How Data Management Can Drive Digital Transformation Your Company

The International Monetary Fund predicts that by the end of 2022, two-thirds – 65% – of global GDP will exist in digital processes. Businesses that have completed the digital transformation process will generate more than half of the world’s increasingly digitized value. In this changing digital environment, taking the reins of data management and digital transformation in your organization has become a critical challenge on the road to success.

The global market value for digital transformation technologies will more than double over the current five-year period of 2020 to 2025 – rising from $469.8 billion in 2020 to $1,009.8 billion. At the same time, the value of business-generated data appears to be on a comparable ascending arc, having nearly doubled from $34.6 billion in 2019 to $52.3 billion at the end of 2021.

Despite the opportunities for growth these figures represent, the endemic problem of bad data remains a potentially catastrophic risk to mitigate in organizations– particularly those in the healthcare field where strict data privacy regulations apply. In this guide, you’ll learn what data management and digital transformation are and how sound data management practices can steer your organization through this transition period.

Key Takeaways:

• Accelerated digital transformation creates value throughout the global economy and the healthcare industry.

• Implementing data management practices can help your organization achieve digital transformation.

• Secure and accurate data enables predictive data analytics that yields actionable insights into your decision-making processes.

Video: What Are the Benefits of Data Management and Analytics? Get Started with Digital Transformation

What Is Data Management?

Data management refers to the set of disciplines and practices that use and leverage big data as a valuable resource. Many disciplines fall under the umbrella of data management. These include:

• Data Governance: Assigns roles and establishes responsibility for data management plans in organizations.
• Data Quality Assurance: Maintains data quality through processes of profiling and cleansing.
• Data Storage and Warehousing: Provides secure digital storage for company data accessible to the right people and compliant with any applicable data privacy regulations.

As a whole, data management practices have a twofold purpose. Firstly, in an environment where both cybercrime and the remote access points it targets are on the rise, data management focuses on protecting organizations and their customers from costly breaches of sensitive data. Secondly, managed structured data has the potential to yield valuable predictive insights and drive enhanced decision-making.

Nevertheless, for most organizations, the technical challenges and hurdles to establish effective data management practices continue to inhibit their ability to meet data science security standards and extract meaningful analytics for decision processes.

What Is Digital Transformation?

Image source: https://financesonline.com/digital-transformation-statistics

Digital transformation has become a vogue topic since the beginning of the COVID-19 pandemic in the spring of 2020. In response to the increased need for digital service capacities created by work-from-home practices and travel restrictions, 97% of enterprise decision-makers pushed for accelerated digital transformation in their organizations. For healthcare organizations, embracing digital transformation promises reduced operating costs and better treatment at the point of care through technologies such as telemedicine, AI-enabled medical monitoring devices, and blockchain-secured electronic health records.

In practice, this transformation refers to digitizing physical – mostly paper – and analog company assets and taking business processes and customer interactions online. The digital transformation process varies widely depending on the industry and how companies interact with their customers. In most offices, it means a shift away from paper-based processes and implementing a shared and remotely accessible digital document platform. For sales, marketing, and other interactive roles, it likely involves making customer interactions digital and omnichannel through social media, dynamic digital content, and online self-service platforms.

Wherever your organization falls on this spectrum of digital transformation processes, one consequence remains inescapable- an exponential rise in the amount of data you create and manage.

3 Ways Data Management Can Drive Digital Transformation

Applying data management best practices can streamline your digital transformation plans at any stage of the process. Here are three practices healthcare organizations can use to get started.

1. Map Your Data and Assess Your Current Data Storage Processes

Digitizing your data storage processes while maintaining day-to-day operations can be a significant challenge. Nevertheless, good data management begins with comprehensive digital storage–locally, in the cloud, or through a hybrid solution.

The first step involves mapping your data to understand what data you capture and how and where it is stored. For businesses early in digital transformation, this likely means identifying paper processes and cataloging archives with the intent to invest in optical character recognition (OCR) technology and document management software.

Image source: https://www.hsb.nl/the-importance-of-deduplication-and-adjudication-in-identity-management-solutions

If your data is scattered across various legacy systems and cloud environments, those already practicing digital storage will likely have widespread data redundancies. Having a certain amount of data replication in high-value databases that mitigates the risk of catastrophic data loss is part of the cloud storage business model. However, as replicated instances compound, your storage costs bloat. Weeding out redundancies through systematic deduplication can lower overall storage costs by 20-40% and leave you with more consistent and accessible data. Additionally, consolidating and cleaning data lowers the frequency of duplicate health records that can lead to poorly or incorrectly informed treatment.

2. Ramp Up Your Network Security Staff and Toolkit

Average dwell times – the time a malicious network intrusion goes undetected–across industries remain around 56 days. Enterprise-level security teams field more than 10,000 firewall and intrusion detection systems alerts daily. Most are ill-equipped to triage and investigate these alerts, which are false positives, leaving potentially costly real threats in the backlog stack. While many upper-level decision-makers remain unconvinced, successful data management requires convincing all stakeholders to treat network and data security as an investment. In the case of private patient records, data breaches represent an escalated level of concern where the Health Insurance Portability and Accountability Act (HIPAA) applies.

3. Leverage Data Analytics to Guide Decision Processes

Properly maintained enterprise data can change business plans from guesswork and hunches into informed strategies that reflect what’s happening in your business, the markets, and online discourse. Predictive analytics grounded in sound data management practices that keep your information secure and accurate have become vital to success in the era of digital transformation. 94% of enterprise decision-makers report that data analytics enhance their digital transformation programs. With data-driven companies now being 58% more likely to exceed revenue goals than their peers, your organization can feel confident incorporating data analytics into your decision-making processes.

A recent example of predictive analytics in clinical assessments strengthens the case for prioritizing data-driven processes in healthcare delivery systems. In 2020, the US National COVID Cohort Collaborative used 174,568 cases of patient data from Covid hospitalizations to model early signs of severe trajectories. Within months, the application of the study’s data modeling lowered the mortality rate for patients screened for early detection from 16.4% to 8.6%.

Master Data Management for Healthcare Organizations with Coperor by Gaine

Gaine offers healthcare organizations in need of data management tools an industry-first master data management platform designed by healthcare professionals for healthcare professionals. Providing seamless integration of data across your organization and your contracted partners with an unapparelled data model, Coperor creates a single source of truth visible throughout your organization.

For a real-time demo of Coperor’s master data management platform, contact Gaine today.