Successful companies treat stored data as an asset and therefore take steps to manage it well. Data can give any business an edge over its competition, provided they can store and leverage it with confidence and competence. Well-managed data possesses three main qualities: accuracy, security, and proper usage.
Even the most accurate data ages poorly. Because old data will waste resources and potentially alienate the customer, businesses must engage in regular and reliable data cleansing and profiling. Data cleansing describes the process of creating and maintaining accurate, relevant, and streamlined information. Data profiling functions as a pre-requisite to cleansing by making by compiling statistics from the company’s existing data sources. An in-depth data profile can identify anomalies and outliers as well as provide tags, keywords, and categories.
Once the data profile has granted an understanding of the data’s composition, the business is in the ideal position to engage in data cleansing. The process of cleansing involves four steps:
- Field Formatting – Serves as a means to standardize terms and format, avoiding unwieldy and inconsistent formatting.
- Component Partitioning – Further refines standardization by breaking data into smaller chunks such as names, purchasing history, contact information, etc. This enhances quality automation.
- Content Checks – Act as a safeguard against improperly defined fields and erroneous data.
- Duplicate Removal – Deletion of duplicate data sets to improve efficiency.
The processes of data profiling and cleansing should repeat as necessary, though the exact timeframe will vary according to a company’s needs and its data’s nature. As an additional safeguard, businesses can employ a data quality firewall to validate new data upon entry.
Businesses naturally seek to protect data assets without compromising performance and so employ both internal and external security measures. While there exist many options for server security, data tends to be more vulnerable to internal threats like disgruntled employees, lost access keys, or lax usage policies. While these threats cannot be eliminated, businesses can mitigate them by using and enforcing a central point of authorization.
The final point of proper data management involves its proper use, otherwise known as data governance. Good governance clearly dictates the company’s set of rules regarding data usage. These rules cover elements like inter-departmental sharing rules, data privacy regulations, frequency of updates and maintenance and assignment of teams to those tasks, and data monitoring, among others. Large enough companies will often create a Data Steward position to guard against unsound practices and ensure proper use.
Accurate, safe, and adequately governed data can reduce operational costs while giving the company a competitive edge over its rivals. Data forms as much as a company asset as finances and personnel, and like any asset, it requires nurturing, protection, and attention to be of any use.
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