What is a data warehouse?
Summary
TLDRPaul 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
📊 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.
🚀 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.
🗄 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.
Mindmap
Keywords
💡Data Warehouse
💡Systems of Record
💡Single Version of the Truth
💡Decision Support
💡Performance
💡Normalization
💡Data Persistence
💡Business Intelligence (BI)
💡CRM (Customer Relationship Management)
💡Flat Files
💡Master Data
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
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