3 Things Your Company Can Do With Metadata

Metadata is data that describes data, and it’s used in a variety of digital processes involving analytics. Large companies often use automated metadata management systems to keep track of information across huge databases, but even if you don’t use automated metadata management, metadata is still important for business.

If you’re unsure of how metadata applies to you, below are three things your company can do with it:

1. Segmenting Customers

When organizing customer data, segmenting customers into groups is often a good idea. This allows your marketing team to get a better idea of which groups of customers are interested in which products. With this information, your business can target different customers with personalized marketing and advertising to increase sales.

To do this, you will need to collect metadata and enter it alongside customer profiles after a sale. Always make sure to get consent before collecting and using customer data.

2. Organize Databases More Efficiently

As mentioned above, many large companies use metadata to organize large volumes of information. Databases containing product information, customer records and more can quickly become an unorganized mess when working with hundreds of thousands of pieces of data, and unorganized data can lead to a loss in productivity.

By using metadata to tag different pieces of information, you can then organize and classify this information for faster recall in the future. This can also be helpful if you plan to use contextual search functionality in a database.

3. Improve Data Reporting Accuracy

Metadata can also potentially improve accuracy in data reporting. When dealing with a large amount of unorganized data, you may end up missing something during an analysis. If you don’t have your data tagged and cataloged, it can be difficult to conduct an audit without starting your analysis all over again from the beginning.

By tagging data with metadata, you can compare the results of your analysis against your metadata. If the numbers match, you can have confidence in your analysis. If they don’t match, you can quickly determine what was missed to conduct a faster audit.

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How Does AI Work When It Comes To Data Catalogs?

Data catalogs play an integral role in managing and governing data. They're crucial to the success of data-driven organizations, keeping massive datasets well-organized and accessible. But old-school catalogs have many limitations.

They require manual cataloging, eating up valuable time and resources. Plus, they can struggle to keep up with changes in data sources, resulting in inaccuracies and poor data reliability. Pair that with a lack of support for unstructured data and effective lineage tracking; traditional catalogs can be more frustrating than anything else.

Real-Time Big Data Analytics

Businesses today rely on real-time big data analytics to handle the vast and complex clusters of datasets. Here’s the state of big data today read more

3 Benefits of Data Sharing

Sharing data among team members and partners can make a huge difference in how efficiently your company can act on data insights. Although data sharing can cause trouble if it isn’t handled properly, the benefits of streamlined data sharing outweigh the risks in most cases.

If you’re considering creating or re-evaluating a data sharing plan, below are three benefits of data sharing to keep in mind:

1. New Perspectives

One of the biggest benefits of data sharing is that you have the opportunity to gain new perspectives. Even the most talented data team in the world can become complacent over time when dealing with the same types of data over and over. Using a platform for data sharing allows you to bring fresh perspectives to data that your company may not have received when relying on the same analysts over and over. In almost all circumstances of data analysis, getting multiple sets of eyes on a problem is a good thing.

2. You Ensure Credit Attribution

While dealing with large datasets, it can be easy for attribution to get lost over time. When data comes from different sources and is analyzed by different people, you want to provide credit where it’s due. This can be tough when your team is the only source representing the data your company works with, but through the use of a platform for data sharing, all the moving pieces in a project can receive proper attribution.

3. Data Becomes Reusable

One of the more important benefits of data sharing is that it allows data to become reusable. When your team is finished analyzing and making use of data, it can be passed along to partners to help them.

This breeds stronger partnerships and can also be used as a source of attribution back to your company. In the age of generative search where entity SEO is more important than ever, having citations and attributions spread across the web is important.

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What is Data Profiling?

Profiling is a crucial part of data preparation programs. It's the process of examining and analyzing data sets to gain more insights into their quality. Having mountains of data is the norm in modern business. But accuracy can make or break what you do with it.

Profiling helps you learn more about how accurate and accessible your data is while giving your teams more knowledge about its structure, content, interrelationships and more. This process can also unveil potential data projects, highlighting ways you can use your data assets to boost the bottom line.

Types of Data Profiling

There are three primary forms of profiling.

The first is structure discovery. This process is about formatting data to ensure everything is uniform and consistent. Statistical analysis can give you more insight into your data's validity.

The second type of profiling is content discovery. With the content discovery, the goal is to determine the quality of the data. It helps identify anything incomplete, ambiguous or otherwise null.

Finally, we have relationship discovery. As the name implies, it's about determining how data sources connect. The process highlights similarities, differences and associations.

Why Profiling is Necessary

There are many benefits to profiling data. Ultimately, the biggest reason to include it in your data preparation program is to ensure you work with credible, high-quality data. Errors and inconsistencies will only set your organization back. They can misguide your strategy and force you to make decisions that don't provide the desired results.

Another benefit is that it helps with predictive analysis and core decision-making. When you profile data, you're learning more about the assets you hold. You can use this process to make predictions about sales, revenue, etc. That information can guide you in the right direction, making critical decisions that help generate growth and success.

Organizations also use profiling to spot potential issues within their data stream. For example, the content discovery phase highlights errors and inconsistencies. Chronic problems may point to a glaring issue within your system, helping you spot quality data issues at their source.

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What is a Data Onboarding Process?

Gathering data about your customers is a critical part of the marketing puzzle. You need to understand who they are to perfect the customer experience while obtaining knowledge about their unique needs. If you operate entirely online, gathering that data is relatively easy. But what if you have to transfer offline data to an online environment?

For example, you may interact with prospects at events or during an in-store sales process. The information you gather in offline environments stays separate from what you collect online, creating potential headaches in managing your customer relationship management strategies.

Data onboarding is about connecting offline records with online users, painting a complete picture of a customer's journey with your company.

Why Data Onboarding Processes are Important

The goal of data onboarding is to consolidate data about your customers regardless of where they came from or what stage in the buyer's journey they enter your online environment. For example, say that a prospect learns about your company in person. They might buy a product, creating the first customer records.

In the future, they might want to continue supporting your business through online purchases. Onboarding ensures that your marketing and sales teams are up-to-date on that customer's history. It allows your teams to provide the most impactful marketing materials that cater to every customer's needs.

How Data Onboarding Software Improves Efficiency

Traditionally, reconciling offline and online data is an arduous process. Basic offline data includes customer names, email addresses, contact information, etc. Meanwhile, online data refers to digital IDs, transaction data, device information, etc.

The onboarding process requires you to match that information to create a complete profile for every customer. Usually, that would involve manual data inputs and reconciliation. Not only is that resource intensive, but it also leads to quality and governance issues.

Data onboarding software improves efficiency across the board. Teams can quickly create, edit and manage data from a single platform. The software becomes a single source of truth for all teams accessing the data while improving accessibility to all stakeholders. Features like automation can make quick work of matching and anonymization, optimizing how your organization uses customer data.

Read a similar article about enterprise metadata management here at this page.

What is a Data Governance Manager?

Modern companies collect and manage mountains of valuable data. Data is an asset that helps businesses remain competitive and constantly push for improvement. Some of that data might contain sensitive information. Organizations can suffer in many ways if it gets into the wrong hands. Not only is there a risk of theft, but losing private information about customers could lead to hefty fines, lost consumer trust and more.

Modern businesses must have set processes and policies to ensure that all data stays safe. That's where a data governance manager comes in.

Understanding the Role of a Data Governance Manager

Simply put, data governance is about setting internal standards and policies that dictate how companies handle data. It covers how it's stored, collected, processed, utilized, etc.

A governance manager is a person responsible for overseeing those policies. They may use a data governance tool to develop internal best practices, enabling secure access.

Think of a governance manager as an overseer. They're the point of escalation when quality and security issues arise, and they typically act as the go-between for various teams. They work with business leaders and data teams to develop policies that benefit the organization. A governance manager's goal is to improve and protect the value of data assets and ensure regulatory compliance.

What Does a Data Governance Manager Do?

This job is complex, but the main focus is to develop a company-wide governance framework. The goal is to establish accountability and set standard protocols that everyone in the organization will follow when using data. They work to mitigate risks, control access and ensure compliance across the board.

Data governance tools can simplify things and streamline many processes. For example, governance managers may establish permissions and accountability processes to record any changes made to data. In that case, tools can automate permissions and implement the necessary checks to ensure that individuals only view information relevant to their position.

A good data governance tool can also take advantage of encryption technology to add another layer of protection against hacks and data breaches.

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