Six steps to transform your organization through data

Today’s organizations understand the importance of digitization to transform their operations and out-compete their rivals. Companies that complete successful digital transformations achieve twice the growth of their peers.

Data is key to digitization, with organizations that harness data and share it effectively increasing efficiency, innovation, collaboration and improving decision-making. Data transformation is central to digital transformation. However, often data is siloed within businesses or not made available in ways that it can be easily understood and used by all employees and external partners.

What is needed is to embrace data transformation alongside digital transformation. This goes beyond simply collecting, storing and analyzing data or transforming data into new formats. Instead, it is about using data to fundamentally transform how the organization functions and operates. This means putting in place the right governance processes and structures to make data easily accessible and reusable.

Julien Lévy, Associate Professor and Academic Director of the Innovation & Entrepreneurship Center in Paris defines data transformation as “Innovating the way we do business, by leveraging data to boost productivity, develop the business and create value for customers. In other words, data transformation is an organizational innovation policy, and the tool of this policy is data and its processing.”

Data transformation delivers multiple benefits, including:

• Increased productivity through improved efficiency
• Better decision-making driving superior performance
• Greater agility, enabling companies to react more quickly to changing conditions
• Improved collaboration, both internally between departments and externally with partners
• Higher levels of innovation and the generation of new products and services based on data

Given the benefits of data transformation, how can you successfully implement it within your organization? It requires a holistic six step approach that brings together people, data, technology and processes to create data-centric organizations that are agile, innovative and efficient.

Businesses now have access to enormous volumes of data. Embarking on multiple, disconnected projects to turn this data into value will lead to duplication, wasted effort and missed opportunities. Instead, data transformation should start by defining a clear data strategy. What are your business aims and objectives and how does this relate to data? Setting this North Star and clearly outlining what data means to the business will direct your program, gain buy-in from senior management and focus your efforts on innovation.

As part of your data strategy, take the time to map and understand your current data landscape, including the datasets you own, where they are located, the technology stack and the internal skills and capabilities your people possess. This will help identify any gaps that need to be filled – and opportunities to be exploited. As part of this process many organizations are now appointing a Chief Data Officer, to define and lead their data transformation strategies.

Data transformation means making your data available to all employees so that they can use it effectively. However, the majority of staff are not data specialists, and may not feel confident in using data during their working lives. To overcome this obstacle, organizations have to create a data culture that involves everyone in data transformation and gives them the skills and knowledge to harness data effectively.

Ensuring everyone is part of data transformation programs dramatically increases their effectiveness. McKinsey research found that transformation projects where at least 7% of employees felt involved were twice as likely to deliver better total shareholder returns. This is because employees understand the importance of data and are using it effectively to innovate, make better decisions and increase their productivity.

Data transformation programs are often large-scale, covering the whole organization. They therefore need to be managed carefully, with strong governance processes in place to guarantee their effectiveness. Governance spans three specific areas:

• Data governance – ensuring that all data is consistent, well-documented, secure, compliant and follows corporate guidelines, wherever it is generated and however it is used.
• Technology governance – putting in place the right technology tools to support your data transformation strategy, with an agile infrastructure that covers the complete data lifecycle from collection to sharing and re-use.
• Project governance – it is vital that the entire data transformation program is well-managed and governed. This is particularly important when creating pilot projects and use cases across the business. These need to be documented, monitored and evaluated to ensure that they are benefiting the business.

Governance requires a combination of the right people, processes and tools and has to be driven by senior management yet involve the entire company, especially data owners in different departments, spanning data, technology and all projects.

Choosing the right enterprise data architecture is crucial to data transformation. Too often organizations try to centralize data and how it is managed, leading to rigid, inflexible infrastructures that stifle innovation. Individual data owners in business units feel sidelined and uninvolved in data transformation, holding back adoption and success.

In contrast the emerging data mesh architecture decentralizes management to data owners across the business. While they have to follow agreed guidelines around governance, they are responsible for maintaining and updating their datasets, working collaboratively with their colleagues. This increases buy-in, accelerates innovation and reduces the risk of projects failing. Cross-functional teams work together to create data products that meet specific needs, transforming data into value. As data mesh is technology agnostic, teams can use the tools that best meet their needs, confident that they will seamlessly integrate across the data stack to enable the free flow of data.

After creating the framework to collect data and putting in place a data-centric culture, organizations need to ensure that everyone can access it through an internal data portal. This has to provide a one-stop shop for data, so that employees can simply log on and view/ and re-use relevant data based on their needs. Data portals need to combine security and the protection of confidential information with ease of use. That’s why organizations are increasingly transforming their data portals into data marketplaces that provide the same seamless and intuitive experience as an ecommerce shopping site. This increases usage and ensures that employees can independently find, navigate, use and rate datasets from across the organization.

The vast majority of employees don’t have the technical expertise required to analyze data using sophisticated analytics/business intelligence tools. Often these programs are complex to learn and expensive to scale beyond business analysts. To democratize data and make it easily understandable, businesses should therefore focus on creating compelling data visualizations, such as dashboards, maps and graphics. These bring context to raw data, and make it meaningful to every employee. This allows them to use and re-use data, encouraging data democratization and widening access to information. As part of the data experience you offer your employees, ensure that it is easy to create visualizations and that they are available in the right formats to match potential uses. While this is the final step in the data transformation journey, it is vital to get it right to ensure data is used effectively by all – it must be simple and intuitive for everyone to share and reuse data across the company.

Harnessing data successfully requires a clear strategy and ongoing program that involves everyone across your organization. Following these six steps will deliver effective data transformation and enable your businesses to become data-driven, enabling greater agility, innovation and efficiency, whatever sector you operate in.