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“The key characteristic of predators is that they do not think like the established competitors.”

Information Advantage’s Rick Tanler

What is the information culture of your enterprise? Is now the time to change your information culture? Who is responsible for changing the information culture? These are the important questions as organizations begin to make data warehouse content available on their intranet.

When information services managers talk about data warehousing, the focus is on the management of alpha-numeric content stored in databases and the application software for user-initiated reporting and analysis of this data. But consider this: The World Wide Web is the largest data warehouse on earth. It informs millions of users every day. The Internet has been successful, in part, because the emphasis has shifted from personal computing to global information sharing—from delivering application features to delivering content. This is the most valuable lesson of the Internet and it alters the information culture of the Enterprise.

The adoption of Internet technologies, to build secure Intra/Extranets, provides organizations with a remarkably efficient and highly capable means of informing everyone—employees, affiliates, customers, and prospects. Arguably, the most valuable sources of internal business intelligence are contained in the data warehouses.

The discussions of three technologies—data warehousing, online analytic processing (OLAP) and intranets—have become intertwined. The result is a powerful blending of technologies that offers new ways to support the information needs of users. How will your organization seize the opportunity? The answer may lie in changing the information culture of your enterprise.

Virtually every company has an information culture. It is reflected in the priorities established and the methods used to manage, distribute, and use information. The most common part of the information culture is the spectator. The spectator views information as being essential to monitoring every change that impacts business operation from a transaction perspective. Spectators describe their data warehouses as ‘the source of the data for reporting and analysis.’ To them, the focus of information services is on meeting users’ requirements for reports. Every organization starts by being a spectator.

The more interesting segment of the information culture occurs in the organization that recognizes that substantial rewards go to the best competitors, not to spectators. These “competitors” view the data warehouse as a catalyst for changing business-management processes in order to become a market leader. The competitor talks about information in terms of how it supports the decision process. Rather than being better at observing what happened, the competitor is intent on making things happen. He recognizes that access to information initiates change.

In The Intranet Data Warehouse (John Wiley & Sons), I differentiated these two cultures—spectators and competitors—by suggesting that one focused on meeting users’ decision support requirements while the other focused on the requirements to improve decision implementation. This separation worked until I began to recognize that there is a third kind of information culture: predators.

Predators occur in an organization that changes the fundamental rules of competition. The predator establishes a new market in its attack on existing markets. chúng tôi is a predator. By creating a virtual bookstore, Amazon found a way to avoid the capital intensive “brick and mortar” retail business model. The key characteristic of predators is that they do not think like the established competitors.

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Business Intelligence Vs Data Warehouse

Difference Between Business Intelligence vs Data Warehouse

Hadoop, Data Science, Statistics & others

Data Warehouse (DW) is simply a consolidation of data from various sources that set a foundation for Business Intelligence, which helps in making better strategic and tactical decisions. So I can say Data Warehouses have business meaning baked into them. Database stores data from different sources in a common format, and The Warehouse is like Godown (Big Building), where many things may be stored. Still, Data- Warehouse works with intelligent algorithms like Indexing, which helps locate and retrieve easily and on the same concept.

Data Warehouse is similar to a relational database aimed at querying and analyzing the data rather than for transaction processing. It usually contains historical data derived from transaction data but can include data from various sources. Data Warehouses hold data, in fact, Tables (Tables that cover numbers such as revenue and Costs) and Dimensions (Group Facts by different attributes like region, office, or week).

I will use certain abbreviations like BI for Business Intelligence and DW for Data Warehouse, as it’s easy to write. So far, I hope you have got enough understanding about both Business Intelligence and Data Warehouse concepts which are so commonly used in the Data Analytics Domain. These are so mistakenly used that even people in this domain are unsure what and when to use them.

Now let’s understand exactly what business intelligence is, which has created so much confusion in the Analytics industry as some people use both terms interchangeably. Lots of discussions are going on the internet.

A Business Intelligence system tells you what happened or is happening in your business; it describes the situation to you. Also, a good BI platform represents this to you in real time in a granular, accurate, and presentable form.

I will tell you why it is so intelligent; using Data is simple. Data is accumulated over a significant amount of time from several disparate sources.

But now, a fundamental question arises about where this data is. This data is stored in the Data Warehouse (DDS, Cubes). And BI systems use Data Warehouse data and let you apply chosen metrics to potentially massive, unstructured data sets and cover querying, data mining, online analytical processing (OLAP), reporting, and business performance monitoring predictive and prescriptive analytics.

So now let’s compare Business Intelligence and Data Warehouse to better understand by comparing.

Head-to-Head Comparison Between Business Intelligence vs Data Warehouse (Infographics)

Below are the top 5 comparisons between Business Intelligence vs Data Warehouse:

Key Differences Between Business Intelligence vs Data Warehouse

Following are the differences between Business Intelligence vs Data Warehouse:

BI means finding insights that portray a business’s current picture (How and What) by leveraging data from the Data Warehouse (DW).

BI is about accessing and exploring an organization’s data, while Data Warehouse is about gathering, transforming, and storing data.

DW outlines the actual Database creation and integration process along with Data Profiling and Business validation rules. At the same time, Business Intelligence uses tools and techniques that focus on counts, statistics, and visualization to improve business performance.

BI deals with OLAP, data visualization, data mining, and query/reporting tools. In contrast, DW deals with data acquisition, metadata management, data cleansing, data transformation, data distribution, and data recovery/backup planning.

DW teams use tools like Ab Initio Software, Amazon Redshift, Informatica, etc., while BI teams use tools like Cognos, MSBI, Oracle BI, Pentaho, QlikView, etc.

Software engineers, mainly Data Engineers, deal with DW, while top executives, Managers deal with BI.

Business Intelligence vs Data Warehouse Comparison Table

Below is the comparison table between Business Intelligence vs Data Warehouse.

Basis for Comparison Business Intelligence Data Warehouse

What it is System for deriving insights related to business. Data Storage: historical along with the current.

Source  Data from Data warehouse. Data from several Data sources and applications.

Output Business reports, charts, graphs. Data, in fact, and dimension tables for upstream applications or BI tools.

Audience Top executives, Manager Data Engineers, Data Analysts, and Business Analysts.

Tools MSBI, QlikView, Cognos, etc. Ab Initio Software, Amazon Redshift, Informatica.

Conclusion

So I finally want to conclude this article as BI tools like QlikView, MSBI, and Oracle BI all access data from Data Warehouses. And let business users create more granular and presentable reports, graphs, and charts, which help top executives to make more effective business decisions in different functional areas like finance, supply chain, human resources, sales & marketing, and customer service.

Recommended Articles

This is a guide to Business Intelligence vs Data Warehouse. Here we have discussed head-to-head comparison, key differences, and a comparison table. You may also look at the following articles to learn more –

What Is Data Mart In Data Warehouse? Types & Example

What is Data Mart?

A Data Mart is focused on a single functional area of an organization and contains a subset of data stored in a Data Warehouse. A Data Mart is a condensed version of Data Warehouse and is designed for use by a specific department, unit or set of users in an organization. E.g., Marketing, Sales, HR or finance. It is often controlled by a single department in an organization.

Data Mart usually draws data from only a few sources compared to a Data warehouse. Data marts are small in size and are more flexible compared to a Datawarehouse.

In this tutorial, you will learn-

Why do we need Data Mart?

Data Mart helps to enhance user’s response time due to reduction in volume of data

It provides easy access to frequently requested data.

Data mart are simpler to implement when compared to corporate Datawarehouse. At the same time, the cost of implementing Data Mart is certainly lower compared with implementing a full data warehouse.

Compared to Data Warehouse, a datamart is agile. In case of change in model, datamart can be built quicker due to a smaller size.

A Datamart is defined by a single Subject Matter Expert. On the contrary data warehouse is defined by interdisciplinary SME from a variety of domains. Hence, Data mart is more open to change compared to Datawarehouse.

Data is partitioned and allows very granular access control privileges.

Data can be segmented and stored on different hardware/software platforms.

Types of Data Mart

There are three main types of data mart:

Dependent: Dependent data marts are created by drawing data directly from operational, external or both sources.

Independent: Independent data mart is created without the use of a central data warehouse.

Hybrid: This type of data marts can take data from data warehouses or operational systems.

Dependent Data Mart

A dependent data mart allows sourcing organization’s data from a single Data Warehouse. It is one of the data mart example which offers the benefit of centralization. If you need to develop one or more physical data marts, then you need to configure them as dependent data marts.

Dependent Data Mart in data warehouse can be built in two different ways. Either where a user can access both the data mart and data warehouse, depending on need, or where access is limited only to the data mart. The second approach is not optimal as it produces sometimes referred to as a data junkyard. In the data junkyard, all data begins with a common source, but they are scrapped, and mostly junked.

Dependent Data Mart

Independent Data Mart

An independent data mart is created without the use of central Data warehouse. This kind of Data Mart is an ideal option for smaller groups within an organization.

An independent data mart has neither a relationship with the enterprise data warehouse nor with any other data mart. In Independent data mart, the data is input separately, and its analyses are also performed autonomously.

Implementation of independent data marts is antithetical to the motivation for building a data warehouse. First of all, you need a consistent, centralized store of enterprise data which can be analyzed by multiple users with different interests who want widely varying information.

Independent Data Mart

Hybrid Data Mart:

A hybrid data mart combines input from sources apart from Data warehouse. This could be helpful when you want ad-hoc integration, like after a new group or product is added to the organization.

It is the best data mart example suited for multiple database environments and fast implementation turnaround for any organization. It also requires least data cleansing effort. Hybrid Data mart also supports large storage structures, and it is best suited for flexible for smaller data-centric applications.

Hybrid Data Mart

Steps in Implementing a Datamart

Implementing a Data Mart is a rewarding but complex procedure. Here are the detailed steps to implement a Data Mart:

Designing

Designing is the first phase of Data Mart implementation. It covers all the tasks between initiating the request for a data mart to gathering information about the requirements. Finally, we create the logical and physical Data Mart design.

The design step involves the following tasks:

Gathering the business & technical requirements and Identifying data sources.

Selecting the appropriate subset of data.

Designing the logical and physical structure of the data mart.

Data could be partitioned based on following criteria:

Date

Business or Functional Unit

Geography

Any combination of above

What Products and Technologies Do You Need?

A simple pen and paper would suffice. Though tools that help you create UML or ER diagram would also append meta data into your logical and physical designs.

Constructing

This is the second phase of implementation. It involves creating the physical database and the logical structures.

This step involves the following tasks:

Implementing the physical database designed in the earlier phase. For instance, database schema objects like table, indexes, views, etc. are created.

What Products and Technologies Do You Need?

You need a relational database management system to construct a data mart. RDBMS have several features that are required for the success of a Data Mart.

Storage management: An RDBMS stores and manages the data to create, add, and delete data.

Fast data access: With a SQL query you can easily access data based on certain conditions/filters.

Data protection: The RDBMS system also offers a way to recover from system failures such as power failures. It also allows restoring data from these backups incase of the disk fails.

Multiuser support: The data management system offers concurrent access, the ability for multiple users to access and modify data without interfering or overwriting changes made by another user.

Security: The RDMS system also provides a way to regulate access by users to objects and certain types of operations.

In the third phase, data in populated in the data mart.

The populating step involves the following tasks:

Source data to target data Mapping

Extraction of source data

Cleaning and transformation operations on the data

Loading data into the data mart

Creating and storing metadata

What Products and Technologies Do You Need?

You accomplish these population tasks using an ETL (Extract Transform Load) Tool. This tool allows you to look at the data sources, perform source-to-target mapping, extract the data, transform, cleanse it, and load it back into the data mart.

In the process, the tool also creates some metadata relating to things like where the data came from, how recent it is, what type of changes were made to the data, and what level of summarization was done.

Accessing

Accessing is a fourth step which involves putting the data to use: querying the data, creating reports, charts, and publishing them. End-user submit queries to the database and display the results of the queries

The accessing step needs to perform the following tasks:

Set up a meta layer that translates database structures and objects names into business terms. This helps non-technical users to access the Data mart easily.

Set up and maintain database structures.

Set up API and interfaces if required

What Products and Technologies Do You Need?

You can access the data mart using the command line or GUI. GUI is preferred as it can easily generate graphs and is user-friendly compared to the command line.

Managing

This is the last step of Data Mart Implementation process. This step covers management tasks such as-

Ongoing user access management.

System optimizations and fine-tuning to achieve the enhanced performance.

Adding and managing fresh data into the data mart.

Planning recovery scenarios and ensure system availability in the case when the system fails.

What Products and Technologies Do You Need?

You could use the GUI or command line for data mart management.

Best practices for Implementing Data Marts

Following are the best practices that you need to follow while in the Data Mart Implementation process:

The source of a Data Mart should be departmentally structured

The implementation cycle of a Data Mart should be measured in short periods of time, i.e., in weeks instead of months or years.

It is important to involve all stakeholders in planning and designing phase as the data mart implementation could be complex.

Data Mart Hardware/Software, Networking and Implementation costs should be accurately budgeted in your plan

Even though if the Data mart is created on the same hardware they may need some different software to handle user queries. Additional processing power and disk storage requirements should be evaluated for fast user response

A data mart may be on a different location from the data warehouse. That’s why it is important to ensure that they have enough networking capacity to handle the Data volumes needed to transfer data to the data mart.

Implementation cost should budget the time taken for Datamart loading process. Load time increases with increase in complexity of the transformations.

Advantages

Data marts contain a subset of organization-wide data. This Data is valuable to a specific group of people in an organization.

It is cost-effective alternatives to a data warehouse, which can take high costs to build.

Data Mart allows faster access of Data.

Data Mart is easy to use as it is specifically designed for the needs of its users. Thus a data mart can accelerate business processes.

Data Marts needs less implementation time compare to Data Warehouse systems. It is faster to implement Data Mart as you only need to concentrate the only subset of the data.

It contains historical data which enables the analyst to determine data trends.

Many a times enterprises create too many disparate and unrelated data marts without much benefit. It can become a big hurdle to maintain.

Data Mart cannot provide company-wide data analysis as their data set is limited.

Summary:

Define Data Mart : A Data Mart is defined as a subset of Data Warehouse that is focused on a single functional area of an organization.

Data Mart helps to enhance user’s response time due to a reduction in the volume of data.

Three types of data mart are 1) Dependent 2) Independent 3) Hybrid

Important implementation steps of Data Mart are 1) Designing 2) Constructing 3 Populating 4) Accessing and 5)Managing

The implementation cycle of a Data Mart should be measured in short periods of time, i.e., in weeks instead of months or years.

Data mart is cost-effective alternatives to a data warehouse, which can take high costs to build.

Data Mart cannot provide company-wide data analysis as data set is limited.

10 Best Data Warehouse Books You Should Read In 2023

Books To Read About Data Warehouse

The top 10 derivative books cover a broad spectrum of topics related to data warehousing, including data modeling, ETL processes, project management, and implementation with various technologies. They are here to guide you so you can invest your valuable rupees in books worth your time.

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Hadoop, Data Science, Statistics & others

10 Different Data Warehouse Books

# Books Author Published Rating

1. The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition Ralph Kimball 2013 Amazon: 4.7

2. Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema  Lawrence Corr, Jim Stagnitto

2011 Amazon:4.6

3. Database Systems: Introduction to Databases and Data Warehouses   Nenad Jukic, Susan Vrbsky, Svetlozar Nestorov

2024 Amazon: 4.5

4. Building a Scalable Data Warehouse with Data Vault 2.0 Daniel Linstedt, Michael Olschimke 2024 Amazon:4.4

5. The Kimball Group Reader: Relentlessly Practical for Data Warehousing and Business Intelligence Remastered Collection

2010 Amazon:4.6

6. Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing (Agile Software Development Series)  Ken Collier

2011

Amazon: 4.3

7. Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale Tom White ‎  2024

Amazon 4.5

8. Building a Data Warehouse: With Examples in SQL Server (Expert’s voice) Vincent Rainardi 2008

‎ 

9. Big Data: Principles and best practices of scalable real-time data systems Nathan Marz and James Warren

2012

10. Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems Martin Kleppmann 2024 Amazon: 4.8

Let us look at the Data Warehouse books and see which one best suits your needs:-

1. The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition

Authors: Ralph Kimball and Margy Ross

Get this Book here

Book Review

A great addition to a businessman’s most used tools for knowing about BI/DW. The Book does well to promise to be helpful to both students and professionals alike. This reference book is necessary if you are thinking about repetitive analogies that will permanently reside in your brain.

 Key Takeaway from that Book

The reading material includes well-made case studies from retail sales, financial services, etc., to derive the meaning of the Book’s concept in real life.

It contains value chain introduction, procurement transactions, bus matrix, order management, accounting, etc.

Authors: Lawrence Corr and Jim Stagnitto

Get this Book here

Book Review

The authors have done an excellent job presenting the topics of the agile manifesto with the BEAM approach that is perfect for people who want another feather in their cap through data warehousing. The author uses visual representation tools such as diagrams and little characters to make the text seem less daunting. The readers get a glimpse of modeling a data warehouse from working on the whiteboard to star schema and various other topics throughout the Book.

 Key Takeaway from that Book

The topics of modeling business events, modeling star schemas, and design patterns for high-performance fact tables are explained in great detail for users to understand completely.

A unique feature of the Book is that we get summaries at the end of each chapter to glance back at the topics just read.

3. Database Systems: Introduction to Databases and Data Warehouses

Authors: Nenad Jukic, Susan Vrbsky, and Svetlozar Nestorov

Get this Book here

This Book has a good command over operational and analytical database systems and creating ERDplus, relational and dimensional models. This Book is an introductory guide to use in university classes.

 Key Takeaway from that Book

Ideal for students looking to start as novices in data warehousing who want to practice with many exercises and practice sheets.

The excellently portrayed topics are ERD diagrams and relational entity models.

4. Building a Scalable Data Warehouse with Data Vault 2.0

Author: Daniel Linstedt and Michael Olschimke

Get this Book here

Book Review

Welcoming you to a world of data vaults in data warehousing, this Book takes you on a wild ride touching the concepts of SQL Server Integration Services, data quality services, and master data services. Many topics are discussed in great detail with suitable diagrams and required references at the end.

Key Takeaway from that Book

This Book is for anyone wanting to dive into the data vault though it was a little outdated in 2023.

5. The Kimball Group Reader: Relentlessly Practical for Data Warehousing and Business Intelligence Remastered Collection

Authors: Ralph Kimball, Margy Ross, Bob Becker, Joy Mundy, and Warren Thornthwaite

Get this Book here

Book Review

The authors have incorporated their year’s knowledge of data warehousing in the final series of the anthology, which is more comprehensive and expanded from the previous editions. The Book requires a prerequisite knowledge of the basics of the data warehouse discussed.

 Key Takeaway from that Book

The Book covers 65 new articles and distinguishable features which were not there in the previous editions.

The book website has content with design tips available and is easily accessible for the general public to consume.

Outlines topics such as fact tables, ETL subsystems, data quality, BI application best practices, etc., briefly.

6. Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing (Agile Software Development Series)

Author: Ken Collier

Get this Book here

Book Review

The Book is easy to follow and precise, with information about agile analytics. The Book contains topics such as filibuster analytics and some truths about agile analytics, which are easy to understand.

 The key takeaway from that Book

The topics of management methods and technical methods are heavily focused On Agile analytics.

The primary topics of these books are modeling business events, modeling business dimensions and facts, and measures and KPIs.

The Book is a complete meal for someone hungry to know much about agile for evolving excellent design, version control for data warehousing, and user stories for BI systems.

7. Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale

Author: Tom White

Get this Book here

Book review

Tom White does an excellent job at capturing the core of Hadoop fair and square without meandering about nonsense. This book is a guide for everyone new to Hadoop open-source software. This Book is for anyone with some prerequisite knowledge of the open source.

 Key Takeaway from that Book

The Book covers topics like how MapReduce works and administering Hadoop, Avro, parquet, and Hbase.

The Book contains ample code and theory to get you invested in Hadoop.

8. Building a Data Warehouse: With Examples in SQL Server (Expert’s Voice)

Author: Vincent Rainardi

Book Review

A thorough guide to building a data warehouse simply yet eloquently. Promises an exhaustive list of data extraction, metadata, the architecture of data warehousing, etc. Experienced developers, managers working in the data warehouse field, and admins of DW are the main target this Book has been aimed for.

 Key Takeaway from that Book

The Book thoroughly discusses building reports, populating the data warehouse, and data modeling.

The Book is built around a cluster of real-world cases with which the author had a chance to interact.

A must-have if you want access to practical implementation in a DW book using Microsoft SQL.

9. Big Data: Principles and best practices of Scalable real-time data Systems

Author: Nathan Marz

Get this Book here

Book Review

It is essentially a guide for people interested to know how technologies fit together in big data and how to choose the ideal tech stack for a specific problem. Recommended for anyone who wants to skim over the surface of the data warehouse.

Key Takeaway from that Book

An organized accumulated read-on data model for big data, serving layers, and stream processing.

Lambda architecture is the major attraction of this Book, and the code blocks of teaching Hadoop are written in Java, which can confuse some readers.

10. Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

Author: Martin Kleppmann

Get this Book here

Book Review

A contemporary guide to unlocking concepts of modern networked applications that will stay in your mind long after you finish the Book. A primary focus is on the functionality of each model rather than the practical implementation.

Key Takeaway from that Book

The Book is divided into three parts: foundations of data systems, distributed data, and derived data.

The illustrations make the chapters enjoyable, while the text is helpful with accurate code snippets.

Batch processing, trouble with distributed systems, data models and query languages, and replication are some topics that are heavily focused on.

Recommended Articles

Our Top 10 Data Warehouse books compilation aims to be helpful to you. For more such Data Warehouse books, EDUCBA recommends the following,

Imc 2023: Qualcomm Is Ready For The 5G Revolution In Smartphones, Connected Cars

The next generation of wireless broadband is said to bring widespread changes to the way we communicate and connect, and we got a glimpse of that yesterday at India Mobile Congress 2023 venue.

Laying the Foundation of 5G

“Our team at Qualcomm has been preparing for 5G deployment for three years now”, said Malladi, as he began addressing a room full of journalists. The completion of each step in the process, according to him, was a huge milestone as it enabled the team to start working with infrastructure partners for equipment based on the specifications.

To give us an idea about the work that went into bringing the technology to life, Durga walked us through the multi-step process leading up to the commercial trails. According to him, the initial tests performed in association with tech giants like Ericsson, Nokia, Samsung, etc. involved a massive 5G contraption.

Qualcomm apparently worked (and is still working) with 19 OEMs, who are popular both globally and locally here in India, to bring down the size. He decided to remain tight-lipped about the OEMs, though. However, he did show us a prototype device, which, he claims was used for early commercial trials. We were given an exclusive look, so no pictures were allowed.

What are the other devices consuming 5G Technology

The primary objective at Qualcomm, as Malladi explains, was to help large manufacturing plants go wireless. This is something that’s easier said than done, as the manufacturing plants in question have armies of robots working on a low-latency network. Why go wireless? Well, it was simply because there was a lot of demand from manufacturers for added flexibility.

But what’s next? What are the other devices we’ll get to see apart from commercial machinery and industrial IoT relying on ultra low-latency computations? Well, the 5G boss, as they call him at Qualcomm, believes that 5G will vastly help in the autonomous field, allowing cars to connect and talk to each other at the same time. Not only will it help regulate the way we drive our vehicles, but it’ll also create a safe environment for all us.

As soon as the technology is ready for a commercial rollout early next year, it looks like we’ll start seeing an influx of “Always Connected Laptops” from various manufacturers as well, powered by Qualcomm’s chipsets. Of course, you can go out and pick up one of these laptops today, but they’ll start going mainstream once more OEMs are ready to with their devices.

Look, besides the obviously known applications, no one knows how the deployment of 5G will change our lives or change the way we access wireless networks. Will we still have VoLTE equivalent of voice technology in the 5G scenario? When can we expect OEMs to bring 5G smartphones to the budget space? There are a ton of unanswered questions. Questions, which, I believe can be answered only after the commercialization.

The first phase of 5G deployment will begin as early as Q2 2023 in North America, Europe, Korea, etc.

But, Qualcomm, as I know, is an invention company that’s been developing the building blocks of 5G technology for years. So, whenever we are at the cusp of these changes, I am sure Qualcomm will continue to fuel the progress, and I am really excited about what’s in store for us.

The Benefits Of Using A Customer Data Platform Cdp

A Customer Data Platform looks to fulfil that age-old promise of centralizing all the customer data, and it is looking good

Marketers know that data management is the key to data-driven marketing. Traditional methods for trying to bring customer data together into a “360 customer view”, failed to solve the complete problem. But there is a new player in town, and its called Customer Data Platform (CDP). Now it is knocking on your door. Should you let it in?

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Bringing together customer data in a CDP

An un-siloed and full view of the customer is not a new concept. Yet the 360 customer view has always turned out to be the just out of reach for the marketer. Custom MarTech integration projects are known to be an especially tough cookie.

A Customer Data Platform looks to fulfill that age-old promise of centralizing all the customer data, and it is looking good. In a typical CDP setup there are three layers or functional stages: Data, Decisioning and Delivery. At the minimum CDP works on the Data layer, bringing together and making available the data from multiple sources. Other CDPs also offer extended Decisioning and Delivery functions.

I have written about CDPs, especially the difference between a CDP, CRM and DMPs.

And that is still a thing. More than half of organizations rate their own marketing and customer experience approach to be disconnected. Only 10% finds their MarTech stack is tightly integrated, with Europe doing a bit worse than North America.

But wait… creating a customer profile or even a 360 customer view doesn’t bring any value by itself.

So what are the benefits of using a Customer Data Platform? and would you need one? Let’s look at a case study that explains the value of connected data.

HelloFresh lost customer reactivation case

An example of a Customer Data Platform use case comes from Hellofresh. The HelloFresh food Box subscription service uses many channels. Facebook retargeting via custom audiences, Sendgrid for emails, Appboy for mobile push, Twilio for SMS, their own site(s) and integrated personalized offline letters using Optilyz. They wanted to connect these while keeping the current set-up.

Through the use of CDP they were able to coordinate the different channels, but also do cross-channel campaign testing. They tested several channels against each other and also different combinations (like the winning combination with email + direct mail). Just setting this up would have been an enormous effort without a CDP.

Then bring back the tracked conversions of the different channels and campaigns.

A CDP helps to do so, without the manual effort and while tracking the results for every user.

Overall, the integrated setup allows companies that have a CDP to easily find the right datasets for possible campaigns, then plan the right campaigns for an A/B/C/…-testing and measure the results.

Here is the case presented by CDP CrossEngage. Reactivate or rather win-back former customers and increase the effectiveness of a personalized direct mail.

Sending an email teaser first doubles ROI and Conversion Rate of the direct mail. It makes sense that the results are better than a single Direct mail, but also much better than an email discount and direct mail after. Which makes the case for A/B testing different scenarios.

Still quite a simple campaign if you look at it like this, but automation requires that the all the data and the multiple execution channels here are communicating with each other.

Another finding was that young people react better to the direct mail moment than older. As a result, HelloFresh could optimize their campaign and send direct mails more often to younger people (and spend more budget on this audience).

In my eyes, the less complex you can keep your marketing campaigns, the better. And especially the first iteration of your campaign.

Benefits of unified customer data

Research by the CDP Institute showed that Personalization, Insights and cross-channel measurement rank as the most popular benefits for Unified Customer Data.

You could see why these would be beneficial to marketers. The numbers might be a bit slanted though, as the respondents could answer multiple uses. For instance, if you have a loyalty program (or thinking about one) obviously your would already need to connect all the program data and personalize.

Importance of Time to first Value and Data Activation

If you are trying to create a CDP business case, the Time to First Value is an important consideration. CDP implementation can be faster than custom, single point integrations. This is because of the built-in integrations and data management features. But the value is the use of the data, also called “Data Activation”.

Start with one or two use cases that have a direct impact on profits and can be implemented quickly. This will help to increase internal support for the data-driven marketing as a whole. Then everything after that is gravy.

Later, the use-case can be built out, or more uses added. A first use case is often a lighthouse project. By having the infrastructure in place, the first project also has a signal effect for numerous follow-up projects.

Think about the maturity on dimensions a CDP offers:

Level of personalization: from placeholders for names to individual website and email content as well as individual customer journeys

Level of automation and refined Decisioning

Level of segmentation: from big groups to almost 1:1 communication

Data maturity: add more data points, from profile, product, and behavioral data to intent and value based.

Add more touchpoints and channels for Cross-channel campaigns.

Begin with quick-wins so the CDP can pay for itself. The quick-wins come in different forms. Sometimes it is called a pilot, a Minimum Viable Product (MVP) or the use of the CDP in a single campaign. While the roadmap will be different – the stages look the same. Starting small with an implementation time of one to three months to reach first results.

Finding your use cases and the Benefits of a CDP

The benefits of a Customer Data Platform boil down your own use case(s). These will provide the value of the CDP. Yet there are some other benefits that go beyond the single use cases and more into the long-term vision of company data management.

1. Cross-channel attribution

While the current measurement and attribution maturity of your company might not demand it (yet). The Case of HelloFresh shows that running campaigns over multiple channels asks for a flexible reporting and attribution. The CDP will ready the organization for a more comprehensive view of the outcomes of Marketing activities and attribution across channels.

2. Agility and future-ready infrastructure

A CDP is built to be a central hub to connect data sources and delivery platforms, where sources can be connected as they are introduced and use it anywhere to drive better customer experiences.

3. Democratization of Data

Traditional IT-managed databases have a built-in bottleneck (namely the IT resources). A CDP democratizes the access to customer data and lets the data be used directly by the departments that generate the associated value. Marketing, customer service, business intelligence, they all depend on the access to the data and customer touchpoints.

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