Your Digital Transformation Initiatives Will Fail if You Don’t Focus on the Quality of Data

Your Digital Transformation Initiatives Will Fail if You Don’t Focus on the Quality of Data

Already a hot topic in corporate boardrooms and faced with accelerating demand due to events like the COVID-19 pandemic, organizations are quickly taking their digital ambitions into reality. In 2021, Gartner estimates that USD 4 Trillion will be spent on IT initiatives worldwide as more businesses, as well as government authorities, realize the potential of digital to unlock hidden value within consumer segments and citizen governance.

As organizations embark on the road to digital supremacy, they need to get the foundations right so that the digital building blocks stacked up above it can sustain their momentum going forward. In the digital age, data is the new oil and digital applications need to leverage the power of data to deliver the best results for organizations that depend on them.

However, in 2020 alone, each human created 1.7 MB of data every single second and the challenge now is harnessing this data to procure insights for growth. For best results in digital transformation initiatives, there is a critical requirement of assuring quality data for use by different digital systems within an organization.

Data quality is the norm by which accuracy, completeness, and freshness of a data object are evaluated before it is qualified as a candidate for supply to a digital transformation exercise. One of the key reasons why several digital transformation initiatives at numerous organizations fail is due to the lack of awareness around data quality and not enforcing it for mission-critical projects within the organization. 

Let us examine the key reasons why data quality is important in ensuring a sustainable digital transformation journey for an organization:

Better Compliance

In 2020, the fines for violation of the GDPR data privacy policy increased by over 40% when compared with the figures 20 months prior. Businesses of all sizes process and ingest huge volumes of data about customers and this data may also include sensitive privacy and other confidential credentials of a customer. For compliance with regulations like GDPR, it is important to identify, classify and segregate data in relevant datasets that will be consumed by enterprise applications. Data quality checks serve as the governing body that ensures that the right data is validated for consumption by thousands of processes and systems in an organization’s operational workflow.

Fosters Accurate Machine Learning

AI and machine learning are some of today’s most promising technology innovations that have found their way into almost all mainstream consumer applications. A whole new dimension of customer experience is born when machines are enabled to learn and mold their intelligence to a level where they can autonomously respond to scenarios by applying logical processing. However, for machine learning to achieve full steam, it is important for the learning algorithms and systems to be supplied with the most contextual datasets for self-learning. Machine learning evolves by evaluating patterns and behavior of entities in different conditions. This knowledge is obtained in the form of data and without data quality, machines may arrive at inferences that are far from ideal.

 Avoids Huge Financial Setbacks

Did you know that 15 to 25% of the revenue for several companies is lost when they deal with bad quality data? Add to that, the tight deadlines and imminent market conditions where consumers demand-responsive digital systems, there is a huge recipe for disaster in the absence of data quality and governance. Knowledge workers have been found to spend more than half of their time dealing with data quality problems and this loss eventually adds more weight to decisions that may curtail digital transformation initiatives due to cost overruns.

Better Customer Relationships

Today, customers come into an organization’s business value chain in numerous ways. Irrespective of the source, it is important to ensure that they are provided with the right choices that lead them from a lead stage into an actual business transaction. Marketing and business leaders believe that the key to this conversion lies in how effectively organizations can leverage accurate insights from the vast data they have about customers and their behavior. With quality data such as their geographical preferences, favorable price ranges, choice of payments, etc. it will be easier for businesses to create an engagement roadmap for every customer.

Gartner in 2020 said that there will be a whopping USD 12.9 Million loss which will be suffered due to poor data quality amongst global organizations every single year. As more businesses and organizations pursue rapid digital transformation, it is imperative that the focus on data quality is emphasized and made aware to decision-makers across industries. If organizations can ensure better quality data through efficient management and advisory from experts, they can get a head start in their ultimate digital ambitions. The stories that your enterprise’s data can weave are limitless, and it is up to you to decide on which story to entertain at a given time with efficient data quality control.