Data Governance Tutorial

Summary

This tutorial delves into data governance, highlighting its critical role in organizations amid escalating data and stringent regulations. It distinguishes data governance from data management, emphasizing governance's focus on overarching policies and processes, while management deals with the practical execution. The video outlines the necessity of data governance for ensuring data quality, security, and compliance, and discusses the pivotal roles of data owners, stewards, and champions. It advises starting with a clear scope, documenting data sources, and maintaining data integrity for effective governance. The tutorial also stresses the importance of regular reviews to adapt to the evolving data landscape.

Takeaways

• 📈 Data governance is increasingly important due to the exponential growth of data and new regulations around data management.
• 🔐 It encompasses the rules, processes, and accountability concerning data, aiming for its routine use, harmonization of sources, and controlled access.
• 👤 Data governance involves defining data ownership and ensuring data is managed and updated correctly by those responsible.
• 🤝 The difference between data governance and data management is that governance sets the structure and rules, while management implements these rules in day-to-day operations.
• 🏢 Good data governance ensures quality data is accessible to the right people efficiently and avoids data redundancy or unauthorized access.
• 👥 Key roles in data governance include data owners, data stewards, subject matter experts, and data champions, each with specific responsibilities and areas of expertise.
• 📋 The process of implementing data governance begins with identifying who's involved, defining the scope of data to be governed, and documenting available data sources.
• 🔍 Data mapping is crucial for understanding how different data sources relate and combine to form a complete picture of the data.
• 📘 Metadata provides essential information about data, such as format and content, aiding in the understanding and use of data sets.
• 🔒 Data integrity is a critical aspect of data quality, focusing on maintaining the accuracy, validity, and consistency of data throughout its lifecycle.
• ♻️ Data governance is not a one-time task but requires periodic review and updating to adapt to changes in data volume, types, and usage patterns.

Q & A

What is data governance?

Data governance refers to the rules, processes, and accountability around data. It involves ensuring that data is used routinely, sources are harmonized, access is granted to those who need it, and ownership and management of data are clearly defined.

Why is data governance important for businesses?

Data governance is crucial as it helps maintain the quality and security of data, ensures compliance with regulations, and allows data to be used effectively within an organization. It prevents issues like multiple databases with the same information or unauthorized access to systems.

How does data governance differ from data management?

Data governance outlines the overall structure, rules, processes, and accountability for data use, while data management is the hands-on implementation of these governance rules. It involves the day-to-day tasks of ensuring the governance policies are followed.

What are the roles involved in data governance?

Roles in data governance include data owners or sponsors, who have decision-making power and are accountable for data accuracy, data stewards who oversee the data on a day-to-day basis, subject matter experts who understand the data content, and data champions who promote good data practices.

What is the role of a data owner in data governance?

A data owner has ultimate decision-making authority and accountability for the data they oversee. They ensure the data is correct, up-to-date, and that those working under them comply with data governance rules.

Why should an organization start with a specific scope when implementing data governance?

Focusing on a specific scope when starting with data governance helps prioritize areas that are most critical or have regulatory compliance requirements. This approach increases the likelihood of successful implementation and avoids the overwhelm of trying to control all data aspects at once.

How does data mapping play a role in data governance?

Data mapping helps understand how information in one data source relates to another, creating a more complete picture. It is essential for combining data from various sources and ensuring that the data is used accurately and consistently across the organization.

What is metadata and why is it important in data governance?

Metadata is information about the data, such as format, content, and field descriptions. It provides a guide to understanding what data fields contain and how they should be interpreted, which is vital for maintaining data quality and consistency.

Can you explain the concept of data integrity in the context of data governance?

Data integrity refers to the stability, accuracy, validity, and consistency of data throughout its lifecycle. In data governance, maintaining data integrity ensures that data remains reliable and trustworthy as it is accessed, moved, and used within systems.

Why is it necessary to periodically review data governance policies?

Data governance policies need periodic review because data and its usage are constantly evolving. Regular checks help ensure that policies remain relevant and effective, adapting to changes in data volume, types, and user needs over time.

Outlines

00:00

📊 Introduction to Data Governance

Jen introduces the concept of data governance, emphasizing its growing importance due to the exponential growth of data and increased regulatory oversight. She outlines the goals of data governance, which include ensuring data is used routinely, harmonized, and accessible only to authorized individuals. Data governance also involves data ownership and ensuring data is managed and updated correctly. Successful governance considers the who, what, when, where, how, and why of data, controlling security, and ensuring compliance. The tutorial aims to differentiate data governance from data management, with the former focusing on the overarching structure and rules, and the latter on the practical implementation of these rules.

05:01

🔑 Roles and Responsibilities in Data Governance

This section delves into the roles involved in data governance, particularly highlighting the data owner or sponsor who has decision-making power and is accountable for data accuracy. It discusses how larger organizations may have multiple data owners overseeing different data types, such as manufacturing or sales data. The paragraph also introduces other roles like data stewards, subject matter experts, and data champions, who work closely with the data and are crucial for effective data governance. The importance of a data governance committee in larger organizations is also mentioned, which is responsible for making decisions, resolving conflicts, and standardizing data usage across the organization.

10:02

🚀 Getting Started with Data Governance

The paragraph discusses the practical steps for organizations to begin implementing data governance. It suggests starting with a focus on who is involved and what data needs to be governed. It advises against trying to control all data from the start, recommending instead to focus on top priorities, such as areas tied to regulatory compliance. The paragraph also stresses the importance of documenting available data sources, understanding how data is currently used, and involving those who are knowledgeable about the data in the governance process to avoid future complications.

15:02

🔍 Data Mapping and Metadata

This section explains the importance of data mapping, which shows how information from different sources relates and combines to form a complete data set. It uses sales data as an example, illustrating how order information, customer data, and inventory data can be mapped together based on common identifiers. The paragraph also introduces metadata, which provides information about the data, such as format and content, and is crucial for understanding and maintaining data quality.

20:02

🛠 Data Quality and Integrity

The focus here is on data integrity as a subtopic of data quality, which concerns the stability and reliability of data throughout its lifecycle. It discusses the importance of maintaining data accuracy, validity, and consistency to prevent data from becoming corrupted or inaccurate as it's used and moved within systems. The paragraph also touches on data scraping as a method to create structure where it may be lacking and to ensure that data used for critical decisions, like warranty claims, is accurate and relevant.

25:04

🔄 Continuous Data Governance

The final paragraph emphasizes that data governance is not a one-time task but requires ongoing attention and periodic review. It stresses the need for policies and guidelines to evolve with changing data and business practices. The tutorial concludes with a call to action for viewers to apply the principles discussed, and it invites feedback and sharing of the tutorial with others who might benefit from the information.

Keywords

💡Data Governance

Data governance refers to the rules, processes, and accountability structures surrounding the management of data within an organization. It is crucial for ensuring that data is used correctly, securely, and in compliance with relevant regulations. In the video, data governance is described as encompassing not just the rules but also the effective use of data to benefit the organization. It involves determining who has access to data, ensuring data quality, and maintaining data integrity throughout its lifecycle.

💡Data Management

Data management is the practical implementation of the rules and processes outlined by data governance. It involves the day-to-day tasks of ensuring that data is organized, stored, and accessed according to the governance policies. The video distinguishes data management from governance by highlighting that while governance sets the structure and rules, management is about executing those rules and handling the operational aspects of data handling.

💡Data Ownership

Data ownership is the concept of having designated individuals or roles within an organization who are responsible for the accuracy, accessibility, and maintenance of specific data sets. The video explains that data owners or sponsors are typically higher-level individuals within an organization who have the authority to make decisions regarding their data and ensure compliance with data governance policies.

💡Data Stewards

Data stewards are individuals who work closely with data on a regular basis and have a deep understanding of the data's content and usage. They are responsible for the ongoing maintenance and quality of the data they steward. In the video, it is emphasized that involving data stewards in data governance projects is vital because of their practical knowledge and insights into how data is used within the organization.

💡Data Quality

Data quality pertains to the accuracy, consistency, and reliability of data. The video script mentions that maintaining data quality is a critical aspect of data governance, as it ensures that the data used by the organization is trustworthy and useful. Good data governance practices help in preventing data from becoming outdated or corrupted, thus preserving its integrity.

💡Data Integrity

Data integrity refers to the stability and consistency of data over its lifecycle. It ensures that data remains accurate and valid as it is used, moved, or processed within an organization. The video discusses the importance of data integrity in avoiding the 'telephone game' effect, where information becomes distorted as it is passed along. Maintaining data integrity is a continuous process that involves checks and balances to ensure data accuracy.

💡Data Sources

Data sources are the origins or locations from which data is collected or generated. The video script discusses the importance of understanding data sources in the context of data governance, as it helps in determining the ownership, accessibility, and usage of data. It also touches upon the challenge of managing multiple sources that may provide the same information, requiring decisions on which source is the primary or authoritative one.

💡Data Mapping

Data mapping is the process of understanding how different data sets relate to each other and can be combined to provide a more comprehensive view of the information. In the video, data mapping is used as an example of how sales data, customer data, and inventory data can be linked together to form a complete picture of a transaction. This process is crucial for effective data integration and analysis.

💡Metadata

Metadata is data that provides information about other data, such as its structure, format, and content. The video script describes metadata as a descriptor of data fields, offering a guide to what information is contained within a data set. It is essential for understanding the context and usage of data and is often used in creating data dictionaries that help users navigate and interpret data accurately.

💡Data Scraping

Data scraping is the process of extracting data from various sources, often when there is no direct relationship or mapping between the data sets. The video gives an example of data scraping in the context of warranty claims, where mileage data might need to be pulled from a text box and matched with warranty coverage data to determine eligibility. This technique is used to create structure and relationships where they do not naturally exist.

Highlights

Data governance is crucial for every business due to the exponential growth of data and increased regulations.

Data governance encompasses the rules, processes, and accountability surrounding data usage.

The goal of data governance includes harmonizing data sources, controlling access, and ensuring data ownership and management.

Data governance is about more than just rules; it's also concerned with making data useful to the organization.

Data management is the implementation of data governance rules, focusing on the day-to-day work of data.

Good data governance ensures quality data is accessible to the right people in an efficient manner.

Data governance involves multiple roles, including data owners, stewards, subject matter experts, and champions.

Data owners have the ultimate decision-making power and accountability for data within an organization.

Data stewards and subject matter experts play a crucial role in understanding and guiding data usage.

A data governance committee is responsible for making decisions and resolving conflicts regarding data usage.

When starting with data governance, it's important to define the scope and prioritize areas like regulatory compliance.

Documenting data sources, understanding data usage, and mapping data are essential steps in data governance.

Data mapping shows how information from different sources relates and combines to form a complete data set.

Metadata provides information about the data, such as format and content, aiding in understanding and usage.

Data scraping is used to capture and relate information when direct mapping between data sources is not possible.

Data integrity is a key aspect of data quality, ensuring data remains accurate and consistent throughout its lifecycle.

Data governance policies should be regularly reviewed and updated to keep up with the evolving data landscape.

Transcripts

in the 15 years i've been working in

analytics i've seen a

growing focus on data governance in this

data governance tutorial i'll go over

what data governance is

why it's important for pretty much every

single business or organization

and what sets apart good data governance

from

poor or even non-existent data

governance at other companies

hi i'm jen i help people learn about

analytics skills and careers

check this video description for

additional resources

[Music]

as the amount of available data has

grown exponentially over the last decade

and more regulation has been put in

place

around data and information management

many organizations have started to think

more about data governance

so what exactly is this data governance

data governance is the rules

processes and accountability around data

there are multiple goals of data

governance you want the organization to

use data in a routine way for sources to

be harmonized

for the people that have access to it to

be people that

need to have access to the data and for

people that shouldn't have access

to not have access it also means

ownership

of the data who's responsible for it

being right

who's responsible for it being managed

and updated correctly

successful data governance considers the

who

what when where how and why of the data

that it's governing

while controlling the security of the

data and ensuring compliance

among many other things data governance

should also be

concerned with how can this data be made

useful to the organization

how can we do more than just have a

giant storage location for information

you may have also heard about data

management what's the difference between

data management and data governance

the main difference is data governance

outlines

the overall structure that should exist

it has the rules it has the processes

the accountability

it's more about what should happen how

should things happen

and data management is more about

implementing all of those rules it's the

hands-on

everyday work to ensure that that

governance the

that's been put in place is being

followed so it's the it

teams executing on it it's the

day-to-day

management of information and access

requirements and

whatnot that people may need when it

comes to the data

if you want to know more about data

management i'll link to a video

on my second channel for avant analytics

my consulting company

now let's talk about why data governance

matters

i mentioned that data governance isn't

just about the rules it's also about the

use of the data and making

it useful really good data governance

implementation means that quality data

is accessible to the right people and

only the right people

in an efficient way throughout the

organization

it means not having multiple databases

that have the same information

or access to systems for certain people

that shouldn't have them

it's making sure that these are in place

so that there's a consistent

understanding of

who has access why they have access and

what they're doing with that information

that exists

let's talk about getting started with

data governance what does that

actually look like practically for an

organization that's implementing it or

focusing more on it

when it comes to data governance one of

the first things that you want to think

about

is who's involved typically there are

multiple roles for a very structured

larger organization

that is implementing data governance or

has been working on it for a while

sometimes in smaller organizations or

ones that are new to data governance you

might see these roles overlap

let's talk about what each of those

roles are first though the first role is

the data

owner or this data sponsor these are

people that have ultimate

decision-making ability about the data

and have ultimate accountability for

that data being correct and up-to-date

typically these people are going to be

higher up within the organization

and have the ability to order or

ensure that those working beneath them

are complying with

what is outlined in the roles that are

defined as part of data governance

there are typically many different data

owners and sponsors when it comes to

data governance

often overseeing specific types of data

so in an organization you might have a

manufacturing data owner data sponsor

who's responsible for

maintaining everything related to a

product that's being manufactured

you may also have a sales data owner

or even maybe a segment of sales you

might have a commercial

and a residential data owner depending

on the different applications that your

company is working with

they're responsible for owning providing

and following whatever guidelines are

outlined for their set of data

you can still have multiple data owners

even for companies or organizations that

aren't dealing with physical products

for instance in a local government

organization you

may have one person that's responsible

for voter registration

data and one that's responsible for

real estate tax data and one that's

responsible for

home ownership data for the locale

so regardless of what scale what type of

product or service

that your organization has or is

providing

you can still have multiple different

data owners

if you're in a super small company if

you've got a dozen people maybe there is

just one person but even then sometimes

you have multiple

data owners if you have people

responsible for different

parts of the business in addition to

data owners

in larger organizations you'll also have

data stewards

subject matter experts and data

champions these are people that

are working more regularly with the data

that really understand the content of

the data

they should ideally be consulted in any

data governance project

because they understand the more on the

ground

work that's being done with this data

they understand the different

uses that people have for it why certain

people may need access

that an executive may not see an

immediate answer for

any time that these people are left out

of the data governance process

there usually end up being a lot of

headaches and hoops to jump through

in the practical application of using

that data

there's still room to question here

whether just because someone

had access in the past do they still

need to have access

but incorporating involving these people

that have more knowledge

can really help improve that process and

make sure that you're not backpedaling

and having to redo a lot of work later

on after you roll out these new rules

larger organizations also typically will

have a data governance committee

this group is ultimately responsible for

all the decisions that are made

if there's conflicts between different

groups they can help resolve them

if there's decisions that need to be

made or implementations that maybe need

made to standardize

how the data is used or stored or

accessed across the organization

this committee can act as a central

resource to make sure that

the data of different types isn't

implemented in a lot of different ways

across each different area so maybe

instead of having

one set of sales database with one

access point to

that and then a separate application

that deals with client information or

production information

the data governance committee can look

at it and say how can we

integrate these better how can we have

one location how can we make it

similar regardless of the type of data

you're looking for

which can usually lead to a more

streamlined process

overall and make it much simpler to

implement changes rather than dealing

with maybe a dozen different

types of systems to access data you

consolidate to

much fewer which can pull all of the

resources

into one location and make it easier to

teach people

how to access the data that they need to

access

even if it's contained within one system

this doesn't mean

automatically everybody has access the

nice thing with the actual

implementation is you can still have

different roles that allow

access to different pieces of data but

having that centralized location can

make it a lot easier

for individuals or groups within the

organization

that need to use data from multiple

different sources to complete their work

in addition to establishing who's

involved one of the very first steps

that you should take concerning data

governance

is to think about the scope of the data

that you want to govern

it's really tempting to say we want to

control it all

but the reality is unless you're a very

very very small company

it's usually not practical to try to

control

everything from the start instead

think about what your top priorities are

so

an easy solution for this is if you have

areas of data management that tie

to government or regulatory compliance

this is a great place to start with your

data governance because it's not just

about your company

or your organization it's about are you

meeting the requirements

of the law so focus on that area and

then as you have

pieces in place you can expand further

and further

but anytime that you try to take

everything within the scope of work that

you're doing

you are much more likely to fail it's

much more likely to take a lot longer

to make the same type of progress

because you're trying to take care of

everything at once

instead of one piece at a time an easy

comparison is

think if you tripped and fell down the

stairs if you

cut yourself and were bleeding profusely

and you had a broken arm and you hit

your head

you ideally yeah you would fix

everything at once

but the company that is going through

this with their data

you fix the thing that's going to hurt

you the most so if you fell down the

stairs and you're bleeding

you stop the bleeding that is the most

immediate pressing concern that doesn't

mean you

ignore the broken bone or the potential

head injury

but you take them in order of what is

the most serious that i deal with first

the same is true anytime you're working

with data what's the most immediate need

what's going to have the most immediate

consequence

negative consequence if i don't do

something about it

and then once that's taken care of you

can move on to the next thing

if you're not dealing with compliance

issues or something that's otherwise

urgent you still need to set some sort

of scope

in this case you can just pick an area

that may have

a lot of advantage to working with or

just pick an area

sometimes people get too hung up on

making sure they pick the right area

that they don't just take action

so if you're not sure what to do pick

something say that you want to

work on client data as the first step of

governance

and then you can move on to the next

step or pick manufacturing data whatever

you do

don't let it stop you from doing

something this can also sometimes inform

who is involved in the data governance

process up front if you're just getting

started

and you have people that you know are

eager and want to be involved

that can be a guideline for what you

pick to work on

or if you pick what to work on that

could inform who should be involved in

that process

now that you have the who and the what

it's time to move into more detail

document what data you have available

these are your data sources

what information is in this data where

does it come from

do you have multiple sources that are

providing the same information

who owns the data who's an expert in it

how often is it updated who checks to

make sure it's updated correctly

who accesses it and what do they use it

for when they do access it

answering these types of questions can

really help you make a more informed

decision on what rules

processes and accountability that you

put in place regarding

a specific type of data before you jump

right into making rules it's important

to understand

how people are currently using the

information

and why they're using it in that way

otherwise

again you end up with a poor

implementation

you end up making more work for people

that

still have to get their job done but now

they have someone who doesn't have any

idea what they're doing

making decisions about what they can and

can't have access to and how they're

going to access it

this doesn't mean you're not going to

bother people by the decisions you make

for data governance

there are going to be people that are

unhappy with the decisions you've made

but they tend to be a lot more receptive

to change and the organization is

typically a lot more receptive overall

when you've at least taken the time to

listen to them

account for their concerns factor that

into the decision

you're making and at least make an

informed decision

even if you know that makes things more

challenging for some individuals or some

teams or department it's easy for people

to think about how

they think the data should be used it's

a completely different story to know how

it

actually is being used it's rare that

there

aren't some surprises along the way of

how people are using

information sometimes because they can't

access what they really need and so

they're substituting

and making adjustments to existing

information

to be able to do the work that they need

to get done you may also find that as

you start exploring the data that's

available that there are multiple

sources for the same

data in this case you have a decision to

make

do you still retain the information from

multiple sources

which is your primary authoritative

source if there's a conflict

for instance a simple example of this is

in the automotive industry

if someone files a warranty claim for

their vehicle

there are multiple ways that the company

can get information about that

they can get mileage information based

on what's manually submitted on the

warranty claim how much the dealer or

the customer reports

in terms of mileage there was on the

vehicle at the time that the repair was

scheduled

however with newer vehicles that have a

lot of remote technology

they can also read this information off

the control units on the vehicle

so if there's a conflict there if

there's a difference between the mileage

that the dealer says and what the

vehicle says

unless there's a known issue with the

vehicle where the mileage would be

reported wrong

typically you're going to want to

prioritize what the vehicle says what

the control units say

automatically because there's usually

less room for error there

you can run into this with all sorts of

data where you'll have these conflicts

even if you don't see immediate

conflicts it's still good to set a

priority of what is your main source

what is going to be the authority when

it comes to

the accuracy of that data as you look

into the available data

in most areas you're going to find that

the data being used

doesn't just come from one singular

location it's usually made up of a

variety of different sources

for instance let's take sales data sales

data might sound like it's one

complete isolated thing however most

sales data consists of client

data information about who made the

purchase

it consists of actual sales data like

what was the sales date what was the

sale amount what was the exact order

that was placed and it often consists

of some sort of inventory or production

information

this isn't quite as typical for

something off the shelf

where client information isn't reported

but if you're working for an

organization that provides a service or

provides any sort of customized product

or even products that offer multiple

variants

this production information probably has

information on

if somebody orders shirt for instance

what color did they order what size did

they order

was their inventory to fulfill it so all

of these different pieces

are in themselves individual data sets

that are brought together to form the

one complete

set of data the one source of

information that we think of the sales

data to combine all of these we get into

data mapping data mapping tells us how

the data the information in one of our

sources

relates or maps to data in another

source

to combine to give a more complete

picture so in the example of that sales

data

we would have the order information

linked to customer information

probably based on a customer name or a

customer id

so if you look in the customer file you

have an id

or you have a name there that is

identical

and unique for an individual for a

company that

is doing the purchase and then in the

order you have that same unique

identifier that same unique name that

same unique number

so that when you match up you look for

the same thing in one

and two and that's how you tell the

systems to combine this information how

to map this information

same thing with the order and maybe

inventory data where

you have the part number that was

ordered the service that was ordered

and then in your inventory or your

service information

you have probably more detail so you

have

part number one in your order you have

part number one

in your inventory database and in your

inventory you talk more about the

details of that

and so you combine those then we have

cases where the mapping isn't direct

so to map customer to inventory there's

no direct mapping there's no

direct single relationship they only

relate or map

together because of that order

information

that's a fairly simple step to get there

sometimes it can be more complicated

sometimes there can be two

three or more steps in between to

connect these different pieces together

make them relevant make them relate to

each other another piece of information

that you'll typically have or should

have about your data is

metadata think of this as information

about the data

what type of format should it take by

default

what type of information is contained

within it so for instance let's talk

about order information

metadata would describe every field that

exists in that set of data

so if we have our date our metadata

would tell us

what format that it's in let's say it's

a

a month day year format then

what does it contain a short description

so

date of order from customer or date

order received

it gives a almost a dictionary of sorts

and

sometimes you'll see it called a data

dictionary which describes

the information that's contained within

that data set metadata and a data

dictionary

aren't always exactly the same but in

general they're giving you more

general information about what's

contained there so that everyone can

understand what content should exist

there

and does exist within those types of

data fields

ideally different tables of data or

different data sets will have

clear mapping of how they relate even if

they have to go through one or

two other tables in the intermediate to

connect

one to the other however that's not

always

reality and when that's not the reality

sometimes we need to use data scraping

to be able to

capture the right information for

instance

maybe we need to know the mileage of

that vehicle

when there was a warranty claim but what

if the warranty database doesn't have

mileage in that case maybe we ask that

someone

puts that information in a text box when

they submit the claim

data scraping is going and finding that

and automatically pulling it out

to see how it relates so how might it

relate on a warranty claim

well most vehicles have a fixed mileage

or

age limitation on the warranty so if

this process has to be done through data

scraping

we'll scrape out what the mileage is and

then

map that to the warranty coverage to see

is that mileage within the limits is the

age within the limits

does it qualify to be covered or does it

not meet the criteria is it too old does

it have too many miles those sorts of

things

so that's a simple example of data

scraping sometimes it can get more

complicated

in general think about it as a way to

try to create structure where

not much structure exists i mentioned

data quality

which has a lot of different subtopics

that could

easily be ours on their own i'm not

going to get into all of those today

but there is one area that i do want to

talk a bit about data integrity

is a subtopic within the data quality

area

and data integrity is not just

what's the overall quality of the data

but it's how stable is our data how

routine

is it can we always trust it how is it

updated how do we know that it's not

corrupted think of data integrity as how

well the accuracy

validity and consistency of data is

maintained across

its life cycle that is from the moment

that we first

collect that data does it remain the

same

does it remain consistent does it remain

to be true does it continue to be

accurate

as we move it around within our systems

as we put it into different tools as

different people start to use it

do we maintain that integrity of

information

so we don't essentially end up with the

telephone game that you might have

played in school where you whisper

something in some person's ear at the

start and by the time you've gone

through

15 different people the message out the

other end is very different than the

message at the start

the same thing can happen with data for

a variety of reasons

it could be system problems that create

this

challenge it can also be multiple people

being involved that don't understand the

context they don't understand

where why how the data was collected

and they make assumptions a lot of times

there's assumptions

at every step and by the time you get to

the end

then it's not really representative of

what you started with

this doesn't always have to be the case

and as long as you're aware of it

it's something that you can put more

things in place to check you could have

someone that checks

the source data and the end result data

to see

do they match do they properly convey

the right

information are they still accurate are

they still valid

really checking to make sure that you

don't end up with a completely different

picture than what you started with

maintaining data integrity and data

quality throughout the life cycle

isn't just a one-off thing you don't do

it and then it's done

it's also about having checks in place

to make sure everything continually

functions as expected

if you have data automatically pulled

into a system every day

what checks do you have in place to make

sure it all happened accurately how do

you catch when mistakes were made

so that somebody doesn't need to stumble

into a problem with it

and raise an alert how do you automate

some of that to help

ensure that that integrity that quality

is maintained

all along on an ongoing basis ultimately

all of this work of data governance

should lead to sets of rules

processes and policies that are applied

across the business to make sure that

you have

good data being used in a good way

throughout the organization

it's the right people accessing the

right data at the right time with the

right amount of accountability

this work should inform business

policies as well as data management

as with data integrity and data quality

data governance in general

is not a do it once and forget about it

forever sort of thing

it constantly needs to be rechecked as

fast as information is growing it's

exponentially growing every year

you may be getting more information

tomorrow than you were getting yesterday

and you may have different people doing

different things with it than they were

in the past so it's important to keep up

with that

if you set up data governance policies

now even if they're perfect

chances are very high that two years

from now they're not going to be perfect

something is going to have changed so be

aware of that that doesn't mean making

changes every day every week every month

but it does mean that you have some

periodic schedule that you come back and

review

and check and make sure that your

policies your rules your guidelines are

really keeping up with the information

that's there

rather than being really reactionary

it's already a little reactionary to

only follow up once a year

or whatever frequency it is but at least

then you only have a small thing you

need to react to instead of

five years from now two years from now

realizing that

none of the rules that you put in place

are being followed because they're no

longer relevant they no longer apply to

the information that's available

and how people really need to use that

data

to effectively run the business to

effectively do their jobs

i hope you enjoyed this data governance

tutorial if you did enjoy it please

consider

giving it a thumbs up and sharing it

with someone that you think may benefit

from it

thank you so much for watching