Intelligent Data Management for Digital Transformation

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In today’s progressive and forward-looking era, businesses are hardly functional without leveraging the technology around them. In 2020, an EY report shows that companies ahead in digital transformation share habits that are leading to improved financial performance. The ever-evolving digital landscape means competitive business models targeting the operations of the current business setup in unique ways. Both to sustain popularity amid escalating competition, and to beat the competition, companies must continue to look ahead.

The most effective way companies can do this is by undergoing a digital transformation. This applies to businesses that are still using traditional models of operation as well as those that come with a digital heritage but might want to tweak their setup. Now, digitization can involve a complete overhaul of numerous kinds but data management is a key component when making this transformation.

Why Businesses Must Leverage Data Management for Digital Transformation

Setups that have successfully reaped the benefits of digitization achieved it by addressing the quality of data available at their disposal. Data management has become the pivotal focus for businesses given the exponential rise in information availability compounded with an equally intense fall in the cost of data storage and cloud computing. This has resulted in most businesses replacing descriptive analytics with pre-embedded analytics to better inform decision making.

Restructuring business operations to a digital-first setup is a daunting task. It has been revealed that the number one factor hampering businesses from effective data management and a successful transition is the lack of efficient data architecture. Businesses that wish to avoid the hassle must follow a systematic transition process and here are the steps to it.

Data Strategy

Embarking on an ambitious expedition of digitizing data warehouses, without a set strategy in place, can lead to failure. The vision for transformative data management will only bear fruit once it aligns with realistic expectations of the value that’s expect to derive from it. Start with strategizing on sound data architecture, eliminating disposable systems, and defragmenting data. This results in reduced IT costs and greater returns.

Data Governance

Erecting a sound data architecture and implementing data management strategies boil down to effective governance and here’s how to achieve it.

  • A Top-Down Leadership Involvement

Effective data management and governance are contingent on leadership support. Without buy-ins from the C-Suite, implementation is unlikely. Large scale companies can follow up by creating data-governance councils that highlight the potential challenges and benefits of effective data-governance for full-scale digitization. Data domains must be created and handed over to thought leaders responsible for overseeing and managing data elements.

  • Align Data Governance with the Overall Digitization Model

Data supervision must be intricately linked with digital transformation, enterprise modernization, and omnichannel creation. Thorough integration of data-governance with in-house transition mechanisms yields value at the point of production and consumption.

  • Prioritize Data and Apply Appropriate Regulatory Pressures

Instead of dealing with data on a holistic basis, categorize it by domains and then further prioritize data assets within each domain. This slims the governance focus to points of criticality and makes the process more effective.

Another point to bear in mind is that governance intensity must remain need-based. Industries vary based on the regulatory pressures they face and data-governance must align with these to prevent unnecessary complexities.

Data Architecture

Remodeling data architecture regularly can bring businesses the highly sought-after opportunity of data-monetization. The foundation of this architecture are data lakes consisting of consolidated data in its raw state for easy storage and inclusive accessibility within an organization. Information ingestion of such sort can prevent businesses from drowning in mismanaged data swamps and gain value. An in-built data-governance system can help businesses achieve this transformation.

Data Innovation

Augmenting the current intelligence environments with AI, predictive analytics, and related technologies is the next phase. While it increases data complexity, it can prove vital for incremental user experiences for long-term retention. Businesses must try 360 engagement that involve bringing innovation and commitment to every facet of the business from customer retention to supplier management.

Final Words

The purpose of digital transformation is to augment one’s customer base through better engagement and quality delivery. Data management is the gateway to a smooth transition. Mastering this new currency of the digital landscape will enable a head start, competitive edge, and operational value for the business needs.

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