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How to use data as a strategic asset

Bilde av Terje Vatle
by Terje Vatle

14. Jan 2021, 9 minutes reading time

How to use data as a strategic asset

Research suggests that most organizations see the value of using data more strategically, but many struggle with getting there. Here are some tips on how to get started.

Strategic in this context refers to data continuously being on the agenda at the executive level of organizations, recognizing it as an important and uniquely valuable resource. Cultivating this mindset starts by assessing just how valuable your data really is. 

The concept of managing, measuring and monetizing information (where information is a processed and prepared form of data) with the same regard as any other asset is referred to as infonomics. According to Gartner, by 2022, 30% of leading organizations will formally adopt infonomics practices and value their information assets, maintaining a balance sheet for internal purposes.


Quantifying the strategic importance of data

Every aspiring data-driven organization will at some point have answered a roaring «yes» to the fundamental question of whether data is important. But fewer have worked out the answer to the follow-up: Just how important is it? 

Making an effort to quantify what data means within your organization, is a crucial exercise, not just because the end-result is valuable going forward. But because the effort itself contributes towards a central data-cultural goal: making every department of the organization willing to invest, learn from mistakes and continuously improve. Choosing that path will eventually show how data has continuous value – and as such, long-term strategic value.


A little how-to

So where to begin? For a data asset to have a truly strategic function, it needs to be used to support a specific business goal. These can be KPI-based goals – both long-term and short-term. One commonly sought goal, for example, is to reduce the organization’s environmental footprint. As a data-driven organization, you seek to get as complete a picture as possible to create a path towards your goal. A goal such as this would often require data from several sources as well as domain competencies.

Therefore, large, multifaceted goals need to be broken down into operational processes, and you need to map out your data availability and relevance vs. the effort necessary to succeed. The organization should answer questions such as:

  • Do we have the required data already? And is it:
    • Accurate: Does the data correctly describe the real world objects you would like to monitor?
    • Complete: Does the dataset contain the required data?
    • Consistent: If data is found in multiple places, is it consistent across all instances?
    • Timely: Are the datasets sufficiently up to date and are we aware of any lag?
    • Unique: Are real world objects represented only once? 
    • Valid: Are the conditions for a specific field satisfied? 
  • Where do the data assets come from?
  • How can we get access to the data assets? 
  • How can we ensure the proper context and quality of the data assets?

Answering these questions should help you get a clearer picture of the effort necessary to succeed. «Effort» in this context refers to a wide range of cost-related activities and measures such as:

  • Enabling the organization to request insights, prepare data, analyze data, interpret and communicate findings, and apply insights in business decisions
  • Hiring, training or buying missing competencies or capacities
  • Assigning ownership to each key data asset with responsibilities for data quality, data definitions, how to use it correctly and more
  • Managing privacy, compliance and data security
  • Performing legal and ethical reviews
  • Performing data integration, data quality assessments, preparation, analysis, insight distribution, decision execution and feedback loops
  • Working long-term with data quality improvements throughout the organization, from business processes where data is captured to analysis where it is used
  • Ensuring proper technology infrastructure is fit-for-purpose, including i.e. data management, data analysis, data visualization
  • Purchasing external data 

Once all of this is mapped out, the strategic significance of your data assets can be quantified as its tangible contribution to your value creation and competitive edge, minus the above costs.

Leading organizations today organize the work around the strategic value of data assets in a product management function, typically directly under the CDO. This function is tasked with generating measurable economic benefits from available data assets i.e. by: 

  • Mapping out an inventory of existing internal and external data assets
  • Evaluating methods of monetization of such data assets
  • Adopting monetization ideas from other industries
  • Test ideas for feasibility i.e.: 
    • Is it usable or practicable?
    • Would the idea have a broad appeal?
    • Is it scalable?
    • Is it manageable? 
    • Do you have the technology? 
    • Is it economical?
    • Is it legal? 
    • Is it ethical? 
    • Is it ecological? 
  • Prepare and package information for monetization
  • Establish and cultivate a market for your information products

One of the biggest obstacles for many organizations is that the value of data itself is still largely unrecognized. Establishing a product management function could be a first step towards understanding the value and potentially monetizing the strategic potential in your data.


A case-in-point: Environmental footprint

Continuing the above example of aiming to reduce the environmental footprint, where does one start? First off you need to identify the primary drivers of sustainability within your organization. This can be anything from energy use and logistics to overproduction and waste management.

Let us assume your organization has a manufacturing facility, and you have identified product packaging as one driver of sustainability – and a potential point of improvement. Then the organization needs to answer the question: Do we have data on our yearly packaging waste? And if no: What would it cost us to acquire? If you deem the investment i.e procurement and integration of sensor systems, training etc. as too costly, you could move on to a second driver for the time being. Do we, for example, have data on the mileage of our transportation vehicles? 

If the answer is yes, the organization now has a starting point for viewing – and utilizing – data as an asset to reach a strategic goal. Needless to say, the branding and consumer trust aspect of this approach should not be underestimated.


The cost of doing nothing

There are other factors in the equation as well, one of them being the cost of doing nothing. Not choosing the path of data might put your organization on the fast track to irrelevance. The organization can over time attract fewer talents – and innovate less. Additionally, there is the risk of simply being left in the dark about crucial factors affecting success, such as buyer behavior trends or product and service profitability.

On the other hand, data can lead the way to entirely new opportunities. Seeing data as a fundamentally strategic asset will open the blinds beyond the current business model – all the while becoming more resilient to disruption.

For further reading, please check out Deloitte’s article Data as an asset or Build a Data-Driven Enterprise by Gartner. Also worth taking a look at Applied Infonomics: Seven Steps to Monetize Available Information Assets and Don't Just Talk About Information as a Strategic Asset, Manage It Like One! by Gartner. Gartner articles require a subscription. 

Do not forget to keep an eye on this space – and never hesitate to contact us for more information about this subject.

Download free e-book: Building a data-driven organization

Terje Vatle

Terje Vatle

Terje Vatle is Chief Technology Officer at BI Builders following global market trends within data & analytics. Terje has a technology and advisory background, and focuses on how to make organizations achieve their goals by becoming more data driven.

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