Do you want to discover a new data revolution?  

Central for de-central data teams: start acting as a (temporary) data owner to service business requirements.

Set up data service desks to be flexible for business requirements.

Data is often seen as the raw material to produce new products, frequently with analytics and AI as the innovative machinery enabling the end-result. Recent years have proven that the value of data more often serves as an enabler of multiple business results, leading to efficiency savings, profits, and the ability to maintain existing markets while expanding into new ones

Data as fundamental oil

Whether it is automated payments and invoicing, online customer interactions, or digital manufacturing, data is the underlying oil that can make your business operations run smoothly.

Or does it? Why is it that, despite the abundance of data, businesses often run sub-optimally, sometimes even relying on manual activities, in this digital age?

“So, if data is the new oil,

why isn’t everything running smoothly?”

All companies generate, receive, and process data to some extent. Data is abundant these days. So, if data is the new oil, why isn’t everything running smoothly?

Here is why: the data itself is complex, and the usage of data is complex. Many companies have tried to resolve this combined complexity through centralized standardization. Many projects aimed at establishing a single data model have become famous, often leading to disappointments. Alternatively, data solutions seek refuge in technology, often resulting in an increase in applications, which can add to the complexities instead of alleviating them. Above all, centralized standardization requires control, which does not adequately serve the business.

Move from control to empower

The very essence of any business is flexibility, the ability to innovate and develop new products and markets. The business needs to be facilitated by data. So, move from controlling towards empowering.

Empowering means understanding that there is no one-size-fits-all when it comes to data, as in the above-mentioned complexities. Data is very similar to machinery. Just compare the oil for ball bearings to petrol. They share the same raw material but differ in volume, characteristics, substance, and processing for different purposes.

How do we see the solution?

With the extensive rise in data volume, complexity, and velocity, a central data team supported by data stewards and architects is no longer sufficient. It requires more decentralized teams that can facilitate specific business needs while adhering to central requirements. Use the motto: control only where needed – for example, using one standard product or client ID across systems, and facilitate where possible, such as adding an additional product ID to support a regional process. Of course, this requires more effort. It is evident that any additional data requires more maintenance. However, the benefits for the business are immediate and significant. There is no need for the business to change processes, systems, or reporting. Immediate possibilities emerge to make more local variations of products and insights, facilitating specific market requirements. This approach maintains the possibility of working with central initiatives and the option to upgrade or downscale data where possible without affecting central requirements.

“The answer is easy, the deployment is more complex “

How do you facilitate this? The answer is easy; the deployment is much more complex. Have dedicated data teams in place with a close relationship to the business. That data team should consist of senior, well-trained data management experts, data analysts, and data engineers to facilitate and guide the local solutions, including the link to central platforms. The team should be able to answer business questions through a so-called service desk. Such a service desk requires a thorough understanding of the business processes and systems and the translation of data requirements into existing (or missing) data within systems. Preferably, the service desk should have the capability to identify, flag, and resolve regulatory questions on privacy, financial legislation, and health legislation. Make sure that the service desk is enabled by a ticketing workflow, including a dashboard displaying their effort and impact. Finally, that data team should be able to guide the business stakeholders in the best approaches and solutions. Don’t expect business stakeholders to deliver data requirements; they will have business requirements. If you didn’t know better, this team almost acts like a data owner.

Of course, some data needs to be strictly governed and controlled. There are overarching business requirements (e.g., insights in sales volumes) that require consistency and quality of trusted data. Identify these key data elements and manage them with a strict and tight regime. These key data elements can. for example, be linked to key reporting, identified as being used for most processes, or be the primary key within multiple systems. Current data volumes can make this identification a tough job. A good start can be using the Dublin Core standard to identify the right regime. The standard uses the following guidance:
– which data is related to which process, system, product and report?
– where is data being used?
– why is the data needed (purpose)?
– Who uses the data?
– How is data labelled and referenced?
– What is the relevance of the data (e.g., static or dynamic)?
– How is data related to other data?

New way

Teams acting as (temporary) data owners is a new, almost revolutionary way of looking at data. The traditional view, based on data standards (e.g., DAMA, DCAM, ISO), all revolves around governance and ownership. That view is based on having data ownership within the business. If you step away from that theoretical view and fall back on lessons learned, then don’t expect business stakeholders to take up sufficient data ownership. For decades, they have perceived data as a by-product.

Most business stakeholders will stay away from data ownership simply because it is unknown territory for them. It is up to the data team to translate do’s and don’ts regarding data and take up data ownership for the business. In theory, this might even support the embracing of data ownership by business stakeholders through the principle of show and tell.

So, a team of experts is required. Such a team goes beyond the effort of some companies to “simply” assign a data steward who reports on the content of specific fields within systems. Companies should build dedicated teams across the organization, which will often need to invent the wheel themselves. The way of working will differ per objective. For project goals, make sure you can act fast, agile, and dedicated. For sustainable solutions, ensure that you stay completely aligned with company standards (and enhance a few where needed) to avoid the “not invented here syndrome.” For any purpose, make sure you take the time to understand and align data, data requirements, and business requirements. And actually build solutions – not just on paper, but within apps, databases, data pipelines, and systems.

All of this will require a solid, robust, and senior data leadership team which can manage, sustain, guide and facilitate data responsibilities. Invest in that team.

For examples on data standards, visit: DAMA, DCAM or ISO