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Companies care so much about data distribution because it often gets locked into organizational silos, becoming inaccessible to those who could use it. Data doesn’t take much time to get locked up in a silo. There’s rarely any intention to do so, yet it still happens.
The potential of data is quite limited when it’s stuck in a silo. It may have localized value, but chances are it can do so much more when it can move around an organization.
Moving data throughout an organization can be challenging but well worth it. There are innumerable benefits to distributed data, whether you’re:
- An executive looking to optimize operations
- A data analyst making sense of complex data sets
- A data-interested employee curious about the potential of data
Let’s explore some of these challenges and benefits.
What is Data Distribution?
Organizational data distribution is understanding how data is spread out or distributed within an organization and how that data can be used to make smarter decisions and gain a competitive advantage. Data distribution can help identify trends and patterns and optimize operations. This can include data about employees, customers, products, sales, or other aspects of an organization’s operations.
For example, an organization might collect data on employee salaries. The distribution of this data might tell us how many employees are in each salary band and whether there are outliers, i.e., employees who earn significantly more or less than others.
The Benefits of Good Organizational Data Distribution
Strong organizational data distribution is essential for harnessing the full value of the data your organization generates daily. Gaining meaningful insights into your data can drive business success in several areas.
Let’s dive into some critical benefits of solid organizational data distribution.
By understanding how data is distributed within your organization, executives and top decision-makers can make informed choices based on real-time data insights. Such decisions can affect operational efficiency, customer experience, and even bottom-line revenue.
Companies that effectively manage and distribute their data can make faster (and better) decisions, respond more quickly to market changes, and gain insights that will help them with innovation. This edge can help them stay ahead of the competition and earn and/or maintain a reputation as a market leader.
Better customer insights
By analyzing customer data and understanding its distribution, an organization can gain a more complete view of its customers and their needs. For example, companies can optimize their engagement strategies for better reach and engagement by understanding how customer data is distributed across different touch points such as social media, email, or websites.
Organizations can also better personalize their interactions with customers. If they can understand that a specific customer purchased a particular product in the past, they can offer personalized recommendations or discounts.
Stronger data governance
Strong organizational data distribution can help organizations:
- Identify potential data quality issues and take steps to correct them
- Better control of who has access to sensitive data
- Protect data and sensitive information from unauthorized access or breaches
- Streamline compliance with regulations like GDPR, CCPA, HIPAA, and other data privacy laws
Data governance and distribution go hand in hand, as security and access measures are critical when data flows throughout an organization. Data governance tools will help execute governance policies and procedures so your data flow is efficient and secure.
The Challenges of Organizational Data Distribution
Because data is distributed across multiple systems and departments, challenges can make gaining a complete view of your data difficult. Data may be in different formats or stored in so many disparate systems that it is difficult to find and integrate it all effectively. Let’s examine some common challenges and how organizations can address them.
Data silos aren’t just bogeymen—they’re real hurdles to effective data management and insights. When each department manages its data in isolation from the rest of the organization, crucial insights may be inaccessible. Worse, the org may never know those insights are there.
Data silos create inconsistencies or errors in data analysis, especially as different business units might define or interpret the same data differently. Plus, the more data structures, definitions, and formats an organization has, the harder it is to integrate data from disparate sources.
Speaking of, data integration can be a considerable hurdle in distribution. Inconsistent data definitions, formats, and naming conventions challenge unifying data.
Integration can also be a time-consuming and resource-intensive process. Some organizations may need to invest in expensive specialized tools or technologies that seem overwhelming or deterrent. There is also the layer of data governance, as setting up any integrations requires a clear understanding of data privacy regulations and governance policies and procedures, which can add more time and resources to integration projects.
The more data is spread across different systems and departments, the more difficult it becomes to control who can access it. Several factors contribute to security challenges in data management and distribution:
- Access control: With murky or undefined access controls, it can be challenging to understand and restrict who has access to sensitive or confidential data. This can make it easier for unauthorized individuals and/or bad actors to access or steal data
- Data privacy regulations: Many organizations are subject to data privacy regulations like GDPR and CCPA. These and other programs place strict data management, storage, and protection requirements. Siloed and disparate data can make it challenging to ensure that data across the organization is managed in compliance with those regulations
- System vulnerabilities: Each system that manages or stores data is vulnerable to cyber attacks, breaches, and other security threats. The risk of a successful attack increases when data is distributed across multiple systems
No data go-to-market strategy
Many organizations that want to monetize their data or create new revenue streams have not adequately planned how to distribute their data and take it to market. There are three components necessary to building a go-to-market data strategy:
Logistics of storage and data packaging
Orgs first determine how they will package the data scattered across their organization and make it available to data buyers. It is also important to consider data federation, or centralizing data to be accessible from a single location. Data warehouses and/or data lakes (or a combination like Databricks’ Delta Lake) are standard, trusted methods of data federation.
Identifying a target audience
Whether aiming to sell or share data internally or externally, you must understand your target customer. To identify your target audience, start by defining your goals. Do you want to improve customer engagement, optimize specific operations, or create a new revenue stream?
Then, identify your audience’s needs and what data is already available. What unique qualities will your data have? You should also analyze your data to determine whether your trends, patterns, and insights are relevant to your audience.
Know what you want to sell
There are many different types of data to sell, and you must know what you want to make available. Is it operational data that could help with supply chain management? Do you want to provide financial data to help organizations with risk management? Or do you have customer data that advertisers, marketers, and other businesses can use to improve ad targeting?
Determine what data you have that can be useful and whether there’s demand for such data.
Internal vs External Data Distribution
Internal data distribution involves sharing data and information between departments, among individuals within a department, across locations, or even across a network of business partnerships (e.g. consultancies, lawyers, affiliates). Internal data provides many insights about business operations but often provides a limited scope that does not consider insights across a market, industry, or economy.
Many organizations also want to avail themselves of external data—to augment their internal data and further refine their decision-making.
External data distribution, therefore, occurs with stakeholders outside of an organization. This includes customers, vendors, suppliers, distributors, and more.
Internal and external data distribution face unique challenges to overcome.
Internal data distribution challenges
Internal data distribution can create silos where data is stored and managed in isolated systems or departments. Siloed data makes it difficult for IT to understand the data business units require or the source of that data, quickly leading to workflow bottlenecks. Data silos also make it difficult to get a complete view of organizational data, leading to errors in analysis and reporting.
Reliance on IT
Data fulfillment is a taxing task for IT teams. Between ensuring the requestor has the appropriate security and access permissions and lengthy data preparation, IT can quickly get in the weeds. To save time, IT may fulfill data orders with large data sets, which leaves the requestor to sort through the data themselves.
Security and access privileges
When data is siloed, say, between a CRM and ERP, access quickly becomes an issue. If one system stores prospect information for the sales team and the other stores purchase data, it’s reasonable for both sales and marketing to access that data of their own accord. This makes it challenging to ensure the data is accessible only to those who need it.
If a business unit cannot centrally access data, IT may need to grant each person requesting access to their own privileges, which is time-consuming and vulnerable to inconsistencies. Without enterprise-wide security policies, a company is at the mercy of each department’s security procedures and protocols. This is risky, confusing, and messy.
External data distribution challenges
Effective metadata management requires clear, consistent data definitions, structures, and naming conventions. If that metadata is inaccurate or inconsistent, it can impact how external parties interpret it. Metadata management tools can help organizations manage their data effectively across different systems, departments, and external parties.
Data privacy and security
Again we see that data distribution requires secure, compliant data. External data must still adhere to privacy regulations, which require careful data access management and storage. When sharing data externally, management must also be cautious for access controls, encryption, and other security measures to keep the data safe from unauthorized access or theft.
Data marketplace metadata limitations
In the name of standardizations, Data marketplaces typically limit what types of metadata can and cannot be displayed. Limiting the information a provider can convey about their product can cause the right data customers to inadvertently bypass the data product, as they don’t have the correct information to understand what it contains. Businesses usually choose data marketplaces because they have an established audience, but if that audience can’t find the product due to display limitations, a business can’t take full advantage of that marketplace’s activity.
How Revelate Eliminates the Challenges of Data Distribution
Revelate enables data distribution across formats, platforms, and organizations with very few technical inputs. Because Revelate is platform agnostic, we can help teams extract data from any source. Our data platform processes, prepares, and distributes data through our data marketplace. Plus, we offer a cost-effective, accessible platform for organizations of all sizes, whether established enterprises or growing businesses.
Revelate leverages automation so that when a data consumer requests your data in the marketplace, a series of triggers will fulfill the order. First, Revelate references your customized security and access policies to determine if the user can access the data. Second, Revelate extracts data from its source location and then prepares and processes it for distribution. Finally, Revelate distributes the dataset to the customer through the data marketplace.
Because this process is automatic, anyone can fulfill orders, not just your IT department. Data customers can use self-service to request datasets based on products in your data marketplace, and an employee can bid a data product on a customer’s behalf.
Effective organizational data distribution is critical to realizing the benefits of your data assets. While this process can be complex and challenging, it doesn’t have to be. Revelate helps organizations with every step, from data integration and governance to data quality, enrichment, and distribution.
Want to see how Revelate can help you generate value from your data assets? Book a demo with us today.