Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Data Governance Handbook
Data Governance Handbook

Data Governance Handbook: A practical approach to building trust in data

Arrow left icon
Profile Icon Wendy S. Batchelder
Arrow right icon
$19.99 per month
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (5 Ratings)
Paperback May 2024 394 pages 1st Edition
eBook
$27.98 $39.99
Paperback
$49.99
Subscription
Free Trial
Renews at $19.99p/m
Arrow left icon
Profile Icon Wendy S. Batchelder
Arrow right icon
$19.99 per month
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (5 Ratings)
Paperback May 2024 394 pages 1st Edition
eBook
$27.98 $39.99
Paperback
$49.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$27.98 $39.99
Paperback
$49.99
Subscription
Free Trial
Renews at $19.99p/m

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!
Product feature icon Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!
Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
Subscribe now
View plans & pricing
Table of content icon View table of contents Preview book icon Preview Book

Data Governance Handbook

What Is Data Governance?

As a data professional, some of the most frustrating conversations you will have about data governance will be about data programs feeling like a series of constraints versus a strategic enabler and that you are slowing business down vs. enabling excellence. Having led data transformations in three Fortune 500 companies, I have heard my fair share of these same messages. In my humble opinion, this is feedback; feedback that we are speaking in “data speak” and have not created a business case that is centered on value generation from the lens of our stakeholders. Rather, we have delivered a business case that is focused on data needs vs. business needs.

From a stakeholder’s perspective, there are a plethora of forces at stake in driving business: generating revenue through the sales teams, marketing to existing and potential customers, economic factors, and supply chain challenges. Data is a part of all of these critical business components, but it is not the first thing that comes to mind for our stakeholders. It is embedded in how business runs. It is a part of the day-to-day. It does not and should not feel like a standalone function.

Therefore, it’s our job to serve the business and to make it feel seamless to the business stakeholders we enable. When things feel like friction, it’s not necessarily because we’re not supported; it’s because we are one of many problems leaders are facing. Often, this comes in the form of a lack of buy-in or pushback, a seemingly endless number of questions, or simply a lack of engagement. For data professionals, conversations like this often end in frustration and the underfunding of the data governance program. I have seen this scenario over and over again in organizations firsthand and have heard it from data executives in every single industry. Far too often, it ultimately ends in the failure of a chief data & analytics officer to survive in the organization.

The question is, why?

Over the course of the next 17 chapters, I will explain why Chief Data and Analytics Officers fail to establish themselves as strategic business partners in their organizations and how you can overcome these common pitfalls and succeed. I will cover everything you need to know to build a case for data governance, rally your organization to support you, deploy a strong data governance program, leverage core data governance solutions, and apply all of this in a case study for a fictitious financial institution. Let’s dive in.

What you can expect to learn

Throughout this book, I promise to be transparent and direct about my experiences, and we’re going to start strong: governance programs fail because we have failed. We have failed to explain data governance in a way that makes sense to our business stakeholders. We have failed to deeply and intimately understand how our solutions will drive business success. In short, we have failed to explain in terms of business value. Conversely, the most successful data executives I have had the opportunity to work with have been successful because they deeply understand their company. They have spent the time to intimately understand the business, have crafted data solutions that enable business success and have successfully explained the benefits in terms of business results vs. data results.

As we go deep into these topics, I will not make assumptions about your experience implementing a successful data governance program. I will start with the basics by grounding you in definitions and the foundational capabilities and will build on how to launch a successful and impactful program, complete with the measures for success that will resonate with executive management and, ultimately, the board of directors for your organization. In the end, we will complete a case study to bring it all together. By the end of this book, you will have all you need to launch a program and deliver with excellence in your own organization. No longer will your organization be overwhelmed by data and underwhelmed by insight. We will change the narrative together.

In this chapter, we will ground ourselves in the basics of data governance and how it relates to adjacent capabilities. Then, we will define the components of a data governance program, why each component matters, and why we treat data governance as an enabler for business value. Subsequent chapters will dive deeper into the fundamental capabilities of a data governance program and how to implement them.

We will cover the following main topics:

  • What is data governance?
  • What’s driving the increasing need for data governance?
  • A brief overview of the data governance components
  • Data governance as a strategic enabler
  • Building a business case for your company
  • When and why to launch a data governance program

What’s driving the increasing need for data governance?

As I meet with data professionals across industries, it is abundantly clear that data governance is more important than ever. Executives are expecting more from data, but without the proper investment, it is harder than ever to respond at the speed of business.

So why is it increasingly difficult to respond to our executives at the pace of the business? There are a number of key factors, including the continuous rise in the following:

  • Data volume: We have more data today than yesterday (everyday!). In fact, the amount of data doubles every two years. Yet, we cannot expect to double our efforts or double our staffing or technology spend.
  • Regulation: The regulatory landscape is evolving, increasing expectations for how data is handled. In the United States, at the time of this writing, six states had signed privacy and data protection legislation into law. This increases the complexity of compliance for data handling.
  • Expectations: Executives’ expectations are rising, but our use of data is not. In a recent Tableau survey, >80% of CEOs wanted their organizations to be data driven, but less than 35% percent of employees felt their data was used in decision making.
  • User base: More individuals than ever are engaging in data, wanting it for their own use but needing to trust it. It puts our governance professionals in a position to add tremendous value by providing trusted, well-governed data to our organizations.

We have to become more innovative and more embedded, leveraging more technologies (e.g., automation and AI) than ever before. We talk about what that means for our customers. But what does it mean for us? If it’s difficult to answer key, basic business questions today, how do we expect to do it in two–three years with more data than ever? We must take this sense of urgency and build capabilities that will scale and last as our volume, complexity, expectations, and user base continue to grow at an unprecedented rate.

What is data governance?

Before we dive in, it’s important that we ground ourselves in basic definitions. During my first role in data management, we made the mistake of assuming that our stakeholders around the organization were aligned on what data we were referring to when we were discussing a particular domain of data. After several months of having difficult conversations on scope (if a particular data element, report, or system were in scope), we realized that we needed to go back and ground all stakeholders in a few very simple questions.

Data governance is the formal orchestration of people, processes, and technology by which an organization brings together the right data at the right time with the right controls to enable the company to drive efficient and effective business results. This formal orchestration should control, protect, deliver, and further enhance the value of data and create equity for an organization. Data governance is active and is delivered through capabilities, including the following:

  • Metadata management
  • Data lineage
  • Data quality
  • Data architecture
  • Mastering data
  • Data operations

We will explore these core capabilities, among other methods, in detail in subsequent chapters. The capabilities that make up a successful data governance program are defined slightly differently in just about every organization. Therefore, it is important that we define them here for the purposes of this book. Feel free to use the vocabulary in this text within your organization or the common language of your business.

Important note

Take the time to build a quick reference guide that defines the most basic terms used around your data governance program (e.g., data, governance, metadata, and so on). Make it accessible to the whole organization as a quick reference guide. Add to it as needed.

Data versus information

I want to point out that there is a passion for the use of data versus information terminology among industry veterans. Some practitioners are firm in their beliefs that these terms are not the same and should not be used interchangeably. Others use them synonymously without much thought. In my humble opinion, either can be appropriate for your organization. The important point is to distinguish between the two so that your organization understands the definitions and how to use them appropriately in your organization. Personally, I do not believe either position is correct or incorrect. It is far more important that you meet your stakeholders where they are and that your organization agrees on the alignment you choose to use. For the purpose of this book, I will use the term “data” primarily, and I will be sure to be specific about what that means.

Use case – financial services company

In my very first data governance position, we launched a robust and multi-million dollar transformation to comply with a regulatory requirement around data management and regulatory reporting. About six months into the effort, we found we were really struggling to define what was “in” vs. “out” of the scope of the program. After several curricular and passionate conversations, we learned that we were not able to scope well because, ultimately, our stakeholders had differing views about what constituted “data” vs. “metrics.” We ended up building a full-blown methodology to ground the company and our regulators on how we thought about the reports so as to be in scope, built a full list of all reports, and documented whether each one either met the criteria or did not meet the criteria, and this was to be available for a credible challenge to anyone or any group interested. Instead of debating it theoretically, we documented the criteria with specificity and then clearly articulated the justification.

What I learned in this experience was two-fold: you cannot make assumptions regarding what people know or don’t know when scoping a data program, and that you must have grounding definitions that can be socialized, agreed to, and documented so that all involved could remain grounded.

I’ll ask us to do the same throughout this book. Please come back to these definitions as needed so we can be aligned.

What data governance is not

Too often, companies have a tendency to blame problems on the data and/or the data team. Data governance (team or program) is not the solution to every problem. Data, like air, is everywhere in an organization, and it truly takes the entire organization to manage it well. Similar to the quality of air when a fire breaks out, poor data moves through an organization like smoke moves from a fire. The strong management of data requires prevention, detection, and correction, and to manage data well requires the entire company to be on board. A single data team cannot unilaterally solve every data problem. It will take the involvement and action of the organization at large to drive change and manage data effectively.

Secondly, data will never be perfect. If you or your executive team is expecting perfection from data governance, I would urge you to adjust your expectations. To ensure we align on what the appropriate expectations and objectives of a successful data governance program are, we must define success. To do that, we must start with the objective of data governance.

The objective of data governance – create business value

To put it simply, companies exist to increase value for stakeholders. When it comes to data, there is one very important objective of data to increase equity for stakeholders. Managing data effectively is one of the ways companies can increase value for their organization.

Figure 1.1 – A simple value equation

Figure 1.1 – A simple value equation

An asset is something of economic value that is owned by an organization. A liability is an obligation (either current or future) that decreases the overall value of the organization. Thus, when assets minus liabilities result in a positive value, the organization has an increase in value (i.e., has created equity), whereas when assets minus liabilities results in a negative value, the organization has a decrease in value (i.e., has reduced equity).

The same mindset can be applied to data. Data can impact equity in a number of ways. Equity can be created through addressing and minimizing operational risks by sustaining regulatory compliance, avoiding fines and penalties, and increasing or creating revenue. I break this concept down into two key subcomponents to manage data governance more specifically. These two subcomponents (assets and liabilities) are directly influenced by my formal training as an accountant and IT auditor, and this tends to resonate well with management when they translate data solutions into measurable value (ideally, monetary value, but may also consider the time value of employees).

Important note

Data is an asset when it creates value for the organization.

A few examples include:

  • Curated datasets that are used for multiple purposes
  • Customer health scoring
  • An authorized provisioning point
  • A data model used for predictive modeling

Important note

Data is a liability when it creates risk for the organization. Data can be both of these things but cannot be either (for example, a data solution may create value and reduce risk).

A few examples include:

  • Non-cataloged data
  • Data that has not been classified and, therefore, not appropriately secured
  • Data leaks/breached data

Ideally, organizations should manage the liability of data while maximizing data as a strategic asset, such that data equity is created. Depending on your business and the maturity of your data governance practices, either asset management or liability management may be a bigger priority.

Data governance should create data equity by increasing the value of data as an asset and minimizing data liabilities. I encourage you to come back to this framing as you apply the principles in this book to your own organization. As you pitch data solutions, consider this:

How is this solution increasing the value of my data (increasing the asset) and/or decreasing the liability?

Both are of value. The momentum created by delivery should translate directly to an increase in data equity over time.

An example of a data asset might be a curated dataset that is reliable because it has clear ownership, is of high quality, and can be leveraged for multiple business purposes organization-wide. An example of a data liability might be as simple as an organization not knowing what data it has, where it lives, or what to do with it. This carries a risk to the company from a security perspective, but also, the lack of accountability means that individuals may be using the data inappropriately for decisions that it is not fit for, increasing the company’s risk of making a decision that it shouldn’t be based on data that were never intended to be used for that particular purpose.

The measurement of the value of an asset is unique to each organization, but in short, being able to tie back the impact to the organization is a good guiding principle. The following are a few example questions to consider as you attempt to value the data asset:

  • Does this asset enable additional revenue? How much?
  • Does this asset save time? Can you calculate the hours saved by an hourly rate for an individual to calculate the person-hours saved?
  • Does this asset improve customer satisfaction? Can this satisfaction be translated or calculated into value for the organization in terms of additional spending or increased customer retention?
Figure 1.2 – Data assets, liabilities, and equity formula

Figure 1.2 – Data assets, liabilities, and equity formula

Data assets may provide value across these components, and value should be calculated accordingly. The most important part of this valuation exercise is not the calculation itself; rather, it is the alignment and agreement with the business. Once you have calculated the value, it is important to go to the business and ask for their feedback. Do they agree with your assessment? If yes, then you have a fully vetted value for your data asset. If not, work with the business to iterate on your data asset valuation until you reach an agreement. If you skip this important step (vetting the value), data teams often are seen to be overselling their value to the organization. This immediately undermines your credibility in the organization. Agreeing on the value of the business supports a strong business relationship and provides credibility of past success when seeking future investment into data solutions.

The measure of the liability portion of the equation is of equal importance. Like data assets, the measurement of the liability carried by an organization’s data will vary based on your organization.

Important note

It is not as simple as more data equals more liability.

Rather, the less the data is managed, the higher the liability. When data is unmanaged, the risk to the organization is higher.

A great example is security risk. When an organization does not understand where data is, it cannot effectively or adequately protect it. This comes at a high risk (liability) to the organization and could result in a data leak or, worse, a data breach. Here are a few questions to consider when calculating your organization’s data liability:

  • Do data liabilities increase the risk to the organization? How much? Are there fines or regulatory penalties we could be subjected to as a result of this liability?
  • Does liability drive inefficiencies in our business? Can you calculate the hours incurred by an hourly rate for an individual to calculate the person-hours impacted due to the inefficiency (for example, a manual process vs. an automated one)?
  • Does this liability impact customer satisfaction? Can this satisfaction be translated or calculated into a decrease in value for the organization in terms of additional spending or decreased customer attrition?

Once you have assessed your data asset value and data liability value, you can apply this to calculate data equity. The idea is to increase the equity over time. This initial calculation can serve as your baseline by which to calculate progress over time. Organizations also may like to leverage a data maturity model to measure progress; however, these models can be interpreted widely in an organization and do not take into account the business value associated with data solutions. Instead, they focus on the development of data capabilities, which do not always translate well for executive management. I prefer to focus on business value rather than an organization vs. a maturity model.

We will not dive into data monetization efforts in this book. The economics of the monetization of data is expertly described in Doug Laney’s book, Infonomics, and I would highly recommend his book to anyone looking to dive into the monetization of data further.

Left arrow icon Right arrow icon

Key benefits

  • Develop a solid foundation in data governance and increase your confidence in data solutions
  • Align data governance solutions with measurable business results and apply practical knowledge from real-world projects
  • Learn from a three-time chief data officer who has worked in leading Fortune 500 companies
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

2.5 quintillion bytes! This is the amount of data being generated every single day across the globe. As this number continues to grow, understanding and managing data becomes more complex. Data professionals know that it’s their responsibility to navigate this complexity and ensure effective governance, empowering businesses with the right data, at the right time, and with the right controls. If you are a data professional, this book will equip you with valuable guidance to conquer data governance complexities with ease. Written by a three-time chief data officer in global Fortune 500 companies, the Data Governance Handbook is an exhaustive guide to understanding data governance, its key components, and how to successfully position solutions in a way that translates into tangible business outcomes. By the end, you’ll be able to successfully pitch and gain support for your data governance program, demonstrating tangible outcomes that resonate with key stakeholders.

Who is this book for?

Chief data officers, data governance leaders, data stewards, and engineers who want to understand the business value of their work, and IT professionals seeking further understanding of data management, will find this book useful. You need a basic understanding of working with data, business needs, and how to meet those needs with data solutions. Prior coding experience or skills in selling data solutions to executives are not required.

What you will learn

  • Comprehend data governance from ideation to delivery and beyond
  • Position data governance to obtain executive buy-in
  • Launch a governance program at scale with a measurable impact
  • Understand real-world use cases to drive swift and effective action
  • Obtain support for data governance-led digital transformation
  • Launch your data governance program with confidence

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : May 31, 2024
Length: 394 pages
Edition : 1st
Language : English
ISBN-13 : 9781803240725
Category :
Languages :

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!
Product feature icon Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!
Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
Subscribe now
View plans & pricing

Product Details

Publication date : May 31, 2024
Length: 394 pages
Edition : 1st
Language : English
ISBN-13 : 9781803240725
Category :
Languages :

Packt Subscriptions

See our plans and pricing
Modal Close icon
$19.99 billed monthly
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Simple pricing, no contract
$199.99 billed annually
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just $5 each
Feature tick icon Exclusive print discounts
$279.99 billed in 18 months
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just $5 each
Feature tick icon Exclusive print discounts

Frequently bought together


Stars icon
Total $ 154.97
Data Governance Handbook
$49.99
Solutions Architect's Handbook
$59.99
Fundamentals of Analytics Engineering
$44.99
Total $ 154.97 Stars icon

Table of Contents

23 Chapters
Part 1:Designing the Path to Trusted Data Chevron down icon Chevron up icon
Chapter 1: What Is Data Governance? Chevron down icon Chevron up icon
Chapter 2: How to Build a Coalition of Advocates Chevron down icon Chevron up icon
Chapter 3: Building a High-Performing Team Chevron down icon Chevron up icon
Chapter 4: Baseline Your Organization Chevron down icon Chevron up icon
Chapter 5: Defining Success and Aligning on Outcomes Chevron down icon Chevron up icon
Part 2:Data Governance Capabilities Deep Dive Chevron down icon Chevron up icon
Chapter 6: Metadata Management Chevron down icon Chevron up icon
Chapter 7: Technical Metadata and Data Lineage Chevron down icon Chevron up icon
Chapter 8: Data Quality Chevron down icon Chevron up icon
Chapter 9: Data Architecture Chevron down icon Chevron up icon
Chapter 10: Primary Data Management Chevron down icon Chevron up icon
Chapter 11: Data Operations Chevron down icon Chevron up icon
Part 3:Building Trust through Value-Based Delivery Chevron down icon Chevron up icon
Chapter 12: Launch Powerfully Chevron down icon Chevron up icon
Chapter 13: Delivering Quick Wins with Impact Chevron down icon Chevron up icon
Chapter 14: Data Automation for Impact and More Powerful Results Chevron down icon Chevron up icon
Chapter 15: Adoption That Drives Business Success Chevron down icon Chevron up icon
Chapter 16: Delivering Trusted Results with Outcomes That Matter Chevron down icon Chevron up icon
Part 4:Case Study Chevron down icon Chevron up icon
Chapter 17: Case Study – Financial Institution Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Full star icon 5
(5 Ratings)
5 star 100%
4 star 0%
3 star 0%
2 star 0%
1 star 0%
Johnnie Sep 15, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Just finished another “data centric” book. A big part of managing data across an enterprise includes establishing an effective data governance practice. I was excited about being asked to review this book.The author identifies the book’s audience as Chief data officer (CDO), data governance leaders, data stewards, and engineers. I found it to be a good reference for anyone who works with data (especially data engineers, data scientists, database professionals and data analysts) - I’m still wondering when we will see an increase in “Data Protection” books by Cybersecurity professionals.The book opens with a practical definition for data governance - Once the reader begins to understand the significance of data governance, the book transitions to getting “buy in” for data governance policies.Data governance models are discussed (example: federated, semi-federated, and hub spoke model) - as well as data governance roles and responsibilities.How do you define a successful Data Governance program? What is Metadata, Data lineage and Data quality? What is the Relationship between data management (Data Operations) and data architecture?How do you implement automation in a data governance practice and how do you communicate data governance insights and concerns to multiple audiences (tech and business)?
Amazon Verified review Amazon
aubrey Sep 15, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a standout resource for anyone involved in data, technology, or business strategy. What really sets this book apart is how it breaks down complex governance concepts into clear, actionable steps. Wendy provides practical frameworks that show how data governance directly ties to business outcomes, making the material relevant for both experienced data professionals as well as those newer to the field.If you're looking to build trust in data and gain a solid understanding of effective governance practices, this book is an invaluable guide.
Amazon Verified review Amazon
Alberto Vicente Sep 02, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book effectively covers data modern challenges, such as stakeholders buy-in, from advocates to team structure, communication, data management, and data-driven decision-making, providing practical case studies and frameworks. Batchelder’s insights offer a strategic roadmap for enhancing data governance to support organizational growth and innovation. This guide is a must-read for anyone looking to strengthen their data governance practices.
Amazon Verified review Amazon
Eli Sep 24, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Before I start, the techniques in this book can be used to pitch almost anything to management. Having a clear, concise, and simple vision, having a readiness model, how to refine the readiness model: This can be applied to AI, Software, Hardware, etc. I think the author went above and beyond in not only going from start to finish on how to craft a data governance strategy; but also how to deal with the various challenges that come from starting one at a large organization. I used a few techniques in the book when crafting my own data governance strategy and they have worked well so far. I'd highly recommend the book to anyone that wants a high level overview of how to pitch and manage a data governance effort. Note that this is not a technical book and as such doesn't come with
Amazon Verified review Amazon
Gideon Kory Feb 07, 2025
Full star icon Full star icon Full star icon Full star icon Full star icon 5
What are the charecteristics of a great sponsor to support us in leading the Data Goveernance program? They push us to build the business case that would resonate with the executives, engage stakeholders and ask for resources to deliver the desired outcomes. It's all about building Trust in Data and it's more mportant that ever before. Not just a handbook, a true desk reference book. Read more
Amazon Verified review Amazon
Get free access to Packt library with over 7500+ books and video courses for 7 days!
Start Free Trial

FAQs

What is included in a Packt subscription? Chevron down icon Chevron up icon

A subscription provides you with full access to view all Packt and licnesed content online, this includes exclusive access to Early Access titles. Depending on the tier chosen you can also earn credits and discounts to use for owning content

How can I cancel my subscription? Chevron down icon Chevron up icon

To cancel your subscription with us simply go to the account page - found in the top right of the page or at https://subscription.packtpub.com/my-account/subscription - From here you will see the ‘cancel subscription’ button in the grey box with your subscription information in.

What are credits? Chevron down icon Chevron up icon

Credits can be earned from reading 40 section of any title within the payment cycle - a month starting from the day of subscription payment. You also earn a Credit every month if you subscribe to our annual or 18 month plans. Credits can be used to buy books DRM free, the same way that you would pay for a book. Your credits can be found in the subscription homepage - subscription.packtpub.com - clicking on ‘the my’ library dropdown and selecting ‘credits’.

What happens if an Early Access Course is cancelled? Chevron down icon Chevron up icon

Projects are rarely cancelled, but sometimes it's unavoidable. If an Early Access course is cancelled or excessively delayed, you can exchange your purchase for another course. For further details, please contact us here.

Where can I send feedback about an Early Access title? Chevron down icon Chevron up icon

If you have any feedback about the product you're reading, or Early Access in general, then please fill out a contact form here and we'll make sure the feedback gets to the right team. 

Can I download the code files for Early Access titles? Chevron down icon Chevron up icon

We try to ensure that all books in Early Access have code available to use, download, and fork on GitHub. This helps us be more agile in the development of the book, and helps keep the often changing code base of new versions and new technologies as up to date as possible. Unfortunately, however, there will be rare cases when it is not possible for us to have downloadable code samples available until publication.

When we publish the book, the code files will also be available to download from the Packt website.

How accurate is the publication date? Chevron down icon Chevron up icon

The publication date is as accurate as we can be at any point in the project. Unfortunately, delays can happen. Often those delays are out of our control, such as changes to the technology code base or delays in the tech release. We do our best to give you an accurate estimate of the publication date at any given time, and as more chapters are delivered, the more accurate the delivery date will become.

How will I know when new chapters are ready? Chevron down icon Chevron up icon

We'll let you know every time there has been an update to a course that you've bought in Early Access. You'll get an email to let you know there has been a new chapter, or a change to a previous chapter. The new chapters are automatically added to your account, so you can also check back there any time you're ready and download or read them online.

I am a Packt subscriber, do I get Early Access? Chevron down icon Chevron up icon

Yes, all Early Access content is fully available through your subscription. You will need to have a paid for or active trial subscription in order to access all titles.

How is Early Access delivered? Chevron down icon Chevron up icon

Early Access is currently only available as a PDF or through our online reader. As we make changes or add new chapters, the files in your Packt account will be updated so you can download them again or view them online immediately.

How do I buy Early Access content? Chevron down icon Chevron up icon

Early Access is a way of us getting our content to you quicker, but the method of buying the Early Access course is still the same. Just find the course you want to buy, go through the check-out steps, and you’ll get a confirmation email from us with information and a link to the relevant Early Access courses.

What is Early Access? Chevron down icon Chevron up icon

Keeping up to date with the latest technology is difficult; new versions, new frameworks, new techniques. This feature gives you a head-start to our content, as it's being created. With Early Access you'll receive each chapter as it's written, and get regular updates throughout the product's development, as well as the final course as soon as it's ready.We created Early Access as a means of giving you the information you need, as soon as it's available. As we go through the process of developing a course, 99% of it can be ready but we can't publish until that last 1% falls in to place. Early Access helps to unlock the potential of our content early, to help you start your learning when you need it most. You not only get access to every chapter as it's delivered, edited, and updated, but you'll also get the finalized, DRM-free product to download in any format you want when it's published. As a member of Packt, you'll also be eligible for our exclusive offers, including a free course every day, and discounts on new and popular titles.