Trust takes years to build, seconds to break and forever to repair
Information in a client relationship:
In today’s business environment, the relationship between organizations and their clients is increasingly multidimensional, whether the clients are individuals, organizations or combinations of the two. And increasingly, a dimension of that relationship involves transacting with information. Consider:
- Products or services provided to the client rely, to a greater or lesser degree, on information that is provided by the client, enriched with other sources, or developed organically by the organization,
- In the course of providing service, the organization takes in and may retain client information to directly or indirectly enable, enhance or enrich client experience. For example, client account information, CRM data, payment information, loyalty profile information,
- In many settings, organizations retain details of transactions for record-keeping purposes, required by regulations or industry standards.
- In other settings, information taken in during a transaction contributes to enriching a dataset or training an algorithm, which in turn improves subsequent transactions
An element of client and customer loyalty is the belief in the ongoing usefulness and quality of the products and services, and trust that the organization will not violate the implicit or explicit terms of their relationship.
So what is the CDO’s role in preserving trust?
Data is playing an increasingly prominent role in most organizations’ products and services, whether as net-new data-oriented offerings, or by enriching existing products and services, or helping to optimize internal decision-making and operations. So how does data play into client trust? Three ways..
Data becoming part of products and services:
As data becomes more integral to products and services, it becomes a more important part of the client experience. Depending on the use case, the breadth, depth and range of data used to enrich the product/service will increasingly become a competitive differentiator. Just like the race to add features to on-up the competition, the richness of the data-sets will be used to distinguish one offering from another. For example,
- The AI features of a consumer electronic device (enhanced by a richer training data-set),
- The relevance and number of true-peer companies represented in a data set used to recommend new or improved business practices,
- The number and range of inputs into a cognitive engine used to forecast business trends,
- The range of inputs and sensors measuring performance on an industrial device, and the real-time analytics optimizing performance, and
- The number of additional data sources used to enrich a dataset licensed to clients, and the ability to adjust quickly.
Data vision describes the ways an organization wants to integrate data into products and services, and the data strategy lays out how the organization plans to get there. The CDO is responsible for coordinating the data vision and ensuring execution of the data strategy including sourcing and managing data through its lifecycle.
So it follows that the more the product or service relies on data to meet client needs, then the more the CDO is key to deliver on those data capabilities. And the more the organization demonstrates the ability to deliver value, the more the client will trust the organization and their brand.
Quality and reliability are central to trust and a client’s desire to engage with an organization. Trust that the quality and reliability will remain is key to maintaining an ongoing relationship. This is true whether at the consumer level, where the transaction involves buying a product, or choosing a doctor or bank, or at the corporate level, buying products or supplies, or engaging an advisor or a BPO.
As the products and services become more dependent on data, issues with quality and integrity of the data can have a greater impact on the product or services, which affects the reputation of the organization and the sustainability of the client relationship. Revisiting the examples from above, consider the following:
- What if the AI features of the consumer electronic device can’t respond to queries appropriately, or worse, actions are inconsistent?
- What if the datasets used to base business recommendations are outdated, or the reference companies aren’t peers?
- What if the data used to train a cognitive algorithm is representative of the business or transactions being modelled?
- What of the sensors are tuned for metric units but comparative data is in imperial units? and
- What if the organization doesn’t have rights to the data used to enrich a dataset licensed to a client?
Assessing risks to the quality of data starts with a data risk management cycle to understand what can reasonably go wrong, and the impact those events can have on the products/services relying on the data. Flowing from this, an organization should implement a right-sized set of governance and management processes. These not only catalog data with a common ontology and taxonomy, but they track data lineage through its lifecycle from generation/acquisition, through use, and ultimately disposition. Ideally, this overlays all key systems and processes in an organization, but pragmatically, they should prioritize the more impactful data (hence the use of the term “right-sized”).
As the CDO should be the business owner of the data governance and management processes, it follows that properly ensuring the quality of data augmenting client-facing products and services is the CDO’s responsibility. This connects the CDO directly to the trust the clients have in the products and services provided by the organization.
The third leg in a CDO’s stool is data protection. Data used to enhance products and services belongs to someone. And that “someone” generally has an expectation for the protection of their information, expressed through a combination of policies, contracts and regulations.
When a client hands their over information to an organization, they generally do so with the expectation of getting something in return — usually some sort of service or added value. The hallmark of a great business relationship is when the client feels comfortable sharing their most important information – relatively openly and seamlessly – in order to get proportional value in return, without having to worry whether the organization will accidentally or maliciously mishandle the information in any way.
So it follows that in order to ensure the client’s expectations are met with respect to handling of their information, the CDO needs to have a clear understanding of where the information is, what is it being used for, who has access to it, what are the constraints and limitations around its use, and what the client expectations are in the event of misuse/exposure/breach and finally, retention/disposition requirements. The CDO also needs an understanding of the softer elements, meaning, what are the unstated expectations for handling the information that are baked into the relationship with the client, and how can they be met. The CDO converts these to information protection requirements they provide to the CIO, CISO, HR, Physical Security, etc., within their organization.
Failing to treat information in line with requirements and expectations can lead to a variety of consequences, including regulatory fines, brand damage and loss of client trust.
As relationships between organizations and their clients gets more complex, and involves the transfer of increasingly valuable data, its incumbent on the CDO to understand and help the organization meet client expectations with respect to use, quality and protection of data. In this way, the CDO helps preserve client trust.
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