Categories
Data Management

Data Mapping + Compliance – How To Get It Right

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.

This means a messy set-up, that is highly manual and the time taken for knowledge to disseminate throughout the company is yearly (or longer).

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 It is time-consuming and quickly out of date.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.

We want to change this. Our promise with the Wult platform is to reduce manual data mapping tasks by up to 90%. That knowledge gap is also too long. We believe with our platform we can reduce it by over six months.

How does our platform do this? With a bottom-up approach.

 

We build the data mapping process from the actual data

The Wult data mapping feature takes a bottom-up approach starting with the actual data in the data silos. We connect to your data silos and use this to build out data mapping.

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.  We know that the average company uses multiple silos, and mapping these can take a lot of time. 

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.

This saves time, and reduces the knowledge gap.

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

 

2. Why data sampling isn’t good enough.

While many companies will focus solely on how data schemas are defined as part of their data mapping, more and more companies will do analytics on data samples to further understand the data. 

When mapping data, most companies will focus mainly on defining data schemas.

As part of a more robust mapping strategy, companies will also do analytics on data samples. These analytics are done to gain a better understanding of their data.

In this part of our data mapping series, we will argue that whilst this is an excellent first step, data analysis requires high amounts of coverage to full compliance.

 

Why do we analyze data sets?

One of the main goals of data mapping is to map out which kinds of sensitive data are kept by an organization. 

In today’s world, companies must understand which data types they hold and what they are liable to do with these kinds of data under the relevant data protection legislation.

It’s also essential to analyze datasets to understand data quantity and inform data retention. But more on that later.

 

The problem with data analysis today

Whilst data analysis is a great step towards a better understanding of data, and thus better data compliance, in its current form, there are some issues.

The problem that we see most with data mapping analysis is the coverage of the samples. Companies are using smaller data samples that they believe to be representative.

 

The great coverage question

For sampling to be an efficient tool, you have to ensure enough coverage for the sample to represent what you are trying to measure. 

The more variable parameters you investigate, the larger the sample size you typically need. 

You, therefore, end in a situation with a tradeoff between the insights you can get and the amount of analysis you put in.

At Wult, we’re trying to index data better to provide a complete insight into data structure, sensitive data and quantity.

The Wult data mapping platform creates a privacy index on top of all your data, so you can get insights into all your data for any parameter you might choose. 

With this, you can answer questions like:

  • How does my customer data overlap across data sources? Answered by understanding exactly which emails or other identifiers belonging to customers are present in any given data silo.
  • How many identifiers am I holding with a given segment?
  • Quantify segments across any parameter. This can help you understand if you fall under new regulations such as the Virginia Consumer Data Protection Act (“VCDPA”) that applies if you process data of more than 100,000 consumers in Virginia.
  • Which types of data are stored together and to which extent?

On top of this, the entire mapping process is fully automated, so the DPO and data team will never spend any time handling manual processes. 

And the system is reading data in real-time, ensuring your data mapping is always up to date. This reduces the chance of missed fields, giving you a complete understanding of your data siloes.

Data analysis has significantly developed in recent times. But to truly build a compliance-first ecosystem, companies need automated systems that deliver representative insights into company data structure.

 

3. The benefits of data mapping bridging data silos.

When mapping data in an organization, it can be very easy to get stuck in a certain way of looking at things.

Once you have chosen your angle, it can be challenging to adapt and change when new data is added or data changes significantly.

You can be left with an incredible amount of work to remap the data with a different perspective or goal.

This is another reason that we built our data mapping platform.

 

What do you mean by data silo?

A data silo is usually a data set containing a single data source with the same characteristics. A company or organization will usually have multiple data siloes, with different kinds of data with different characteristics, legal implications and sensitive fields.

A data silo doesn’t contain all of a company’s data or the dimensions needed to do effective data mapping. That’s why we build our platform to work with segments.

 

What is a segment in data mapping?

A segment is a new way of looking at your company’s data. Rather than looking at your data through the lens of a silo, segments allow you to look at a combination of siloed data that are grouped differently.

An example of a segment is customers. You may have data in different siloes, but our platform will let you combine these siloes to create a clear overview of multiple datasets.

 

Why does segmentation help to improve data mapping?

Clear overview + understanding your segment

By segmenting your data by user, for example. You can see a clear overview of what types of data you have in different silos. In this example, you might find that you are collecting sensitive fields in one dataset that you weren’t aware of.

Often it isn’t easy to build a complete understanding of the different types of data being collected in a segment.

The number of entries in a segment isn’t quantifiable. It’s an unknown.

Let’s look at an example to see how this can affect a company.

Companies usually have a CRM that includes name, email and phone number. Combining this with a finance system would likely add banking information and address into a separate silo.

And by combining this with your marketing tool, you might also know a customer’s gender and age bracket. Again this is siloed.

If you look at that data silo per silo, it is unclear how much data you are storing, which can be a considerable risk.

This same issue extends to data volume. Consider you have 60,000 customers in one system, 25,000 in another, and 10,000 in the last. How many do you have? Somewhere between 60,000 and 95,000.

Only by creating an identity graph of all your siloed data will you know for sure and can act accordingly.

 

Retention

The same thing applies to retention. A segmented view of your data allows you to understand where you might be collecting more data than you need to?

Companies shy away from implementing retention policies because it can be daunting to map data across siloes accurately. It becomes easier to keep as much data as possible.

But this can lead to several problems, especially as regulations shift and employees change.

Segments make retention easier, as implementing these policies across siloed datasets is challenging. These are usually specific to a silo, even where retention policies exist, making full retention impossible.

 

Compliance

Unconnected, siloed data is not a good place in today’s world. As compliance grows, businesses need a universal view of their data to comply with quickly changing regulations.

Alongside this, the world of data is now an international one. But the world currently lacks universal data regulation. This means different processing laws in different regions.

For example, you may have siloed data stored in both the EU and the US. In a customer segment, you may combine these, meaning that you are moving data from the EU to the US.

This means that data for EU citizens is hosted in the US, which can create regulatory problems. Our platform can help you to identify these issues and alert you to the need for a transfer impact assessment. Something very different from disjointed siloed datasets.

Our platform allows you to understand which type of data in which silos are split or duplicated across different regions. This specifically helps with the problem of cross international data regulation.

Regular alerts mean proactive data mapping. And finally, segmented data mapping means that governance platforms such as the Wult platform can alert you of changes. Moreover, these changes can be segment-specific.

For example, alerting the DPO when a new type of PII is found.

Or giving instant updates when data is stored in a new country.

Segments can open up siloed data. It’s an approach that will help improve the data mapping process, enabling automation, better retention, and improved compliance.

 

Get in touch to learn how Wult can help with Data Mapping





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    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.

     

     

    Categories
    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.

    Categories
    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.

    Categories
    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:

     

    Accuracy

    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.

     

    Completeness

    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.

     

    Timeliness

    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.

     

    Relevancy

    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.

     

    Consistency

    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?

    Compliance

    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.

     

    Results

    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.

    Categories
    Data Management

    What Is Data Governance And Why Is It Important

    Introduction

    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.

    Example:

    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.

    Example:

    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.