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In this comprehensive blog, we’ll tell you everything you need to know about data governance so you can start making the most of your data—today.
I will throw caution to the wind and presume you know just how crucial data is right now.
If you don’t, you really should.
It’s the key driver of growth for modern businesses, but you can forget about outsmarting your competitors if you don’t manage it well.
Seriously. It’s that important.
But, don’t take my word for it—let’s take a look at the numbers. Thirty years ago, we were still in what I like to refer to as the ‘filing cabinet phase.’
Those days are gone. Since the advent of the internet, the amount of stored data has exploded. In fact, by 2013, 90% of the world’s data had been created in just two years prior.
By 2025, analysts predict that users will create 463 exabytes of data every day—that’s the same amount of information stored on 212,765,957 DVDs.
The data age is here, but what does that mean for businesses?
In a nutshell, you need to up your data governance game.
And here’s how to do it.
Don’t worry. I won’t make the next part too painful. Data governance is a lot easier to define than you might think.
I’ve done my best to keep this as straightforward as possible, so here we go:
Let’s break that down.
Data is being created and stored at lightning speeds, and with this stockpile of data comes responsibility. It’s pretty simple. If you’re responsible for any third-party data, you are obligated by law to govern it correctly.
Compliance is one of the critical drivers of data governance, but there are others.
Another significant catalyst is big data management. It’s easy to mix up data governance and data management, but the two terms are different.
Whereas governing big data refers to introducing company-wide policies and processes, management involves enacting them on a day-to-day basis.
Another important driver is customer satisfaction. Tedious link? Okay, it sounds like it, but it becomes a lot clearer when you drill a little deeper.
When you govern data efficiently, it’s much easier to share it. If a customer requests a data set—that could be anything from PII or performance data on a particular stock or asset—the quicker they can get access to it, the more satisfied they’ll likely be.
Even the fact that a business can share this information at all is a benefit.
And this translates directly into a business benefit because efficient decision-making practices lead to growth.
When an organization has access to governed data, it’s far easier to make better judgment calls. With qualified data, businesses can determine what has worked in the past, what hasn’t, and everything in between.
If you want to implement these models company-wide and manage them on an ongoing basis, you’re going to have a lot of work on your hands.
It’s necessary to implement traditional strategies over multiple systems and tools, and, by design, they focus on one primary driver: compliance. Consequently, these strategies don’t help much with data literacy, the most significant factor in widespread data use.
Traditional governance follows the DAMA framework.
DAMA International has been in the data governance game for over three decades, and they have done some incredible things during that time.
But there is a problem with their framework of governance. Not only is it prescriptive, but at times it’s intrusive too. Let’s go ahead and dissect the terminology described in the framework—piece by piece.
These master blueprints enable users to manage data integration, control data assets and align data investments with business strategies.
You require a data architecture group to:
From a governance perspective, data architects are responsible for:
Data Modeling and Design
Data modeling and design processes are directly comparable to data architecture. However, where data architecture processes provide an overview of a company’s data management requirements, data modeling and design are secondary.
Data governance duties:
Data Storage and Operation
Most organizations have various databases (SQL, No-SQL, data lakes, etc.), maintenance systems, backups, encryption protocols, and other activities.
Teams responsible for data storage and operation must:
From a data governance perspective, the following should be accessible:
Appraising stored data using fixed acceptance standards to ascertain its quality is called data auditing and validation.
Once an organization decides on its data storage methods, the challenge is to ensure regardingt remains secure. When data is stored on-prem, it’s down to dedicated IT professionals to develop security systems that prevent third-party access or alteration.
But this challenge doesn’t end with external threats. Data security protocols should also prevent unauthorized users within an organization from accessing or manipulating prohibited data sets.
Data security goals:
IT security teams use various tools and techniques like encryption, antivirus software, malware attack prevention, and more to achieve these goals.
Data Integration and Interoperability
Data engineers are usually responsible for creating and managing these data pipelines.
Data integration goals:
Data integration requirements:
Document and Content Management
Data exists in many formats. It could be a PDF, text file, JPG, or one of many other document types. Several steps must be followed. They include organizing and categorizing data, developing storage solutions, implementing workflow protocols, editing the data, publishing, and archiving.
Unstructured data requires governance, and here’s why:
Reference and Master Data
Although similar, reference and master data are two separate things.
For example, a trader may well be aware of the tickers representing each stock in the global stock market even if they don’t possess any other detailed information about the stock itself.
All of the following are the Master Data Management (MDM) activities:
Data Warehousing and Business Intelligence
Traditionally, IT teams used an Extract, Transform, and Load process (ETL) to upload and store data in a data warehouse. This way, data is moved in batches and on daily schedules.
But there’s a limit to how much data you can move at once, so in a traditional data governance model, data warehouses often require updating. This method also requires a lot of resources, including CPU, memory, and bandwidth.
BI groups can enable business acceptance by:
Key objectives include:
Metadata is intrinsically linked to data quality because the information contained within it gives data provenance. But without a system in place that automatically analyzes metadata and uses it to categorize and qualify this provenance, it’s impossible to get the most from metadata on a large scale.
Metadata management objectives:
The governance team must establish metadata standards and guidelines.
Data Governance Business Glossary
Employees in every organization will inevitably use different terms to describe the same thing. From a data governance perspective this can be very challenging.
That’s why a business glossary is so important. Using metadata, as mentioned above, it presents users with clear definitions and standardizes internal vocabulary.
Business glossary objectives:
Data quality team objectives:
Identify and champion data quality improvement practices through various process improvements
Traditionally, data governance programs were so expensive that stakeholders needed a clear justification for the investment.
The return on investment (ROI) of a traditional data governance program is pretty hard to calculate (we’ll get to calculating the ROI on modern examples later), so the maturity model was developed to better communicate the process with sponsors and stakeholders.
So what does the maturity model look like? Here’s IBM’s version:
Level 1: Initial
There is no awareness, lots of silos, and no governance program in place.
Level 2: Managed
An organization begins to realize the importance of data and how it can benefit from it. Companies start seeing data as an asset.
Level 3: Defined
Data regulation and management guidelines are better defined and more widely implemented. Integration with existing company processes has started, while regulatory rules are refined and made less ambiguous. Technology is used in a more efficient way to manage data.
Level 4: Quantitatively Managed
At this stage, all projects follow the data governance guidelines and principles, while data models are documented and made available throughout the organization. Assessable quality goals are set for each project, data process, and maintenance task.
Level 5: Optimizing
There is a reduction in the cost of data management, and data becomes easier to administer. Operations are streamlined and easier to navigate. Data governance becomes an enterprise-wide effort that improves productivity and efficiency.
As we covered (in great detail) earlier on in this blog post, traditional data governance deals with multiple departments and various functions.
Trying to align departments with data sources, not to mention one another, is a massive headache. Traditional data governance approaches don’t provide an easy way to measure the success of a data governance program. So, it’s often difficult to justify the investment.
Although incredibly complex, traditional data governance is outdated. Today, it cannot achieve the efficiency, cost-effectiveness, and simplicity of modern data governance tools.
What’s required is a centralized, value-driven platform that’s easy to implement and manage.
But what does that look like?
Today, data governance is defined by the level of value it can bring to an organization—especially when quality data is the foundation of this value.
In the early days of data governance, there was a great deal of focus on developing specific data architecture. Now, using modern data governance technologies, architecture diagrams are automatically built using raw data.
To advance data-driven decision making in an organization through trusted insights.
To ensure data compliance across various data privacy laws and internal data policies.
To improve the efficiency and productivity of IT and data teams.
It’s pretty simple. You can encourage data-driven innovation in a company and make better business decisions when data is:
But don’t get ahead of yourself. Before an organization can innovate with its data, there are a few extra steps to take first.
Data literacy is the process by which an organization puts in place measures to ensure all data users within that organization receive education that enables them to consume data confidently.
A comprehensive data literacy strategy enables companies to avoid mixed messaging and cross-department confusion.
To build a culture where users can utilize data effectively, the way custodians distribute, store, and manage the data must be transparent.
And transparency leads to trust.
But transparency doesn’t mean making all data in an organization available to everyone—that would make your data almost impossible to govern correctly!
Instead, it’s vital that a company clearly states where and what its data is, where it’s coming from, who is using it, who owns it, and whom to contact if you need access to it.
Once you have a data-literate staff, transparent data sets, and a culture of trust in that data, the next step is to make it accessible. Without access to the data they need to develop new concepts and approaches, users can’t innovate.
Ideally, all users will have equal access to the data they require—as long as no restrictions are in place to protect PII or other information. You can achieve this through smart cataloging and classification.
Self-service analysis happens when business users develop into business analysts. After giving users access to data, they can train, experiment, and innovate. Eventually, these users will transform into analysts using the data available to make better business decisions.
The more methods you put in place to streamline the data governance process, and track KPIs, the better the data’s quality becomes.
Organizations need to make a concerted effort to improve their data quality to get the best from it. Once a data-literate staff can access and analyze this data, they can determine the specific KPIs required to track its performance.
Compliance is a driving force behind data governance practices today. There are three key areas to consider if you wish to address compliance issues in modern data governance.
Standardizing data is a crucial step to ensuring compliance. When you standardize data, it is easier to track and compare.
Once standardized, data is easier to identify, enabling organizations to classify and tag it. Understanding data is vital if you want to ensure compliance.
Data lineage refers to the lifecycle of data—where it comes from and where it’s been.
From a compliance perspective, documenting data lineage enables organizations to achieve many objectives, including more efficient regulatory reporting, improved data governance through access to historical data, and the ability to expose any discrepancies or potential security threats.
Chief Information Officers (CIO) and Chief Data Officers (CDO) are expected to do more with fewer resources. There is a powerful drive to transform existing data models into modern systems, but several fundamental processes are required to achieve this.
Data discovery processes ensure that an organization’s data is easy to find, access, and understand regardless of where it is stored. The best way to achieve this is through a data discovery platform, but how do provisions like these improve efficiency?
Generally, data is stored in multiple locations with countless different admittance measures in place in an organization. With data discovery systems in operation, data is easy to locate because it is searchable.
This process slashes the time it takes to find and understand a particular data set. And, when data is discoverable, it can be collaborated on. Data and IT teams can work together to use the data available to them to develop data-driven growth strategies.
Impact analysis, in this context, concerns the processes IT and data teams undertake to determine the impact of data management decisions downstream.
Impact analysis enables these teams to work more efficiently before rolling out a significant data management protocol because they can systematically weigh up the pros and cons of any imminent decision.
The first step of impact analysis is to do a business assessment. Using smart tools, both data and IT teams can quickly assess how introducing specific changes will impact profits, workflow, and more.
Metadata provides context to information enabling users to work with it more effectively.
You must fully understand the data you are using to get the most out of it. That’s why in modern data governance, one of the most important drivers of efficient data analysis is managing this metadata.
When managed correctly, metadata makes every aspect of modern data governance more effective. It provides accurate information to calculate impact analysis.
Now you know what a modern data governance model is, it’s a good time to talk about who uses it.
At the top-end of the scale, the most cutting-edge modern data governance programs allow for progressive implementation, enabling users to develop data governance programs at their own pace.
The ROI of a data governance program is value-driven, always use-case specific, and not intrinsically tied up with tangible profits—at least not in every circumstance. So, to calculate the ROI, you have to look at the governance program as a whole.
It’s straightforward to calculate the ROI in regards to improved efficiency because you’ll quickly learn how much time you’re saving your data teams, and, of course—time is money.
Self-service has a significant impact on ROI. When users gain access to platforms that make it easy for them to find and use data independently, the economic impact can be huge.
One report by Forrester included analysis from seven companies that had used a modern data governance tool. Over three years, it found that:
It regarding is challenging to calculate an exact ROI from a compliance perspective, but it’s easy to work out the savings you could make by not falling foul of regulatory guidelines.
There are lots of data protection laws but let’s look at the ones with the biggest fines attached:
And these aren’t just empty threats. The worst rule-breakers of the EU’s GDPR got hit with the following penalties:
The most challenging ROI to calculate surrounds data-driven innovation because it is both company-specific and slow to mature.
However, you can split this ROI into two—benefits to business leaders and business users.
With business users, even when there is a trusted data delivery platform in place and all the data required to innovate is at a user’s fingertips, it’s difficult to predict when and how innovation will happen.
Over time, teams will build more use cases with the technology available to them. Eventually, there will come a pivot point where this new use case, say a recommendation engine, for example, is rolled out.
Even then, you need to have people use the technology first to find out how popular it is and what the ROI will be.
The first step in your data governance journey is finding the best governance tool for the job.
You’ll need to get a little introspective and figure out what you want to get out of your data governance program. Find out what you need and go with a tool that meets these expectations.
The winning tool should support most, if not all of your data sources and enable you to realize your key goals—within budget!
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