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How to Break Down Organizational Silos and Unlock Value From Your Data
Are organizational silos holding you back from driving value across your company?
Organizational silos prevent companies from achieving a holistic, connected customer view across their business divisions. They hide data relationships that, when accessible across divisions, present greater insights, patterns of referral, and growth opportunities.
In this article, learn about the challenges of the silo mentality and how to break down the barriers using a customer intelligence solution that combines Entity Resolution and network generation to create a connected view of customers. With this solution, you unlock data value to gain a holistic understanding of your overall client population and their relationships so you can provide better client service and further growth.
The problem with organizational silos
For large companies, organizational silos enable specialized servicing of clients across different business divisions or departments. However, they also segregate data, removing the ability to see how:
The same client is engaged across different departments.
Multiple clients across departments might be related to one another – for example through legal ownership structures or directorships.
Existing clients in one department can provide referrals to help win new business in another.
On top of this lack of visibility and clarity, each department operates according to its own narrow view of a customer, with no appreciation of the broader relationships. Also, engaging the same customer independently across various departments often leads to poor customer experience.
By breaking down the organizational silos, you create a single customer view based on quality, connected data. This view opens your company to new ways and opportunities to engage customers, speak with them, and even provide services to them.
Customer data solutions that fall short
Organizations typically apply different customer data solutions within a particular division. However, they aren’t sufficient in establishing a full picture of the client network across all divisions. Let’s look at a few commonly used solutions and why they aren’t effective at breaking down the silo mentality.
Customer data platforms
Customer data platforms (CDP) connect customer data across multiple sources to drive more relevant and timely engagement across channels. They use deterministic matching, such as whether records have the same email addresses, to build the identity and profile of an individual across digital and marketing platforms to better understand those journeys.
These data platforms come with the following limitations:
No network or graph capabilities to establish relationships between clients and across different lines of business
No ability to do probabilistic matching or fuzzy matching that infers matches based on a broader set of attributes
Can’t handle complex customer segments like large corporations or high net worth clients
CDPs are more suited toward B2C client engagement, such as in retail and small business, but lack the capabilities that organizations require to achieve a holistic view of their customer base across B2C and B2B.
Customer relationship management systems
Customer relationship management (CRM) systems capture and track the accounts, contacts, leads, opportunities, and engagement data from frontline teams. Often each department has its own CRM instance. To create a single customer view across divisions, organizations might attempt to consolidate these instances into a single, unified CRM.
These systems have the following challenges:
Matching technology is unsophisticated, and some CRMs use a fixed-data model that can be restrictive when creating a single view
CRM vendors often have specific applications tailored to particular client segments – for example, Corporate or Wealth Management – forcing separation of this data
They struggle to integrate other organization-wide data, such as product usage, payments, and transactions, or large third-party data sets which are needed to understand relationships between clients and prospects
They lack sophisticated network or graph capability to define and visualize relationships
Data lakes
Data lakes are a catchall for data from different areas of an organization. Using in-house analytics, data science teams try to aggregate and process raw data to gain insights.
The biggest challenge of data lakes is the extensive time and effort the in-house solution requires linking the data together. Highly paid data scientists and analysts spend their resources preprocessing and cleansing data to create a match. Instead, they should be analyzing a prepared single view of data and creating insights to drive value across your organization. Also, these solutions are difficult to operationalize at scale.
How to break down silos and unlock data value
While customer data solutions have their own advantages, they aren’t able to create a connected customer view across organizational silos that you can use to drive the cross-line of business collaboration. To understand what these concepts mean, let’s break down each one.
Generate a connected customer view
To connect your customers across silos, you need a knowledge graph of your internal and external customer data that provides an enriched view of your customers and their relationships. This view helps you understand both the customer, their broader connections, and the profile of those connections.
For example, a bank may be able to identify that a Corporate client has:
A fast-growing subsidiary that is being serviced by the Business bank
A major shareholder who is a Private bank prospect and shares an address with a Retail customer
Recently started trading as part of a supply chain with two other high-value Corporate clients
Together, this information and level of detail create a connected customer view of your customers and potential customers across all business divisions.
Drive collaboration across lines of business
Organizations often manage their various divisions in isolation. As a result, they don’t effectively leverage the scale of their customer base because they don’t understand all the connections across those different segments.
With a connected customer view, you break down the barriers between organizational silos, creating the opportunity to drive collaboration across lines of business. In doing so, you gain a clear understanding of how all your customers are linked so you can offer better service to your clients and prospects, do more targeted prospecting, and accelerate growth.
A significant advantage of this collaboration is the faster acquisition of new customers. For example, a bank can automatically identify where existing retail or wealth customers have connections to high-value commercial or corporate bank prospects. Then, it can flag these opportunities to the appropriate relationship managers.
Connect data silos with Customer Intelligence
Creating a connected customer view and driving collaboration across departments starts with a customer intelligence solution. This will provide a centralized customer decisioning engine that pulls together external and internal information across different sources. It supplies unique network insights to power more personalized engagement and accelerate growth.
To create a connected customer view so you can work across data silos, a customer intelligence solution requires two key components:
Entity Resolution to pull information from internal and external sources to build a single view of people, businesses, and addresses. It yields much greater accuracy than traditional data-matching techniques.
Network generation to establish relationships between all people, businesses and addresses and create a knowledge graph of your customers and non-customers.
After you create the connected customer view, you apply analytics across those networks to automatically highlight opportunities where you can engage customers or prospects. For example, you might predict that an existing commercial bank client is likely to IPO in the near future and prompt engagement with its major shareholders as potential private bank clients.
You might also create more personalized engagement with your existing customers by understanding their broader social connections. Or you might try to gain insights into your customers’ connections to other businesses they interact with so you can suggest more targeted products for them and provide custom pricing.
Customer Intelligence driven by Decision Intelligence
A customer intelligence solution built using Entity Resolution and network generation creates even more value when your data end users gain from its insights.
Once you have a connected customer view from the customer intelligence solution, you can make the insights available to your decision-makers across departments. You can also integrate the output with other systems, such as a CRM system, and make the information available to customer service agents or individuals in other business sectors.
Learn how customer intelligence creates a connected customer view and holistic understanding of your customers and prospects.
