Data Asset Management | The What, Why and How
Introduction To Data Asset Management
Every company born before the digital age is undergoing some form of digital transformation to better manage their data to deliver better outcomes. It’s a huge priority, because without progress on this front, the already large gap between the digital native businesses like Alphabet, Amazon, Facebook, Tesla etc, will continue to widen.
This is both a technical and a strategic problem. Technical because the complexity of legacy systems and decades of poorly managed data has created a big mess to fix. Strategic because if the company doesn’t think about its data in the right way, it will never achieve the kind of results that have catapulted digital natives into being the world’s most valuable companies.
Orientating technically, around datasets, without first orientating strategically, around the purpose and goals of the organisation, the task of transformation is near impossible. The volume and complexity too large, the overriding question becomes ‘where do we focus?’ Without that question answered, the only way a business can move forward is to do technical fixes that do improve the situation, but can’t be proved to be the most effective activities to undertake.
Inverting to the business end makes it simpler – create a concept that makes the way data creates value clear. This means making the data into specific business assets, like the UK customer data asset, or the employee data asset. A specific data asset includes multiple datasets.
Why Data Asset Management Matters
Asset management means managing something valuable in a safe, consistent and efficient way to get the most out of it. For physical assets, there are standards like ISO 55000, and before that PAS55. Meeting these standards means an organisation can prove to their shareholders, customers, partners, local communities, governments and others, that it manages important assets well.
We don’t have anything like this for data. Frameworks like ISO 8000, ISO/IEC 25012 and DAMA-DMBOK are too theoretical. They don’t talk about tangible actions needed to create real change. Without good management discipline, more is spent on fixing data than on using it. Unlike physical assets, like a car, using data doesn’t degrade the condition of it. The condition of data, for the jobs it needs to do, is determined by the ecosystem around it. Data problems are symptoms of business problems, typically a lack of alignment, communication, clarity of definition and policy follow-up and enforcement.
Take a company that builds and runs shopping centres. One part of the business gets a contractor in to build a new wing. It’s a big job that will take 18 months. Procurement don’t understand what the Estate Management team managing the new wing need to run it. Procurement don’t know Estates need the data for drains needs to be recorded precisely, nor do they know those records should include exactly when the drains were installed, so they can be changed in three years when parts rust and degrade. All Procurement know is they need a ‘map of the drains’. This is misalignment and miscommunication.
The new wing is built. It looks great. The contracts give a PDF map of the drains, and a list of when the drains went operational. But operational means when the whole system was finished, they were installed and exposed to the elements nine months before.
When Estates get brought in, not only do they now have to map and survey every drain again, they have to do it for every heat vent, hot water pipe, Wi-Fi router and more. Then the tenants come in, rent their unit and start their building work. Their contracts specify keeping detailed records of when and how they change them. But this is never enforced, because the position that fulfils that role has a high turnover. Estates face the same challenge again. This time with 100 units all making different changes at different times.
These problems would be avoided if data was treated as an asset like the new building itself, but it isn’t. Data is invisible, complex, assumed, and as we see later, overshadowed by technology.
How To Manage Data As An Asset
- 00:44 – Data is important
- 02:02 – Why is data important & what’s changed that data has become so important?
- 03:11 – Data is a significant asset, but it’s not managed as an asset
- 03:38 – Digital-born companies understand that data is the lifeblood of the organisation
- 05:06 – CEOs agree that data is critical, but organisations invest in tech, not data
- 06:50 – Only 3% of data is fit for purpose
- 07:58 – The hidden cost of bad data
- 08:41 – The data maturity ladder
- 09:25 – The data problem unpacked
- 13:55 – How do we solve it? Improving ROI on data assets
- 16:02 – Data asset management in action
Data Asset Management vs. Data Governance vs. Data Management
To put it simply:
Data management is data centric, focused on technical data management, like understanding the system of records, managing master data, metadata etc. so the data is better able to support the business.
Data governance is business centric, being the process of controlling data and processes to make data more usable and compliant with legislation and best practice.
Data asset management is value centric, focused on organising collections of data assets to create more value for the stakeholders the organisation serves.
Each is important. Data management being the most granular and technical. Data asset management is about managing data through its life cycle, from collector or creation, through storage, use and manipulation and eventual disposal. Once you’ve addressed the big questions of what to do with your data, data governance helps you manage it. This means it’s not a case of data asset management vs data governance, more a case of data asset management AND data governance.
Data asset management, data management and data governance explained by analogy
The analogy of going on a journey is a helpful way of explaining the difference:
Data management would involve asking questions more focused on the car. Is the fuel tank filled, the tyres pumped up, the brakes responsive and the transmission in good working order?
Data governance is like checking how well you, the driver, can handle the car. Have you passed your driving test, are you able to use the cruise control safely and are you wearing your glasses?
Data asset management is focused more on the point of the journey and arriving safe and ready to participate in that. It includes the context. There’s a big difference between what you need to go on a two-week road trip and a 15-minute pop to the shops. Indeed, for the trip to the shops, the car may not be the best mode of transport to use if you only need coffee.
Digital Asset Management
There is scope for further confusion between the terms data assets and digital assets. While these terms are sometimes used interchangeably, they do not mean the same thing.
The definition of a digital asset is a broad category, being anything that exists in digital form that you own the rights to use. They range from the photographs taken for your website and advertising to the volumes of information you have about your customers. Some of the most common digital assets include Word documents, photographs, video, audio files and others. Characteristics of a digital asset include:
- Assets that are digital in nature.
- Those that are uniquely identifiable.
- Those that provide value to the company.
Because digital assets are uniquely identifiable, they can be searched for and discovered. Digital asset management software allows you to make your digital assets easily searchable while protecting their integrity. Put simply, it’s about managing file storage and access in the most effective way. An important area of course, one that can deliver efficiency savings and given revealing insights about how the organisation works. But it is not going to deliver the strategic advantages that data does, because it’s not focused on improving decisions and delivering value directly.
An Effective Data Asset Management Framework
In our data leadership report, we revealed that 91% of business leaders say that data is a critical part of their organization’s success. Two thirds say that they see data as a material asset in their business. Yet just one third manages their data to the same standards as other business assets. The two thirds that don’t, aren’t just risking getting left behind, but also risking expensive and embarrassing mistakes.
A data asset management framework follows the same questions used to manage any kind of asset. Asset management is a consistent way of developing, operating, maintaining, upgrading, and disposing of assets efficiently. There are huge fields of work dedicated to the management of all sorts of assets. At its simplest though, there are eight questions that underpin good practice.
- What are our assets?
- Which are most and least valuable?
- What do we want to achieve with them?
- How are we going to do that?
- How do we know we’re on track with that?
- How is the condition of our assets changing?
- How are we responding to those changes?
- How do we get the best ROI out of the asset?
The Biggest Barriers To Data Asset Management
The biggest barriers to data asset management are not technical, nor technological, despite data asset management software not existing yet. The biggest barriers to data asset management lie in the way businesses are used to managing their other assets. The balance sheet is a critical artefact businesses use to manage their assets. At present, International Accounting Standards don’t allow for data to be accounted for in a way that represents the unique nature of data as an asset class. Inclusion on the balance sheet would send a strong signal to the business of data’s value and importance.
But until accounting standards change, which will take many, many years, the next best approach is to place a monetary value on data, so people in the business understand the value of their data assets. Given that this value typically accounts for at least 20% of the organisation’s total value, it would be a bad businessperson that does not invest in managing their data assets for better results.
Data Asset Management Resources
The benefits of treating data as an asset
The benefits of doing a data maturity assessment
Data maturity models – measuring the health of your data
Different data valuation methodologies
Data landscape – navigating the data jungle
Data quality vs data condition: the power of context