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Originally published by New Context.

Today, digital transformation vs. digitization is a common topic of discussion. It��s not uncommon for the terms to be used interchangeably, though this is technically incorrect. Digital transformation is a broad concept that applies to all aspects of a business. Digitization, on the other hand, is a process that helps to drive the transformation. When both operate in tandem, it��s possible to create a more customer-driven experience.

A big part of this is data. The ability to use data in a way that��s seamless and collaborative while also ensuring its protection isn��t exactly straightforward. Baking data security strategies into the process can help enterprises enjoy the benefits without the risk. In this article, we��ll describe the difference between digital transformation and digitization, how they relate, and how to apply them securely.

Digital Transformation vs. Digitization

Global spending on digital transformation efforts . Digitization will become a vital part of these initiatives. As a result, it��s crucial not to compare digital transformation vs. digitization as apples to oranges. Instead, we must understand how the two complement one another.

Digital Transformation

Digital transformation is an overarching strategy that will impact all parts of the business. It centers on replacing old, outdated manual methods and procedures with technology-driven ones. Typically, a digital transformation will target business processes, models, domains, and culture. By addressing all these components, leaders are far more likely to have a successful transition. In other words, we are transforming how the business operates: legacy processes are altered or completely removed as a consequence of bringing new technologies to bear. The efforts result in a combination of increased speed or efficiency, reduced costs, greater versatility, and/or improved customer satisfaction.
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Digitization

Digitization is a critical component of digital transformation. It is the process of converting hand-driven manual information into a fully digital format. A standard example occurs when old client files are scanned into a computer system and organized with software. Anytime you see an office full of filing cabinets removed and replaced with a computer-based interface for the same information, you have witnessed data digitization.?

Digitization can also refer to converting manually intensive or human-oriented tasks into automated or semi-automated computer processes, such as using chatbots and artificial intelligence to rapidly respond to inquiries, instead of making customers wait for a human representative.?

Combining Transformation and Digitization for Data-Driven Strategies

Firms across industries are targeting customer-focused strategies to improve services and offerings. This new focus is possible through the vast availability of data generated from digitization and digital transformation efforts.

Data provides empirical evidence��rather than speculation��that helps companies plan processes and predict customer behavior. Consider traditional A/B testing. Actual users receive multiple options based on the same concept. The data collected during this experience will tell the firm which option resonates more with its intended audience. The empirical evidence allows the company to guarantee a more positive customer experience.

However, there is a major hitch in data collection that could limit its effectiveness. That problem lies in customer trust. Almost half of Americans have had their private data breached some time within the past five years. Trust barriers limit connections with consumers who��ve been burned before by the bad data storage practices of companies. Any company that wants to take a customer-driven approach must adopt practices and principles that guarantee the safety of this private information. These security measures not only protect you against a breach but also become a strong selling point to your audience, loudly stating: ��We respect you and the trust you place in us.��

Overcoming the Risks in a Data-Driven Strategy

Customer experience improvement is a common goal in digital transformation. of organizations report that meeting customer expectations was the primary driver in their transformation strategy. However, all those efforts go to waste when the plan exposes personal information and breaches trust. It��s vital to incorporate security at the deepest layers of the program to protect the integrity of data while also harnessing its full potential. Here are just a few ways to manage this:

It��s essential to be transparent about security measures in any digital transformation or digitization program. Customers must trust an organization to share their data. Having clearly established security protocols builds that confidence. Transparent security helps companies to take advantage of a customer-driven strategy while maintaining system integrity.

1. Security as Code: Security as code adds protective measures into the very foundation of the program. Developers code in things like tests, gates, and other security checks to allow for continuous monitoring and improvement of the system. Security as Code creates a scalable solution ideal for controlling ever-changing data; security as Code is not some grandiose process, and shouldn��t add complexity to your development process or tooling stack. For example, if your test suite includes assumptions that test an input to ensure it is hardened against SQL injections, you��re writing security as code!
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2. Automated Compliance: The requirements involved in managing customer data can vary widely by industry. For example, those in the healthcare industry must contend with HIPAA, while financial sectors are more concerned over the Gramm�CLeach�CBliley Act. Applying the right compliance standards to data, and automating the methods of enforcement, ensures companies meet legal responsibilities to customers.
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3. Cyber Threat Intelligence: Customer data will always be the target of bad actors, and they are forever changing their tactics. Open-source intelligence is incredibly valuable, as it allows companies to stay up to date on threats specific to their industry. With the right cyber threat intelligence, they can understand why specific data is at risk and assess attack sources.
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4. Data Tagging: One significant risk to data security is disorganization. A firm can��t protect what they don��t understand. Data tagging practices ensure companies can establish risk levels and provide the right standards of protection for confidential assets. Fortunately, the process of digitization provides the perfect opportunity to ensure that every piece of incoming information is appropriately tagged with the right classifications. Digitization is a one-time, often labor-intensive process, best to get maximum benefit for the effort!

It��s essential to be transparent about security measures in any digital transformation or digitization program. Customers must trust an organization to share their data. Having clearly established security protocols builds that confidence. Transparent security helps companies to take advantage of a customer-driven strategy while maintaining system integrity.

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