Saturday, November 16, 2024

Implement value objects in EF

Important characteristics of value objects

Value object implementation in C#

How to persist value objects in the database with EF Core 2.0 and later

Persist value objects as owned entity types in EF Core 2.0 and later

Additional resources

 Tip


This content is an excerpt from the eBook, .NET Microservices Architecture for Containerized .NET Applications, available on .NET Docs or as a free downloadable PDF that can be read offline.



.NET Microservices Architecture for Containerized .NET Applications eBook cover thumbnail.


As discussed in earlier sections about entities and aggregates, identity is fundamental for entities. However, there are many objects and data items in a system that do not require an identity and identity tracking, such as value objects.


A value object can reference other entities. For example, in an application that generates a route that describes how to get from one point to another, that route would be a value object. It would be a snapshot of points on a specific route, but this suggested route would not have an identity, even though internally it might refer to entities like City, Road, etc.


Figure 7-13 shows the Address value object within the Order aggregate.


Diagram showing the Address value-object inside the Order Aggregate.


Figure 7-13. Address value object within the Order aggregate


As shown in Figure 7-13, an entity is usually composed of multiple attributes. For example, the Order entity can be modeled as an entity with an identity and composed internally of a set of attributes such as OrderId, OrderDate, OrderItems, etc. But the address, which is simply a complex-value composed of country/region, street, city, etc., and has no identity in this domain, must be modeled and treated as a value object.


Important characteristics of value objects

There are two main characteristics for value objects:


They have no identity.


They are immutable.


The first characteristic was already discussed. Immutability is an important requirement. The values of a value object must be immutable once the object is created. Therefore, when the object is constructed, you must provide the required values, but you must not allow them to change during the object's lifetime.


Value objects allow you to perform certain tricks for performance, thanks to their immutable nature. This is especially true in systems where there may be thousands of value object instances, many of which have the same values. Their immutable nature allows them to be reused; they can be interchangeable objects, since their values are the same and they have no identity. This type of optimization can sometimes make a difference between software that runs slowly and software with good performance. Of course, all these cases depend on the application environment and deployment context.


Value object implementation in C#

In terms of implementation, you can have a value object base class that has basic utility methods like equality based on the comparison between all the attributes (since a value object must not be based on identity) and other fundamental characteristics. The following example shows a value object base class used in the ordering microservice from eShopOnContainers.


C#


Copy

public abstract class ValueObject

{

    protected static bool EqualOperator(ValueObject left, ValueObject right)

    {

        if (ReferenceEquals(left, null) ^ ReferenceEquals(right, null))

        {

            return false;

        }

        return ReferenceEquals(left, right) || left.Equals(right);

    }


    protected static bool NotEqualOperator(ValueObject left, ValueObject right)

    {

        return !(EqualOperator(left, right));

    }


    protected abstract IEnumerable<object> GetEqualityComponents();


    public override bool Equals(object obj)

    {

        if (obj == null || obj.GetType() != GetType())

        {

            return false;

        }


        var other = (ValueObject)obj;


        return this.GetEqualityComponents().SequenceEqual(other.GetEqualityComponents());

    }


    public override int GetHashCode()

    {

        return GetEqualityComponents()

            .Select(x => x != null ? x.GetHashCode() : 0)

            .Aggregate((x, y) => x ^ y);

    }

    // Other utility methods

}


The ValueObject is an abstract class type, but in this example, it doesn't overload the == and != operators. You could choose to do so, making comparisons delegate to the Equals override. For example, consider the following operator overloads to the ValueObject type:


C#


Copy

public static bool operator ==(ValueObject one, ValueObject two)

{

    return EqualOperator(one, two);

}


public static bool operator !=(ValueObject one, ValueObject two)

{

    return NotEqualOperator(one, two);

}

You can use this class when implementing your actual value object, as with the Address value object shown in the following example:


C#


Copy

public class Address : ValueObject

{

    public String Street { get; private set; }

    public String City { get; private set; }

    public String State { get; private set; }

    public String Country { get; private set; }

    public String ZipCode { get; private set; }


    public Address() { }


    public Address(string street, string city, string state, string country, string zipcode)

    {

        Street = street;

        City = city;

        State = state;

        Country = country;

        ZipCode = zipcode;

    }


    protected override IEnumerable<object> GetEqualityComponents()

    {

        // Using a yield return statement to return each element one at a time

        yield return Street;

        yield return City;

        yield return State;

        yield return Country;

        yield return ZipCode;

    }

}

This value object implementation of Address has no identity, and therefore no ID field is defined for it, either in the Address class definition or the ValueObject class definition.


Having no ID field in a class to be used by Entity Framework (EF) was not possible until EF Core 2.0, which greatly helps to implement better value objects with no ID. That is precisely the explanation of the next section.


It could be argued that value objects, being immutable, should be read-only (that is, have get-only properties), and that's indeed true. However, value objects are usually serialized and deserialized to go through message queues, and being read-only stops the deserializer from assigning values, so you just leave them as private set, which is read-only enough to be practical.


Value object comparison semantics

Two instances of the Address type can be compared using all the following methods:


C#


Copy

var one = new Address("1 Microsoft Way", "Redmond", "WA", "US", "98052");

var two = new Address("1 Microsoft Way", "Redmond", "WA", "US", "98052");


Console.WriteLine(EqualityComparer<Address>.Default.Equals(one, two)); // True

Console.WriteLine(object.Equals(one, two)); // True

Console.WriteLine(one.Equals(two)); // True

Console.WriteLine(one == two); // True

When all the values are the same, the comparisons are correctly evaluated as true. If you didn't choose to overload the == and != operators, then the last comparison of one == two would evaluate as false. For more information, see Overload ValueObject equality operators.


How to persist value objects in the database with EF Core 2.0 and later

You just saw how to define a value object in your domain model. But how can you actually persist it into the database using Entity Framework Core since it usually targets entities with identity?


Background and older approaches using EF Core 1.1

As background, a limitation when using EF Core 1.0 and 1.1 was that you could not use complex types as defined in EF 6.x in the traditional .NET Framework. Therefore, if using EF Core 1.0 or 1.1, you needed to store your value object as an EF entity with an ID field. Then, so it looked more like a value object with no identity, you could hide its ID so you make clear that the identity of a value object is not important in the domain model. You could hide that ID by using the ID as a shadow property. Since that configuration for hiding the ID in the model is set up in the EF infrastructure level, it would be kind of transparent for your domain model.


In the initial version of eShopOnContainers (.NET Core 1.1), the hidden ID needed by EF Core infrastructure was implemented in the following way in the DbContext level, using Fluent API at the infrastructure project. Therefore, the ID was hidden from the domain model point of view, but still present in the infrastructure.


C#


Copy

// Old approach with EF Core 1.1

// Fluent API within the OrderingContext:DbContext in the Infrastructure project

void ConfigureAddress(EntityTypeBuilder<Address> addressConfiguration)

{

    addressConfiguration.ToTable("address", DEFAULT_SCHEMA);


    addressConfiguration.Property<int>("Id") // Id is a shadow property

        .IsRequired();

    addressConfiguration.HasKey("Id"); // Id is a shadow property

}

However, the persistence of that value object into the database was performed like a regular entity in a different table.


With EF Core 2.0 and later, there are new and better ways to persist value objects.


Persist value objects as owned entity types in EF Core 2.0 and later

Even with some gaps between the canonical value object pattern in DDD and the owned entity type in EF Core, it's currently the best way to persist value objects with EF Core 2.0 and later. You can see limitations at the end of this section.


The owned entity type feature was added to EF Core since version 2.0.


An owned entity type allows you to map types that do not have their own identity explicitly defined in the domain model and are used as properties, such as a value object, within any of your entities. An owned entity type shares the same CLR type with another entity type (that is, it's just a regular class). The entity containing the defining navigation is the owner entity. When querying the owner, the owned types are included by default.


Just by looking at the domain model, an owned type looks like it doesn't have any identity. However, under the covers, owned types do have the identity, but the owner navigation property is part of this identity.


The identity of instances of owned types is not completely their own. It consists of three components:


The identity of the owner


The navigation property pointing to them


In the case of collections of owned types, an independent component (supported in EF Core 2.2 and later).


For example, in the Ordering domain model at eShopOnContainers, as part of the Order entity, the Address value object is implemented as an owned entity type within the owner entity, which is the Order entity. Address is a type with no identity property defined in the domain model. It is used as a property of the Order type to specify the shipping address for a particular order.


By convention, a shadow primary key is created for the owned type and it will be mapped to the same table as the owner by using table splitting. This allows to use owned types similarly to how complex types are used in EF6 in the traditional .NET Framework.


It is important to note that owned types are never discovered by convention in EF Core, so you have to declare them explicitly.


In eShopOnContainers, in the OrderingContext.cs file, within the OnModelCreating() method, multiple infrastructure configurations are applied. One of them is related to the Order entity.


C#


Copy

// Part of the OrderingContext.cs class at the Ordering.Infrastructure project

//

protected override void OnModelCreating(ModelBuilder modelBuilder)

{

    modelBuilder.ApplyConfiguration(new ClientRequestEntityTypeConfiguration());

    modelBuilder.ApplyConfiguration(new PaymentMethodEntityTypeConfiguration());

    modelBuilder.ApplyConfiguration(new OrderEntityTypeConfiguration());

    modelBuilder.ApplyConfiguration(new OrderItemEntityTypeConfiguration());

    //...Additional type configurations

}

In the following code, the persistence infrastructure is defined for the Order entity:


C#


Copy

// Part of the OrderEntityTypeConfiguration.cs class

//

public void Configure(EntityTypeBuilder<Order> orderConfiguration)

{

    orderConfiguration.ToTable("orders", OrderingContext.DEFAULT_SCHEMA);

    orderConfiguration.HasKey(o => o.Id);

    orderConfiguration.Ignore(b => b.DomainEvents);

    orderConfiguration.Property(o => o.Id)

        .ForSqlServerUseSequenceHiLo("orderseq", OrderingContext.DEFAULT_SCHEMA);


    //Address value object persisted as owned entity in EF Core 2.0

    orderConfiguration.OwnsOne(o => o.Address);


    orderConfiguration.Property<DateTime>("OrderDate").IsRequired();


    //...Additional validations, constraints and code...

    //...

}

In the previous code, the orderConfiguration.OwnsOne(o => o.Address) method specifies that the Address property is an owned entity of the Order type.


By default, EF Core conventions name the database columns for the properties of the owned entity type as EntityProperty_OwnedEntityProperty. Therefore, the internal properties of Address will appear in the Orders table with the names Address_Street, Address_City (and so on for State, Country, and ZipCode).


You can append the Property().HasColumnName() fluent method to rename those columns. In the case where Address is a public property, the mappings would be like the following:


C#


Copy

orderConfiguration.OwnsOne(p => p.Address)

                            .Property(p=>p.Street).HasColumnName("ShippingStreet");


orderConfiguration.OwnsOne(p => p.Address)

                            .Property(p=>p.City).HasColumnName("ShippingCity");

It's possible to chain the OwnsOne method in a fluent mapping. In the following hypothetical example, OrderDetails owns BillingAddress and ShippingAddress, which are both Address types. Then OrderDetails is owned by the Order type.


C#


Copy

orderConfiguration.OwnsOne(p => p.OrderDetails, cb =>

    {

        cb.OwnsOne(c => c.BillingAddress);

        cb.OwnsOne(c => c.ShippingAddress);

    });

//...

//...

public class Order

{

    public int Id { get; set; }

    public OrderDetails OrderDetails { get; set; }

}


public class OrderDetails

{

    public Address BillingAddress { get; set; }

    public Address ShippingAddress { get; set; }

}


public class Address

{

    public string Street { get; set; }

    public string City { get; set; }

}

Additional details on owned entity types

Owned types are defined when you configure a navigation property to a particular type using the OwnsOne fluent API.


The definition of an owned type in our metadata model is a composite of: the owner type, the navigation property, and the CLR type of the owned type.


The identity (key) of an owned type instance in our stack is a composite of the identity of the owner type and the definition of the owned type.


Owned entities capabilities

Owned types can reference other entities, either owned (nested owned types) or non-owned (regular reference navigation properties to other entities).


You can map the same CLR type as different owned types in the same owner entity through separate navigation properties.


Table splitting is set up by convention, but you can opt out by mapping the owned type to a different table using ToTable.


Eager loading is performed automatically on owned types, that is, there's no need to call .Include() on the query.


Can be configured with attribute [Owned], using EF Core 2.1 and later.


Can handle collections of owned types (using version 2.2 and later).


Owned entities limitations

You can't create a DbSet<T> of an owned type (by design).


You can't call ModelBuilder.Entity<T>() on owned types (currently by design).


No support for optional (that is, nullable) owned types that are mapped with the owner in the same table (that is, using table splitting). This is because mapping is done for each property, there is no separate sentinel for the null complex value as a whole.


No inheritance-mapping support for owned types, but you should be able to map two leaf types of the same inheritance hierarchies as different owned types. EF Core will not reason about the fact that they are part of the same hierarchy.


Main differences with EF6's complex types

Table splitting is optional, that is, they can optionally be mapped to a separate table and still be owned types.

Additional resources

Martin Fowler. ValueObject pattern

https://martinfowler.com/bliki/ValueObject.html


Eric Evans. Domain-Driven Design: Tackling Complexity in the Heart of Software. (Book; includes a discussion of value objects)

https://www.amazon.com/Domain-Driven-Design-Tackling-Complexity-Software/dp/0321125215/


Vaughn Vernon. Implementing Domain-Driven Design. (Book; includes a discussion of value objects)

https://www.amazon.com/Implementing-Domain-Driven-Design-Vaughn-Vernon/dp/0321834577/


Owned Entity Types

https://learn.microsoft.com/ef/core/modeling/owned-entities


Shadow Properties

https://learn.microsoft.com/ef/core/modeling/shadow-properties


Complex types and/or value objects. Discussion in the EF Core GitHub repo (Issues tab)

https://github.com/dotnet/efcore/issues/246


ValueObject.cs. Base value object class in eShopOnContainers.

https://github.com/dotnet-architecture/eShopOnContainers/blob/dev/src/Services/Ordering/Ordering.Domain/SeedWork/ValueObject.cs


ValueObject.cs. Base value object class in CSharpFunctionalExtensions.

https://github.com/vkhorikov/CSharpFunctionalExtensions/blob/master/CSharpFunctionalExtensions/ValueObject/ValueObject.cs


Address class. Sample value object class in eShopOnContainers.

https://github.com/dotnet-architecture/eShopOnContainers/blob/dev/src/Services/Ordering/Ordering.Domain/AggregatesModel/OrderAggregate/Address.cs


 


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Documentation


Implementing a microservice domain model with .NET - .NET


.NET Microservices Architecture for Containerized .NET Applications | Get into the implementation details of a DDD-oriented domain model.


Using Enumeration classes instead of enum types - .NET


.NET Microservices Architecture for Containerized .NET Applications | Lear how you can use enumeration classes, instead of enums, as a way to solve some limitations of the latter.


Designing validations in the domain model layer - .NET


.NET Microservices Architecture for Containerized .NET Applications | Understand key concepts of domain model validations.


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Training


Module


Persist and retrieve relational data by using EF Core - Training


This module guides you through the steps to create a data access project. You connect to a relational database and construct create, read, update, and delete (CRUD) queries by using Entity Framework Core (EF Core).

Wednesday, November 13, 2024

Scrum Expansion Pack (AI as a Team member)

في أواخر 2023 وبداية 2024، Scrum.org (بقيادة كين شوابر، واحد من مؤسسي السكرام) أطلقت الـ Expansion Pack دي مش عشان تغير السكرام، لكن عشان "تدعمه" بقوة الذكاء الاصطناعي. الفكرة مش إننا نغير الـGuid، الفكرة إننا نغير "الطريقة".

إليك التفاصيل:

1. مفهوم "الذكاء الاصطناعي كعضو فريق" (The AI Teammate)

الـ Expansion Pack بتدفعنا لمرحلة إن الـ AI ميبقاش مجرد "أداة" زي الـ Calculator، بل Digital Collaborator.

 * في الـ Daily Scrum: ممكن الـ AI يكون هو اللي بيحلل الـ Burndown Chart ويقول للفريق: "يا شباب، بالسرعة دي إحنا مش هنخلص الـ Sprint Goal، محتاجين نتحرك في التاسك الفلاني".

 * التكامل: بيعلم الفرق إزاي يعملوا "Onboarding" للـ AI كأنه موظف جديد، له صلاحيات وله حدود.

2. تسريع الـ Feedback Loops (السرعة القصوى)

جوهر الـ Agile هو إننا نتعلم بسرعة. الـ Expansion Pack ركزت على إن الـ AI بيخلي الـ Sprint نفسه أسرع:

 * Backlog Refinement: الـ AI بيساعد الـ Product Owner إنه يحول "أفكار العميل المشوشة" لـ User Stories واضحة ومكتوبة بـ Acceptance Criteria مظبوطة في ثواني.

 * Coding & Testing: مش بس بيكتب كود، ده الـ AI دلوقتي بيعمل "Pair Programming" مع المطورين، وده بيقلل الـ Bugs اللي بتظهر في آخر الـ Sprint.

3. إدارة "التعقيد" (Handling Complexity)

السكرام أصلاً معمول عشان "المشاكل المعقدة". الـ Expansion Pack بيوضح إن الـ AI بطل في الحتة دي:

 * بيقدر يربط بين بيانات جاية من "السوق" وبيانات "الفريق" عشان يساعد في اتخاذ قرار الـ Pivot or Persevere (نكمل ولا نغير المسار).

 * بيقلل الـ Cognitive Load (الحمل الذهني) على الفريق، فبدل ما تقعد تفكر في "إزاي أكتب الـ Test ده"، بتفكر في "إيه هي القيمة اللي العميل هياخدها".

4. الـ Definition of Done في عصر الـ AI

دي نقطة جوهرية في الـ Update: الـ DoD لازم يتغير.

 * لو الـ AI هو اللي كاتب الكود، هل الـ DoD بتاعنا بيتضمن "مراجعة بشرية للثغرات الأمنية اللي الـ AI ممكن يغلط فيها؟".

 * الـ Expansion Pack بتنبهنا إننا لازم نحدث معايير الجودة بتاعتنا عشان نضمن إن "شطارة" الـ AI متبقاش "كارثة" تقنية بعدين (Technical Debt).

5. أخلاقيات العمل والمسؤولية (Accountability)

السكرام واضح: الـ Accountability عند البشر. الـ Expansion Pack بتقول "الـ AI ملوش ذنب". لو الـ AI غرق المركب، الـ Scrum Team هو المسؤول. عشان كده البحث بيأكد على أهمية الـ Transparency (الشفافية)؛ لازم الكل يكون عارف الـ AI عمل إيه بالظبط وإزاي.

الزتونة:

الـ Scrum with AI Expansion Pack مش كتاب قوانين جديد، ده "Update" لعقلك أنت كـ Scrum Master أو Developer. بيقولك: "يا بطل، الـ AI بقى موجود في المكتب معاك، لو معرفتش تدخله في الـ Sprints بتاعتك وتخليه يشيل عنك الهري الكتير (Maximize the work not done)، هتلاقي فريق تاني بيسبقك بمسافات".

تخيل المشهد: "أول يوم في الـ Sprint"

​القعدة بتبدأ والكل فاتح اللاب توب، ومعاكم "زميل" جديد موجود على الشاشة اسمه "AI-Bot" (ده الـ Agent بتاعكم).

​1. الـ Backlog Refinement

  • البشر: الـ Product Owner بيقول: "يا جماعة العميل عايز ميزة الدفع بالكريبتو".
  • الـ AI Agent: بيعمل سكان لكل الـ الـ User Stories اللي فاتت والـ Documentation، وفي ثواني بيطلع لك Acceptance Criteria كاملة، وبيقولك: "خلي بالك، الميزة دي هتضرب في كود قديم عندنا في السيستم بتاع 2023، أنا طلعت لك الأماكن اللي محتاجة تتعدل".
  • النتيجة: وفرت ساعة نقاش في "هو إحنا محتاجين نعمل إيه بالظبط؟".

​2. تقدير الجهد (Story Pointing)

  • البشر: المطورين بيستخدموا Planning Poker وبيقولوا "دي تاخد 5 نقط".
  • الـ AI Agent: بيتدخل ويقول: "بناءً على سرعة الفريق في الـ 5 سبرينتات اللي فاتوا، الميزة دي فيها تعقيد في الـ API، أنا شايف إنها تاخد 8 نقط عشان نكون في الأمان".
  • النتيجة: تقدير واقعي جداً مبني على بيانات (Data-driven) مش بس على "إحساس" الفريق.

​3. تحديد الـ Sprint Goal

  • البشر: الفريق بيتناقش في الهدف.
  • الـ AI Agent: بيقترح: "بما إننا عندنا 3 تاسكات متعلقة بالأمان، إيه رأيكم نخلي الـ Goal بتاعنا هو 'تأمين بوابة الدفع بنسبة 100%'؟".

​4. توزيع المهام (Tasking out)

  • الـ AI Agent: "أنا ممكن أشيل عنكم كتابة الـ Unit Tests والـ Documentation للميزة دي بالكامل، ركزوا أنتم في الـ Logic المعقد والـ Integration".
  • النتيجة: هنا بقى طبقنا مبدأ Maximize the work not done (بواسطة البشر طبعاً).

Sunday, November 3, 2024

Data exploration

Data exploration: An introduction for data analysts

Data exploration is the foundational phase of data analysis, where you familiarize yourself with your dataset. It's about understanding its structure, identifying potential issues, and beginning to formulate questions for deeper investigation.

Data exploration encompasses a diverse range of activities, each designed to reveal different aspects of your dataset. These activities can be broadly categorized into three core areas: understanding your data, uncovering relationships, and formulating hypotheses.

Understanding your data

This phase involves getting familiar with the individual variables and their characteristics within your dataset. The first step is to identify the types of variables you're working with. Are they numerical (continuous or discrete) or categorical (nominal or ordinal)? Understanding the nature of your variables is fundamental for choosing appropriate analysis techniques and visualizations. Next, you'll calculate summary statistics for each variable to gain a quantitative understanding of their central tendencies, spread, and distribution. These statistics, including mean, median, mode, range, variance, standard deviation, skewness, and kurtosis, offer a quick summary of your data's main features.

Visualizations are a powerful tool for exploring data. Histograms, scatter plots, box plots, and other visual representations can reveal patterns, trends, and outliers that might not be readily apparent from raw numbers. They allow you to "see" your data and gain intuitive insights.


A histogram revealing a trend.

Missing data is a common challenge in real-world datasets. During exploration, you'll identify and address missing values. This might involve imputing missing values based on patterns in the existing data, removing rows or columns with excessive missingness, or employing specialized techniques designed for handling missing data.

Lastly, in this stage, you'll identify outliers—data points that significantly deviate from the majority. Outliers can be indicative of errors, anomalies, or interesting phenomena that warrant further investigation. Identifying and understanding outliers is crucial for ensuring the robustness and reliability of your analysis.


Uncovering relationships

Stock market information graph that is showing correlation.

Once you have a good grasp of individual variables, you'll move on to exploring relationships between them.

For numerical variables, you'll calculate correlation coefficients to quantify the strength and direction of linear relationships. Correlation analysis helps you identify potential dependencies or associations between variables, which can inform further modeling or hypothesis testing.

When dealing with categorical variables, you'll create contingency tables to examine their relationships. Cross-tabulation reveals patterns of co-occurrence or dependence, helping you understand how different categories interact.

Visualizations also play a crucial role in uncovering relationships. Scatter plots, heatmaps, and parallel coordinate plots can visually depict relationships between multiple variables, often revealing complex interactions that might be difficult to discern through numerical summaries alone.


Exploratory Data Analysis (EDA) is an iterative process of visualizing, summarizing, and transforming your data to uncover unexpected patterns or insights. It's a creative and open-ended approach that can lead to the discovery of novel hypotheses and research questions.


While generative AI can automate tasks and find insights, it cannot replace human intuition and expertise. By combining the computational power of AI with the critical thinking and domain expertise of human analysts, we can maximize the value of data exploration. Validating the insights generated by AI is crucial, as without a deep understanding of the subject matter, the information produced can be misleading or meaningless. The expertise of human analysts remains essential for accurately interpreting and applying the insights to specific fields.


By the end of this video, you'll be able to recognize the importance of formulating clear and concise prompts when using generative AI for data analysis and apply strategies for creating effective prompts that elicit meaningful results. Generative AI needs clear instructions to deliver the results you're looking for. These instructions are prompts. Well-crafted prompts help you uncover valuable insights, while poorly constructed ones might lead to irrelevant results. Why are clear and concise prompts so important? Let's explore the benefits of improved precision, reduced time, and increased accuracy. Let's start with precision. A vague or ambiguous prompt can generate results that are not directly related to your goals. Imagine asking a librarian for some books on history. You might end up with a stack of random titles, none of which align with your specific interests. In contrast, a precise prompt such as books on the history of ancient Rome would yield much more relevant results. Similarly, when you're working with generative AI, a clear and specific prompt ensures that the AI focuses on the exact insights you seek. This precision is important when you're using generative AI to explore large data sets, summarize key characteristics, or even brainstorm initial hypotheses for further investigation. A well-defined prompt acts as a guide, leading the AI through your data to uncover the specific insights you need. Effective prompts will also help you save time. By investing time upfront to craft clear and concise prompts, you can significantly reduce the overall duration of your data analysis projects. Clear prompts empower generative AI to swiftly comprehend your objectives and deliver targeted insights. This eliminates the need for manual data shifting. By streamlining the data analysis process, clear prompts enable you to focus on higher-level tasks such as interpreting results, drawing conclusions, and making data-driven decisions.


While AI can be a valuable asset in data exploration, its insights should be carefully evaluated and compared to those obtained through traditional methods and domain expertise. Generative AI models can sometimes produce outputs that are plausible-sounding but factually incorrect or misleading, and they may overlook subtle nuances that could be critical to the analysis. The evaluation process is an ongoing cycle of refinement and improvement.


Assessing the validity of generative AI insights: A framework for data analysts

Ground truth comparison

It's important to be cautious and critical when using GenAI tools. A systematic approach to validating insights generated by GenAI is essential to ensure the reliability and trustworthiness of data-driven decisions. Ground truth comparison serves as a benchmark, involving comparing the AI-generated insights against established facts or trusted sources. This anchoring in reality helps identify any discrepancies or potential inaccuracies, ensuring that the AI's outputs are aligned with the tangible world. However, establishing ground truth can be challenging, especially in domains where objective truth is elusive or constantly evolving, requiring a combination of multiple trusted sources and expert opinions to triangulate the AI's outputs and assess their validity.

Statistical validation

Statistical validation is an indispensable component of the assessment framework, providing valuable insights into the reliability of AI-generated outputs through methods like confidence intervals, hypothesis testing, and sensitivity analysis. It transforms AI outputs into quantifiable measures of confidence, enabling informed decisions based on robust data-driven insights. However, statistical significance does not always equate to practical significance, so results should be interpreted in conjunction with domain knowledge and practical considerations.


Sensitivity Analysis

Sensitivity analysis is crucial for identifying potential biases or limitations in an AI model. By varying input parameters or assumptions, we can see how the model’s insights change, revealing areas where the model might be overly sensitive or prone to errors. This process acts as a stress test, helping us understand the model’s vulnerabilities and guiding us towards a more nuanced understanding of its capabilities. It also helps in detecting biases by varying inputs related to sensitive attributes like race, gender, or age, ensuring the AI’s decisions are fair and equitable.

Cross-Validation

Cross-validation helps prevent overfitting and enhances the generalizability of the AI model. By dividing data into subsets and testing the model on each, we can assess its performance across different scenarios. This ensures that the model’s insights are not just artifacts of the training data but are applicable in broader contexts. Techniques like k-fold cross-validation or leave-one-out cross-validation offer varying levels of rigor and efficiency, helping gauge the model’s adaptability and practical utility.

Domain Expertise

Domain expertise is invaluable in validating AI-generated insights. Subject-matter experts critically evaluate AI suggestions, ensuring they align with industry practices and are contextually relevant. Their insights help fine-tune the AI’s outputs, making them more accurate and applicable. Involving domain experts throughout the AI development process enhances the quality and relevance of the AI’s outputs, guiding the selection of training data, model design, and result interpretation.

Bias Detection

Bias detection involves scrutinizing patterns to identify any discriminatory outputs. By systematically varying input parameters related to sensitive attributes, we can observe if the AI’s outputs exhibit any biases. This proactive approach ensures that the AI’s decisions are fair and equitable, enhancing trust in its deployment.