Empower Your Business with Self Service Data


Table Of Contents

Businesses are dealing with more data than ever before. Because data is so important these days for making effective business decisions, it’s imperative that all stakeholders within an organization have easy access to the data they need.

But, of course, there are challenges with that fact:

  • Ensuring that the quality of company data is up to par, organized, and usable
  • Ease of access to data is often hindered by local and federal regulations and laws, company data governance policies, and the ability to find the data that one needs.
  • Implementing and/or building a system that allows easy access to data while at the same time enforcing the appropriate data governance can seem overwhelming or out of scope in terms of budget.
  • Educating employees, including those who are not tech-savvy, on accessing data using certain systems or processes may take significant resources.

Regardless of these challenges, the reality is that, ironically, data is causing the world to move faster in virtually every respect. So data is both the solution to and cause of businesses needing to make faster decisions to keep up with a quick-paced world. Businesses need to be able to address the challenges associated with data access or simply fall behind.

In this article, we explore how data self service makes the entire business workflow more efficient and flexible, and enables business leaders to address key challenges quicker and more effectively.

What Is Self Service Data?

Data that can be accessed on demand by stakeholders via a readily available platform, such as an online data marketplace, is referred to as self service data. 

With data self service, stakeholders get on-demand access to relevant data that they can then use to make more informed business decisions.

Why Self Service Data Matters

Typically, data consumers must go through the company’s IT team to access certain data sets. With so much data flowing through an organization at one time and such a high need for it to make business decisions, it would take an astronomical amount of resources to fufill every data request. Most data consumers within an organization want access to datasets to use for business intelligence, and providing self service data access through a marketplace solution is the first step.

Benefits of Self Service Data

Benefit Description
Less reliance on IT Instead of going to IT to obtain datasets, the user can access it themselves, freeing up IT resources.
Insights without the wait Instead of waiting for data to be manually prepared and packaged, they can access it immediately and begin gleaning insights.
Higher data literacy Self service data in an organization often increase data literacy as users become familiar with various applications and tools that help them use the data and have a more active role in maintaining data integrity and governance.
Increased speed to insight When stakeholders can quickly access the data they need, they can work faster and make smarter decisions.

Use Case: Self Service Data Analytics

A wide variety of users often need to be able to access and analyze data without relying on IT or BI specialists for support. Self service data analytics offers that ability through robust data analytics tools that are able to interpret and present datasets in a visual way, such as through charts, graphs, tables, and more. This allows all types of professionals to view and glean insights from data without needing someone else to interpret the data for them or having to contend with difficult-to-use data analytics software.

Challenges of Enabling Self Service Data


Given all the technology available to businesses today, implementing a self service data solution would be easy. However, there are a variety of challenges that businesses need to be aware of, and establish strategies to overcome before the implementation of self service data can be a success.

1. Dealing with Multi-Cloud and Hybrid Systems

If you ask the typical enterprise employee how many systems and applications they use on a daily basis, chances are they’re going to start counting using their fingers as they rattle off a list. These days, it’s not unusual for companies to use various systems, including a combination of cloud systems and legacy systems, which likely include on-premises. Each system will have a different approach to data collection, security, and access, which complicates matters when you want to provide democratized access consistently across the entire organization.

Solution: Approaching data governance holistically even with multiple systems, both cloud and on-premises, is doable using a data marketplace solution like Revelate. With Revelate, built-in centralized security, provided by our partner Immuta, allows you to manage how your data governance is applied to marketplace purchases or downloads, regardless of the dataset’s origin.

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2. Handling a Variety of Data Formats, Both Structured and Semi-Structured

Businesses of all sizes need an effective process for storing data in different formats. Structured, semi-structured, and unstructured data all need to be stored properly so that they can be accessed and used as needed.

Solution: Up until just a few years ago, businesses would look for a data warehouse or a data lake to store structured, semi-structured, and unstructured data, respectively. These data repositories would then be managed centrally by a team of data scientists and other data professionals.

However, a more robust solution called data mesh has recently emerged as a more effective solution for data management. At a high level, data mesh aims to eliminate the centralized management team from becoming a bottleneck for data access. Instead, it shifts the majority of the responsibility surrounding governance and access of certain data to the appropriate subject matter experts (e.g., HR management oversees employee data, customer service oversees customer data, etc.).

The people who know the data best can have more control over its use and access under a data mesh system. This decentralized approach to data management doesn’t eliminate the central data management team but instead shifts them to a more supportive role and empowers them to use their skillsets on more challenging and complex data tasks.

3. Finding Sufficient Storage to Hold Rapidly Increasing Volumes of Data

Storing data using on-premises systems has long been outdated, with cloud systems being the preferred way to store large amounts of data. Determining the best cloud data migration means understanding how cloud data storage works and the costs associated with it.

Solution: Organizations like Amazon, Google, and Microsoft have implemented cloud-based storage solutions capable of storing and moving terabytes of data across systems. These solutions often work in tandem with their other software offerings, enabling companies to build an entire data ecosystem using their solutions.

However, getting data out of that ecosystem, let’s say to fulfill a customer’s order, isn’t often straightforward unless that customer is also using whichever company your organization has chosen. Revelate is platform agnostic, meaning that it can extract datasets from any system and transfer them to any target, allowing self service data to occur regardless of which system the data consumer is using.

4. Harnessing Enough Power and Ability to Process Large Volumes of Data

Processing large volumes of data, including analyzing it to determine where it belongs, what identifiers it needs, the quality of the data, and more, needs the help of different systems. Big data processing involves distributing the workload demands across multiple systems to complete the ETL (extract, transform, and load) process. For example, a data lake may integrate with other platforms like relational databases or a data warehouse to organize the unstructured from structured data. In other cases, data preparation or data mining tools may be used to preprocess the data for certain applications. In any case, processing big data takes a lot of power, and it’s often a struggle for companies to develop a cost-effective solution.

Solution: Enlisting the help of a big-data-as-a-service provider gives companies access to the processing power they will need to handle large amounts of data. Cloud-based big data providers have the ability to scale compute resources appropriately so that there’s just enough power to handle the workload for a certain project.

5. Making Data Accessible From Anywhere, Anytime

Another challenge that organizations face with self service data is making data available at any time and anywhere. After all, one of the main points of self service data is convenience, which means on-demand access.

Solution: Implementing Revelate as a data marketplace solution is a cost-effective way to ensure that data consumers can access data anytime and anywhere. Datasets can be requested from an eCommerce-like storefront (that can be customized for internal and external audiences). Then the system performs the appropriate checks and balances to ensure that your company’s data governance policies are followed. Once the automatic checks determine that the data consumer can access the data, they can download it immediately.

6. Implementing Security Controls and Governance Needed to Meet Corporate and Government Requirements

It may seem overwhelming to implement the appropriate security measures holistically while following ever-changing legal requirements, especially in multi-cloud and hybrid systems. Failing to do so can result in fines, penalties, and even a business shutdown in more severe cases.

Solution: Using a centralized security solution enables security teams to manage all systems, applications, and data movements using one system. Immuta is one such system that can apply security to an organization’s entire modern tech stack and data infrastructure, allowing teams to apply and manage security holistically.

7. Providing the Right Tooling for Bi and Analytics

One of the main use cases for internal data consumers is likely to be data analytics. Professionals want to be able to access understandable data so they can make quick and effective business decisions. Choosing the BI and analytics tools is paramount to ensuring that they can access and interpret the data they need.

Solution: Look for a self service data analytics tool that integrates seamlessly with your business workflow and plays nicely with the data ecosystem and governance policies that your company has created. Examples of self service data analytics tools include:

  1. Alteryx
  2. Cognos Analytics (by IBM)
  3. Microsoft Power BI Pro

8. Conducting Thorough End-User Training So Data Users Can Be Truly Self-Sufficient

Providing the right tools so users can easily access data is great, but they also need the training to use those tools effectively. Prioritizing effective onboarding sessions for self service data analytics tools, as well as any self service data tools like a data marketplace, will help ensure that the end user understands how to use these tools to glean insights.

Solution: Providing end-user training helps with enforcing data governance. When the end user understands their role in keeping data secure (e.g., not sharing passwords, the importance of using MFA, how data governance contributes to data integrity), then they will want to do all they can to protect unauthorized access to the very data that helps them do their jobs better.

Self Service Data Discovery and Access Management

Before self service data initiatives can be enabled, data needs to be discovered, and access needs to be managed to ensure that the users can only gain access to the data that they need. This often requires using a variety of tools, as outlined in the table below:

Data Tool or Method Explanation
Self service Data discovery Used for two purposes:

  1. Gathering data from outside sources to augment internal data to get a complete picture of business performance.
  2. Gain complete visibility into internal data, including all sources, and which data needs the highest levels of security, and which does not. This assists with ensuring regulatory compliance and compliance with company data governance standards.
Data catalog tools Assist with assigning information to different datasets. Similar to how a library organizes books based on information like author, genre, edition, and description of what it’s about, data catalog tools help an organization assign information to and categorize datasets so that they are easily searchable and accessible, regardless of their source.
Automations Automations eliminate human error that can happen through manual fulfillment processes and enable stakeholders to gain access to data anytime and anywhere.

With Revelate, users can access data anytime through an online data marketplace platform while automations in the background perform the necessary checks and balances to ensure that only the right users gain access to the right data.

Steps for Implementing Self Service Data


Implementing a self service data solution can be a complex process involving various considerations, such as data ingestion, storage, governance, and security.

To successfully implement a self service data solution, businesses should consider the following steps:

1. Identify the Data That Needs to Be Shared

Not every dataset that an organization has needs to be shared, so it’s important to identify what information should and should not be shared, and to what extent. This is another great reason why data mesh is so helpful because the subject matter experts that understand specific data the best can determine the extent to which it should be shared, rather than putting that burden on IT teams who may not fully understand the appropriate use cases of the data.

2. Determine the Governance of That Data

For data that is sharable or being monetized, the appropriate data governance measures should be implemented. This includes what credentials someone needs to access an entire dataset or a portion of it and how automations can enforce that governance when a dataset is requested through a self service data platform like a data marketplace.

Of course, sensitive or confidential data should get special attention, especially with regard to compliance with regulatory requirements and laws. In the case of sensitive data, additional oversight may be necessary by a central authority within the organization (such as a data science team) as a secondary authentication method to ensure that the data is being accessed by the right person for the right reasons.

3. Package the Data Into Data Products

Once data that can be shared or monetized has been identified and data governance for that data has been determined, it’s time to package that data into products that BI or analytics programs can make sense of, or that users can otherwise utilize in different ways.

The approach to packaging data should consider the various use cases of that data and its completeness of it. For example, if you’re wanting to provide a data product that contains all the sales figures for Widget A over the last fiscal year, then you’d need to package all the financial information from different applications and sources like the company’s website, payment processing systems, and financial records to build a complete dataset.

Once the appropriate data is gathered, it needs to be prepared so that BI or analytics can read it or that users can apply it to various programs. This can be extremely time-consuming to do manually, especially when users request different formats.

Revelate can automate the process of extracting, preparing, and packaging data into products and distributing them for sale via a customizable marketplace.

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Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!

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4. Determine the Delivery Methods for That Data

Once data is prepared, packaged, and ready for distribution, you’ll need to determine the best way to get that data to the people who need it. For most organizations, the best way to deliver data to different audiences (internal and external) is through a data marketplace. With a data marketplace, you can list various data products the same way an eCommerce website would list physical products, with descriptions based on the data product’s metadata, as well as images.

Most data marketplaces, however, have limitations on how data products can be displayed, which can hinder searchability and understanding of the data product for the data consumer. Revelate alleviates this problem by providing a fully customizable marketplace solution so that you can choose how data products are displayed to suit your organization’s needs.

5. Make the Data Product Available to the Consumer

Once all the other pieces are in place, it’s time to make the data product available to the data consumer. Ideally, this entire process should be automated to really provide a true self service data experience. Using a data marketplace like Revelate, data products aren’t uploaded to the platform and stored there, rather, when a request is made, a data product is extracted from the source, placed into a temporary S3 bucket, and then distributed to the user from there.


Self service data is essential to implement for organizations that want to be able to maximize the potential of their data. By democratizing access to data through self service data systems, organizations can enjoy the following benefits:

  • Increased speed to insight
  • Insights without the wait
  • Higher data literacy
  • Better insights based on holistic knowledge

But before these benefits can be realized, organizations need to make a plan for meeting the potential challenges that self service data can present, including:

  • Dealing with a variety of source systems (multi-cloud and hybrid)
  • Storing structured, semi-structured, and unstructured data, as well as determining storage for increasing amounts of data
  • The processing power that will be needed to analyze and organize data
  • Security and governance
  • Which self service data analytics tools and BI applications to use
  • End-user training on how to use a self service data platform and related tools

By recognizing the steps and potential challenges with self service data, organizations can adequately prepare themselves for implementing this data democratization solution.

One of the essential components in a self service data implementation is a self service data marketplace. Revelate is a fully customizable data marketplace solution incorporating security, access, and automations to support a holistic self service data platform experience.

Learn more about Revelate today. Get Started.

Unlock Your Data's Potential with Revelate

Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!

Get Started