Simplifying Organizational Data Distribution

Simplifying Organizational Data Distribution [Challenges & Solutions]


Table Of Contents

What is data distribution? The data distribution definition refers to when organizations control the movement of their data both internally within the organization itself or externally to partners, vendors, suppliers, and other stakeholders.

There’s no doubt that organizations can gain a myriad of benefits from their data, whether it’s providing it to internal stakeholders to make better, data-driven business decisions or providing it to external data consumers via data monetization or data sharing efforts. Traditionally, data order fulfillment took monumental resources and time for organizations as highly-technical professionals would need to extract, prepare, process, and distribute datasets to waiting clients and internal stakeholders.

What’s more, fears surrounding data sharing, mainly with security and privacy of data, would often result in executives erring on the side of caution and forgoing data sharing efforts “just in case”. These days, data distribution is much easier, and many of the original fears and hesitations that organizations had in the past have been alleviated thanks to emerging data science technologies like automations, machine learning, and artificial intelligence. However, that doesn’t mean that challenges with data distribution don’t exist and that organizations don’t have to overcome them before they can implement an effective data distribution strategy.

Implementing an effective data distribution process in an organization still requires organizations to find solutions for certain challenges, including strategies behind internal and external data distribution for taking data products to market (the how who, and what) as well as handling data security and privacy as it moves inside and outside of an organization.

In this article, we’ll explore the main challenges organizations face with data distribution and how Revelate as a data fulfillment platform provides the solution to most of these common distribution of data challenges.

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The Challenges of Organizational Data Distribution

Not Having a Strategy for Taking Data to Market

Many data providers lack a strategy for taking their data to the market. There are three components to such a strategy that covers the how, the who, and the what of data distribution.

1. The How

The first thing that organizations need to think about is how they are going to package the data scattered across different formats and in different locations to allow it to be available for data buyers. Centralizing data so that it’s accessible from a single location is known as data federation. Use of data warehouses and/or data lakes (or a combined solution, such as Databricks’ Delta Lake) is ideal, but not every organization has these technologies in place.

2. The Who

The next thing to think about is who the organization is selling or sharing its data to. Target audiences for data can be internal or external. For instance, a parent organization with multiple child companies could create a subscription-based service where the child companies get access to real-time data for a recurring fee. Alternatively, a group of organizations in an industry (such as healthcare, for example) could get together and create a data sharing ecosystem where data is shared between these organizations based on a mutual agreement.

 3. The What

Finally, organizations need to deliberate on what type of data they want to make available. For example, a financial institution may want to sell or share lending and investment data to help other financial organizations with risk management. On the other hand, a large delivery organization may want to share route data with municipalities such as cities or towns so they can use that information to develop their public transportation systems. Determining what data your organization has that can not only be of use, but that has a demand is an important consideration for taking data to market.

Inability to Execute Effective Data Distribution

Distributing large data sets is complex. Without a good understanding of the brass tacks of distributing and managing data, it is difficult for organizations to execute data distribution effectively. Distribution of data is not just about moving a data chunk from one folder to another but rather getting it into the hands of the right people at the right time. IT departments often handle traditional data distribution efforts and depending on the scope of data fulfillment orders, can be extremely resource and time intensive.

Understanding how your organization currently handles data distribution and taking the time to identify improvements in each area goes a long way to establishing an effective organizational data distribution strategy.

The following are common challenges that organizations face with effective data distribution:

Poor Data Management

Ensuring data quality, consistency, and accuracy are important factors for any organization’s data management protocols. Poor data management can occur for a number of reasons:

  • Lack of standardization
  • Inconsistent access controls
  • Poor network security
  • Data duplication

This, in turn,  leads to the following problems:

  1. Data Silos: Organizational data is fragmented and isolated in different departments, meaning that decisions aren’t considering the entirely of organizational data. This could result in negative outcomes with regard to business intelligence.
  2. Poor Quality Data: The people working in different departments may get inaccurate information due to incorrect data. This can have cascading negative effects throughout the organization, from loss of clients to loss of revenue, and reduced operational efficiency and productivity.
  3. Duplicate Data Entry: Duplicate record entries take place in databases without proper checks on them by managers or administrators of the system. This wastes time and money for an organization as duplicate records need to be maintained separately, which uses up resources like storage space and can result in substantial data storage costs.

Data Security

Whenever data is shared, whether internally or externally, concerns about its security, encryption, licensing, usage rights, etc. are bound to arise. Data in flight needs to remain encrypted, while stored data needs to conform to privacy and access-related regulations. Organizations that fail to meet regulatory requirements for the security and privacy of data can be served with hefty fines.

Why Revelate Helps Eliminate these Challenges

Revelate is an end-to-end data fulfillment platform that enables data distribution across formats, platforms, and organizations safely and securely, with very few technical inputs. Revelate is platform agnostic so that organizational data can be extracted from any source. From there, the data platform handles processing, preparation, and finally distribution of data via a fully customizable, white-labeled data marketplace that’s cost-effective and accessible for organizations of all sizes, from enterprises to growing businesses.

Revelate aims to eliminate the burden on IT departments having to handle data distribution by taking advantage of automations. The way that it works is that when a data consumer makes a request on your data data marketplace, it sets off a series of triggers. First, checks and balances are run to determine if the user can have access to the data set (security and access measures are established in advance and are fully customizable). Second, the data is extracted from the source location, prepared, and processed for distribution. Finally, the dataset is distributed to the customer through the data marketplace. This process all happens automatically, meaning that anyone can fulfill data orders⁠—the customer can do it on a self-service basis, or a salesperson or other employee can execute the process for a customer.

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The Difference Between Internal and External Data Distribution

The Difference Between Internal and External Data Distribution

Internal data is data produced by an organization, while external data is data retrieved from an outside source. When an organization relies entirely on its internal data, they often do not see the big picture regarding the status of its business within a market, an industry, or even an entire economy. This limits effective decision-making as decisions are made with a narrow scope of information.

With the plethora of data available, it makes sense that organizations that want to make effective data-driven decisions would want to take advantage of the lucrative nature of augmenting their internal data with external data.

A Capgemini Research Institute report, Data-sharing masters: How smart organizations use data ecosystems to gain an unbeatable competitive edge, which surveyed 750 industry executives in 12 countries, as well as over 30 industry experts and academics, found that data sharing ecosystems—a group of organizations that share their data with each other—provide the following benefits:

  • Improve customer satisfaction
  • Provide new revenue opportunities
  • Boost productivity
  • Help reduce costs

In addition, 48% of organizations plan to launch new data ecosystem initiatives, and 84% of those will do so within the next three years from the reports publication date. This highlights the importance of external data distribution and that organizations see the value in augmenting their internal data with external data sources.

Here’s a quick breakdown of the differences between internal and external data distribution:

Internal Data Distribution External Data Distribution
Data sharing occurs within an organization. This can involve sharing information between departments or individuals within a department or even across locations. Data sharing occurs with stakeholders outside of an organization, including customers, vendors, suppliers, distributors, and more.

But data distribution, whether it’s internal or external, isn’t free of challenges. The benefits may be many, but overcoming challenges with both distribution types is essential.

Common Challenges of Internal Data Distribution

  1. Relying on IT to Fulfill Data Orders 

Relying solely on IT to fulfill data orders can create process bottlenecks that can quickly bring projects to a halt. Data fulfillment is a taxing task for IT teams. The resources that go into having to find and isolate data ensure that the person that’s asking for it has the appropriate security and access permissions to be viewing and using the data, and preparing the data so that its in a usable state often mean that data orders don’t get filled in a timely fashion. To save time, IT departments may fulfill data orders with large data sets—leaving the recipient to sort through the data themselves to find what they need.

  1. Siloed Data Makes Access Difficult

If data is siloed, it can be difficult to identify where it resides within an organization. This can lead to frustration on both sides of the equation. IT is not always aware of the data required by business units and may be unable to identify the source, leading to workflow bottlenecks quickly.  In addition, siloed data makes it difficult for organizations to conduct accurate analysis and reporting across departments.


  1. Consideration for Security and Access Privileges  

Organizations are responsible for protecting their data. This often means restricting access to certain business units or to specific individuals within those units. Data can be siloed by default if a company uses multiple systems and platforms—such as a CRM, ERP, and accounting software.

For example, if one system stores customer information and another stores product inventory information, it’s difficult for both departments to view the same data at once. It can be difficult to ensure that data is kept secure and only accessible to those who need it.

Data distribution also presents an opportunity for fraud or misuse if the organization doesn’t have clear policies around access and security. If a business unit cannot access data centrally, it may be necessary to grant each person who needs access to their own set of privileges. This can make it difficult for the organization to maintain control over who can do what with the data.

Finally, It can be difficult to establish and maintain a secure data ecosystem that is accessible by multiple departments. For example, an organization may have enterprise-level security policies in place for its data warehouse, but each department has its own set of security procedures and protocols. If these policies don’t align with each other or with external regulations such as privacy laws, they could cause internal friction and confusion.

Common Challenges of External Data Distribution

  1. Reliance On IT for Data Order Fulfillment

With regards to IT teams, the same challenges with external distribution present themselves: fulfilling orders in a timely manner is still time-consuming and resource-intensive, but the problem is perhaps compounded when it’s external customer data orders since there’s more pressure for swiftness as well as accuracy, and dumping large datasets for external customers to sort is both a security risk and doesn’t give a good impression.

  1. Scalability

Without an effective data distribution solution, scaling external data fulfillment orders can quickly become impossible or at least extremely difficult. Until CME Group became a Revelate customer, they struggled with making the large amount of data that they had accessible to external customers. As a financial organization, CME’s wealth of data (which includes integrated third-party datasets as well) is extremely useful to a wide variety of organizations to discover insights to capture market opportunities. This example represents so many organizations that struggle with managing vast amounts of datasets that are in demand, where manual fulfillment processes simply were not scalable enough to capture all possible customer opportunities.

  1. Metadata Limitations on Data Marketplaces

Data marketplaces typically limit what information (metadata) can and cannot be displayed in an effort to keep data products standardized, which limits what the data provider can convey about their product in the metadata. This could potentially cause the right data customers to bypass the data product because they don’t fully understand what it contains.

Businesses usually choose data marketplaces because they have an established audience, but if that audience can’t find the product because it’s not displayed in a way that makes sense, then the business won’t be able to take full advantage of the activity of the marketplace.

How Revelate Addresses Internal and External Data Distribution Challenges

Breaking it down, the main challenges of data distribution, whether it’s internal or external, fall within the categories of:

  • Data fulfillment efficiency
  • Data access and security
  • Scalability with data distribution

Revelate, as a data fulfillment platform, addresses these challenges by providing a fully customizable data marketplace that automates the entirety of an organization’s data fulfillment supply chain from start to finish. This means that no intervention is needed from the point that a data customer requests an order to fulfillment.

Security and access permissions established by your organization are established in advance through data marketplace access (which can be set to public, private, or hybrid) and through checks and balances once a dataset is requested. Because Revelate is platform agnostic, datasets can be extracted from any source. The automated preparation and processing step ensures that the datasets are fully prepared to the data customer’s specifications before being downloaded to the data marketplace.

This means that anyone can fulfill data orders, not just your IT department. Self-service options allow data customers to request datasets on their own based on data products displayed on your data marketplace, or a salesperson, administrator, or other frontline employees can request a data product on a customer’s behalf.

What’s more, your data marketplace can handle simultaneous orders—meaning that data orders are filled in a timely manner, whether it’s one or one hundred orders being requested at one time.

The Benefits of Good Distribution of Data

The Benefits of Good Distribution of Data

When data distribution is done effectively, the benefits can be seen through increased operational efficiency, better business insights, and ultimately better business decisions being made.

Here are a few of the specific benefits of good distribution of data:

Data Distribution Benefit Description
Increased market intelligence When data is readily available to those who need it, organizations can better understand their target market(s), including the ebbs and flows that affect business opportunities.
Better customer insights Gaining a better understanding of customers so it’s easier to sell to them is a goal of businesses in all industries. Data-driven insights remove bias, focus on facts, and provide concrete information on customer behavior, including buying patterns, seasonality, and more, allowing businesses to adjust goals and objectives accordingly.
Accountability and ownership Part of effective organizational data distribution and governance is establishing accountability and ownership of data. Departments should own and be responsible for their data alongside data stewards who ensure that governance policies are followed as data flows throughout (and outside of) an organization.
Better data governance When data distribution best practices are followed, it supports better organizational data governance. Data governance and distribution go hand in hand since security and access measures must be ensured as data flows throughout an organization. Data governance tools assist businesses in executing governance policies and procedures and ensuring governance technologies are working as intended.
Create new revenue opportunities When organizational data distribution strategy is rock-solid, it’s easier to capitalize on the revenue-enhancing opportunities that data brings. Whether it’s through better decision-making or the discovery of new products or services that the business can offer, effective data distribution is behind these efforts and can pay off in dividends.


Organizational data distribution is an extremely lucrative business endeavor that can greatly increase an organization’s revenue, help them make better business decisions, and overall positively impact every aspect of its operations. But behind the scenes, data distribution has to be done effectively, or an organization won’t see the results it’s looking for.

Taking the time to analyze each of the common challenges with the effective distribution of data, and identify ones that are perhaps unique to your organization, is paramount for creating an effective and efficient data distribution strategy. Once challenges are identified, steps can be taken to overcome them by following best practices with data governance and data science as these relate to effective data distribution.

If you’re looking for an effective tool to help you monetize, share, or exchange your organization’s data, then Revelate’s data marketplace provides the perfect solution.

Book a demo with us today to learn more!

Simplify Data Fulfillment 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