S3T Playbook: Gaining Buy-in for Data Modernization
Many enterprises won't be ready to participate in the AI revolution without data modernization.
Embracing Data Modernization: Strategies for Success
Generative AI and a renewed interest in LLM model training is putting a new spotlight on the need for data modernization - which almost always requires a migration from legacy to more modern cloud based platforms, and is no easy journey. How to prepare yourself and your team.
Executive Summary
The rapid advancement of Generative AI (GenAI) and increasingly powerful large language models (LLMs) is driving a sense of urgency among leaders who do not want to get left behind. But many of these same leaders are now confronted with the unpleasant reality that their data enterprises are not ready to participate in the AI revolution - and won't be - without significant investment in data modernization.
GenAI apps using foundational LLMs pre-trained on Internet data, have captured imaginations, but represent a small percentage of the world's most valuable "data fields" (the data analogy to "oil fields"...locations where high value concentrations exist). Across multiple industries, vast concentrations of high value proprietary data sit locked up inside legacy data warehouses or dispersed across departmental databases. These data assets are underutilized, pinned down by access rules and sustained at barely viable levels in cost-prohibitive legacy platforms, processes and vendors.
The net effect is that this highly valuable data is accessible only by the teams who have the skills to keep it locked in place; other teams with the skills to unlock the value of the data using more modern technologies are not allowed to get near it.
Astute data professionals recognize a data strategy trifecta for unlocking the maximum value of data:
- the largest most unfiltered data sets
- the most powerful AI processing
- the top software and data talent
The challenge for change leaders: how to inspire and guide an organization to leave behind its status quo and embark on a data modernization journey toward this trifecta?
Significant investment in data modernization is not just an option but a necessity
Data locked up inside legacy systems, cycled through convoluted poorly managed processes, or governed by strict access policies may be unusable in Generative AI or Large Language Model processes until significant changes or investments are implemented.
But data modernizations are fraught with complexity, costs, and perhaps worst of all politics. When a large data asset migrates from one generation of technology to the next, decision authority and budgets inevitably change hands. Departmental data stores, owned and operated for years or even decades by a comfortably entrenched panoply of teams, vendors and systems, now must be transitioned to new platforms managed by teams and vendors with newer skills. Underneath all of this lurks the ever present risk that the go-live will be messy, or that the data quality of the new system will fall short of expectations.
No wonder that leaders approach data modernizations with such trepidation, and that the number of organizations who consider themselves data-driven is actually falling.
8 Critical Strategies for Driving Successful Data Modernizations
Here are eight strategies to help navigate this complex and politically charged journey.
In each of the strategies we look at 2 change leadership levers:
- Explanations: How to give the right words to people who need to understand and embrace the changes. Stakeholders will need the right explanation that addresses their concerns, or corrects their misperceptions about what the change will mean.
- Empathy: How to stay mindful of the concerns of impacted individuals and teams. The ability to persuade and guide cannot be separated from the ability to exercise awareness of the human impacts in the situation.
#1. Start by building a clear data strategy
MIT and Thoughtworks surveyed 350 senior data technology executives on their data strategy and data modernization plans. Their findings published in the joint report Modernizing data with strategic purpose indicate that successful data modernizations start with a well established data strategy. In fact 71% of those embarking on a data modernization effort have had a strategy in place for longer than 2 years.
- Explanation: We can follow an inclusive thoughtful process that will gradually build and refine a data strategy, that starts with a depiction of our current state (including pain points and unmet needs), shows the desired future state destination, and the steps for getting there. When we do this we will have an actionable approach for data modernization and its benefits. A good data modernization roadmap enables the delivery of value to occur as we move from current state to desired future state.
- Empathy: Recognize that strategy often drives several types of anxiety in stakeholders. These may include concerns that the strategy is unrealistic or non-actionable or concerns that the strategy creates a new set of winners and losers - with implications for the stakeholders and their organizations.
#2 Take the time to develop a clear data-backed explanation of the benefits
It's essential to clearly articulate the benefits of data modernization that are relevant to the specific organization, backed with compelling data, as well as examples of how other companies have succeeded.
- Explanation: Describe the specific opportunities for operational efficiency, enhanced decision-making capabilities, and the ability to leverage AI for competitive advantage. Back the explanation with detailed supporting data. This may take time to develop (as the MIT study suggests above).
- Empathy: Recognize that leaders may be skeptical or overwhelmed by these changes. Look for, and share success stories from other organizations that have successfully transformed their data infrastructure, demonstrating real-world benefits and providing a blueprint for success.
#3 Address concerns and build trust:
Key insight for building trust: most stakeholders are not achieving wins at the rate they wish they were. If you want to build trust, focus on showing how you can create more wins together. As noted above good data modernization roadmap enables the creation of wins - opportunities for the teams to deliver recognized value - as we move along the journey from current state to desired future state.
- Explanation: Describe the path from current to future state and the opportunities along that path to create wins together. Be transparent about the process, timelines, and how data will be managed and secured. Emphasize the security measures and compliance standards in place to protect data in the cloud.
- Empathy: Acknowledge the anxiety and trust issues that come with partnering and working together in new and different ways, as well as the anxiety associated with relinquishing data ownership (where that's a factor). Assure stakeholders that their data will be protected and that these changes are in their best interest.
#4 Work hard to preserve high stakeholder involvement:
This will not come easily. Stakeholders want to be involved but are buried in day to day urgencies. So prepare to expend significant effort and creativity to solve this one. Key Tactic: Focus on conversations (what needs to be conveyed) NOT just meetings (events that block time). Conversations can be facilitated in an inclusive way via IM, calls, meal events, or other means - ideally a mix to ensure everyone is on board and involved. No easy answers - it just takes sustained focus and a little creativity to ensure full engagement.
- Explanation: We need our key stakeholders centrally involved in the planning process to ensure their concerns are heard and addressed. As we define new roles and responsibilities, we want to ensure that stakeholders understand how their expertise will continue to be valued.
- Empathy: Understand that stakeholders might feel sidelined or undervalued. Engaging them early and often can help alleviate these concerns and foster a sense of collaboration and shared purpose.
#5 Bring everyone along through inclusion: incremental implementation with heavy support and training.
Taking an inclusive approach will help reduce risk of costly conflicts or organizational silos that fester over time. Implement data modernization in incremental phases to reduce disruption and allow stakeholders to adjust gradually. Include plenty of training, mentoring and other forms of support that enable the organization to learn and adapt. Start with pilot programs to demonstrate quick wins and build confidence in the new systems. Slower sometimes is the fastest way.
- Explanation: We will provide comprehensive training sessions to help stakeholders understand the new systems and processes. By offering continuous support and resources we will help everyone adapt to the changes.
- Empathy: Recognize that learning new systems and ways of working can be daunting. People are watching for indicators that they matter, and that they are being set up for success. Providing ongoing support and resources shows a commitment to their success and eases the transition.
#6 Keep AI and other emerging tech in perspective through empirical hands on approaches instead of hype driven discussions
While its important to highlight and capitalize on opportunities presented by GenAI (and other emerging technologies), its also important to be aware of emerging technologies can drive counterproductive politics and power shifts, or at least the perceptions about them that are not helpful. Do not allow a duality of "cool people" vs "non-cool" people to evolve. Maintaining an inclusive empirical approach that emphasizes hands on experimentation, can help the entire effort stay rooted in real world possibilities - much more so than allowing a select exclusive group to ideate behind closed doors.
- Explanation: Explain how advanced technologies fit into the data strategy and data modernization plan. Data modernization can enable us to leverage AI capabilities, such as predictive analytics and personalized services, which can drive business growth. Encourage collaboration between data owners and AI teams to identify and pursue new opportunities.
- Empathy: Acknowledge the excitement and potential that AI brings. Remember some will be fearful of being left behind or excluded. Keep it inclusive. Highlighting the opportunities in a way that shows how everyone can play an important role in them can help shift the focus from the fears and challenges to the exciting possibilities.
#7 Build a constructive data-driven culture that mitigates power dynamics:
Define and promote a culture that values data-driven decision-making and continuous improvement, and uses data to empower everyone: the entire organization, its partners, and its customers. Many data driven culture attempts focus on being data driven while forgetting to focus on the empowerment aspect. This creates an unhealthy entitlement culture where those with access to the data get the most air time in the c-suite and drive agendas which are not transparent to, or understood by the rest of the organization. This can be the achilles heel of analytics organizations, resulting in deterioration of culture and employee engagement, as well as resentment/resistance against the analytics organization.
To counter this, ensure leadership champions this kind of data modernization culture and visibly supports the empowerment of all, not just some. Recognize and reward contributions from stakeholders who adapt and contribute to the modernization efforts.
- Explanation: We define a data driven culture as a culture that uses data to empower our entire team: our entire organization, our business partners and our customers. Data modernization is not just a chance for some to up their game, it's a chance for all of us to up our game together. We will recognize and reward people who lean in, learn and drive this forward, no matter what part of the organization they are from. We will also establish frameworks that ensure equitable data ownership and access, and seek to prevent power imbalances.
- Empathy: Recognize that cultural shifts can be difficult. Leadership support is crucial in modeling the desired behaviors and fostering an environment where data-driven decision-making is the norm. Understand the fear of losing control or power. Ensuring equitable ownership and recognizing contributions can help mitigate these concerns and foster a sense of fairness and collaboration.
What You've Just Learned: Embracing Data Modernization
You learned how to help others understand the Need for Data Modernization
- Generative AI (GenAI) advancements highlight the urgency for data modernization.
- Outdated data system may impede the organizations ability to leverage AI.
- High-value proprietary data often remains underutilized due to legacy systems.
You learned the Data Value Trifecta
- Largest, most unfiltered data sets.
- Most powerful AI processing capabilities.
- Top-tier software and data talent.
You learned about handling the challenges and necessities of data modernization
- Significant investment in data modernization is essential.
- The process is complex, costly, and involves political challenges.
- Migration to new systems can lead to power and budget shifts, and risks in data quality.
You learned 8 Critical Strategies for Success
- The first 4 strategies guide you to start with a clear data strategy and roadmap from current to future state, a data backed explanation of benefits in order to address concerns, build trust and drive high stakeholder involvement.
- The last 4 strategies help you and your team drive inclusive incremental implementation that keeps emerging tech in perspective, and builds an empowering data driven culture with the support and recognition of leadership.
Now you are ready to consider how to implement this learning in your work and share it with your team. Reflect on how these lessons can be gradually introduced and help your organization successfully leveraging AI and modern data systems.
Data modernization is a challenging but necessary journey in the age of AI.
It is not just a technical shift but a cultural one. By addressing concerns with empathy, building trust through transparency, and highlighting the long-term benefits, we can encourage a smoother transition. These strategies will help leaders navigate the complexities of data modernization, ultimately positioning their organizations for success in the AI-driven future.