How to Make a Data-Driven Digital Transformation Work
In recent years, data has taken center stage as a vital currency in determining which companies succeed in the digital age, owing in part to the adoption of cloud technologies. As a result, businesses are now transforming their data ecosystems and operations as a critical step in reimagining their businesses for the digital age.
The primary goal of these digital transformations is to effectively leverage modern technologies such as artificial intelligence and machine learning for data insights that can help customers be better served. With businesses relying on "gut feeling, experience, or opinion" for less than half of their decisions, gaining clear insights from data can be difficult.
With global spending on digital transformations expected to reach $1.8 trillion in 2022, an 18% increase over the previous year, it's more important than ever for businesses to get this project right.
Whether a company is in the planning stages of such sweeping digital change or has already begun the journey, understanding what a digital transformation truly means is beneficial.
What Exactly Is Digital Transformation?
The ongoing process of integrating new technologies, culture, and procedures around data into an organization in order to increase competitiveness and better serve customers is known as digital transformation. Because every digital interaction offers the opportunity to personalize or improve experiences, it's no surprise that data is at the heart of many digital transformations today.
Another important goal of a data-driven digital transformation is to create an environment in which everyone can easily consume and produce data.
Digital transformation is a journey, not a destination to be reached. Companies that are successful have laid the groundwork for ongoing innovation and growth as new data technologies emerge.
Other advantages may include:
- Decisions based on insights that improve performance
- Increased operational efficiency and increased employee productivity
- Customer experiences that are more engaging and personalized
Although what works for one company may not work for another, modernizing existing data technologies and infrastructure is an important first step. This can include converting an on-premises legacy system to the cloud, developing a data streaming platform to enable real-time insights, or migrating data to an enterprise-wide data lake.
Key Features of Digital Transformations
So, how do companies that have successfully undergone a data-driven digital transformation look? They frequently exhibit some or all of the following characteristics:
- Real-time data delivery for faster insights
- Data as a product treatment
- Capabilities for self-service with built-in governance
- Data ownership federation across business lines
- Policy and tooling centralized for managing data warehouses, data lakes, and low latency data stores
What Is the Distinction Between Digital Transformation and Data Migration?
Data migration is the process of moving data from one system to another. This could imply switching between locations, formats, applications, or all of the above. Most organizations begin a data migration because they have outgrown their legacy platform and want to replace it with a new system while keeping the same dataset. Often, the new platform provides significant advantages such as lower costs, increased security, or improved performance. A data migration has a target destination and an end date.
The goal is to restore what was in the old system while making as few changes to the data structure and processes as possible.
While it may appear that data migrations are simple, 83% of them fail or exceed their budgets and schedules. They also necessitate extensive planning and follow-up activities after the migration. Data quality issues or a lack of standardization across data sets can cause issues that slow the process of moving data into the new system. Regardless of the challenges, data migration is a critical and necessary step for businesses today, especially as more companies embrace the benefits of cloud technology.
The following are the primary steps in a data migration:
- Conversion of data
- Profiling of data
- Data purification
- Validation of data Quality assurance
- Data transfer
Many data migrations today are part of a larger transformation strategy involving the transition from on-premise data infrastructure to the cloud's seemingly limitless storage and scalable compute resources.
According to Gartner, more than 70% of companies have moved some of their workloads to the cloud.
While data migration is an important part of many digital journeys, it is not a digital transformation in and of itself. A complete transformation must consider changes in culture, process, accountability, and data governance in addition to technology.
Furthermore, unlike a migration, which has a set destination and well-defined steps to get there, a data transformation is ongoing and will look different for each business.
Culture Is at the Heart of Transformation
Transformations are only long-term and successful when they are accompanied by long-term cultural change, which involves cultivating behaviors and attitudes that support the company's new direction and objectives. According to a BCG study, 70% of digital transformations fail to meet their goals. The "people dimension," or the operating model and culture required to advance a company's digital plan, was a sticking point for many of these failed initiatives. Changes in organizational structure or associate skills may be required. Implementing new ways of working with data across organizations successfully establishes a cycle of continuous improvement for businesses.
When to Migrate Data vs. When to Transform Digitally
Some businesses may discover that they do not need to plan for an entire transformation in order to achieve their goals. Many smaller businesses, for example, are much more agile than large enterprises and can make changes quickly without having to overhaul their legacy systems or processes. Furthermore, some businesses are compelled to plan and execute a data migration due to a looming deadline, such as running out of space in their on-premise system.
However, when a company has control over the timing of each, tackling data migration and wider transformation separately is usually preferable. While data migration has the advantage of having a target and a time frame, comprehensive transformation takes much longer and requires more resources. Companies must also give themselves room to learn and grow from the inevitable mistakes that will occur during a transformation journey, which should not be linked to the successful implementation of a data migration.
Best Practices for Approaching Digital Transformation
While each company's transformation journey will be unique, the following are some of the best practices that are emerging as more companies adopt data-driven transformation strategies.
Create a Clear Data Strategy
A comprehensive data strategy can serve as a road map and guide for all stakeholders involved in a larger transformation. The strategy should not be developed in isolation, but rather with the participation of key members from across the organization. It should include both an overall business goal for the transformation and goals for individual business lines. The strategy should also be designed in such a way that it can adapt and change in response to new business conditions and disruptive technologies over time.
Communicate at Various Organizational Levels
Focus on effective communication at all levels of the organization, as well as the creation of a feedback loop. Before implementing any data strategy, bring together all key stakeholders, including data engineers, data consumers, line of business managers, and executives. Discuss each stakeholder's goals, frustrations, and desires, as well as key meeting dates.
Make Your Case to Customers
A true transformation cannot take place unless your employees are on board. Each leader is responsible for convincing each end user of how their lives will improve in the new world. Understandably, change is difficult and can cause anxiety about the significance of current roles as well as skepticism about whether the change is beneficial. Businesses can cultivate a supportive and empowering culture for data end-users by clearly and thoughtfully communicating the value of the transformation to each employee and how a role will change, if at all.
A Cultural Shift is Required
A true digital transformation necessitates changing an organization's culture in order to sustain the adoption of new organizational structures or operational processes in the foreseeable future.
Changing the way data is perceived and used throughout your organization is a must. A significant cultural shift occurs when the organization begins to treat data as a product, with data end users acting as consumers and data sets delivered as products with their own SLAs.
The Transition from Centralized to Decentralized
Adopting a decentralized approach helps address the significant increase in data complexity and volume that results from moving to the cloud. The model of a central data team managing data requests from across the organization can become a bottleneck, unable to support the new data ecosystem and demand for faster insights.
A Constant Data Journey
Many businesses are recognizing the importance of investing in data as a critical component of their digital transformation journeys. The challenge for most of us is recognizing that deeper, long-term change is about more than just data, but rather how your organizational culture complements and enables new ways to produce, consume, and govern data.
Companies must develop data strategies, best practices, and cultural shifts to ensure that transformation efforts continue well beyond the current environment. Businesses will be set up for continuous innovation for the benefit of their customers if they approach each step with the full scope of technological and organizational changes in mind.
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