The Evolution of Enterprise Data Management Requirements

Enterprise data management requirements have evolved drastically over the last few years, with today’s businesses facing more pressure than ever to implement proactive strategies. Enterprise data management isn’t, however, a passing trend or product of herd mentality – genuine operational demands are driving this movement. Companies are urgently looking to make data more accessible and more meaningful, a task that would allow cold, hard data to inspire decisions, empower efficiencies, and ultimately drive growth.

For some first-movers, their data is already standardized, converted to usable forms, and stored in a way that’s both accessible to users and secure. Unfortunately, most enterprises weren’t so vigilant and now find themselves scrambling to get on top of their data, which is inevitably growing and changing day by day.

In this article, we’ll investigate the enterprise data management and how it has transformed over time. Let’s get started.

The Changing Face of Data

The sheer quantity of data in existence is growing at alarming rates. In 2018, the global datasphere totaled 33 zettabytes. By 2025, data is expected to surpass 175 zettabytes. What’s more, almost one-third of the world’s data will require real-time processing.

What does that look like in practice? Today, 5 billion consumers interact with data daily. By 2025, that number will be 6 billion – 75 percent of the world’s population. Estimates suggest that each connected person will have at least one data interaction every 18 seconds.

The amount of data is growing, but so too are the types of data companies are handling. Data streams contain everything from financial information and inventory figures to unstructured data sourced from social media and the Internet of Things (IoT).

All of this data in its varied forms must be:

• Centralized

• Organized

• Accessible

• Secure

Achieving these is the mission of enterprise data management. But as the face of data grows and shifts, so too will data management requirements.

What Is Enterprise Data Management?

Enterprise data management describes a business’s capacity to do the following:

• Integrate data into decision-making

• Govern data

• Secure data

• Distribute data from several streams

• Transfer data between partners, subsidiaries, and applications

Effective enterprise data management is not an easy task. It takes a comprehensive understanding of your organization’s data and an intelligent, proactive, agile management strategy.

Components of enterprise data management – Modern-day enterprise data center management requirements demand organizations to tackle the following critical elements:

Data integration – Successful enterprise data integration sees organizations move and consolidate varied data into one, central, accessible place. Data integration ensures all the disparate forms of data are not just accessible but also useful. Data integration may involve virtualization, propagation, and consolidation.

Data governance – Data governance includes the processes and policies in place that ensure the security, quality, and integrity of data. In the face of a swelling tide of data, enterprises’ responsibility to safeguard this data becomes increasingly important. Governance encompasses the guidelines relating to policy enforcement, authority, and obligation.

Data security – Data security relates to the measures in place designed to protect data at all points of its lifecycle, including both in transit and at rest. Data security strategies protect against theft, leaks, and breaches, as well as maintain integrity and minimize the risk of corruption.

Meet Enterprise Data Management Requirements with a Future-Proof Strategy

With the above components accounted for, you can craft a future-proof enterprise data management approach that meets your requirements today – and in tomorrow’s data-rich world.

Here are a few best practices to keep front of mind:

Assess – A time-consuming process, yes. But one that cannot be skipped. For businesses to implement an effective data management strategy, they must understand the ins and outs of their data flows, as well as the many various types of data they handle. Teams must be certain that the processes they commit to reflect the nuanced landscape of their organization’s data.

Identify deliverables – Data management can be nebulous. It’s crucial for companies to identify what it is, precisely, they aim to achieve with a data management strategy.

• What are the objectives?

• What is the full scope of the undertaking?

• How will success be measured?

Data projects can fast become colossal undertakings, especially as data requirements become more significant. A phased approach with incremental goals can be an effective method of implementation.

Set policies and procedures – Determining and documenting policies and procedures is essential in guiding how data will be recorded, stored, and transferred. Proper standards can help mitigate risks of security breaches, corruption, and data loss.

Further to this, if an organization operates in a highly regulated industry, they must identify their obligations and set policies that guarantee compliance. This not only protects data but also helps businesses avoid fines and preserve customer trust.

Prioritize quality – Bad data is worse than no data, and embracing a culture of quality will assist organizations in preserving security, integrity, and, ultimately, data worth.

Invest in talent and technology – Without the right people and the right technology at the helm, any business’s enterprise data management strategy is doomed from the outset. Look for forward-thinkers that can keep up with changing demands and opt for tools that enable seamless scalability and flexible features.

What’s Next with Enterprise Data Management

Never before have enterprises been faced with such a significant quantity of data. And while the sheer amount of data offers a whole host of exciting opportunities for innovation, it also imparts a heavy burden.

How you manage your data is important today – that much is clear. But as more and more information enters the datasphere, enterprise data management may just have the power to make or break a business.

Michael Brenner is a keynote speaker, author and CEO of Marketing Insider Group. Michael has written hundreds of articles on sites such as Forbes, Entrepreneur Magazine, and The Guardian and he speaks at dozens of leadership conferences each year covering topics such as marketing, leadership, technology and business strategy. Follow him @BrennerMichael.

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