[Forbes] The Role Of Data In The Age Of Digital Transformation

It might be an understatement to say that today’s business environment has become hyper-competitive, and the companies that aren’t continuously reinventing their business — with data at the core — will end up watching from the sidelines while their market is disrupted. Data technologies, science and processes are rewriting the rules of business and propelling organizations toward digital transformation.

Digital transformation, and the radical rethinking of how an enterprise uses technology to meet customer expectations and dramatically affect performance, is happening at a dizzying pace. In fact, IDC predicted that global spending on digital transformation technologies and services was expected to increase by nearly 20% in 2018 to more than $1.1 trillion.

At the foundation of the radical rethinking vital to digital transformation is intelligent management of the proliferation of data throughout the enterprise. I believe that the adoption of advanced analytics, artificial intelligence and ultimately the success of any digital transformation demands two critical elements: trust and understanding of data enabled through effective data quality and governance initiatives.

Taming Your Data

Business leaders intent on digital transformation must first look at their data and how they will quickly cleanse, review and blend business-critical data from different systems across the enterprise. In addition, the harmonized and cleansed data must be able to be easily migrated into new systems error-free to accommodate the reinvention of the business. Neglecting the flow, quality and governance of data will inevitably negate any return on investment in technology and undermine digital transformation initiatives.

According to Forbes Insights, while “the challenges posed by improving data quality can be daunting and obscure the benefits and possibilities that good-quality data enables … the costs of doing nothing are high. Gartner measures the average financial impact of poor data on businesses at $9.7 million per year.”

Getting the governance piece right is key as well, particularly in complex data landscapes. Information governance uses a set of defined roles, processes and policies to help manage data assets and ensure their integrity, accuracy and security. Without these structures and controls, data assets lose much of their strategic value. Without effective information governance, no one can be certain about what data assets an organization has, who controls them, what information they can provide and how they should be used.

Good governance also increases the utility of data from new sources — both inside and outside the organization — and supports compliance efforts with regulations such as HIPAA and the General Data Protection Regulation (GDPR).

But beyond business and compliance requirements, data trust and understanding driven by established policies and rules form the basis for effective use of the algorithms used in machine learning or artificial intelligence (AI). Every AI application works because of rich and expansive data, and these applications are at the heart of digital transformation. A detailed information governance program reinforces AI models, making their predictions more sound for yielding empowered business decisions.

Crowdsourcing Data Quality Management

Using a crowdsourced approach, an organization can effectively automate data quality management and governance. This approach allows different departments to impart their knowledge while at the same time help drive complete transparency and accountability for maintaining data quality.

Organizations can use automated workflows to trigger error notifications and remediation processes when the rules are broken. Employees with knowledge of and specializing in particular data can correct data errors and issues based on the workflow triggers. The rules are then updated in real-time to reflect specific data criteria, such as compliance and regulatory updates, brand standards, etc.

Ultimately data quality can be maintained at high levels by driving ownership of the data to the people who use it most frequently and understand it the best — typically at the business level rather than in IT.

Final Thoughts

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