Data Compliance

Data Compliance – What Is It & How To Get It Right

Whenever you interact with a customer, user, or employee, there is a high risk of data being used or exchanged. In today’s world, it’s nearly impossible to get by without access to some data protected by some legislation.

It seems that data breaches and privacy issues are happening on a more common basis. From Equifax to Facebook, companies face stricter rules when processing, managing and using data in their day to day business.

Your company can’t afford to ignore data compliance. Today it’s more important than ever to get it right. This post will help you understand what data compliance is, how it works, and how you can protect your business.


What do we mean by data compliance?

Data compliance is the process of following regulations that stipulate how it should manage the organization’s digital assets.

These regulations can be different based on geography, and there are often multiple regulations that a company must comply with when dealing with data daily.

The data that the regulation is referring to is usually PII (personally identifiable information). Still, in other cases, it can include financial information and additional information related to an individual or business.


Data compliance v data security

It’s important not to confuse these two terms. They might concern similar goals, such as minimizing the risk that a company is exposed to. Data compliance is specifically compliance with legally stipulated standards. Data security refers to all the processes and guards that are used when managing and interacting with data.


Why is data compliance important?

Failure to comply exposes your company to huge risks

The most obvious motivations for focusing on data compliance are that a failure to do so creates an extreme risk to your business. In some cases, this risk can represent the end of your business, but with legislation stipulating huge fines, it’s essential to get on top of compliance.

These risks can be financial or reputational. For example, in the EU, GDPR stipulates that in the event of a data breach, fines can reach up to 10 million Euros, or up to 2% of a company’s entire global turnover.


Your companies reputation is on the line

For other businesses, especially consumer-focused ones, the damage can be irreversible and can dramatically negatively affect a company’s reputation.

Consumers need to trust the companies that use their personal data, and a failure to do compliance properly can have the effect of customers leaving in droves, and have a nasty effect on your customer retention.


Compliance is an opportunity to build a smarter company

Today’s consumers have become more concerned about how their data is used and where it is used. Businesses must take these concerns seriously or face the consequences.

However, getting compliance right can positively affect consumer loyalty and win business by setting clear differentiating factors with the competition. Many in the tech space have scoffed at Apple’s new approach to consumer privacy, and the effect on consumers might not be realized soon.

But, putting compliance front and center of your mission statement can have a hugely positive effect on how new and existing customers perceive you.


Challenges with data compliance

Data compliance doesn’t fit neatly into a single department

Who takes ownership of compliance? This may be clear in larger organizations, but for smaller businesses, it’s not always clear where it fits.

The fact that there are multiple roles and departments for compliance makes it harder to build an effective process. Businesses need a solution that can work neatly with multiple teams and across departments to provide a holistic view of data compliance.


Data compliance isn’t plug and play (yet)

There are usually many toolkits for most existential business concerns that can help solve issues quickly and with little implementations. For data compliance, this isn’t true.

This is why Wult is building an end to end compliance toolkit. We hope it will help companies get on top of compliance with minimal effort and reduce compliance workload.


To understand compliance, you need to understand your data and where it comes from

To comply with data legislation, you need to understand all of your data sources. What type of data do you collect, how is it used, and what guard is there protecting subjects?

This can get messy quickly, but with useful tagging and categorization in the ingestion and processing phases, this process can be significantly improved. Planning can allow for much easier compliance once multiple people across several departments are using data.


Data compliance is different, depending on where you are

For larger companies, there are multiple regulatory concerns for the same type of data. This adds another layer to data compliance, making it difficult to track and understand where errors might have occurred.

If you are a global company, you need an efficient way of complying with GDPR, CCPA, and other legislation based on where the data was collected or how the data is eventually used.


Building a better data compliance strategy

For modern businesses, it’s more important than ever to get compliance right. A good data governance strategy requires a few things to work effectively.


Start with the customer in mind

When looking at data compliance, you need to start at the beginning. Your customer’s data is essential, and they are trusting you with it to manage it correctly. This means that compliance should extend to your customers and how they communicate with you.

Your customers should be able to do more than provide you with data. Compliance means providing a channel for communication with your customers. How can they see the data that you have on them? How can they engage with you, and how can you respond to their questions effectively and in a timely manner?

Wult’s compliance platform has been designed to work from your first customer through to processing 000’s of daily data requests. We help businesses instantly set up opt-out and tools to honor any customer data requests.

Alongside this, we offer you a dashboard where requests can be actioned, audited, and alerts can be raised within your team with any compliance issues or risks, should they emerge.


Include stakeholders in the journey

For key stakeholders, data compliance is one of the most critical issues. They want to know that compliance is being done correctly, and they need a way of auditing compliance efforts to track company progress.

Wult’s data compliance platform keeps all stakeholders informed and up to date with a powerful global view of compliance within a company. These views help improve compliance and identify errors or breaches before it’s too late.

This holistic view can be extended to different teams to ensure that your whole organization is pitching in with their expertise.


Bridging the gap between data and legal

Make sure that you keep your data and legal teams on the same page. Traditionally there has been a disconnect between the legal teams that exist to protect the companies interests and the data teams that love to move fast and break things.

Bringing both together and giving them a complete view of compliance increases the speed that data can be used and ensures that compliance is done with maximum efficiency.


Focus on how fast your company can react

Building a data compliance strategy requires you to assess how quickly you can react to data breaches, errors, or alerts. After all, reacting quicker can be the difference between no fine and multiple thousands of dollars.

All in one solutions like Wult are designed to provide a holistic view of compliance. With helpful alerts and integrations into your daily workflow, your company is better prepared for any data related issues.



For modern businesses, data compliance can be an existential issue. Many companies are still relying on outdated systems to alert them of errors or breaches in data processing.

With increased scrutiny on companies that use data, businesses must develop a data compliance strategy that reduces the risk they are exposed to.

On top of this, building a powerful data compliance strategy also presents an opportunity to provide value for customers. By offering instant access to customer data, creating a holistic view, and engaging team members across the business, it’s possible to turn data compliance into a powerful reassessment of how your business approaches data management.

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.


What Is Data Integration? Best Practices + Tools

What is data integration?

Data integration is the process of combining and consolidating data from multiple sources to provide a single holistic view.

The data integration process is often the beginning of many routine data processes, from transformation, mapping, and data analysis.

This process is usually one of the initial phases of a data supply chain. It’s fundamental that businesses get it right, as it can affect data processes and action further along the data pipeline.

Data integration had no single approach, but there are common elements consistent from case to case. These usually include a series of sources that are integrated using multiple techniques and processes.


Benefits of a high-quality integration system

In today’s connected and data-driven world, it’s rare to find a business that uses only a single data source. In fact, the average company will have several complex operations that require data from multiple sources to operate effectively.

Thus taking multiple data sources and combining them is a technical challenge in itself. A single company may be using data from external sources, their CRM and internal databases, marketing, and analytics tools, as well as customer-facing tools and applications, to name a few.

The combination of these data sources also presents a series of marginal gains. For example, the effect of inputting data into a single system reduces the workload and set up costs of managing multiple datasets and integrating them elsewhere.

Modern companies must be able to adapt and work with multiple datasets. In a typical company, the benefits could look like the following:


Time-saving and efficiency gains

Manual data integration can be a costly and time-draining process. Single task integrations can snowball into repeatedly run tasks, taking up resources each time it needs to be done.

Preparing and analyzing data becoming integrating it takes time and requires careful analysis. Building a robust data integration properly alleviates the stress on these resources at a future date. It removes the need for employees to make connections from scratch each time an integration needs refreshing.

Using an integration tool (such as Wult) can help companies to save even more time. This can reduce the need for coding tasks, saving even more time and resources, allowing resources to be allocated to other tasks, such as analysis.

A good system will also be timely, so data arrives as close to real-time as possible, making it possible for the company to react faster than the competition.


Fewer mistakes and errors

Keeping track of a companies data resources and how they are managed is a lengthy and complicated process. The correct documentation is needed; employees need to have the correct software and setups need to be consistent across teams that work with data integrations.

Also, without a dedicated tool or data integration process, this must be replicated whenever anything changes.


Improved collaboration

Data isn’t a static resource – in larger organizations, it’s shared between teams and can even require transportation across numerous countries and locations.

Companies, therefore, need a secure and reliable solution for integrating data and delivering it to a location that can be used effectively.

Alongside this process, the employees who are using this data will undoubtedly need to make changes and optimize data for their specific needs. A robust data integration system will allow these changes to be effectively tracked and managed so that innovation can be tracked across the business.


More power and usable data

Integration, when appropriately done, forces organizations to optimize and improve the data that is integrated. It facilitates improvements from multiple employees and departments into a single centralized location.

This means that the data is of a higher quality as quality issues are identified and improved. This ultimately means that the data is ready to use in a much better state than before the integration process and can form the basis of effective analysis.


Why data integration – some everyday use cases

The data integration process doesn’t look the same for every company and can vary significantly depending on several factors. Let’s look at some common examples of data integration to understand how it can benefit businesses.


Making business intelligence simpler and more accessible

A single, unified view of many data sources is a powerful BI tool. Businesses can get an overview and rapidly comprehend and analyze available datasets to maximize BI insights. This allows quick and practical insights into the current status of the company.


Creating centralized data containers to power multiple departments

For larger businesses, the integration process will precede the building of a database or data warehouse that is a combination of many data sources.

In these examples, the data will sometimes be relational and therefore, should be queryable, able to run reports, extract relevant analysis, and access data in a consistent form. Data should be integrated correctly and with the correct procedures to work most effectively.


Making use of big data

The more data sources a business uses, the larger the potential amount of data that will need to be ingested and integrated into their system. The amount of data being created is growing rapidly. For companies with a data generating product with a large number of users, the amount of data can grow quickly.

Alternatively, some organizations require big data sets of entire cities to be integrated and available for analysis. Therefore, the data integration effort requires higher sophistication – companies can ill afford it to break or have significant downtime, as this can lead to vast amounts of lost data.


Best practices

There are several different strategies for integrating data, and this choice is often based on many factors that are different for each company.

These are usually the amount of data, the number of data sources, the completeness of the data being integrated, and the characteristics of the data.

Today, these are the primary data integration methods for businesses:


Application-based integration

This data integration method involves an application that helps businesses set up connections to data sources and integrate the required data.

They will likely have a powerful interface to perform these tasks, allowing both developers and users with less technical experience to have an input into the data integration process. They are usually collaborative and will ensure that every stakeholder is on the same page.

Being out of the box, these processes can simplify the process of data integrations. They will most likely come with a suite of tools to assist with data tasks once the data have been integrated, making them excellent value for money and resources.


Manual data integration

This process is a more fundamental approach to data integration. Datasets are manually collected and formatted to match the desired end location.

Of course, this method is very time consuming and requires much manual work. For companies with any more than small amounts of data and numerous data sources, this is not recommended. There is also an increased chance of errors and other issues with the management and maintenance of inputs.


Middleware integrations

Middleware is similar to an application based integration – it sits in between the data source and the end location and usually manipulates and formats the data before sending it to the correct destination.

This is a slightly more manual process, but it can be useful with legacy systems that aren’t supported or data formatting issues arise.

It’s important to note that some modern applications that deal with data integration will have the ability to combine middleware into their workflow, simplifying the whole process.


Uniform access integration

This approach involves a front end system that can visualize data consistently from multiple sources. The data doesn’t leave the source and is stored there, but viewed elsewhere.

The benefits of this method are that the source data remains in different systems, and can be in multiple formats.



Legacy data

Companies need to think carefully about the types of data they are integrating and the format of any legacy data. Newer systems will likely have more identifiers and other fields that may be missing in legacy systems such as time and date.

In these situations, careful planning and using a method with the ability to modify and adjust legacy data as it is integrated can be invaluable.


New data types

As data becomes more widely utilized, new types and formats are being created all of the time. These might differ in type (unstructured, real-time) or source (location, IoT), for example.

Adapting your data integration solution to these changes is part of the process and will ensure that your business can continue to use data effectively and get the best results. Of course, a data integration application approach will be likely to support these new technologies quicker in a thoroughly tested environment.


Third-party data

External data should always be carefully vetted and assessed with regards to quality and accuracy. It can, however, be challenging to get a complete view of how the data was collected. There may also be governance issues to consider before integrating data, and this should be understood in this initial stage before the data is being used throughout an organization.


Integrations management

Data integration doesn’t end once data has been integrated. Managing the integrations must be considered, and in some cases, teams must update and adjust integrations to keep up with best practices.

Again this is simple in a modern application based integration tool. Management of integrations is simple and often collaborative, so your integrations are up to date and secure.


About Wult

Wult lets companies control their data pipeline. From integrations to transformation and governance. At every stage of the data lifecycle, Wult helps teams and organizations collect high-quality data and generate better outcomes from it.

Data Management

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

This is a fully interactive long-form post on data mapping from a compliance perspective. Each week we will publish a new section. You can use the side navigation bar to the right of the page to quickly move between the sections.

Together, we will illustrate how data mapping can be a completely automated real-time process yielding almost endless insights and actions for the DPO and the compliance team.

The sections in the series are as follows (Sections with link have already been published).

    1. How to let automation drive your data mapping process.
    2. Why data sampling isn’t good enough.
    3. The benefits of data mapping bridging data silos.
    4. How real-time data mapping feedback improves compliance.
    5. How vendor changes impact your data mapping.
    6. Linking your internal data mapping to external communication.
    7. The people angle to data mapping – access and ownership
    8. Data mapping from different angles – sources and segments
    9. Vizualize your data mapping
    10. Tying it all together – best in class data mapping.


1. How to let automation drive your data mapping process.

One cause of the most common problems we encounter when speaking with DPOs about data mapping is the manual nature of the process. 

A typical data mapping process is a yearly event where the data compliance team interviews departments, send surveys and go through data samples. The result is a number of word documents or excel sheets containing an overview of the processed data.

Such a process is not optimal. First, the end result is not easily searchable or easy to query. It is time-consuming and quickly out of date. But worst, it is not guaranteed to be correct.

Creating data mapping from the actual data

The Wult data mapping feature takes a bottom-up approach starting with the actual data in the data silos. 

A silo can be anything that holds data and support unstructured data (think Google Drive, Dropbox etc.), API integration of structured data down to more infrastructure-heavy systems like databases. 

By using native integrations into each data source, our platform creates automated data mapping consisting of:

  • Overview of all the data sources and datasets within.
  • Data scanning to understand the data types, data source overlap and more.
  • Segment identification to allow separation between customers, employees etc.
  • Geo-spatial tagging of data.

This approach ensures the highest quality of data understanding. You can trust that all data in each data lake has been indexed and scanned, and the structured format of the data map allows you to build upon it.

The following sections will cover some of the insights and applications you can build on top of your data map.


2. Why data sampling isn’t good enough.

This section is coming soon, check back next week for updates.


3. The benefits of data mapping bridging data silos.

This section is coming soon, check back next week for updates.


4. How real-time data mapping feedback improves compliance.

This section is coming soon, check back next week for updates.


5. How vendor changes impact your data mapping.

This section is coming soon, check back next week for updates.


6. Linking your internal data mapping to external communication.

This section is coming soon, check back next week for updates.


7. The people angle to data mapping – access and ownership

This section is coming soon, check back next week for updates.


8. Data mapping from different angles – sources and segments

This section is coming soon, check back next week for updates.


9. Vizualize your data mapping

This section is coming soon, check back next week for updates.


10. Tying it all together – best in class data mapping.

This section is coming soon, check back next week for updates.











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 uses

Real Estate Data – How Better Data Can Outsmart The Competition

First of all – what is real-estate data?

Real-estate data provides a wide range of information related to properties, from residential and commercial through to industrial property and broader land-based insights.

This data helps property owners, property investors, real-estate companies, and renters make more informed decisions and stay ahead of the curve.

Real-estate data comes in many forms, but often it’s unreliable and not as effective due to delays or inaccuracies.

Getting your hands on the best real-estate data in a structured way can significantly boost productivity and help you stay one step ahead of the competition.


Who can benefit from precision real-estate data?

Having accurate data that is updated and maintained with consistency can be helpful to several different companies operating in the real-estate space.

In practice, the real estate industry is split into smaller categories. For every one of these categories, access to compelling real-estate data can provide a substantial competitive advantage:

Renting and buying industry – for those looking to buy, sell, or rent property data is a powerful way to get ahead of the competition and identify trends that help reach targets.

Real-estate investors and developers – for those in this area, success means identifying trends and new promising location before anyone else — the perfect task for great real-estate data.

Not every real-estate company works in just one of the above areas. For some, it might be beneficial to reuse real-estate data across all three. One thing is for sure, better data means better results, and it’s time to make the change to a more proactive dataset.


What you can do with better real-estate data

Build better real-estate listings

For buyers and renters, it can be confusing and challenging to understand listings and navigate to the perfect property. Having the right metadata around properties can drastically improve this experience and help to nail sales and enhance the customer experience.

People care about things such as crime rates, schools, neighborhood details, local social hangouts. Having this information available can differentiate yourself from the competition.


Make better predictions

Being predictive is big business in the real estate industry. How much should you charge as a landlord, and for how long? How much is a property genuinely worth, and how much is that going to change going forward.

For investors and buyers, having the right dataset can have a considerable impact. Getting a better idea of what’s going on in specific areas can help you to get the best value properties in the best areas before the competition are aware of it.


More accurate valuations

A considerable part of the real estate industry is property valuations. Data has come a long way in helping this process, but with so much at stake with valuations, it’s essential to get precision data to support.

From looking at cap rate models to econometric forecasting, better data means that you can do valuation better. Combined with the right data governance and quality standards, you can conduct more accurate and valuable estimates.


Understand how rents are changing in real-time

As well as valuations, robust real-estate data can help to understand leases and understand how they are changing in real-time. A better view of supply and demand allows better predictions. Better data science teams use better real-estate data.


Smarter investment decisions

Investment is an industry that has always worked with large datasets to get a competitive advantage. Data scientists make use of many datasets to understand the real estate market as a whole.

Standard datasets can portray two properties as similar when, in fact, they are not. More conclusive and alternative datasets can be used to get a better interpretation of the value of a property, area, or region as a whole. That’s why dynamic and precision datasets are becoming so popular in the real estate industry.


What does good data look like?

Common issues

With real-estate data, the best companies know that success comes down to three things:

Lack of accuracy

Lack of frequency (not up to date)

Lack of standardization

We understand that for good real-estate companies to succeed, data needs to be more than just a web scrape. That’s why we did it better.

Wult’s data comes from a multitude of sources; these are continuously checked and updated. This frequently updated dataset means that you don’t just get a static dataset, you get one that updates along with global property changes and information.

On top of this, we built a robust data governance solution and data mapping functionality that can restructure, manage, and distribute our real-estate data in a way that allows you to act quickly and deliver results. From structure to quality, we provide a transparent solution so that everyone using the data can get what they need when they need it.

For more information, please get in touch via the form below. We’ll provide you with a data sample and give you a live demo of our data product.


Get a real-estate data sample

    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; whereas an e-commerce company, public relation agency, a software house, or similar business entities are more likely to 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.

    Data Extraction

    What Is Web Scraping? Best Guide To Extracting Data From The Web

    What is web scraping

    Data is one of the most important assets for a modern business. Collecting data, analyzing it, and cleaning it is a powerful way of improving a variety of business functions.

    But where does data come from? There are many sources, some public, some private. These datasets sometimes cost money, and sometimes they are traded.

    When companies face a problem, they often look for data to solve that problem. We live in a world where information is everywhere, but it’s more prevalent than anywhere on the web.

    It is estimated that the total data on the web if combined and mapped, would total one zettabyte. It’s okay if you had to look that up – it’s a monumentally huge number that is difficult to comprehend.

    The crucial thing to understand about this data is that it’s mainly public. Anyone with a browser can access it. This is the basis of web scraping. Turning information available on the web into datasets.


    Why scrape the web

    So much information exists on the web that the use cases are potentially limitless with web scraping. Most data types can be extracted from the web, and this can be an excellent asset for businesses to function effectively, from leaden to insights and understanding.

    Think of a type of data, and you can probably get it from the web. From email addresses, venue addresses, song lyrics, and categories of fish – the list goes on and on.

    All of this information is readily available. Collecting it, structuring it, and analyzing it can have hugely positive effects on a wide range of businesses. We will explore these in more detail before looking at how web scraping works in practice.


    How does web scraping work?

    To understand how web scraping works, we need to understand how web browsers work and understand websites.

    When you go to a URL in your browser will send a request to a server, which will then send back a response. This response contains the HTML, CSS, JSON, and JavaScript that the browser will then use to form a visual web page.

    The scraper gets this request and based on a set of predefined conditions, and it extracts the relevant data. This is then converted to the correct format.

    There are two main ways of scraping the web – we’ll look at this in more detail below.


    Scraping with python

    One way of scraping the web is to use python and build your own scraper to extract the data that you need from the web. However, even for people that are experienced with python can find more complex data extraction tricky, and starting from scratch is an extremely steep learning curve.

    However, getting started requires you to install python and a few other tools, and you can get started with just a few lines of code (this will be very basic at first).

    Here’s an example:

    from bs4 import BeautifulSoup
    import requests
    url = "<>"
    content = requests.get(url)
    soup = BeautifulSoup(content.text)

    Here’s what is happening – we’re requesting a URL, and once we receive the response, we put it into an object. This is the same as if we manually went to the site and viewed the page source.

    From here, the possibilities are endless, and the more that you practice and run scrapes, the better you will be at getting the information you need. However, there is another way.


    Using a prebuilt tool to extract data from the web

    Another option, instead of building a web scraper from scratch is to use a tool that can adapt to your needs and extract the data you need. Here we’d like to talk about Wult.

    Wult has two main components – an extractor (scraper) and a data management solution.

    The first part is a powered-up version of what we have already seen. It can extract data from the web, structure the data, and output the data that you need for processing.

    It has a few other benefits over a basic web scraper:

    • It can view the web as a human does, rather than just the raw code. This means that it can extract data from JavaScript pages where the data would not be in the source code.
    • It can adapt to changing websites. Wult learns which element you are looking to extract, and it makes smart decisions where it thinks it can see the same data in another place. This means you don’t miss out on relevant data.
    • It can be set up to check for updates. With a basic web scraper, you would have to run the scrape again and then check for changes, and modify your original database.
    • All of this can be set up in a tidy interface, and you don’t need to have any experience with python or coding to make it work.

    The second part is the data stream. Think of this as more of a data analytics tool that filters, modifies, and combines the data into a form that works for you. It can then plug this data directly into your current set up. This is where the powerful automation happens, and again, this is all possible without coding experience, and without having to read through all your API integration docs.


    Use cases for web scraping

    Now that you have seen two ways of scraping and extracting data from the web let’s look at what you can do.

    Better access to company data

    Many sites around the world provide information about businesses and organizations. Collecting and combining these datasets into a single database is a powerful tool.

    A natural scrape flow would be to scrape a directory of companies and extract key information about the business, such as the web URL. From here, you can build a smarter data extractor that can use this URL to find public records of the company in other areas of the web, such as social profiles, investor profiles, etc.

    Very quickly, you can build a very detailed database that includes more detailed information such as the number of employees, category, markets active, and even revenue.

    This is a dream for any sales team to work from. They have a unified database of extensive structured information on every business.

    Wult can help businesses to attain this kind of database without any prior coding experience. Wult’s data stream allows businesses to automatically integrate this data into their current setup and even automate outreach.


    Better sales data and lead generation/prospecting

    The above dataset naturally runs into this one. With company data, you can filter out the relevant companies for your business and then build a robust, automated sales machine.

    Who wants to manually find prospects and then search countless sites for contact details when you can automate the whole process.

    You can build an incredibly detailed scrape that can even filter out leads and qualify them. From here, plug into your outreach solution, and you have a fully automated lead-gen machine.


    Marketing automation

    Web scraping is an incredibly powerful marketing tool that can help to grow your organic channels and build engaged audiences for your business. Web scraping forms an effective part of a B2B marketing strategy.

    Here is a web scrape/data extraction and data analysis/action flow that can yield incredible results for marketers and illustrates the true power of web scraping.

    Step one – identify competitors with a strong social following that offer the same service/product as you (of course yours is better, but what do social media users know).

    Step two – use a smart web scraper to extract the profiles of their followers

    Step three – create a flow that follows them and messages them with information about your products

    This is just one way that web scraping can help marketers. But it doesn’t just have to be on social channels.

    Web scraping can be hugely useful for SEOs looking to find new opportunities and automate a tonne of processes so they can focus on growing their search presence.


    Brand monitoring

    Brand monitoring is becoming a huge part of any business. In the world of e-commerce and retail, routinely checking product reviews and customer feedback is a must.

    Brands often miss out on this, as it can be a lengthy process when done manually. Web scraping and data analysis can help to monitor multiple platforms. They can even consider social reviews to build an incredibly powerful business intelligence tool only from public-facing data on the web.


    Market analysis, big data, and insights

    People think that big data is complicated and unattainable for the average business. But it’s not. With a simple web scrape, you can begin to extract powerful data than can help you to analyze the competition and identify new market opportunities.

    Scraping competitor prices can give you a substantial competitive advantage. Understanding the growth of new products can also help you buck trends and get there first before the competition.

    This can also help you get quick overviews of new areas if you are looking at moving into new, competitive markets. It can also help with financial predictions and stock analysis.

    All this is possible from a simple web scrape.



    Web scraping is a powerful and often untapped tool for businesses that want to be able to speed up complex tasks and processes and automate their business. Extensive information exists on the web. You need to know how to find it.