Written By: Russ Artzt

#ArtztOnData: Making the Most of the COVID-Driven Data Explosion

COVID is causing a surge in online business activity

The pandemic has led to great suffering, but hopefully this will be temporary, with the world soon moving forward to becoming a healthier place. The virus has also wrought immense, likely permanent changes in the way people shop and interact with businesses. For example, according to a study by Marketplace Pulse, eCommerce is up over 44%, year over year, in contrast to an annual growth rate of between 11% and 17% from 2011 to 2019.

This leap in e-commerce has created many new online accounts and customer records, with accompanying buying histories and other rich types of business data. The concurrent growth in online interactivity, in the form of web-based customer service and chats, to name just two examples, has created additional volumes of customer data.

Leveraging new online customer interactions for digital transformation

Some business leaders are recognizing the opportunity presented by COVID-related changes in the relationships between companies and their customers, partners and employees. According to a recent survey by Twilio, 97% of executives say they are accelerating their companies’ digital transformation due to COVID. Ninety five percent of companies surveyed by Twilio are seeking new ways of engaging with customers as a result of COVID. Another 92% say transforming digital communications is now extremely or very critical to addressing their current business challenges.

How data quality and data management issues block transformation

Succeeding with digital transformation involves aligning business and IT strategies with well thought out execution. It also requires having the right data. Without meaningful, high quality data, the systemic aspects of digital transformation will not work—at least in the way they were designed. For example, if a transformation project calls for data analytics software to parse customer feedback contained in customer service records, that process will have the most impact if the customer service data can be correlated with customer records, purchase histories and so forth.


Data silos — Data that needs to be uniformly available could instead be residing in isolated silos. This is nothing new, of course, but as companies take the initiative to harness recent changes in online customer interaction, the inability to span data repositories hampers progress.

Inaccurate, duplicative and non-enriched data — Customer data may contain duplicate records as well as inaccuracies that impede useful data analytics. Duplicative, inaccurate data also negatively affects the customer-facing side of digital transformation. For example, if customer service representatives cannot find records because customer names are misspelled, or they send emails containing errors, that’s bad for the brand. Alternatively, the data on hand could benefit from enrichment. This might mean adding SIC codes to customer records, appending GPS map links for better service and so forth. Enriching customer data adds to the realization of digital transformation goals.

Non-normalization — Data that is not normalized contributes to poor outcomes in digital transformation initiatives. Given that digital transformation frequently involves integrating disparate systems, each with their own data sets, if the data does not match, in terms of field formatting and the like, the integration will not produce the desired results. At a minimum, it will cause problems, e.g. System A might store phone numbers in a “##########” format while System B stores them as “(###) ###-####.” Getting systems A and B to inter-operate as envisioned for digital transformation purposes will require normalizing the data, e.g., getting both phone number data formats to match.

How data orchestration enables digital transformation

It’s not enough to break down the data silos if the data is incompatible and of low quality. Data has to be cleansed, regardless of the system that houses it. The data must also be normalized. If enrichment will help, then the data should be enriched, too.

How does this happen? Data orchestration offers an answer. A data orchestration solution automates deduplication, correction, enrichment and normalization, along with all other core data quality management processes. The solution orchestrates the steps that comprise these workflows. And, a data orchestration solution is able to handle data quality improvement continually and permanently. As the COVID ecommerce and customer interaction trends continue to generate new data, orchestration can be on the job, constantly ensuring that the digital transformation program is always working with clean, correct, and complete data.


The COVID-19 pandemic has been a painful episode, but we will get through it. The resulting speed up of digital transformation plans has been an unexpected outcome, one that has the potential to drive significant business growth. Data is essential for success in any such endeavor. To work, digital transformation demands clean, accurate, normalized data, free from duplicates and other inaccuracies.

To learn more about how data orchestration can fuel successful digital transformation – visit