Good decision making is predicated on good information. A simple statement of the obvious. Who would want to run a business using poor quality decisions? This is all good in theory, what happens in practice is generally less than ideal. I’m going to treat that as a given.
When we proceed down the path of Business Intelligence(BI) and Artificial Intelligence(AI) we assume that our data is good enough and up to the task. Information is tricky stuff, sometimes we are not even aware of it until after the event that generated it has passed. Information also relies on the quality of the underlying data. Data can be defined succinctly, but information must be good enough for the task at hand.
Information governance is about ensuring that sources of information produce adequate and effective data of good quality. One of the principal issues of efficient data collection is knowing what data needs to be collected for an event or function that triggers the collection process. Suppose that you are recording the arrival of passengers at a train station, what data do you collect? Ticket number, destination, size, weight, how they arrived?
If the metrics of decision making are known the source data may be collected. Metrics are defined by combining source data which may be referential or transactional. Referential data is collected when the referential entity, such as a customer is identified for transactional purposes. Referential data, once collected must be maintained on a regular basis. Maintenance of referential data is one of the most important parts of data governance.
Maintaining referential data must be an ongoing task, some aspects can be usefully outsourced, such as credit ratings and corporate addresses. Personal details need to be kept up to date and meaningful, audit trails need to be kept. The concepts of master data need to be explored and responsibility and ownership of data need to be understood. This understanding needs to extend beyond business analysts and management. Data knowledge is an essential part of business culture, as relevant information is essential to the good operation of a business.
Transactional data is the lifeblood of any organization. Transactional data represents business activity and is the source of the fact tables that drive BI and AI. Transactional information is best collected as close to the source, in both time and location, as is possible. It is much harder to determine the essential data of a passenger at the destination than at the origin, purely because the start of a journey starts a transaction, whereas arrival at a destination implies a transaction in progress. Transactional data is inherently mutable, states and values change as the transaction is processed through the system. Process (or is that data?) analysis may indicate that we don’t need to record departures and arrivals as a single transaction. Then a change to the process, such a rewards system, makes it a necessity.
An excellent indicator of the utility of transactional data is how it is used. If it is used ubiquitously and is the one source of truth, it is very healthy data. If there are a variety of shadow systems that produce alternative versions of the truth, there will be issues with the data collections process. When the same information is collected multiple times (I’ve already been asked that a dozen times), that is a prime indicator of transactional torpor. In the same way, that reference data must have a master, transactional data must have a master and ownership. Transactional data often spawns subsidiary transactions in accounting ledgers and order books. These transactions must refer to the same reference data and this requirement must be handled in an appropriate fashion.
The key message here is that data is not separate to process, it is an intrinsic part of any process. Creators and consumers of business data need to have full awareness of what general and specific process rules exist in an organization. Another key concept is DRY (Don’t Repeat Yourself), collect information once. as efficiently as possible. Information Governance should be part of organizational culture. Executive responsibility for information governance must nurture that culture.
Being lean does not require a specific methodology, but lean information governance should be part of any continuous improvement effort. When you are faced with the latest flavour of business improvement, ask yourself if it incorporates better informational awareness into the process.