Digital transformation is the use of new technology to address business problems, and includes initiatives like cloud computing, going paperless, e-commerce, blockchain, online learning, and much more.
Gartner says the key accelerator for digital transformation is an organization’s competency in data and analytics, and by 2022, 90% of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency.
Alarmingly, Forbes estimates that 7 out of 8 digital transformation programs fail. In this blog post, we’ll investigate what they’re doing wrong—and how you can get it right.
Digital transformation starts and ends with data
Our VP of Corporate Sales, Sam Hedberg, believes that a well-executed data strategy is necessary to complete a company’s digital transformation:
Data is the lifeblood that pumps through your digital transformation. Without it, your digital transformation would die.
On Episode 30 of our podcast, DataFramed, Taras Gorishnyy, then a Senior Analytics Manager at McKinsey, identified the necessary moving parts to building a data strategy:
- Create a vision for analytics aligned with company strategy
- Establish strong C-level support, e.g., advocacy, funding, and excitement
- Build a strong data foundation to provide integrated, trusted, and timely data
- Extract value early from several use cases
- Implement process redesign and culture change to ensure the right mix of data skills across the company
Let’s examine how each of these steps in building a data strategy can help you successfully execute your digital transformation.
1. Create a vision for analytics aligned with company strategy
McKinsey says that many companies start their analytics journey by finding out what data they have and determining where it can be applied. But this approach is not scalable. Instead, companies should start by identifying the decision-making processes they could improve to generate additional value in the context of their overall strategy.
These are a company’s critical strategic areas, and typically require cross-functional alignment. But sometimes, large companies begin with a data strategy of hiring a lot of data scientists and launching pilot projects. Often, these efforts ultimately don’t scale, because that’s putting the cart before the horse. Analytics teams that work in a silo will produce limited output.
One of our customers, a financial institution, realized that open source languages were the way of the future and decided that the whole company should be able to speak the language of data. So they turned to DataCamp to help them scale data skills across departments and at every level. With their company-wide upskilling initiative, they onboarded hundreds of employees, using our “teams” functionality to develop custom learning plans by department, learning objectives, and skill levels. They also create assignments for various teams, which keeps employees engaged and actively learning.
2. Establish strong C-level support
In large organizations, different departments are often not designed to work together. For a massive transformation initiative, executive sponsorship is critical to establish a unifying mission across the company.
In 2018, Salesforce conducted a study on digital transformation revealing that 75% of decision makers said their digital transformation efforts were supported by executive sponsorship. Executive support in data and analytics translates to greater chance of success due to more investment dollars, a higher likelihood of execution, and greater organizational alignment.
One of our customers, a national government agency, began a digital transformation initiative this year with the support of their Commissioner. With executive support, they expect to complete their digital transformation in three years, including an overhaul of their digital platforms and a move to online learning focused on upskilling in R via DataCamp.
3. Build a strong data foundation to provide integrated, trusted, and timely data
The common thread for all digital transformation initiatives is data. All of an organization’s systems—from process flows to applications to data centers—rely on high-quality data. Every organization needs to have the processes and capabilities to be able to use their data. This includes data quality management, such as pulling in data from various sources and cleansing it to ensure accuracy, as well as governance to ensure compliance with data privacy regulations like GDPR and CCPA.
This is why our co-founder and CEO Martijn Theuwissen said in an interview with AiThority: “Companies need solid data foundations and tools for extracting, loading, and transforming data (ETL), as well as tools for cleaning and aggregating data from disparate sources.”
And it’s why Dr. Hugo Bowne-Anderson, data scientist and educator at DataCamp, believes data preparation is just as important in the data workflow as machine learning, deep learning, and AI. After all, “your models are only as good as your data.” The nuts and bolts can’t be overlooked—they must be carefully sourced, installed, and tested for your data engine to run properly.
4. Extract value early from several use cases
In order for enterprises to execute an ambitious data strategy that delivers real business results, they need to be able to point to successful use cases. Demonstrating successful use cases is critical to communicate value and urgency across the entire organization. McKinsey says that projects must demonstrate value early and often through quick wins, so different data initiatives may be happening in parallel:
[A] data executive explained how, rather than immediately try to connect disparate systems or create one master system, which could take years, he placed a “layer” over the existing systems that presented a complete view of a process, such as the lending process at a bank, that takes place via multiple systems. He created alerts to signal when steps in the process occur (such as loan approval, when an email is sent to the applicant notifying him or her of the approval, and so on), so that even if each step was completed by a different system, teams could see the whole process as if the systems are working together.
This example demonstrates that extracting value from and collaborating with data is closely tied to the previous step of building a strong data ecosystem.
5. Implement process redesign and culture change
ShopRunner CEO, Sam Yagan, believes his company has incorporated data fluency in their culture:
Because data is so infused in our culture, it doesn’t take long before someone realizes that the person sitting next to them or the person sitting across from them is running his or her own reports and accessing his or her own data.
DataCamp can help every company achieve this gold standard of company-wide data fluency. Our online learning environment supports data strategy execution and helps companies democratize data fluency—at any level, and across every department.
We conducted a survey of over 300 Learning and Development (L&D) leaders, and found that building data fluency is a priority for 89% of companies. The key to achieving this at scale is not just by hiring top talent—it’s by creating a culture of upskilling and reskilling your workforce.
By 2017, 77% of US companies were already using online learning. With so much of the world population working from home today, online learning has never been more relevant.
Online learning is a key part of digital transformation, and DataCamp provides a learn-by-doing approach to democratize data skills across your company. Visit datacamp.com/discover/enterprise to find out more about how we can help you execute a scalable data strategy to complete your company’s digital transformation.