Digital transformation exists to change (fundamentally, rather than at a surface level) how a business operates. Though enabled by technology, delivering technology projects is not the point; the business is being changed to better respond to customer and employee needs. This separates it from “digitization,” which is merely running the same old processes with (hopefully) more efficient technology.
Failure in digital transformation affects around 70% of projects according to a Boston Consulting Group study. These failures often stem from data-related issues such as poor data quality, fragmented systems, and outdated technology.
A common misconception is that digital transformation leaders need to get the company’s data right before embarking on transformation efforts. Not only does this mindset lead to years and millions of dollars lost since data is only contextual to its use, but delaying transformation to get the data right is not really an option — making data fit for purpose needs to be part of the digital transformation journey itself. There is no "getting company data right," there can only be "getting the right data for your use case."
Data refinement should be integrated into the transformation process. To ensure success, data teams must transition from mere benefactors to active contributors, fostering collaboration, accelerating iteration, and promoting transparent communication.
Digital + Data/AI Transformation: They’re More Similar Than We Think
Moving from theory to practice in digital transformation often leads to failure, comparable to data and AI initiatives, with a failure rate of 50% to 75%. Just as Everyday AI advocates for integrating data and AI into everyday organizational practices, successful digital transformation requires organic integration over time.
- The program exists to spend a budget against a grand vision, but not enact fundamental change.
- Technology delivery becomes the focus, instead of tangible improvement in customer experience.
- The need for hard work and deep consensus to avoid the above leads to a “lift and shift” mentality, by which the semblance of ongoing success is created but the benefits are one-off cost reductions.
Pfizer serves as a prime example of successful organizational transformation by scaling data science efforts to extract exponential value from their extensive data. By setting clear goals, achieving global alignment across business and technical teams, and systematically breaking down internal barriers, Pfizer significantly scaled their data science efforts. They now manage over 3,000 concurrent data projects, hundreds of thousands of datasets, and nearly 1,000 contributors to the data process.*
In alignment with this discussion, Jeff McMillan, Chief Analytics and Data Officer for Morgan Stanley Wealth Management, emphasizes a shift in perspective, viewing AI not as the end goal but as a tool for intelligence enhancement and informed decision-making. This perspective underscores the importance of thoughtful integration of people, processes, and technology, as exemplified by Pfizer's successful organizational transformation.
Why Migrating to the Cloud Isn’t an Architecture Strategy
When it comes to cloud migration, many (if not most) people and teams think putting the data all in one bucket is the end of the journey. In our experience at Dataiku working with hundreds of multinational organizations, the value of the initiative is often an afterthought (if it’s a thought at all).
It’s worth noting that:
- Migrating all data to the cloud is not totally risk-free — yes, cloud storage can be cheap, but for some organizations (especially ones that are 100+ years old and have incredible amounts of historical data), not cheap enough to put every datapoint that’s ever been collected. There’s some data that’s valuable on the day it’s collected, some a week later, some three to four years later. But what about after seven years? It’s worth putting some thought around what data really needs to be in the cloud, because after all …
- All data will never be in the cloud. Most IT teams don’t consider the fact that business people plan for a world where data will pretty much never all be in one place. At Dataiku, we talk to people every week who might have 60%-80% of their data in some big data platform, but inevitably some extremely important thing — like, for example, a list of product codes — comes out of some other business process. It’s in peoples’ inboxes, it’s in XYZ SaaS tool, not in the cloud
The bottom line: cloud migration in and of itself doesn’t mean data is getting more meaningful or useful from a business perspective, so for it to be a strategic move with positive outcomes, there should ideally be a larger goal. In other words, cloud migration can (and should) be part of that goal, but it shouldn’t be the goal.
How to Help Everyone Work in a Way That Makes Value Delivery Easier
Broadly speaking, poor data is commonly cited as a reason for digital transformation failure (i.e., “All of our fancy digital platforms went live and nobody used them because the platforms didn’t communicate and data remained inconsistent across them”). While teams cannot afford to delay data and AI transformation to wait for data quality to be perfect, a key contribution of those teams to digital transformation is precisely to help deliver quality data because:
Digital transformation initiatives will fall short when the effort is spent only on shiny new customer-facing technologies (think websites, apps, experiences) without laying the necessary data foundations to make these scale robustly. But, to be clear, this should be part of the process and not something that will be done and dusted prior to beginning the transformation journey.
For example, Jeff McMillan, Chief Analytics and Data Officer at Morgan Stanley Wealth Management, cites data quality as one of the decisive factors to becoming an intelligent organization. To control data quality, Jeff advises organizations to have a data quality infrastructure, metrics around accuracy, a clear definition of what “quality” means to the organization, and people who are specifically accountable for the accuracy and monitoring of data quality on a daily basis.
Improving data quality, along with using data and analytics as a whole, is a continuous process (i.e., Morgan Stanley has monthly governance meetings to talk about any data quality issues, infrastructure to take in data quality problems and evaluate it to determine a solution) and can take years. However, doing so within a centralized data repository can keep roadblocks to a minimum, as it avoids multiple sources of truth, inconsistencies, and other concerns.
So, ideally, digital transformation success depends on the quality of the data flowing between new processes, but existing data on customers, products, etc. was generated by old processes. As a result, leaders are often blinded by these problems until it’s too late and “going agile” to build websites and apps won’t be a turnkey fix.
Embedding data and analytics across the enterprise successfully, on the other hand, depends on enabling people to easily access data relevant to them and work with it to deliver business outcomes faster or more reliably. Ideally digital transformation creates more relevant data and makes it more accessible while generating opportunities for the people empowered and upskilled by data initiatives to continually improve their processes.
Maintaining scalability and resilience involves avoiding outdated working methods. While digitally transformed processes address inefficiencies, continuous innovation is essential for sustaining progress. Data and AI transformation empowers SMEs to address ongoing challenges and fosters in-house capabilities for future digital transformation phases.
Expanding data and AI initiatives across business units is crucial for deploying more models and driving high-value outcomes. Leveraging unified platforms for transitioning from data to insights fosters effective collaboration among diverse teams. Engaging the right teams from the beginning ensures solutions align with customer and employee needs while prioritizing transformation goals over mere reliance on technology.
In setting up self-service data initiatives, GE Aviation ensures the incorporation of business needs by collaborating with various business lines. They further involve a wide range of stakeholders, combining grassroots efforts within the business with executive support to increase program visibility and advocacy. This approach highlights the prioritization of customer and employee needs as the ultimate goal of transformation, beyond mere technological advancements.
Conclusion
According to McKinsey, “digital transformations require cultural and behavioral changes such as calculated risk taking, increased collaboration, and customer centricity.” As discussed, digital transformation requires fundamental, organization wide change and a strategy to both mitigate risks in data programs and execute them effectively (whether basic or complex, Dataiku can assist). Similarly to AI transformation, executives embarking on digital transformation journeys must:
- Not solely focus on technology — people and processes are crucial (i.e., failure to involve everyone in the enterprise or adequately communicate role impacts can hinder technology adoption).
- Align with the company’s short- and long-term business objectives (i.e., establish plans with budget and ROI benchmarks for driving and tracking value from digital transformation projects).
- Assess existing technology strategies and infrastructure (i.e., identify gaps and prioritize projects within the digital transformation effort).
- Establish proper programs (i.e., with the right operating model that aligns with the organization’s structure).
Organizations face numerous challenges with digital transformation. However, implementing best practices outlined in this blog can facilitate success, showing how digital transformation and Everyday AI complement each other to enhance ROI and efficiency.
*Information from a VentureBeat discussion with Dataiku's Kurt Muehmel and Pfizer's Chris Kakkanatt