Spencer Nguyen - August 11, 2022
Modern technology has enabled businesses to collect all manner of data. Managing data as an asset includes website interactions to drilled-down explanations of what makes a company’s strategy successful, this information informs initiatives, aids decision-making, improves business processes, and provides a way to measure success toward specific objectives.
However, having enterprise data is not a strategy or a competitive advantage in and of itself. Data assets have to be managed in such a way to support the company’s data strategy.
Strategic Data Management
Treating data as an asset is critical because it enables the data to be used strategically. Too often, data management is treated as a tech problem for the IT department, or it gets wrapped under departmental management when really it is a business issue, but that’s a mistake. Data is not a byproduct of business processes. There is a big difference between data and information.
Data vs. Information
While data vs. information are used almost interchangeably in many circles, there is a vital distinction. Put simply, information is the product that comes from data that has been processed and converted into a usable format. In other words, big data only adds business value once data analysis is complete. More importantly, the end information is only as good as the data quality.
The Importance of High-Quality Data
Digital transformation and most current business models rely on high-quality data sets to inform their initiatives. The right data sources are critical to their success, but converting big data into usable information can be complicated. Data is dispersed in different formats, and there may be data governance issues, regulatory, or other data management complications. Treating data as an asset is an approach to managing those challenges.
Categorizing Data as an Asset
Categorizing data as an asset is much more than a shift in understanding data vs. information or realizing the value of enterprise data. Effective data management documents the worth of that data and, in some cases, may even take on a notional value. Data valuation can be accomplished in many ways — the best approach will depend on the nature of the data itself and how readily it can be processed into usable information.
Approaches to Data Valuation
Overall, a sophisticated approach isn’t necessary; data valuation just needs to be transparent enough that any weighting makes sense. For instance, customer data could be scored based on the quality of the data, how much that information increases revenue, and whether that data can be leveraged to reduce costs.
Outlining the Data Valuation Approach
While there will be no one-size-fits-all approach to data valuation, there is a basic process to using data as an asset:
- Create a rough draft of any enterprise data assets.
- Review the data quality of each source.
- Assign a notional value to each data set.
- Create a roadmap of initiatives based on those notional values.
- Establish a process to monitor metrics and adjust notional values.
Protecting Data as an Asset
Data protection is important as well. While data use is one consideration, unlike other assets, it is not the primary consideration in data management. Instead, data quality is the central concern. It has to be reliable, it has to be representative, and it has to be usable. For these three things to happen, enterprise data must be protected. DreamFactory solutions offer data protection that seamlessly integrates into data analytics and company information systems.
Valuing Data as an Asset
Data has an inherent value; it is a strategic asset. “Data, in the right hands, is often as valuable as land, buildings, and equipment,” explains Dr. Henna A. Karna in CFO. “If an insurance company, for example, can make better underwriting decisions than its competitors because of an enhanced ability to acquire brilliant insights from its data, investors and Wall Street would want to know that for valuation purposes.”
However, data is almost never included on a balance sheet like physical assets (or revealed in any way), and most companies do not have a chief data officer or CDO to oversee data strategy — and that is a missed opportunity. There are several important reasons why you should be managing data as an asset.
Integrating Historical Data With Real-time Data
The digital transformation experienced in most industries is powerful on its own. Something as simple as seeing the customer experience in real-time can inform business strategy, but companies can go a step further by integrating historical data with real-time data to spot trends as they develop. In this way, real-time data analytics can act as proof of business strategy effectiveness.
Data Analytics and Automation
Also, when data is treated as an asset, big data can be interpreted in context. Data analytics ensures that the information produced is compared apples to apples so that customer data is viewed in context and the company can see causation instead of correlation. Moreover, the right approach produces these actionable insights using automation, so the company gets a competitive advantage from each information silo.
Data as a Separate Revenue Stream
Finally, treating data as a business asset is useful because it can produce a new, separate data stream. Many companies can find a market for the data they collect on their customers through their life cycles or as a part of everyday business. For instance, an accounting company may introduce data initiatives to sell information on the total value of new tax breaks for small businesses across different industries.
Integration Is Key to Realizing the Value of Data
Whether your company intends to use data management to gain a competitive advantage, plans to produce a revenue stream from data assets, or simply wants to improve decision-making, managing data as an asset makes it happen. The right big data ecosystem solution can improve profitability by producing actionable metrics that inform company strategy and positioning. Start your free 14 day trial to see how DreamFactory’s API projects can provide your company with a competitive advantage through high-quality automation, data analytics, and real-time risk management.
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As a seasoned content moderator with a keen eye for detail and a passion for upholding the highest standards of quality and integrity in all of their work, Spencer Nguyen brings a professional yet empathetic approach to every task.