Digital Strategy Aligned With Enterprise Data Strategy

Enriched customer 360


Digital transformation is one of the top three priorities for most organizations. A fully-fledged digital strategy includes capabilities like e-wallet, digital customer experience, digital payments, digital marketing and sales, and digital analytics. This article discusses the importance of aligning the digital strategy with enterprise data strategy, to enable a consistent and complete digital experience. It also discusses various options of how to funnel the correct data to the digital apps in order to maintain consistent experiences across all the digital channels.

It is critical to identify and implement sophisticated digital apps, providing digital experience to customers, suppliers, employees and decision makers. These digital transformations, however, will not be successful or will only meet partial goals, if the apps are not funneled with complete, accurate and reliable data in real-time and as needed. It also needed to ensure consistent experience across all the digital channels. Alignment with enterprise data strategy addresses both the real-time data provisioning and the consistent experience across all digital channels by maintaining consistency and standards across the applications.

A successful digital platform necessitates that data being up-to-date, accurate, complete and available in real-time. It also requires that data is enabled with sophisticated technologies that are highly scalable and support variety of digital platforms. A smart digital platform also requires that the data is funneled through the digital apps and is not just complete, consistent and accurate, but intelligent enough to help the digital platform provide the right services to right people. See below how to get the right, consistent and intelligent data to support a digital platform.

Customer 360 is widely practiced by different organizations globally, because it can give answers to questions like:

- What kind of business client are they dealing with?

- What business relations connect the customers to them?

- What kind of relations do they have with the customer?

For a fully-fledged and intelligent digital strategy to perform its functions, Customer 360 view should be incorporated with a few more questions like:

- What will be the client’s position in the future?

- What ideas has the client been trying to express?

- Is the client using the company as a platform for money laundering or criminal activities?

Customer 360 should be further enriched with the client’s transactional, interaction data, social networking and historical data to provide desirable and accurate responses to the above questions, in order for it to support the digital strategy. Because so many of the pre-existing applications were developed during evolutionary times before data was so complex, it is important to now see the need for this alignment of strategies before data becomes much more complex over time.

As the businesses evolved over a period of time, they kept adding various applications to support critical business processes or deriving insights. Data continues to grow at substantial rates, and is often duplicated among different applications. The environment becomes further complex, leading to:

- No centralized customer onboarding process. Customer data is managed by multiple applications, and the data across these applications might not by in sync.

- Multiple versions of data truth, and analytical capabilities leveraging different versions of the data sets and producing siloed insights.

- Multiple analytics teams, some at functional level and other deriving insights of individual business lines.

- Customer interactions data is either in emails or in call center records. Mapping unstructured data to the customer information in structured databases is a complex process or not possible due to technical limitations.

- New Technological Advances have brought in social media platforms such as Twitter, Facebook, and Instagram, where the customer’s interaction activities have risen to greater heights, resulting in extremely tiresome and time-consuming data collection processes. Data can often be inconsistent, causing formatting to be necessary between programs, and thereby increasing the room for error.

The biggest challenges that come along with this type of application and data environment (a very common landscape for most companies), is the data movement across different applications. Due to different data structures, data values and data standards across these the applications, it is a time consuming, complex and expensive process to bring data together and cleanse/standardize it in a consumable form.

For example: a new customer’s information might have been created in one of the customer onboarding applications, and related transactions might have been captured in one or more transaction applications. Moving data from these applications to a one single source, matching, cleansing, standardizing and making it ready to support the digital tools is a time consuming process, is very resource intensive and can be highly expensive.

Another challenge is maintaining consistent experience across all digital channels. Individual digital capability can be stood up with this approach by leveraging data from the domain specific applications, but it is difficult to provide the same or consistent experience across all digital channels.

For example: e-Wallet can be enabled by leveraging the data from credit or debit card systems, digital customer experience can be enabled by leveraging data from the customer onboarding system(s), digital marketing/sales can be enabled by leveraging data from the CRM application, and digital payments can be enabled by leveraging data from the payment applications.

The next challenge often faced is the lack of intelligent data. As the digital marketing/sales apps are leveraging data from CRM, Master data or other marketing systems, it is plain funneling of data from these systems to digital apps. One might not know if we are marketing the products/services to a person or entity that is not involved in any illegal activities, hidden intent, or not knowing an event change in a customer’s life (individual or organization) will limit the organization from cross-sell and upsell using digital platform.

For example: Digital marketing platform might turn out to be a technology upgrade only, providing sophisticated apps to the marketing/sales teams, as well as to the customers, with minimal or no increased revenues – if intelligence driven data is not funneled through the digital apps helping increase cross-sell and up-sell of various products to existing customers.

Next, companies usually find a lack of trust in the data to be quite challenging. Having multiple systems, redundant data stores and broken processes leads to the confusion identifying the system of truth, lineages of the data lifecycle and reliability of standards.

All of this considered, is there a quick and clean way to support digital platforms? Should time and money really be invested in this approach, or are there better options?

Approach 1 – Standardize existing data capabilities, synchronize data across all systems and establish robust data governance to manage ongoing quality.

Approach 2 – Complement existing data landscape with an Enterprise data layer, enabled with big data capability and machine learning mechanics.

In conclusion, digital strategy is not just about digital apps, and having the most up to date customer applications. Underlying data management brings the real success. If data is more efficient and intelligent, the strategy for maintaining can become not only mutually beneficial for both the customers and companies, but it can even generate more revenue.

To best support digital strategy, there are two options that must be weighed by enterprises, to decide which is best for them. One option is to enable all digital apps on data capabilities that are already existing, synchronizing them across all systems. The other approach, complementing the existing landscape with an Enterprise data layer enabled with Big Data, is a much cleaner approach that is faster and more efficient. Both, however, have their individual pros and cons.

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