The structural element so often neglected from digital transformation efforts is data portability. Data portability refers to the ability to transfer data and knowledge assets from one data controller to another while retaining context and meaning, preventing the creation of information silos. Knowledge assets are the value that organisations create that are stored digitally - and prior to digitisation, were scattered across spreadsheets, filing cabinets, libraries, and peoples’ brains.
The types of knowledge assets will vary across different businesses and sectors. For media organisations, for example, it would include articles, infographics, and videos. Specific types of media outlets will have further knowledge assets - where finance publications might store market data, food and cooking publications will store recipes. Outside the media, in healthcare organisations for example, knowledge assets will look very different - primarily patient data, research and analysis of clinical data.
Unless data portability is placed firmly at the front and centre of any transformation project, information silos are inevitably created, which hinder cross-organisational collaboration and innovation. This, in turn, makes it incredibly challenging to rapidly adapt to changing market conditions. Organisations can avoid this by implementing a structured data and technology backbone, defined by McKinsey as “the storage, aggregation, analysis, and provision of data across the organization”, to better manage their knowledge assets.
Usually, even when well-structured data is generated, it ends up locked into different systems which are entirely separate from one another. This means that time is invested into connecting this information manually, wasting precious internal resource and preventing rapid innovation.
Let’s take a law firm as an example. With a structured data backbone that covers all areas of the business in place, the legal and financial team can see at a glance all their clients, case classifications, financial statuses, and how these all connect. In practice, this means that the legal team do not need to spend time communicating to their finance, administrative and leadership teams on administrative matters - leaving their time to be spent on their core business, legal activities.
For an ecommerce business on the other hand, a structured data backbone connects the product catalogue, product categorisations, suppliers, fulfilment and delivery options, transactions, sales
volumes, and website analytics, making it easy for decision makers to understand how changes to one part of their business impacts another. Without this deeper level of insight presented in a simple and structured manner, key learnings that could increase sales could go unnoticed and never be acted upon.
As with all aspects of a digital transformation initiative, it’s better to take a measured and agile approach than to rush in, all guns blazing. Instead of trying to rearrange and restructure all of your data at once, identify which assets are valuable and differentiating, and start there. Companies can from there work towards linking up all their data across all of their systems, from CMS, product analytics, planning and business intelligence to finance, audit and compliance, and so on.
Digital transformation is a worthy undertaking, but good intentions can quickly turn into missed opportunities and lost competitive advantage. It is crucial to consider how you can connect your data and carry its context and meaning across all of your business use cases. Without this data backbone, even a highly strategic digital transformation initiative will not have the desired effect or add value across the business.