Data assets refer to a system, application output file, document, database, or web page that companies use to generate revenues. They are some of the most valuable assets in the technology era, and organizations spend billions of dollars managing such assets.
Maintaining a company’s data assets helps companies improve their decisions, serve customers, and generate new revenue streams. Most technology companies such as Facebook, Google, and Netflix mainly rely on their data assets to engineer new products, improve current products, and create better ways of providing value to their customers.
Organizations collect and store data on various events, information, and transactions. Typically, most organizations store data on their customers’ interests, spending behavior, social media, budgets, strategic plans, etc. The collected information is managed and refined into usable information that enables organizations to serve their clients better and remain competitive in the marketplace.
How to Manage Data Assets
The following are some of the ways that organizations can manage their data assets effectively:
1. Reduce the cost of data
Many organizations tend to store a lot of data that remains unused most of the time, resulting in higher data management costs. It also costs more to store, protect, and archive all this data that remains idle in the company. An organization should aim to reduce data management costs by deleting data that the company no longer needs (or perhaps never needed).
Also, when acquiring new sets of data, a company should only invest in data that they do not have. If they have to acquire a new set of data that they already have, they should destroy the old data. This will help reduce cases of duplicate data that increases data storage costs.
2. Derive more value from existing data
Another way that a company can manage its data assets more effectively is to find new ways of deriving value from its data. For example, a company should re-evaluate the value derived from existing data and determine if there are other ways to use the data to derive more value from it. It may also consider doing things such as selling the data to third parties to earn extra revenue.
An example of a company that earns revenue through its data is Ohio-based grocery chain Kroger. The company sells its product sales data to packaged goods manufacturers, receiving approximately $100 million annually from this extra revenue stream.
3. Data inventory and security
Proper data storage and security are crucial to ensuring the integrity of data. A company should maintain a catalog of all the data they own, alongside a brief description of the data. The description should indicate where the data is stored, when it was created, and how it is used.
Also, the data should be easily accessible to the company’s employees. If they require authorization to access the data, there should be a clear process for seeking approval. A company should also safeguard the integrity of data by limiting access to a select number of employees.
What is a Data Warehouse?
A data warehouse refers to a system that stores large amounts of valuable information used by a company. It is considered an essential component of business intelligence. A data warehouse typically receives various data from multiple sources. It stores both current and historical data in one place, making it easy to access the data and generate analytical reports used in decision-making.
Several steps are usually followed when creating a data warehouse. The first step involves extracting large volumes of data from various sources and bringing them into a single collection point. The extracted data then goes through cleansing to check and correct any errors, with the aim of ensuring that the data stored is of high quality. The data is then converted to a warehouse format for easy storage and access.
The compiled data then goes through sorting, summarizing, and cataloging to be easier to use. As a company obtains more data assets, it updates the warehouse data to keep it up-to-date and accurate. The data is made available for use by professionals for reporting, market research, and decision-making.
Determining the Return on Data Assets
The return on data assets measures the ability of an organization to generate revenues from its data inventory. Every year, companies spend billions of dollars on software, computer systems, process automation, and data management. The return on data assets measures how efficiently the organization has succeeded in profiting from its data.
The return is measured by evaluating revenue lines, cost reduction mechanisms, or closed down poorly performing divisions. Companies determine the profits attributable to data assets by creating visual representations that show key performance indicators, trends, and revenue streams that can be leveraged.
CFI offers the Business Intelligence & Data Analyst (BIDA)® certification program for those looking to take their careers to the next level. To keep learning and advancing your career, the following CFI resources will be helpful: