Business is a complex exercise in organization and human nature. Peter Drucker famously said “the purpose of a business is to create a customer;” yet a lot of things must be done to manage the business that at first glance do not touch the end customer. This is a false notion; everything one does inside the enterprise has some effect which ripples outward to the end customer.
An example might be data management. While this may appear to some to be an internal-only affair, it has a direct effect on dealings with the customer. We can see the obvious such as linking available inventory to web order pages; but links to billing, warrantee, and shipping are just as important for customer satisfaction. When the package fails to arrive, good data handling can make this a trivial issue that makes the customer better appreciate the company; or it can cause customer irritation preventing future orders from this customer and causing angry product reviews posted on Internet sites.
The decision to not properly model a database system because of budget cuts might cause latency in customer care or support. Do product support people know the latest product models and specs? Does sales staff know that certain models have been discontinued or they now come in a variety of colors? Does procurement know what is rapidly selling in the outlets? Does the manufacturing dept. trust sales projection data after hearing that sales has been “challenged” by upper management to increase volume 5% or else? (Don’t bet on it.)
This last is interesting, many groups in a company do not make quick decisions on data coming in because it is late, or inaccurate, or so convoluted that using it for Business Intelligence is impossible. Lack of access to timely and accurate information prevents business agility. When data is not trusted each manager waits for a solid trend to develop over a longer time before they make a decision. Waiting for future data, I would humbly suggest, is the enemy of market agility.
Data Governance requires that the data be examined carefully for sensitivity to external regulations and internal policies. Failure to fully understand these requirements can cause what looks like governance: but just puts unnecessary hurdles in front of people seeking data access. Often this is because sensitive data is mixed up willy-nilly with non sensitive information
Even more probably, because there is not a central system in place to identify and locate sensitive “regulated” information across the enterprise, and then a policy placing it in protected environments.
Non-sensitive information is what most processes require yet often is hard to obtain because of this mixing. Difficulty in getting authorization will, if not linked tightly to a quality definition of the data, result in eventually the wrong people finally getting access to data they do not need but nevertheless will download to their laptops. These laptops are then left in taxicabs.
Understanding data implies defining its meaning, its business importance, the timeliness required by users, its accuracy, usable formats, and also sensitivity to regulatory compliance. The world of data quality overlaps data governance; both of these activities are vital to a business so employees can have access to trustworthy information as soon as possible.
Trusted data illuminates sharp market changes that can quickly drive internal response. When employees really know what’s going on with the business and customers, they make agile decisions positively affecting sales and customer loyalty.
Don’t isolate data quality and data governance from each other; they are both doing the same thing: defining data better so that it can be managed better and easily used to create customers.