What is a data warehouse?

Summary

Paul Felix, founder of Leapfrog BI, explains data warehouses as central repositories for an organization's information, sourced from various systems like CRM and financial systems. He emphasizes their importance for decision-making, offering a single version of the truth, performance for analytics, simplicity for users, and data persistence. Felix clarifies that a data warehouse is not a product or technology but a strategic asset for organizations.

Takeaways

• 📚 A data warehouse is a central repository that consolidates data from various source systems such as CRM, financial systems, HR systems, and operational systems.
• 🔍 The primary purpose of a data warehouse is to support decision-making by providing relevant and accurate information to decision-makers at all levels of an organization.
• 🌐 Data warehouses integrate both internal business data and external data sources to give a comprehensive view necessary for informed decision-making.
• 🗣️ The concept of a 'single version of the truth' is crucial for data warehouses, ensuring consistency in the information provided to users querying customer data, sales figures, etc.
• 💻 Performance is a key benefit of data warehouses, as they are designed to handle large-scale data analysis without impacting the performance of operational systems.
• 🚀 Data warehouses simplify data access for business users by providing a normalized, easy-to-navigate structure that contrasts with the complex backends of operational systems.
• 🗄️ Data persistence is managed within data warehouses to meet organizational needs, often storing historical data that source systems may not keep, which is vital for trend analysis and forecasting.
• 🚫 A data warehouse is not defined by any specific product or technology; it's a concept that can be implemented using various database management systems and technologies.
• 🛠️ The decision to use a data warehouse is driven by the need for better data management, improved decision-making support, and the ability to handle large volumes of data without degrading the performance of source systems.
• 🌟 The value of a data warehouse lies in its potential to be one of the most valuable assets for an organization, enhancing strategic and operational decision-making processes.

Q & A

What is a data warehouse according to Paul Felix?

A data warehouse is a database that serves as a central repository for all of a company's relevant information, collected from various source systems such as CRM, financial systems, HR systems, operational systems, flat files, and master data.

Why is it important to have a data warehouse instead of just using the source systems for decision-making?

A data warehouse is important for decision-making because it provides a single version of the truth, enhances performance by handling large data queries without affecting source systems, simplifies data access for users, and ensures data persistence according to organizational needs.

How does a data warehouse support decision-making in an organization?

A data warehouse supports decision-making by providing decision-makers with relevant, accurate information about the internal and external environment that influences the outcomes of their decisions.

What is meant by 'single version of the truth' in the context of a data warehouse?

The 'single version of the truth' refers to the concept that when a user queries the data warehouse for information, they receive a consistent and unified answer, regardless of who is asking or when they are asking, ensuring consistency across the organization.

Why might source systems struggle with decision support queries?

Source systems are designed for transaction processing, which involves reading and writing small amounts of information. Decision support queries often require processing millions of records, which can overwhelm these systems and degrade their performance.

How does a data warehouse improve the performance for business users?

A data warehouse is designed to handle large-scale data queries quickly, providing business users with timely responses to their questions without negatively impacting the performance of the source systems.

What is the role of simplicity in a data warehouse?

A data warehouse simplifies data navigation for business users by organizing data into intuitive structures, making it easy to retrieve information without having to deal with the complexity of multiple tables and relationships found in source systems.

Why is data persistence important in a data warehouse?

Data persistence in a data warehouse ensures that historical data is retained and organized in a way that supports various business requirements, such as trend analysis and auditing, which may not be the case with source systems that often archive or discard old data.

What are the four reasons Paul Felix gives for why a data warehouse exists?

The four reasons are: 1) Single version of the truth, 2) Performance, 3) Simplicity, and 4) Data persistence.

What does Paul Felix clarify about what a data warehouse is not?

Paul Felix clarifies that a data warehouse is not defined by a specific product or technology. It is not a particular database product like Oracle, DB2, or Microsoft SQL Server, nor is it a specific technology like a relational database or multidimensional cube.

How can Leapfrog Bi assist in building a data warehouse?

Leapfrog Bi can assist by providing expertise and services to help organizations design, implement, maintain, and utilize a data warehouse effectively.

Outlines

00:00

📊 Introduction to Data Warehouses

Paul Felix, the founder of Leapfrog BI, introduces the concept of a data warehouse. He explains that a data warehouse is a central repository for an organization's data, sourced from various systems like CRM, financial systems, HR systems, operational systems, and flat files. The purpose of a data warehouse is to provide relevant, accurate information to decision-makers, which is crucial for the success of the organization. The video aims to discuss why it's necessary to have a data warehouse when data can be maintained in the source systems themselves.

05:02

🚀 Performance and Data Warehouses

The second paragraph delves into the performance aspect of data warehouses. It discusses how systems of record, designed for transaction processing, can be overwhelmed by reporting and analytics queries, which can negatively impact their performance. The data warehouse, on the other hand, is designed to handle such queries efficiently, providing timely responses to business users. It also addresses the issue of data complexity, explaining how data warehouses simplify data access for users by organizing it into easily navigable structures.

10:04

🗄 Data Persistence and the Role of Data Warehouses

The final paragraph focuses on data persistence, emphasizing the need for organizations to maintain data in ways that align with their specific requirements. It contrasts the behavior of source systems, which may only store the current version of data, with the data warehouse's ability to store full audit trails and historical data. This historical data is crucial for analyzing trends and predicting future opportunities. The paragraph concludes by clarifying misconceptions about what a data warehouse is not, such as being a specific product or technology, and encourages viewers to build their data warehouses with the help of Leapfrog BI.

Keywords

💡Data Warehouse

A data warehouse is a centralized repository of an organization's data, designed for reporting and data analysis. It stores historical data collected from various source systems such as CRM, financial systems, and operational systems. In the video, the founder of Leapfrog BI explains that a data warehouse is more than just a database; it is a strategic asset that enables decision-making by providing a single source of truth and supporting complex analytical queries without burdening operational systems.

💡Systems of Record

Systems of record are the primary systems where data is first captured or entered into an organization. Examples include CRM systems, financial systems, and HR systems. The script mentions that data does not originate in a data warehouse but is collected from these systems of record, emphasizing the role of these systems as the initial point of data capture.

💡Single Version of the Truth

This concept refers to having one consistent and accurate set of data that reflects the state of an organization at any given time. The video script explains that a data warehouse provides a single version of the truth by consolidating data from various systems, ensuring that all users and reports rely on the same data set, thus avoiding discrepancies.

💡Decision Support

Decision support in the context of the video refers to the use of data and analytical tools to aid in making informed business decisions. The script highlights that a data warehouse serves as a decision support system by providing relevant, accurate, and timely information to decision-makers at all levels within an organization.

💡Performance

In the video, performance is discussed in terms of the speed and efficiency with which a data warehouse can handle queries and reporting. It is contrasted with the limitations of systems of record, which are designed for transaction processing rather than analytical queries. The data warehouse is designed to deliver fast responses to complex queries, enhancing user experience and decision-making.

💡Normalization

Normalization is a database design technique that organizes data to minimize redundancy and dependency. The script uses normalization to explain why applications' databases are not ideal for reporting and analysis. Highly normalized databases, while efficient for transaction processing, can be complex for users to navigate for reporting purposes, which is why a data warehouse de-normalizes data to simplify access.

💡Data Persistence

Data persistence refers to how long data is kept and how it is stored over time. The video script mentions that organizations have specific needs for data persistence that may not align with how source systems handle data. A data warehouse can be designed to persist data in ways that meet these needs, such as maintaining historical data for trend analysis.

💡Business Intelligence (BI)

Business intelligence encompasses the strategies and technologies used by organizations to analyze data and gain insights to support decision-making. The script positions the data warehouse as a critical component of BI, facilitating the analysis of data from various sources to inform strategic and operational decisions.

💡CRM (Customer Relationship Management)

CRM systems are software applications designed to manage a company's interactions with customers and potential customers. The script uses CRM as an example of a source system from which customer data is extracted and loaded into the data warehouse, highlighting its role in centralizing customer information for analysis.

💡Flat Files

Flat files are simple, non-hierarchical data files, such as CSV or TXT files, that store data in a tabular form. The video script mentions flat files as one of the sources from which data is collected for the data warehouse, indicating that data can come from a variety of formats beyond just database systems.

💡Master Data

Master data refers to the core and consistent data that an organization maintains as a single, golden record. The script includes master data as one of the types of data that are collected into the data warehouse, emphasizing the importance of having a central repository for this critical information.

Highlights

A data warehouse is a central repository of an organization's relevant data, collected from multiple source systems like CRM, financial, HR, and operational systems.

A data warehouse doesn't originate data; it pulls data from existing systems of record to create a single source of truth.

One key reason for building a data warehouse is to create a 'single version of the truth,' ensuring consistency across different business systems.

Data warehouses enable better decision-making by providing decision-makers with relevant, accurate, and integrated internal and external information.

Data warehouses help prevent performance issues that can arise when systems of record are tasked with analytics and reporting, which they are not optimized for.

A data warehouse organizes data into structures designed for efficient queries, offering fast and reliable access to information for business users.

Simplicity is a key advantage of data warehouses, making it easy for users to retrieve and navigate data without complex querying.

Data warehouses allow organizations to persist data in ways that source systems may not, such as storing historical records or maintaining full audit trails.

Source systems may not store full history (e.g., only current customer addresses), but data warehouses can preserve complete histories for analysis.

Data warehouses provide a persistent and centralized view of data, allowing organizations to analyze trends and make predictions over time.

A data warehouse is not a product or technology (e.g., it’s not Oracle or Microsoft SQL Server); it’s a concept that can be implemented using various tools and architectures.

Data warehouses can be implemented using different technologies like relational databases, multi-dimensional cubes, or even in-memory storage.

Decision support systems (like data warehouses) are crucial for both strategic and operational decision-making within an organization.

The complexity of querying highly normalized transactional systems makes data warehouses essential for providing user-friendly access to business data.

Organizations can leverage data warehouses to store large volumes of historical data, which is crucial for conducting trend analysis, forecasting, and business intelligence.

Transcripts

hello everyone my name is Paul Felix I'm

the founder of leapfrog bi today I'm

going to be addressing a question that I

could ask pretty regularly and that is

what exactly is a data warehouse a demo

warehouse is is a it's a database as the

name implies but it's actually a lot

more than that

first of all data does not originate in

a data warehouse instead we're going to

go out to an organization's

source systems such as a CRM or customer

relationship management system your

financial system your HR system your

operational systems flat files our

master data all this information is

going to be collected or copied from

those systems of record and brought into

the data warehouse so the data where

else becomes a central repository with

all of the company's relevant

information so that makes a question why

would we do such a thing

why would we copy data that's already

maintained we know as current in these

systems of record and then place it into

the central repository well to answer

that question we need to really take a

second and talk about how decisions are

made and what is the impact of a

decision or kimo decision on an

organization I think we could agree that

an organization's success is defined or

depends on the cumulative ability of

everyone in that organization's ability

to Creek to make good decisions or to

make decisions that have successful

outcomes so if that's the case then how

do we enable decision-makers to make

good decisions and by decision makers I

want to make sure I'm clear about that

we're not only talking about those very

strategic decisions that are made by

only the executives in the organization

those are very important decisions of

course but we're also talking about

routine operational decisions that are

made on a day to day basis by everyone

in an organization so how do we enable

this full spectrum of decision makers to

make the best decision possible well one

way we do that is

by providing decision-makers with

relevant accurate information a decision

is best made when a decision-maker

understands the environment that

influences that decisions outcome and

that environment is is kind of twofold

first of all we have in organizations

internal information we've talked about

all these business systems already CRM

and financial and so on but you also

have external data such as the weather

the physical environment you have the

financial environments or the markets

all of those external influencers also

need to be brought into a data warehouse

so that they can be integrated with

internal data and provide it to a

decision maker such that that

decision-maker has the best possible

understanding of the environment that

impacts a decisions outcome and this is

why a data warehouse is potentially one

of the most valuable assets that an

organization can possess all right so

why once again why would we take data

out of the systems of record and put in

the central repository the data

warehouse why not just go straight to

the systems of record and use that as a

source of our information to empower all

of these decisions or to provide

information for all these

decision-makers I'm going to provide

four reasons why a data warehouse exists

reason number one single version of the

truth this is probably the most commonly

cited reason for data warehousing it

spans a lot of different concepts I'm

going to give a couple of them here one

example is an organization may have a

number of business systems that track

the same information let's just talk

about customer information you may have

customer information in a customer

relationship management system and you

may have customer information in your

financial system because you're tracking

sales and you may have customer

information even in your operational

systems potentially it's very important

that when a person asks for a list of

customers that they get the same answer

from day-to-day

or from person to person that won't

happen

typically if you're trying to collect

this customer information from each of

your systems individually so providing a

single version of the truth is an

important characteristic of a data

warehouse when we go to that data

warehouse and we ask for a list of

customers it is the enterprise list of

customers any business logic that needs

to be applied has already been applied

when the business user goes to that data

warehouse to retrieve that information

reason number two the performance

performance can really be broken down

into two areas

first of all let's assume that the data

warehouse doesn't exist in that case we

would have no option other than to go to

where the data originates which is the

systems of record to again empower those

business users to get the information

you need to make better decisions if we

go to a system of record and we ask that

system a record to support the type of

reporting analytics that we're talking

about decision support information we're

going to often times bring those systems

to their knees and that's because very

simply systems of records are designed

for transaction processing they're

designed to read very small amounts of

information and write very small amounts

of information at a time think of a

point of sale every time someone makes a

purchase that point of sale records a

record that says here's the purchase

here's the line items of that purchase

here's who purchase it very small

amounts of information now contrast that

with our decision makers requirement a

decision maker may ask to see the

aggregated sales volume for a particular

product quarterback quarter and give me

the comparison for the prior year's

sales for those same products that

requires often millions multiple multi

millions of Records to be traversed and

that type of question or query is going

to often bring those systems to their

knees which which has adverse impact on

the system on the source system because

it's no longer focused only on serving

that as a point of sale system which is

going to be

the performance that's going to be

deteriorated but it's now also trying to

serve this decision support role so the

system of record impact is is definitely

a negative performance implication if we

don't have a data data warehouse on the

other side of the spectrum the business

user is expecting to get an answer to

their question and we are trying to

provide that business user with the best

performance possible because we want

them to use this information we don't

want them to go to some some report and

have to wait for five minutes or ten

minutes or an hour or possibly have the

report delayed by days potentially

because we have to batch process this

thing to limit the impact on the source

systems so the user experience of the

business user is another area where

performance is critical in a data

warehouse once again is one of the roles

of the data warehouse is to deliver a

well performing repository whenever that

user asks the question the data where

else is going to respond in a timely way

because we're going to organize the data

into data structures that are designed

to support that type of question reason

number three

simplicity applications have backends or

databases that are highly normalized and

basically that means once again that

they're designed to carry out

transaction processing they're designed

for very small reads and writes that is

perfect for applications but it is not

perfect for a business user trying to go

out and collect a piece of information

once again our customer example if if

you have a business user that wants to

get a list of customers and they're just

going to one application such as let's

use the customer relationship management

system as an example

well that's CRM system it may store

customer information in two three a

dozen or more tables and consolidate

that information into a single list of

customers with all of the attributes

that we want such as the address a phone

number

whatever it might be the demographics of

that customer is often not at all a

simple process if you compound that

problem by adding in two three four or a

number of and organisation's

applications or source systems you have

a situation that is just insurmountable

for a business user to go out and try to

achieve in any reasonable amount of time

so one of the roles of a data warehouse

is to provide these users a simple way

to navigate data whenever they go to the

data warehouse and they ask for a list

of customers there is a simple list of

customers they ask for a list of

products there it is they won't see the

sales or the past year no problem here's

the date you select the year you want

and it the information is filtered and

returned for you very easily and

intuitively all right reason number four

data persistence this is very simple

organizations have certain needs they

want to persist their data in certain

ways that doesn't always aligned with

the way source systems persist data

let's once again talk about a customer

an application may store customer

information and it may also and now

allow the business user that's

interfacing with that application to

update that customer information such as

an address the application itself may or

may not store a full history of that

customers record in other words the

application may store only the current

version of a customer's address as

opposed to keeping a full audit trail

that says well at this point in time the

customer dress was a and then at this

point in time the customer dress changed

to be them g7 one example of data

persistence here but an application has

a certain behavior it make it it may

create or store the whole audit trail of

a customer record or it may store only

the current version if it's the current

version

well that doesn't support a number of

business requirements that an

organization may have as an example if

we want

to know how a product is selling across

the last two years month to month a

month and we wanted we want to break

that analysis down by location well if

we only know a customer's current

address then that analysis is not

possible we have to know where the

customer was at the time of the sale so

a data warehouse is going to persist

data in a way that meets and

organizations needs if we need a full

audit trail we'll store that full auto

tail trail regardless of how the source

of some decides to behave source systems

also archive data they just simply take

some data offline to limit the load on

on their databases again a data

warehouse is going to have a different

set of requirements often a lot of

history is stored within a data

warehouse and that history is very

important to establish key trends that

that are used through different types of

regression or analytics to determine

what the future opportunity might hold

so again a data warehouse is going to

serve as the organization's area where

data is persisted okay so we talked

about the four reasons why data

warehouse exists and we also define what

a data warehouse is now it's also

important to define what a data

warehouse is not and that is it's not a

product and it's not a technology buy a

prod product I mean it's not it's not

Oracle it's not db2 it's not a Microsoft

sequel server it's not a product that

products are very important of course

products and tools are all very

important in implementing and

maintaining and monitoring a data

warehouse but the data warehouse itself

is not defined by any particular product

a data where else is not a technology

there's many ways of implementing a data

warehouse and technology again is very

important to successfully implementing a

data warehouse but regardless if the

data warehouse is implemented in

a relational database and a

multi-dimensional queue but no sequel

technology or if the data is stored in

RAM or stored on dimensional disk all of

these things are again very important

but they don't define the data warehouse

okay so we talked about what a data

warehouse is it's database we know why

we're creating a data data warehouse

we're going to use it as a decision

support system we know why we're not

going back to the systems of record for

reasons that I gave were data

persistence simplicity performance and

single version of the truth and we also

know what our data where else it's not

it's not a product it's not a particular

technology so now you know exactly what

a data warehouse is go out there get

busy build your data warehouse contact

leapfrog bi and we'll be happy to help