Modern data platform technology has advanced rapidly, delivering greater capabilities than ever. By investing in this new evolution in data and analytics, you can take your business — and your customer experience — to the next level and beyond.
One of the biggest advantages of implementing a modern data platform is business agility. A modern platform helps you deliver actionable insights to your business — insights that were previously unavailable or weren’t delivered in a timely manner. It can also help you to realize cost and resource efficiencies by reducing data complexity and enabling powerful self-service capabilities.
The ability to support more advanced analytics is equally as attractive as the speed and efficiency gained. A modern platform can help you go beyond reporting what happened to predicting what will happen — accelerating your analytics maturity to become more forward-looking and purpose-driven.
While most businesses recognize the potential benefits, there are several motivating factors for why people consider migrating to a modern data platform. These factors include:
- the existing platform is limited and unable to support the monetization of data
- the increased adoption of cloud and DevOps principles for data management
- the current data platform is going out of service
Regardless of the reasons a business is considering the shift, the swelling need for new systems of insights is clear. According to the 2020 Forrester Data Management playbook, 94% of CEOs view data about customer needs and preferences as critical to their strategy. To data architects and engineers, this need may feel removed from the world of data management, but without modern data platforms to power decisions, a business will not be able to keep up in the new customer experience economy.
Key success factors to ensure a successful migration
Once you have decided to move forward, several factors will determine your ability to deliver on the promise and value of the investment in a modern data platform.
Alignment with the business strategy
At the beginning of the project, work backward by envisioning your desired business outcomes. Think through your business goals — and how data supports them. Defining these business outcomes is the first step toward charting a prioritized roadmap that breaks down a complex project into smaller, simpler, more manageable phases.
Willingness to reimagine the future
It’s important to look at the data ecosystem holistically. In addition to identifying the capabilities you need now, look to the future as well. What’s more, think beyond traditional needs to consider architecture trends that may apply to your business, like self-service analytics that empower line-of-business professionals to perform queries and generate reports on their own.
Amidst this reimagining, continue to be realistic about your company culture and team members’ willingness, ability, and capacity to learn new skills and tools. How much disruption can they handle?
Strong partnership with the business
Deciding to invest in a modern data platform requires a strong partnership between IT and the business teams they support. Lack of alignment will result in pushback because of the perceived disruption and the lack of buy-in for the potential benefits of your new platform.
Another common mistake is that without a strong partnership, IT may be refactoring poorly designed data and analytics or capabilities that the business teams may not care about. To succeed, the two areas must not look at themselves separately, but as one. This decision is not simply a decision about technology; rather, it’s for the organization as a whole.
Investment in change management
Implementing a modern data platform is an opportunity to propel cultural change — one that needs buy-in from all parties involved. Structure your data management practices so that, at any given point in time, there is full visibility and accountability of what you’re keeping, throwing away, changing, or adding. Documenting fundamental changes in architecture and processes — and ensuring they’re fully traceable to those involved in the process — is key when rebuilding.
Technologies have matured, and, in many cases, companies no longer need to construct solutions from the ground up. However, with numerous options available, the challenge can be deciding which approach is right and how to balance the sophistication of the solution with the complexity required to manage it.
Below are three steps that a data leader can take to get started:
- Define your strategy: Consult with the business to develop a data strategy that aligns to the business goals and outlines a crawl, walk, run approach to developing the capabilities needed.
- Use proven blueprints: Take advantage of existing reference architectures and successful design patterns.
- Prepare for Change: Ensure your team is prepared for the shift to automation and DataOps.
Your strategy needs to include what you can do in-house, what you should outsource, and what you should buy. From a cultural perspective, you must not think about the platform migration as a multi-year project. Today’s business environment is far too fluid and requires that you roll out the initiative in an iterative way to provide incremental value along the way.
Finally, there’s little value in simply changing platforms as you’re not removing the conditions and constraints that gave rise to the issues that prompted the change. Hosting a broader business architecture conversation and developing a modern data platform that is revolutionary instead of evolutionary will enable data at scale and put your company in a position to standout.
In future articles, we will dive deeper into how to develop your data strategy, selecting the right blueprints for your organization, and the emergence of automation and DataOps.