Data Ecosystems Simplified: Strategy, Architecture, Models & More

Organizations these days have a lot of data⁠—websites, customer information, general files, and overall enterprise infrastructure and applications. The total sum of these components makes up the organization’s data ecosystem. An organization’s data ecosystem provides decision-makers with the information they need to make data driven-decisions that positively impact the business.

These days, organizations are often globally distributed, with resellers, suppliers, stakeholders, business partners, and more existing in different environments and economic climates. This means that for larger organizations, they’re dealing with multiple data ecosystems, often with different governance and access policies. The challenge, then, is ensuring that these ecosystems remain interconnected (to prevent data silos) while at the same time maintaining relevant security, access, and data governance, even as the scale increases.

But before we get into more about the challenges that organizations face with building and managing their data ecosystems, let’s take a few steps back and start with a clear explanation of what a data ecosystem is, the different types, and how they work.

What is a Data Ecosystem?

The definition of a data ecosystem is the collection of data gathering tools, analysis infrastructure, systems, and applications in an organization.

To visualize this, consider that every data ecosystem includes these base components:

1. An infrastructural layer, which is the combination of hardware and software capture and manage data. This includes servers, databases, data warehouses, data lakes, and more.
2. Applications, which include the services and tools that act upon data to make it into usable sets. Revelate is an example of an application that acts upon data to transform it into usable data products that can then be shared or sold.
3. Analytics platforms, which provide the tools that decision-makers, data scientists, and analysts need to understand what the data is telling them, and how to act on that information.

If we look at data ecosystems in the context of data sharing, whether it’s internally or externally, then data ecosystems take place in three main forms:

1. Closed data ecosystems, where data is shared in a closed environment consisting of several data providers with no additional outside access
2. Strategic partnerships, where a small number of providers share data with each other for a specific purpose
3. Open data ecosystems, where data is made publicly available

The value that an organization gets from becoming part of a big data ecosystem is that they can mix data from a variety of sources outside of their own business, and as a result better understand their reputation, their customers, how their business fits within a competitive market, and gain insight and information that simply wouldn’t be possible if they relied entirely on internal data.

In other words, a successful data ecosystem creates a mutually beneficial data sharing relationship between providers by:

• Allowing a low barrier to entry for relevant participants, with clear indications regarding how data is beneficial so that participants are less likely to want to leave the ecosystem and will continue to contribute to it
• Motivating participants to join forces, typically because they have similar goals and interests and see the value of having different stakeholders in the system (such as users and developers)

Examples of how companies can use external data

Benefits of The Data Ecosystem

1. Allows organizations to understand their customers better. Internal data paints a picture of customer behavior, buying habits, and the overall journey from the investigation of a problem to the sale. It allows an organization to understand how a customer interacts with their business, but that’s only one piece of the puzzle.
   Sure, internal data gives some insight into a customer’s pain points, business operations, and much more, but it’s a narrow information path. You’ll get enough pieces of the puzzle to get an idea of what the overall picture looks like, but you’ll miss the details that could lead to better customer service, a lucrative new product or service offering, or a way to optimize your sales pipeline. Augmenting internal data with external data fills in those gaps and allows you to see a complete picture rather than part of one.
   Capgemini’s recent Data Sharing Masters report found that organizations that are a part of data-sharing ecosystems improve customer satisfaction by an average of 15%, improve business productivity and efficiency by 14%, and reduce annual costs by 11% in a period of 2-3 years.
2. Gives organizations a competitive advantage. An organization improving its understanding of their customer is an obvious competitive advantage, but a data ecosystem can go beyond that as well by eliminating data silos between suppliers, partners, distributors, and other stakeholders.
   For example, supply chains can be optimized by analyzing partner data. As a more specific, real-world example, the speed of the COVID-19 vaccine development and rollout in the UK was made possible in part by data-sharing ecosystems among researchers, healthcare companies, and policymakers by allowing them to test efficacy, reduce risks and side effects, and make factual information about the vaccines available to the public.
3. Provides organizations with an additional revenue stream. Selling data can be an extremely lucrative venture for many organizations. From providing additional protection in times of economic uncertainty by having a reliable revenue stream to gaining additional potential benefits from data asset sales. Revelate puts the analytics data from data product sales back into the hands of the provider, allowing them to glean insights to ensure their data monetization strategy remains successful.
4. Creates mutually beneficial partnerships. It can be tough to get all the distributed stakeholders involved in an organization to work seamlessly with each other, but data ecosystems help that cause by making data from any stakeholder highly accessible.
   One example from McKinsey discusses issues that semiconductor companies have with reaching their customers worldwide through distribution and how data sharing provides a solution. By utilizing data sharing to take a more analytical, collaborative approach to distribution, namely by analyzing detailed sales information and industry trends, these companies can more effectively navigate a rapidly evolving landscape and improve business strategies simultaneously.
5. Helps prevent data silos. According to Gartner’s research, business decisions based on old or erroneous data could cost small and mid-sized businesses up to $15 million in losses per year. Data silos lead to hindered and ineffective decision-making, wasted time, reduced productivity, and much more because BI and analytics aren’t considering the entire story surrounding the business. It’s like pointing a flashlight straight ahead in a pitch-black forest and trying to draw a map based on the tiny glimpse of the landscape that you see. When data is unified across the organization and made easily accessible to all (with the appropriate security measures in place, of course), then better decisions can be made, with more successful outcomes.

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How to Overcome Data Modernization Strategy Challenges

Organizations navigating the digital transformation to modernize their data ecosystem do so because of its benefits. However, there are challenges with modernization that must be understood before it can take place effectively.

Organizational Challenges with Finding and Using External Data

Although access to external data is becoming easier in some ways, such as government-run portals that allow organizations to download a wide range of datasets for free or for a cost (data.gov, the portal for the United States, and data.gov.uk, the portal for the UK are two examples) there are still challenges with using external data that organizations have to overcome, which are highlighted in the table below from Deloitte:

The solutions for these challenges isn’t always straightforward, and to compound the issue, Deloitte rightly points out that the longer that organizations take to address these challenges, the less time they’ll have to react to market trends and other lucrative events quickly, potentially putting business revenue at risk.

• Complex data-provider market and data quality. The sea of data marketplaces out there can not only feel overwhelming, especially to a new entrant in the space but also brings questions about potential data quality to light. Every market has its own standards about how data products are displayed, limiting flexibility with metadata descriptions and, subsequently, the ability to find relevant content. This is just one factor that can affect the quality of data that’s being consumed from one organization to the next; not only is the discovery process potentially lengthened, but the overall dataset could prove to be useless to one organization and only partially useful to another, all because of limited metadata. In addition, many data marketplaces limit registered users’ access, preventing exposure beyond a specific audience.
  Revelate’s data platform allows organizations to create a fully customizable data web store that allows data transfer from anywhere to anyone, increasing the availability of data products and allowing organizations to describe them effectively.
• Purchase liability terms and data preparation. Privacy and security is a big consideration for organizations concerning data sharing. Organizations have complex relationships that include internal and external stakeholders and managing data access to ensure the right person gets access to the right data can be challenging. In some cases, the same data set will have some relevant data for a stakeholder, but the rest shouldn’t be privy to their access.
  With Revelate, you can not only create a data web store with multiple access fronts (public, private, or hybrid), but you can also limit specific information within a dataset to users that meet the criteria. For instance, a sales data set that includes information about each salesperson’s quotas and sales goals isn’t relevant to a supplier, but the information in the same dataset outlines the movement of a specific product they provide. With set-out security and access privileges, only the relevant portion of the dataset would be accessible to the supplier.
• Managing relationships between data providers and effectively storing data. From a relationship management standpoint, if the organization hasn’t already, is to shift the thinking from being the main character in a data ecosystem to a participant within it. The very principle of data sharing is that everyone is getting mutual benefits from it, so everyone should consider themselves an equal actor in the ecosystem as well. With Revelate, data remains stored with the provider, and data is only extracted when it’s necessary, following set-out security and access protocols. This means that organizations retain full control over their data while still having the ability to participate in a data-sharing ecosystem.

Data Ecosystem Modernization Challenges for New Organizations

1. 1. Relationships with suppliers, partners, distributors, and other internal and external stakeholders may still be established and may change over time, making managing the entry and exit of providers within the established ecosystem tough to maintain, especially at the beginning.
   2. Scoping and scaling, especially trying to predict what the future will hold in terms of business goals and aligning them with data-sharing initiatives may be hard to determine at such an early stage of the business.
   3. Technical requirements for developing a secure and compliant data ecosystem model and the technology and IT expertise required to categorize, organize, and set out security and access measures for data sets may seem overwhelming in terms of the time and cost required.

Data Ecosystem Modernization Challenges for Growing Organizations

1. 1. Continued scalability of a data ecosystem in a growing organization can potentially cause issues as the organization struggles to mitigate data quality issues and redundancies and implement standardizations for new and evolving datasets across the entire organization.
   2. Inadequate data automation and management are possible as more and more data enters the ecosystem and manual processes are implemented. Automation can be established at the same time, but likely the organization will need time to determine which automations are necessary and need time to develop them alongside security and access procedures.
   3. Data and technical expertise with regard to the use cases of specific data sets versus business value will need to be handled by professionals with an understanding of these factors, mainly through data governance and maintaining data quality.

Data Ecosystem Modernization Challenges for Mature Organizations

1. 1. Inadequate automation to handle the movement of high volumes of data can mean that data discovery and security become difficult, even for organizations that are well-versed in the world of data ecosystems. Manual processes are prone to errors and security risks, so mature organizations should invest time and resources into creating effective automations that streamline high-volume data movement and address security risks without compromising efficiency.
   2. Bureaucracy is extremely common in highly mature organizations with data ecosystems, causing even simplistic processes to be slowed to a crawl as employees and stakeholders are forced to navigate rules and standards in an attempt to be consistent.
   3. Maintaining a holistic data ecosystem can become difficult over time, especially with large enterprises with multiple subsidiaries. These child organizations may replicate solutions from the parent organization in creating their own data ecosystem, resulting in a siloed effect as the data ecosystem develops independently of the parent organization.

Key Drivers for Data Ecosystem Modernization Strategy

The ability to organize and glean value from large amounts of data

While most organizations are focused on growth, the difficulty in gleaning business value from large amounts of data can be difficult without an effective data modernization strategy. Tons of data holding untapped business potential sitting there waiting to be taken advantage of is a huge driver for businesses to take the plunge with modernizing the technology behind their data governance, storage, and handling to create an accessible ecosystem.

Data democratization

The conversation surrounding data democratization is an important one and one that has convinced organizations to rethink how they manage, distribute, and interpret data. The understanding that data sharing has infinite potential benefits when the right data gets into the right hands is a key driver for an organization to modernize its ecosystem, especially when they understand that it can retain control over its data regarding who can access it and when.

Data quality and security

Through modern data ecosystems, data quality, and security can be effectively managed easier than before. While real-time authorization has been a challenge in the past, automations can quickly apply security and access measures to users according to preset conditions. Other tools, such as timed access control or credential-based limited access, can further improve accessibility to data while at the same time ensuring security. When data goes through consistent checks and balances before it’s made available, that data’s trustworthiness also improves.

Different Data Ecosystem Architecture Components

Considerations When Creating a Data Ecosystem

A successful data ecosystem requires thoughtful business strategy, a good understanding of what you want its purpose to be, and how people will interact with it.

• Start small and build up as you go. Starting small and scaling is always a good way to begin any major project, especially a data ecosystem model. Start by discussing design, sharing models, security and privacy, needed technology, and determining a strategy for scaling up over time.
• Consider risks. Legal and data governance risks should be taken seriously and given the time and resources needed to ensure that contingency measures are executed properly.
• Use standard over proprietary. There’s no need to reinvent the wheel when others have already created systems that work. Leveraging pre-built systems and architecture save time, while ensuring functionality through the previous testing.
• How data governance will be established. Create data governance guidelines that fit with your company’s security and privacy standards, plus follow applicable regulations, such as GDPR, the CPRA, and CCPA.

How to Build a Data Ecosystem

Data ecosystems contain three main components:

1. Infrastructure

   Your ecosystem’s infrastructure is like the foundation of a house; walls, plumbing, electrical, and more gets built upon it and relies on that foundation to sustain the rest of the structure. The infrastructure of a data ecosystem model contains:

   • Unstructured Data, which is unorganized files, such as video, audio, or image files. Unstructured data is stored in data lakes, since there’s not enough information about it to place it in any specific location.
   • Structured Data, which is organized files, such as contact information, credit card numbers, or geolocation data. Structured data contains the information needed to be easily searchable in databases, and easily used by ML and AI. Structured data is stored in data warehouses, because it already has the information for the system to understand what it is and where it belongs.

2. Analytics

   Think of analytics software as the front door of our metaphorical house, that allows your teams to access the data within the ecosystem and glean insights from it. Many out-of-the-box data ecosystems include some form of analytics, but they are often rudimentary compared to a dedication solution. Instead, a robust analytics solution often consists of a variety of tools, such as data visualization programs, reporting tools, data mining programs, and open-source programming languages to capture and make sense of the data appropriately.

3. Applications

   The applications in a data ecosystem are like the roof of the house. They act upon the data within the ecosystem to make it usable. A data application analyzes large amounts of data and extracts insights from it to support business decisions. These insights can be visual like reports and dashboards, or they can be more rudimentary like numbers in a spreadsheet.

Conclusion

The conversation around data ecosystems is constantly evolving as new technologies and best practices emerge, but one thing’s for sure: implementing a data ecosystem to handle the flow of data in and out of your organization is paramount to business success.

Contact us today if you’re interested in learning more about Revelate and how our all-in-one data fulfillment platform can support your data-sharing initiatives.

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