[This article was co-authored by Harry Hanelt]
Digital asset: a trendy term or a transformative concept? A digital asset is simply content that is stored digitally, typically in commercial databases. However, in businesses, this straightforward content has many relevant, even crucial forms such as operational data, business processes, business systems, spreadsheets, text files, audio files, PDFs, word documents, HTML documents and more.
If your company is like most from the Fortune 1000, several years back, senior management was running digital transformation plans like it was the virtual gold rush — what value is hidden inside all that content? How could it be sifted to find customer-related data that would give companies a competitive advantage for retention and acquisition?
Digital assets are only as valuable as the content they represent, which can be easily codified, searched and analyzed. The foundation all this harkens back to the data. While some digital assets are mission-critical (i.e., customer-centric), there are also a lot of assets that are not high value. The key to organizing and differentiating these is an excellent digital strategy, which is why there has been a massive push in this space for Fortune 1000 companies over the past 10 years. However, it seems that most strategies have been diluted, forgotten or have become misaligned. Companies need to get back on track, getting to a state where content is easily digitized, then analyzed to find those golden pieces of business value.
A digital asset framework alone is ineffective for a company without that solid digital strategy in place. These intertwined processes are dependent on business and technology working in concert, with full consideration of every business vertical within a firm.
What is your digital DNA?
A digital strategy should mirror the digital DNA of an organization since it permeates all departments, processes and data. However, when a good data strategy is coupled with the digital strategy, digital asset management (DAM) is produced as a way to structure and store all a firm’s digital assets. DAM is not something that each firm needs to invent itself. Powerful DAM tools exist, which can significantly progress a company’s digital agenda. There is research that covers the pros and cons of these tools by firms such as Gartner or Forrester since each program has a particular focus like brand asset management or digital supply chain.
However, a company’s DAM cannot produce the desired digital transformation if the key element of the strategy, data, is weak. It does little good if digital assets have erroneous data or have different labels for the same asset (data taxonomy), causing data corruption. A good example is the process of acquisition and all the challenges that could bring without inducing a standardized data terminology as part of the strategy.
Just as there are tools on the market that address DAM, there are also tools for data management. But before looking at any tools to solve data challenges, a firm’s approach should focus on the components of a comprehensive data management strategy. Otherwise, the risk is building a process around a tool alone. Do the upfront work and take the time to do it well! It will allow for the acceleration down the line. At that point, a more agnostic approach for tool selection can be undertaken.
The following are the components of a data strategy that need to be in place before implementing a digital asset management solution.
- Data governance: Proper governance ensures that important data assets are formally managed. Everything from the current state to lineage is considered. Governance ensures that data conforms to business requirements. Firmwide governance committees need to be established for strategic alliance. Data quality (stewardship) is very much part of this function.
- Master Data Management: MDM represents processes and tools that define and manage the critical core data of an organization. MDM is reliant on a well-prepared data governance plan. As Aaron Zornes has pointed out, “MDM without governance…is just data integration.”
- Metadata management and taxonomy: Metadata is a full data descriptor, and Taxonomy is how that data is categorized. This comes into play as per the previous example of acquisition.
- Data architecture: Physical (e.g. from data centers, to cloud, to HADOOP, etc.) and logical architectures that will change based on the business requirements, and data governance.
- Analytics: Getting the output right. You can have the best tools and tool frameworks, but sideways data, no matter how “pretty,” will provide ugly results.
By first developing a sound data strategy, companies can leverage their digital assets effectively. Cloud migration, legacy technology and API services, will also be better served. A good data strategy will ensure that a company is nimble, and can meet market, compliance and security demands.
Unfortunately, this process can be “no pain no gain.” Trying to use tools to quick-solve the problem of leveraging your digital assets in furtherance of your digital strategy without getting your data in order will impair your success. A tool-driven approach will never gain the advantaged visions of success and will only get negatively compounded when left as the status quo. Foundational work is not easy, but in the long run, your data will determine your destiny.