to see and remove topics that are not interesting.* 

Data science helps marketers to segment the audience in detail by dozens of parameters in a targeted manner. *For example, to offer a product when it is relevant. If you know when your customer runs out of a product (pet food, household chemicals, car coolant and so on), then advertising the product at the right moment is a chance for success.* 
 
### Analytics 

Data science helps marketing to get real-time analytics and quickly adapt to new situations (it changes strategy). It speeds up planning of future campaigns. Data-driven forecasts will answer how pricing will work and what price to choose to maintain and increase sales. 
 
### Budget optimisation

Accurate analytics and predictability of consumer behaviour save the company money. Ads are good for the company's budget when shown on time and on the right platform. Data science helps marketers to understand what content is interesting to the audience so that they do not waste resources on creating unnecessary one. There is always data to forecast future campaigns and optimise current ones. 
 
### Customer loyalty and retention

Attracting a new customer is more expensive than keeping an existing one. Data science shows when the customer starts being less active or stops communicating with the company at all. When the company knows the customer lifecycle, it sends personalised offers or useful content in time [**for retention**](customer-trust). 

## Conclusion

Data science examines data and answers business and marketing questions to test hypotheses, ideas and launch new products and services.  
 
In business, data science is a tool for objective decision-making instead of intuitive actions or subjective experience. Data science analyses information about the audience to create new services and products. In marketing, data science is about optimising platforms and budgets, personalising offers. It is a tool for analytics. 
 
Data scientists work in industry, logistics, banking, telecommunications and other fields where it is possible to collect and analyse data and then apply it for business purposes.

Nowadays, data science helps business and drives innovation. In this piece, you will learn what data science is and how to apply it in business and marketing. 

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Data science allows one to process and analyse data. It also helps to deal with business issues in retail, industry, banking and other fields. 

Data Science: How to Use It in Marketing and Business

Data Science in Marketing and Business

Customer Data Platform

Omnichannel Marketing

Loyalty programs

Drag-and-drop email designer

Personalized mailings

MVT and A/B testing

Real-time analytics

Integration and WebHooks

Forms and Surveys

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