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At first glance the transition of data seems simple. We want this data we currently have assembled together so that we can analyse it, bend it, twist it and potentially discover something that can add business value. However, if you were to dig deeper beneath the surface, the realisation is soon reached that there is a big underlying structure that facilitates that ability. That it extends beyond the confines of a server room, or the IT team.
While the processes are fairly constant and repeated over and over, however due to the inherent complexity of this task, even a small change or deviation through mistake or badly made assumption can have a big effect. Therefore, having a clear vision of how things are meant to happen, when, where and who is responsible is immensely necessary to have a quality end result. Just as a moving truck company with all the intentions in the world to carry your goods safely, if their processes and ways of handling the merchandise does not adhere to good and sustainable quality practice you will end up receiving damaged goods. What is quality and why is it important? When people normally talk about quality, it is used as a descriptive measure for defining the state of something. Quality is not just about the 'what' of something, it needs to be part of the how, where, when and who. It is a state of mind and approach in all those facets and their associated choices to do something that is quantified and realised (or not) through the achievement of standards. The rewards of achieving it give forward to reducing risk, building business agility, creating transparency, growing confidence in your people, processes, your systems and indeed, also your data. But isn't data quality enough? One of the current trends and focus points in the market is centred on data quality. There is currently much of discussion around how to achieve this through master data management. While I do support all the latter initiative as data is the ultimate deliverable in the end, I do believe that this exercise needs to be taken further and that support systems and processes, inclusion of the right people and an overall vision need to be built to support both the process and the people. I believe that the inherent challenge in building an effective, efficient and agile data warehouse and business intelligence solution and its scope of reach and effect within a business is underestimated when considering the degree of input necessary both from a system and person perspective to make it work successfully. What is data governance and why is it important? This brings me onto data governance. Its development is the recognition that data warehousing and business intelligence is not only about the technology, but emphasises a cross organisational collective desire, input and understanding to achieve the following: enable better decision-making, reduce operational friction, protect the needs of data stakeholders, training to deal with data issues through common approaches, building standards and repeatable processes, reduce costs and increase effectiveness through better coordination and having process transparency. Data governance treats data quality as a part of its overall framework. It also deals with policy, standards, strategy, privacy, compliance, security, architecture and integration, data warehouses, BI and management support. It is not there to replace management, but instead to provide the framework and focus for those who need to make the tough decisions to understand a complex environment and develop a clear view of the bigger picture. It strengthens the understanding around all the parts that make up a quality strategy and encourages the notion that data should be seen as an asset critical to the business as opposed to an after thought. What business value can it provide? There are constant competitive and legislative pressures, to name only two forces at play, for business to be agile and responsive. So they remain and continue to grow in increasing their revenue and value, managing their costs and complexity and ensuring their survival through being cognisant of risk and potential vulnerability. All while also adding new products and services, improving customer service through the provision of a consolidated view across multiple business domains, being legally compliant to acts such as Sarbanes-Oxley, determining operational risk through fraud detection and others. The ability to provide the right information, at the right time, to the right people to satisfy the above demands and conditions will more and more grow as a competitive advantage and potentially, in the future, become the differentiator in who survives and who doesn't. Thus, its necessary to realise that in accepting the challenge in what it would take to build that competitive edge it means you need to take on your data as a cross-organisational asset and treat it no differently than you would anything else that determines the future of your business. |
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