The annual company report. The weekly project management meeting. The current sales forecast.
Important decisions are made every day based on the content of such reports and documents. But where does the information that supports this content come from? How is it defined? Who says it’s appropriate to use and for what purpose?
Commonly we make the distinction between the words data and information. Data is more of a computing term to describe the characters, symbols, numbers and media that a computer system is storing. Information, however, carries the context of data that is processed and is, therefore, able to be interpreted by humans. Information can be represented and presented in different forms and perspectives according to the interpretation requirements. And therein lies the problem. How do we define, or govern, interpretation itself? Indeed, how can we develop the ability for interpretation to become more flexible depending on the interpretation requirements but without losing our trust in the information we are using?
Big, social, mobile, IoT – all just data
Companies with ERP systems that manage customer ordering and stock control, for example, often find it difficult to understand sales performance and demand forecasting, not because they lack the data, but they lack the ability to represent product information in a way that supports this method of interpretation. Silos of reporting mechanisms are often created to circumvent this type of problem and each might have its own unique way of collecting data and processing it to improve its quality before releasing it as information. The processing does not necessarily follow any common or corporate rules, or governance, leading potentially to the generation of information that might not be fit for purpose and indeed when linked to other sources of information, might even become totally misleading.
Consider the simple terms “Customer” or “Product”. What do they mean to you? What do they mean to a sales person? A financial controller? An industry regulator? While the interpretation of this information might change, the governance of the data itself must remain coherent prior to any processing to support differing interpretation. Without the governance, we are denying the end users, the information consumers themselves, the ability to understand and trust the information they use. Big data, social data, mobile data, IoT data…. It’s just data. Without some form of governance as our compass, it is arguably impossible to navigate this data.
(Want more on Data Governance? Find our blog post “What Is Data Governance? Do I Need It? And How Do I Get Started?” here)
It is, of course, important that governance initiatives are applied to - and start with - the most critical business data elements, which are typically those including customers, products, assets and employees. One of the most common drivers for the implementation of a governance initiative is any form of business analytics. As the volume and variety of data being made available for processing into information increases, decision makers are being faced with a more complex variety of interpretations possible. Which is good news – as long as they can trust the information on which they are making their decisions.