How to Manage Data as an Asset

A company balance sheet lists current and long-term assets, including inventory, accounts receivable, cash, property and equipment. However, the most important assets — the factors that define the company and provide the greatest competitive advantage — are sometimes less obvious. Employees, customer relationships and corporate data, for example, provide far greater value and competitive advantage than traditional assets such as a stock portfolio or desktop printer.

For years, companies have recognized the linkage between employees and competitive advantage of the business and have invested in their people through training, education and development. More recently, firms recognizing the value of customer relationships have invested in CRM systems and even customer intimacy programs. However, the latter would be useless without safeguarding the most valuable asset of all: information.

Like other corporate assets, information, created from all its data components, has measurable value that is integral to achieving strategic objectives and gaining or maintaining a competitive advantage. Likewise, the value of all your data can increase or decrease depending on how effectively you manage this asset over time. Successful data managers, or stewards, create environments where data is accurate, up-to-date, accessible and well-governed.

Here, we’ll examine how these factors affect the value of your data assets and provide a roadmap for how to increase the return on your information investment.

Unlike Fine Wine, Data Rarely Improves with Age

Managing data has taken on a heightened level of importance as companies strive to be more customer-centric in a bid to compete on a global level. Many have even gone so far as to create new positions, such as vice president of customer experience. The pace of change continues to accelerate, with a truly global economy creating new markets, new products and new customer innovations faster than ever before.

For data to be a true asset, however, it has to be used while still fresh. Using old, inaccurate data wastes time, resources and money - and if it is customer data, it may very well alienate a portion of the customer set. Do not allow your hard-won assets to turn to vinegar while sitting on a shelf! Companies should take three very important actions to ensure the freshness and accuracy of data.

1. Start with Data Profiling
An in-depth data profile to assess the foundation and current state of corporate data will expose its weaknesses and flaws. Some applications provide counts on what percentage of fields are populated, but one can only get a true understanding of data quality by examining data values as well. Key questions to ask include: How many unique keys are not unique? Are there symbols or commands where characters should be? Are the numbers in an appropriate format? Are there fields, such as Social Security number, that are populated with all 1’s or all X’s? By comparing the universe of values within a database, one can identify outliers, anomalies and other questionable data points.

2. Four Steps to Data Cleansing
Once you understand the make-up of your designated data, you can begin to improve the quality of the data, and thus increase the utility of your informational assets.

Format fields: The style and format of customer data is often subject to the personal whim of whoever handles data entry — and even customers are not consistent in this respect if they are charged with entering data on their own (i.e., Web self service). Data normalization helps ensure that consistent terms and formats are used across a given field, so you can identify matches, standardize records and provide a more consistent user experience.

Parse components: Break down strings of data into multiple fields to standardize data elements with greater accuracy. Whether records include names, addresses, contact information, purchasing history or even more complex data often associated with business records, data parsing makes it possible for companies to turn data into the usable components needed to perform automated data quality operations far more effectively.

Check content: Some records contain accurate information that cannot be utilized because it is embedded in the wrong field. Other fields may appear to be populated, but the information is obviously wrong (i.e., all 1’s). Automatically correct these anomalies so that data is fit for use.

Remove duplicates: Once data is standardized, companies can identify matches and duplicate records with a high degree of confidence.

3. Perform Ongoing Data Maintenance
Despite rigorous efforts, it is important to recognize that even the best data goes stale over time. It is an unavoidable occurrence — customers move, update email addresses, change preferences and modify their relationships with companies regularly. Regardless of where your customers reside, it is paramount to keep up with them. A regular data maintenance program is necessary to ensure that your company’s data is kept up-to-date over time, and there are two options for this.

Batch maintenance: Running through the data cleansing steps regularly will uncover and correct issues as they arise. While annual data cleansing should be considered the minimum, companies that conform to data quality best practices will run their maintenance programs on a quarterly basis.

High-quality input: Real-time data quality applications can validate new data as it is being entered, serving as a data quality “firewall,” helping to ensure that bad data never enters the database.

These two processes are distinct and provide different benefits. Managing data quality at the time of data entry, for example, requires speed and reliability on a transactional basis. Meanwhile, batch-oriented applications can be more thorough and complete. Ideally, companies should identify a data quality platform that can support both engines, so they can leverage a shared set of rules and business logic across all data quality initiatives.