Digital transformation initiatives have a new goal to align with and stay ahead in web 3.0. Enterprises don’t want to miss it. They are building products that deliver in-the-moment, personalised experiences to customers. As we know, says Yash Mehta, an Internet of Things (IoT) and big data science specialist, users are producing and sharing data on multiple platforms such as messaging apps, social apps, and others.
We are actually producing 2.5 quintillion bytes of data every day. With the advent of IoT, this will only grow. Therefore, it is imperative to assure seamless interoperability among platforms, and enable the data models with a 360-degree view of customer data in one place.
To put it simply, customer 360 refers to a centralised, integrated dataset that contains all the customer information relevant to an enterprise. Data services instantly make accurate and up-to-date information accessible to all authorised consumers and other stakeholders.
It provides a single point to access customer master data such as name, address, contact, etc., and interaction centre (IC) data such as emails, reviews, feedback, phone conversations, website forms, etc. There are also transaction data that include orders, invoices, refunds & cancellations, payments, etc.
Now, this looks like a regular view that most organisations must be having. In reality, however, most enterprises tend to ignore it. As per the Cross-Functional Customer Data Survey 2021 conducted by Gartner, only 14% of business leaders agreed to have achieved a customer 360 view. These figures are far from positive for a year as crucial as 2021 for digital transformation.
Expectations from a customer 360-degree data model
To ensure the provisioning of relevant data sets only, it is important to enable domain-level federal control. Next, defining quality expectations from data processing deduplication and cleansing is important.
It should also support the integration and unification of all data be it transactional, master data, or interaction data collected from different sources.
Furthermore, it should support dynamic data masking, encryption, and tokenisation at the customer level for optimal data protection. This can be done by implementing data privacy policies & procedures.
Achieving customer 360 with a data product
Now, as we know, using data as products is a super successful data management technique and most platforms support it. Since a customer data product enables a dataset accessible to authorised consumers in real-time and through any method, it is a great medium to actualise a customer 360-degree view.
Moreover, the data quality, privacy, definition, and expectations of a customer 360 differ from one domain to the next. This is where data products can help achieve exclusivity in implementation.
Current limitations
Before proceeding, let’s discuss the limitations of existing customers’ 360 degrees.
Most existing solutions around customer 360 are centralised. This limits their potential to serve the needs of different business domains wherein each has its time-to-value, flexibility, and agility expectations. Here’s more:
- The CRM system has limited scope for the Sales, Marketing, and Services verticals. Enterprises today are handling customer data for a multitude of systems and verticals.
- Data management platforms lack the support for transactional as well as customer interaction data. They are limited to Metadata only.
- The customer data platforms are inefficient in provisioning data integration, data privacy, governance, and quality.
- Data lakes are normally meant for analytical workloads. This limits their use case operational ability and governance.
Innovation – Use of micro-database technology
As mentioned above, a data product for customer 360 addresses the exclusive business needs of data consumers. It can do so by federating the control to respective business domains. However, to fully meet the expectations as discussed the existing data product platforms are bound to pitfalls and further insufficiencies.
Overcoming these insufficiencies demands a different contemporary approach to data products such as using micro-database technology. For example, K2View’s data product platform implements a micro-DB technique throughout its data fabric platform.
It performs all functions of a data management landscape such as discovering, integrating, orchestrating, enriching, governing and storing data for every customer, in one micro-DB only. The fabric maintains millions of such micro-DBS.
Holistic view
Their solution can deliver a holistic, 360-degree view of customer data, in real-time backed by continuous provisioning and transformation. It assures seamless accessibility of customer data to all authorised stakeholders.
It ensures agility, thereby delivering quick value to data consumers. Moreover, since every domain has the ownership for defining their customer 360 needs, there’s no need for cross-enterprise contracting.
Not to miss, data consumers can discover and access customer products easily.
Ultimately, the approach is cost-effective as it enables enterprises to leverage investments with minimum replacement.
What’s next?
The enormous improvements in internet bandwidths will digitise more processes and thus create more data. Subsequently, this would pose a challenge to the data models to match up with the expectations and deliver in-the-moment insights. So far, we discussed how customer 360 with the backing of a customer data product delivers at par. We also highlighted the key benefits of using contemporary technologies such as a micro-DB to unlatch optimal performance.
What’s your opinion on using data products for attaining a customer 360-degree view?.
The author is Yash Mehta, an IoT and big data science specialist.
Comment on this article below or via Twitter: @VanillaPlus OR @jcvplus”
