Data Management

Why Companies Need An End To End Data Governance Platform

Data is everywhere; I’m sure you’ve heard it all before. It’s ubiquitous in modern business. There’s rarely a team or employee that doesn’t use some form of data in their daily tasks. So why then are companies so bad at using it to its full potential?

We’ve been working with data for a long time. It’s painful to think of the vast amounts of underutilized data that should have been repurposed, shared, and put in the hands of the best person when they needed it most.

We believed that there had to be a better way. There should be a smarter option, one that enables data management at the point of collection. It was time to leave behind the messy dark days of data management and onto a more productive way of operating.

We kept coming back to one simple fact: businesses understand the need for a CRM to manage every aspect of your sales process. So why isn’t there a tool that could do the same for data?


Let’s take it back to the start (of the data journey)

It turns out it wasn’t just us who had these problems. When we looked a little closer, we noticed that many companies were having issues in some similar areas. We decided to ask for the top concerns that companies have with data, and here’s what they said:

  • We spend a lot of time maintaining multiple data sources, which adds external development cost without adding too much value.
  • Often we have multiple sources of the same data, which adds complexity when using data.
  • Not having access to data because of a lack of extraction tools.
  • Not having a mechanism to allow co-workers to get access to data in a single and consistent way.
  • We lack a clear overview of data ingestion to monitor business performance.

Many of these companies understood the difficulties they were having, but a lack of foresight in the collection and planning stages led to headaches further down the line. This fact led us closer to the source of the problem:

Companies are doing governance too late, and some aren’t even doing it at all. Missing this (often one chance) for governance is costing companies endless resources at a later stage.


Why we built an all in one governance tool

We realized that data requires the right tools at the beginning of the integration process. Starting governance at the start of the data lifecycle opens up incredible opportunities further down the line.

Companies can be more productive with data, improve compliance, and eliminate headaches when it comes to unifying data across an organization.

So we decided to try to build the CRM for data. And we came up with Wult.

Wult understands governance from the very start of the data journey. Governance is often an afterthought, but using Wult makes governance ubiquitous and straightforward by design.

What do you do when there’s a new data source added to your business? Who is vetting the source, who decides where this data goes and how it’s eventually used? Is there an auditable trail of these processes?

Wult makes new data exciting rather than daunting by establishing rules when data is collected. This isn’t just about excellent plumbing. It requires focus on the rules that govern a company’s data and establishing these at the start of the process.

This way of thinking alleviates headaches further along the line. No more scrambling for proof of compliance once it’s too late. No more messy datasets that are rendered unusable. No more duplicates and a lack of overview. Wult makes it simple to unify data and distribute it to the right teams.


Towards holistic data management

As we focused on universal governance, we also began to notice some hidden benefits emerge. Channeling our data through a governance tool provides a holistic view of data and its use within an organization.

Data is more powerful when combined with other data, access is simple, and schema matching means that there are less formatting issues for companies to face.

All of these processes are dealt with in the collection process, providing a streamlined and efficient way of working with data.

We think that Wult is going to change the way that teams use data within an organization. It will save countless resources and ensure that teams are on the same page from the beginning of the data process.

Transform your outdated data strategy today and make data usage inspiring instead of painful.

Data Management

What Is Data Mapping – How To Do Data Mapping + Examples

The average company is now dealing with large amounts of complicated data systems. With siloed data in many places, linking and managing this data into a manageable centralized database is a priority for many businesses.

The amount of data sources that the average company is using is rapidly increasing. Data comes in many different forms and types, and it can be extremely complicated to ensure that data is structured universally.

That’s where companies are increasingly looking at data mapping. To take control of their internal and external data and find a solution that can organize, structure, and create a unified central data location.


What is data mapping?

Data mapping is the process of matching fields from multiple datasets into a schema, or centralized database. Data mapping is required to migrate data, ingest, and process data and manage data. Ultimately the goal of data mapping is to homogenize multiple data sets into a single one.

Data mapping means that different data sets, with varying ways of defining similar points, can be combined in a way that makes it accurate and usable at the end destination.

Data mapping is a standard business practice. However, as the amounts of data and the complexity of systems that use the data has increased, the process of data mapping has become more complicated and requires automated and powerful tools.


An example of data mapping

To help to understand what data mapping is and how it works, we are going to look at an example of multiple databases where data mapping is helpful. The data we are looking at is related to footballers, and the information is organized into columns and fields and has a different way of organizing the data

(click to enlarge).

Each of these databases has similar and different entries. For example, all of them have an id. The payers and managers have a wage entry, and teams are the only ones that have a field for stadium.

Merging all of these databases into a single entry means that you can query a single database to retrieve information on each. For businesses, this is invaluable as it provides a holistic view of the companies data assets.

Bring databases together requires a map of the fields that clarify and match fields that should intersect. It sets rules on how to hand data from each input, what type it is, and what should happen in the case of duplicates, or other issues.

Here’s our example again, but in with our map connecting the correct fields to produce a single database.

In this example, we have added some smart conversions as are possible in the Wult platform. We have set the currency on the output wage field to convert values from different currencies. We have an inferred field – the platform automatically finds the league and uses this to create a new field with the value. Along with this, a country field is added.

To summarize, data mapping is a set of instructions that allow for multiple datasets to be combined, or allow for a dataset to be integrated into another. This example is more simple, but the process can become exceedingly complicated based on the following factors:

  • The number of datasets that are being combined
  • The amount of data
  • The frequency that the data should be mapped
  • The number of schemas that are involved in the mapping process
  • The hierarchy of the data being combined


Why is data mapping essential?

Data mapping is essential for any company that processes data. It’s mainly used to integrate data, build data warehouses, transform data, or migrate data from one place to another. The process of matching data to a schema is a fundamental part of the flow of data through any organization.

Data mapping is the key to good data management. Unmapped or poorly mapped data will cause issues as data flows to different endpoints within an organization. Mapping is the first step to getting the most out of your data when it reaches integrations, transformations, and when it is stored for future use.

An organization that uses data makes use of data mapping at three main stages of the data flow. These are data integration and data transformation. Let’s take a brief look at data mapping in each of those contexts.


Data integration

Integrating data into a workflow or a data warehouse requires data mapping. In many situations, the data that is being integrated will be in a different form to the data that is being stored in the warehouse (or elsewhere in the workflow).

For a data warehouse, the primary mapping process involves identifying the incoming data, and it’s attributed and matching this to the warehouse schema. Specifically, the process will include looking for areas where the datasets overlap and defining the rules that will govern the mapping process. For example, if both databases have similar information, which one should be used.

Solutions like Wult make ingesting data simple and pain-free in these situations. With unlimited integration sources, you can build a centralized data warehouse that is accurately mapped, clean, and usable from minute one.


Data transformation

Data transformation is all about taking data in a specific format and converting it into a different format or structure. This step can be a crucial stage to prepare information that is ready to ingest into a warehouse or integrate into an application.

Data mapping is vital in this process as it is used to define the connections between data and helps to determine the relationship between datasets.


How to do data mapping effectively

Getting started with data mapping can be a daunting task. However, implementing a robust solution early on in the data lifecycle can save you vast amounts of time in the future and ensure that your data is robust and reliable.

These steps will help you to understand what you need to do before, during, and after initiating your data mapping solution.

Define the data that will be moving. This means that you should look at the tables, fields, and the format of these. Think about the frequency that data will need to be mapped.

Map the data. This stage requires you to map fields in the source data to fields at the destination.

Define any transformation that you’ll need. For example, this could be rules or governance procedures that deal with clashes in data or duplicates.

Test the mapping process. Start with a small amount of data and test to see if the data mapping works as expected.

Once you are happy that everything is working correctly, you can start your workflow or deploy your mapping system. If you are using a platform such as Wult, you can see in real-time where errors occur and attain full visibility at before and after points.

Maintain and update the mapping process. This will require input as new data sources are added with new fields.


Data mapping techniques

So you have been through the process, and you know what you need to do. But how do you select the right tool for data mapping? What options are there, and what techniques can you use to build a robust data mapping solution?


Manual data mapping

This is the first solution to create a data mapping tool for your business. This requires developers to code the connections that match the source data to the final database. For one-off injections of data or custom data types, this could be a viable solution.

However, the scale of most datasets and the speed needed to adapt to how these change in today’s data landscape mean that a manual process can struggle to deal with complicated mapping processes. In these cases, businesses will need to move to an automated solution.


Fully automated mapping

Fully automated data mapping tools allow businesses to seamlessly add new data and match it to their current schemas. Most tools make this p[process available in a UI so that users can visualize and understand the stages that data flows through and map fields at each stage.

Some allow inputs from thousands of different sources, and the mapping process lets users bring data in an agnostic way to their databases and solutions.

The benefits of a fully automated solution are that it provides an interface that means nontechnical employees can monitor and set up data mapping. As well as this, users can check and visualize how their data is being mapped, identify errors quickly, and improve the process simply.

Data Management

What Is Data Quality – Definition + Why It’s Important

Data is big business. It is used across numerous industries, and everyone is talking about the competitive edge that they can get from data.

But as data has grown to become one of the world’s most valuable commodities, data quality has started to dominate the conversation. The opportunity is clear – but to get the best results, companies need to be able to trust the quality of their data.

The simple fact is that many companies are unaware of what useful data looks like. Managing data, filtering, and improving datasets can have a significant impact on results. That’s why we’ve written this post – to help understand what you can do to source better data, and improve on the data already in your infrastructure.


What is data quality?

So what is data quality? Well, there are usually many factors that contribute to a dataset being better than another. These individual factors may be more or less important for each company, depending on the use case.

However, data quality can usually be divided into smaller categories that are as follows:



One of the most important factors to consider with your data, but what does it mean? Accuracy is the similarity between the data and the actual real-world situation it is related to.

Having data that doesn’t represent the real-world conditions presents numerous problems. It can cause incorrect conclusions and can create real issues further along the line.

An example can be seen in location data when a data point is falsely attributed to a store. The data could be inaccurate as the real-world device might not have gone inside the location. The data suggests that the person has, and this can create issues for somebody targeting devices that are aware of a product inside the location, for example.



This refers to how consistent the amount of data is from entry to entry. Having incomplete data means that some of the fields are missing. This means that the dataset as a whole is not as valuable as there may be some insights that can’t be reached due to missing information.

For example, let’s say that you are collecting data from a form or survey. If there is an option to skip the interests field, then your data set will likely have missing information in this field. In the future, somebody may wish to segment this list based on interests. Doing so would probably remove some people that might have the same interests but didn’t submit them in the data collection process.



This term is used to describe the delay between the real-world event and the data. This should be as close as the real-world event as possible as data becomes less effective the further away it is from the real-world event.

As the world changes, delays in data reporting can have huge effects and can drastically limit the effectiveness of data. For example, investors using data to gauge stock performance can get a significant competitive edge if their data is more timely than the competition.



Making sure that the data you use is relevant to the purpose is essential. Clearly defining goals and the types of data needed to achieve these is a crucial part of the data collection process.

Relevancy makes sure that your data is as lean as possible. By including only relevant information, you make it easier to ingest, filter, and manipulate data most efficiently.



When comparing data, the structure must be consistent, so that you can accurately make comparisons and identify differences and trends between two data points.

This extends out to different departments and people that are using the data. If the same dataset is presented differently or formatted differently, then this can cause huge issues. For example, when measuring KPIs, if the underlying dataset is different, then two entities could have completely different ideas of what’s going on.


Why is quality data relevant?


As new regulations relating to data come into play, compliance becomes more of an issue for companies and their data.

Ensuring that data is appropriately collected and managed in a way that complies with internal and external regulations is now a crucial part of any data business. Data quality is a fundamental part of this issue as bad or disorganized data makes it more difficult to prove compliance.



Ultimately useful data is invaluable for companies as it leads to better outcomes and helps them to reach their goals across many departments and areas. Data quality allows each department to make better decisions and achieve their goals.

Poor data can have an opposing effect; it can lead to drastically wrong decisions. That’s why it’s crucial to be able to manage and control data quality from collection through to use.


Benefits of high-quality datasets

Better insights and ability to plan effectively

The more high-quality data that you have, the better your insights are going to be. This allows you to make better decisions, understand what will happen in the future, and plan effectively.


A bigger competitive advantage

This one kind of goes without saying. If you have better data than your competitors, then you are in a much better position. This competitive advantage allows you to act quicker, with more insight, and get better results.


Less time spent fixing problems after ingestion

Better data doesn’t just get you better results; it saves you time and resources. Having consistently high-quality data flowing into your organization makes it easier to generate insights and makes it easier to deliver these to the right people in the organization. It also makes it easier to map data.

Contrast this with bad data, which can require vast amounts of time, adding structure and reformatting into an acceptable state.


Better segmentation, targeting, and attribution

For marketers and advertisers, higher quality data means improved segmentation and better targeting. Collecting quality audience data allows markets to build detailed profiles and match behavior to conversions across the customer journey.


Improved customer experience

Quality data insights can help to build new products and improve how users use existing tools and services. You can be alerted to areas of customer pain and identify an example of why and how customers are dropping off from your funnel.


Improved commercial results

Better data will have a positive effect on commercial outcomes. It will help to reduce waste and ensure that your marketing campaigns are of the highest quality.


Collecting high-quality data

Data quality issues can occur during the collection process and cause huge problems at a later time. It’s vital to get the data collection stage right as doing so is one of the quickest ways to bring in structured, quality data to your organization.

Generally, issues arise because of a lack of tools or structure. Having the right data governance policies is another area where companies should focus to collect high-quality data.


Make a plan

A lack of a plan is going to seriously inhibit the results that you get from data. Plan for how the data is going to be collected, the tools that you’ll need and how to ingest the data in the best way. This should extend to the roles and people that have specific roles in the process.


Define standards

Make sure that your organization has a communally available and agreed-upon definition of what quality data looks like. Everyone should buy into these standards to ensure that your collected data is high quality.

Data Management

What Is Data Governance And Why Is It Important


You’ve already heard how important data is to your business, and you’re aware of how data can help you to grow and reach your goals. But understanding the value of data is just the first step to utilizing it well.

All organizations should prepare for how to use these vast amounts of data that are available to them. This preparation means coming up with a robust plan that ensures data is collected correctly and used properly through the business.

To build this, you will need to look more closely at data governance as well as the why, how, what, who, and where of your business’ data.

Most businesses need data governance, but many don’t realize the benefits that an effective strategy can bring.

That’s why we have written this guide. For every excellent data use case, from AI to machine learning, there should exist a robust data governance strategy. We want to help you understand how data governance can affect data quality and privacy, as well as your business goals.


What is data governance?

Data governance is a set of rules and principles that ensure data quality throughout the entire lifecycle of your data.

Data governance helps to make things more transparent when it comes to data. Without it it’s difficult for people within a business to remain on the same page. It’s harder to understand where data comes from and get the most value from data.

This is much more than a simple rule for your business – data governance is a comprehensive system that can securely control data collection, usage, and understand the quality of different data types against others.

That’s why there are many tools for businesses to add data governance to their business operations.


Why do you need data governance?

Data is quickly becoming the most valuable asset for businesses across all industries. Because of this, there is a lot of data available, and it’s not easy to understand how useful this data is.

Some business models require the purchase of data; others will collect their own first-party data. Either way, many businesses have no way of telling the quality of this data, if it is as accurate as they expect it to be. This can become a huge problem at a later date.

As well as this data collection can be a murky process. Many businesses don’t have the correct data collection procedures in place and fail to understand the laws around data collection, management, and processing in the regions where they operate.

But why do you need data governance?

Here’s a full list of all the areas where a sound data governance solution can have a positive impact:

  • Management – data governance can help to oversee the use of all data assets. It can illustrate their value for different areas of a business and help to understand where optimization can occur.
  • Finance – for finance departments, data governance is all about the protection of sensitive data and systems while ensuring consistency.
  • Sales – for the sales and marketing departments, the use of data is an integral part of the process. Data governance is required to ensure that data is being collected and used correctly and to identify areas of low data quality and effectiveness.
  • Planning – data governance can ensure effectiveness across the supply chain and help to reduce operational costs.
  • Production – the use of data in automated systems requires a high level of data governance.
  • Legal – businesses have more data privacy regulations to comply with, so having a robust data governance system will ensure that all data being used is collected and managed correctly.


Benefits and goals of data governance

What does this mean for my business? What exactly are the benefits of implementing a robust data governance strategy?

With bad data, it’s impossible to make the right decisions at the right time. Collecting the data isn’t enough by itself – you need data governance to bring it all together.

This unlocks your business to use data more effectively. Here are some possible use cases for data governance.

  • Consistently make confident decisions based on reliable and relevant data for the specific purpose and end-user within your business.
  • Comply with data protection legislation and other regulatory requirements by documenting the permissions and usage of data from collection through to use.
  • Built a robust data security system in which data ownership and responsibilities are clear for all involved.
  • Utilize data to improve profits. This can be simply monetizing the data but can be used to drive revenue in other, less direct ways.
  • Build a robust data distribution solution between internal and external data processors.
  • Ensure that data is in the right format and as clean as possible before use, to save time performing these tasks at the point of use.
  • Build a standardized data structure to ensure the same data is suitable for different tasks in different areas of your organization.

There are many more benefits to implementing a data governance strategy for your business.


Data governance components

Now that organizations have the opportunity to capture massive amounts of diverse internal and external data, they need the discipline to maximize that data’s value, manage its risks, and reduce the cost of its management.

Data governance is made up of three core concepts: management, quality, and privacy:


Data management

The first pillar of data governance is the management of data that flows through your organization.

There needs to be the ability to control data from a centralized location and to standardize how data is used in different areas and for different uses.

This may also involve creating a central database along with a master dataset. A key motivator for data management is to ensure that the right people can access the best data when they need it, in a form that is best suited to provide the best insights and yield the best results.


Data privacy

The rise of data privacy regulations, such as the GDPR in Europe and the CCPA in California, has had a significant effect on data governance.

Different data types and purposes come with new challenges as organizations attempt to manage their privacy solutions while making sure they can get the most from the data that goes into their operations.

In this context, data governance is about having full control over the collection, management, and use of data. It requires a system that can understand who can use use the data, how it can be used, and can track this process at it happens.


Data quality

Often, companies have no way of validating or checking the accuracy of the data that they use. Building data quality controls can have a hugely positive impact on businesses, from building better insights to paying only for data that meets the required standards.

Data governance can provide organizations with a powerful system that can help identify inaccurate or inadequate data before it enters their systems. This means that end data uses are based upon better data that has been verified and can be trusted to provide the required insights.


Data governance frameworks

So how do you implement all of this into your business strategy? It requires a data governance framework.

A data governance framework is a number of rules, role delegations, and processes that aim to ensure that everyone using data within an organization is on the same page.


Step one – The mission

The first step of data governance is to ask why. This should be a mission – why are you trying to implement data governance into your organization?

This should explain why data governance is essential. It should be related to business goals, and the relevant stakeholders in the business should endorse it.


Our mission is to build a data governance solution to manage and control the collection of data from our media properties. This will mean that we can monetize our data more effectively and build trust with our unpaid subscribers.


Step two – Choose the areas to focus on

This should include the long and short terms goals of the data governance strategy. You should also define the key metrics that you will use to measure the effectiveness of your data governance program.

As well as looking at the measurement of this, you should think about the funding and resources that are required to implement data governance.


Our long term goal is to ensure that data collected through our media inventory is compliant with data protection regulations. As well as this, our shorter-term goals are to build a filter for inaccurate data and ensure that we make consumer opt-out functionality.

We will measure this by looking at the amount of data that is collectible under data privacy regulations and by looking at the quality of data that is deemed suitable for use (passes our inaccuracy filters).


Step three – How are you going to do this?

The next step is to define the rules and definitions that will form your data governance strategy. Specifically, you will need to look at the following:

  • Data policies – who is responsible for what in the daily management of data. What’s the escalation process, and how is this managed?
  • Data standards – what does useful data look like? How can you make sure that that it’s simple for everyone within an organization to understand this?
  • Data definitions – are there any key terms that need defining to ensure that your data governance strategy works for everyone?
  • Data controls – these are the processes that you will put into action to measure the adherence to the data rules and standards, as well as how your governance strategy is helping to achieve your defined goals.

After this, you can think about the tools that you will need to bring this into operation. This varies according to the needs and the organization itself. For more information and a more detailed look at bringing your framework to life, head to the next section – how to implement data governance.


Step four – Make sure you have the right people for data governance

This should be a two-pronged approach:

Stakeholder engagement

For a data governance strategy to be effective, you need to engage with the key stakeholders as well as the data controllers and data users.

Governance team

For larger organizations, this may be a team, or in smaller cases, it may be a single person. This team should be able to support the activities and governance across different areas of the business. They should also be able to engage with the key stakeholders and be able to process suggestions and manage support.


Step five – Standardise and define where your framework should be applied

The final step is to standardize this framework in the way that it applies to your business. It must be repeatable and easy to implement for every employee who uses data within an organization.

Your framework should be easy to enable but also functional enough to support every employee’s data needs.


How to bring data governance framework to your business

So you have designed a framework, but how can you add this to your company?

There are tools that aim to take control of how data is used in your business. Some of these are extremely expensive and overly complicated.

We built a tool that aims to make data governance accessible – Wult.

Wult takes control of your data tasks. But it does more than this. Wult brings built-in data governance solutions alongside these tasks.

So, for example, Wult doesn’t just help you to find the data you need, clean it, and integrate it into your business’ workflows. It also adds a layer of data governance so that you can understand privacy requirements, create roles, and uses and ensure that your companies procedures are followed throughout the process.


What stage of data governance am I at?

Identifying the stage of data governance your organization is at is a significant step to understand your business needs.

Measuring your organization up against a data governance maturity model can be a handy element in making the roadmap and communicating the as-is and to-be part of the data governance initiative and the context for deploying a data governance framework.

You can use this resource as a guide to understand exactly where your business is and how you can best incorporate a robust data governance strategy based on this.


Best practices for data governance

Every organization is different when it comes to data governance. However, we have compiled some essential tips that will apply to some.

  • Keep it simple – always take a step back and ask yourself if you are overcomplicating things. Data governance needs to meet your goals. To do this, you may need a simpler solution, or it might be easier to change to a data solution that has governance baked in.
  • When it comes to your data governance goals, you need to choose clear and measurable targets. Make sure you celebrate when you meet these and understand why it is when you don’t.
  • Clearly define the roles and responsibilities of everyone who is involved with the data governance strategy. Extend this to everyone within the organization who uses data at any point.
  • Internal documentation and definitions of critical data terms are hugely important for data governance. Try and bake this into your employee handbook and make sure it’s easy to access and makes sense.
  • Build a business case for data governance. Sometimes it can be easy to lose track of why you need a data governance solution in the first place. Linking this to clear business goals is a great way to keep data governance relevant in everything that you do as a business.
  • Educate key stakeholders and get them to buy into your data governance project.

This should be more than enough to get started with your data governance strategy. Remember, you can use Wult to build powerful, governance ready, data solutions for your business.

What is data governance?

Data governance is a set of rules and principles that ensure data quality throughout the entire lifecycle of your data.

Why do companies need data governance?

It's important for businesses to understand the laws around data collection, management, and processing in the regions where they operate.

What are the main benefits of data governance?

There are many, from unified access, better use of data, saved time and money, and more powerful uses for data within an organization.

What's a data governance framework?

A data governance framework is a number of rules, role delegations, and processes that aim to ensure that everyone using data within an organization is on the same page.