Establishing a data quality framework for ingestion and/or creation of data is important to ensure that the resultant information product, as consumed by the enterprise, is of high quality and fit for purpose, says master data and data quality company Pilog CEO and president Dr Salomon de Jager.

Companies should have in place a strategic framework for the management of their master data and metadata classifications to ensure data is created, used and retired accurately and is easily usable across their systems as well as their interfaces with clients and suppliers. This concept links to data as a product and, therefore, the creation of an information product.

"Data has a life cycle, similar to any other input used in industries and commerce, and its value is linked to its use. A lot of data loses its value progressively, hence the need for a data framework to support and accelerate its use throughout an organisation and its ecosystem, and capitalise on its value," he says.

Further, implementing a data quality framework is a one-off effort that provides companies with the opportunity to prepare their organisations for the already large and massively growing streams of data that they will have to navigate to serve their customers and capitalise on market opportunities.

"Unless data is structured using master data and metadata frameworks, organisations will not be able to get ahead of its speed and volume, nor be able to capitalise on the opportunities presented by such rich streams of data as effectively," De Jager adds.

Enterprise data tools, which have proliferated, are also far more effective when used in conjunction with data quality frameworks. Data quality frameworks provide tools with a purchase on organisations' data, improving their functionality and providing greater value, he says.

Similarly, data quality is necessary to enable the use of distributed databases to bring together data from millions of records for organisations.

"If we want to send more data, then we should be publishing our data dictionaries to make our data readable for other business partners and suppliers. The first place to start is with the International Standards Organisation (ISO) 8000 data quality standard, which stipulates the publishing of data dictionaries to support effective and accurate data sharing across different industries, technical disciplines, jurisdictions and quality regimes, among others," De Jager notes.

Competing energy and equipment multinational companies Schneider Electric, Sony and Siemens, for example, are collaborating to publish a dictionary for standards organisation International Electrotechnical Commission (IEC) SC3D Web platform for electric components. This will enable the sharing of structured data and quality parameters of often critical components.

"There are many developments happening in many fields, and industries will have to invent and integrate new terminology for new systems and equipment. Unless we build and publish data dictionaries and add to them, we will not readily be able to share commonalities on a global basis to support increasingly information technology-enabled industries and economies," he notes.

A common dictionary means terms can be exchanged in multiple languages, or in multiple syntax formats, enabling seamless and accurate integration and secure, controllable sharing of information across many processes and uses internally and externally, De Jager says.

Meanwhile, approaching the management of information as a product can inform and improve frameworks for data management, data governance, data quality management and data analytics, says University of Arkansas at Little Rock Professor of Information Science and Acxiom Chair of Information Quality Dr John Talburt.

Organisations focus on managing their data sources, but do not practice product management, as if the products and service they are building are simply by-products of the process, he highlights.

"We collect data and install software systems to build information products and services that produce value for the organisation and for the users of the products and services and, while there is growing realisation that data represents one of the most important assets of an organisation, data resting in a database or data lake only has potential value.

"The value is only realised when it is transformed into an information product or service used to solve a real business problem," he emphasises.

Further, there is a disconnect between the producers and custodians of data and users of the information products and services they create. This is evident in organisations that launch data analytics units in which the data scientists are more focused on solving technical problems than business problems.

Achieving data literacy requires supporting each employee to understand how his or her work contributes to business value realised through the organisation’s information products and services.

"Starting with products can be a useful exercise not only in data quality and data analytics, but data governance as well. For example, by populating your data glossary with the items from your data products, such as reports and key performance indicators, organisations can then trace it back to understand which data sources should go into the data catalogue," Talburt recommends.