Data Strategy: The Difference between Information Architecture and Data Architecture

Information and data – the words are seemingly interchangeable. Data is information. Information, particularly when discussed in terms of technology, is data.In polite conversation this statement generally holds true. But when talking data strategy, information and data are entirely different beasts. Understanding their differences, and correctly handling the data strategy architectures of each, is vital if your organization is to get the most it can from its strategy.

Data vs Information

Austrian-American management consultant Peter Drucker once described information as “data endowed with relevance and purpose”. Examples of raw organizational data, such as sales figures, business costs and customer retention rates, are essentially meaningless when analyzed as standalone figures; but once this data is set in some form of context, usually by combining it with other data, it transforms into information.The raw data of one month’s sales figures will give an organization absolutely zero insight into how they are performing, but give that data some historical context by combining it with the sales figures of the preceding months and you’ve suddenly got tangible information on which to act. The more data combined, the greater the insights that can be garnered from it. Perhaps the sales figures are on an upward trend thanks to your organization entering a new market, or on the back of a new marketing initiative.In short, data can be thought of as the zeros and ones, whereas information is what those zeros and ones are trying to say.

The Differences in Architectures

As part of its data strategy an organization will require two ‘architectures’; one devoted to raw data, the other devoted to the information that can be garnered from that data.An organization’s data architecture will define how data is to be collected, stored, organized, distributed and consumed. Rules must be created to govern the structures of databases and file systems, as well as the processes which connect the data with the areas of the organization that require it. It takes raw data and makes it digestible for the information architecture.Information architecture, on the other hand, aims to give structure to the systems and procedures which convert raw data into useful information. Once the raw data has been delivered with the help of the data architecture, the information architecture is in charge of converting that data into real insights.As an example, data architecture might feed raw data on sales and customer contact into an information architecture system, such as a Customer Relationship Management (CRM) system, where it is amalgamated and analyzed in order to reveal any relationships between customer contact and sales. This can be done channel by channel, region by region.

An Architecture’s Effect on Data Strategy

With data strategy now at the front of any forward thinking organization’s mind, the subsequent focus on architectures is now stronger than ever. An organization’s approach to these architectures will inform its approach to its greater data strategy, perhaps more than many realize.The temptation when formulating architectural strategy is to aim for maximum control. This can be a mistake however, as a highly centralized, highly regulated approach to your data and information architectures will be reflected in a greater data strategy that is inflexible and monodirectional.

As always, there is a happy middle ground, although this middle ground will shift dependent on the needs and wants of your organization. But defining that middle ground will have to wait until our next article, when we tackle data truth.