Accelerating Enterprise Digital Transformation with a Next Generation Database

Chapter 4. Key Takeaways

This chapter focuses on summarizing the key themes and ideas found within the book. Some of these include the decision points where an organization can find value. For example, understanding the trade-offs between real-time analysis and archival data retrieval is an important part of determining how far an organization can go toward digital transformation.

Looking at the long-term horizon can be difficult. But this chapter also includes factors to consider when looking at the way a database platform can be shaped for both the present and the future.

Key Decision Points for the Next Generation Database Platform

Examining the factors necessary to move forward on the digital transformation path is a key theme throughout this book. Whether an organization is beginning on the path or is trying to reach the path’s next milestone, the use of data within the organization will be essential to success.

Business growth is a key driver behind decisions across the entire organization. Digital transformation is a means by which the organization can drive growth and be able to sustain that growth.

New opportunities can be identified when an organization moves analytical processes earlier in the data life cycle. For example, analytical processes frequently help identify new opportunities for revenue. When analysis results are found closer to data capture, the organization can act quickly on those opportunities. Ideally, analytical processes are happening in real time when the data is being captured. However, many of the current database platforms simply don’t have the capability to perform both transactional and analytical processing in real time and at scale. Some platforms can perform both by using a hybrid approach, combining the best elements of transactional and analytical processing into a coherent and modem database experience.

A hybrid approach, combining both transactional and analytical processing, is the goal. Obviously, reaching that goal is not an instant switch but rather requires planning to ensure minimal impact on end users. A key first step is to identify the primary systems in which analytical processes are being executed. Which pieces of the analytical processing can be moved or migrated to a new system? For example, if data is siloed in a legacy system, what options exist to snapshot that data or capture pieces of the data and move or replicate those pieces toward a more real-time analysis?

Other considerations include the following:

• Which legacy systems and data will be crucial to success, and how well does the new database platform integrate with those systems?

• How easy is it to scale up and down with flexibility in the new database system?

• What types of resiliency and reliability does the new platform have, and what are the associated costs of meeting the business requirements?

• How easy is it for developers to work with the new database platform?

• What was the original design paradigm for the platform, and did that paradigm include cloud capabilities natively?

Because each organization is different, other factors may need to be considered when choosing a database platform on which the digital transformation can succeed.

Crafting a Long-Term Road Map

In the near term, it’s relatively easy to see that an open source, community-based focus can be leveraged in such a way as to gain insight and collaborate in new ways. It’s also easy to see that data will continue to grow. From there, assuming that data collection will continue to increase and that the demands on knowledge from that data will also increase appears to be reasonable.

With those factors in mind, a long-term road map should be based on success itself. Deploying solutions that require significant expense to merely provide redundancy is looking not at the current or future needs for a database platform but rather at yesterday’s needs. Technologies built on a standard client/server model, assuming low-latency and reliability, are also not looking toward the future.

Rather than making decisions based on assumptions, the exercise of removing assumptions about connectivity, availability, and reliability helps architect better solutions. Removing assumptions about data size and usage helps further develop the road map. However, certain assumptions can be made, such as the increasing use of AI, intelligent services, and cloud-based deployments to help bring additional fidelity to the road map.

Business agility in the context of long-term planning means finding inflection points where course changes can be made. In an agile context, revisiting the assumptions using the current reality is the primary means to become aware of the need for changes. Turning agility into ability is possible when following best practices for digital transformation.

Summary

Capturing data when that data is increasing in size, speed, and complexity is a difficult but solvable problem. Performing extended analytical processing on data, even at scale, is also a well-known and solved problem. Providing a means to both capture and process data, in real time and at scale, is the next level that will enable insights to be found and acted on quickly. The hybrid transactional and analytical processing concept can help organizations achieve the next step in enterprise digital transformation.