At first glance, the transition of data seems simple. We want this data we currently have assembled together so we can analyse it, bend it, twist it and potentially discover something that can add business value.
However, if one is 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 goods safely; if their processes and ways of handling the merchandise does not adhere to good and sustainable quality practice, someone 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 people, processes, systems and indeed, also a company's 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 a discussion around how to achieve this through master data management. While I do support the initiative - as data is the ultimate deliverable in the end - I believe 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 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 to 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 co-ordination, 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 afterthought.
What business value can it provide?
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.
Anne Botha is technical manager at PBT.
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 the while, 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 grow as a competitive advantage and potentially, in the future, become the differentiator in who survives and who doesn't.
Thus, it is 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.
* Anne Botha is technical manager at PBT.
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