With increasing frequency, organisations are undertaking journeys that require operational and associated technical and data transformations. Some of these are small, others quite radical, but all come with some degree of risk.
Despite this, the organisations believe that these journeys are worth taking because the destination will deliver tangible benefits, including for example:
- cost savings
- increased agility
- improved data quality and reporting or
- even ensuring delivery timeliness for its clients
Have a Safe Journey
For all organisations, any successful journeys rely on three fundamental pre-requisites:
- A well-defined starting location
- A meticulously planned travel itinerary
- A thoroughly specified destination
If any of these specifications is flawed, then the journey runs a significant risk of simply being a meandering sight-seeing tour that haemorrhages the organisation’s resources, time and confidence. Of course, engaging experts who have the necessary knowledge, tools and experience, is a smart risk minimisation strategy that organisations would be wise to adopt. Many of those who ignored this option though, have discovered that their journey has been severely compromised because their starting point was not as well defined as they had believed.
And unfortunately, this realisation has come far too late for some.
Moving into the Cloud, integrating Big Data, or plunging into a Data Lake has been a rude awakening for those who were ill prepared for such endeavours. What it starkly revealed to them, is that they didn’t have a firm grasp on their basic operational data at the outset. And quite possibly, still don’t!
Before they started on their journey they were unable to answer two deceptively simple, yet crucial questions:
1) What data do we have? and
2) Where is it created, stored and consumed?
Without being able to answer these fundamental questions, how can any organisation contemplate a transformation of its data landscape?
Put simply, how can you successfully plan a journey if your starting point is unknown?
It is worthwhile spending some time to find assistance from inside the organisation or, if required, from external consultancies. The trick for success is to find experts who realise the criticality of these questions and have set about establishing the required skills, sophisticated tools, and effective processes to support your entire journey. They are therefore able to reduce risks and amplify benefits for any organisation’s successful transformation.
However, after the journey is completed, these questions will still remain as valid as they were before it began. Therefore there must be ongoing processes that will continually:
- demand answers to these questions
- evaluate the responses and
- maintain a complete and accurate contemporary definition of the organisation’s data landscape
But none of this will matter unless there is a determination within the organisation to make the resulting definitions pervade its strategies and operations. There is no point in having such definitions hidden in rarefied repositories which only a handful of people can access!
The Data Architecture Function
Defining the data landscape and embedding the definitions across the organisation is the concern of the Data Architecture function within an organisation. The resulting definitions and processes should definitely form essential cornerstones of any Data Governance or Master Data Management (MDM) frameworks that the organisation has evolved.
And it is worth emphasising the importance of the processes in these definitions.
How many times does the simple question of how an organisation has implemented MDM elicit the response along the lines of ‘Oh we use our ETL Tool for that …’
Data definitions are not a technological problem.
And whilst tools can help in all sorts of ways, it is the people and processes that will collectively agree and define the map of the organisation’s data landscape in the form of its Enterprise Data Models. These data models are the ones that will allow an organisation to hold up a mirror enabling it to ‘Know Thyself’.
Enterprise Data Models As Maps
I strongly believe that the Enterprise Data Models from one of the most critical components for any organisation in order for it to survive and thrive in today’s turbulent data times.
If these models and their management processes are not at a mature state, then the organisation would be wise to use any transformation journeys to also make progress on their evolution.
Transformations are necessarily a time of change for organisations. Change spells risk. For organisations that are charting unknown territory, their first step should be to engage relevant expertise before starting out. This will minimise the risks and additionally maximise the benefits.
But in addition, I would strongly urge all organisations to make an honest assessment of the degree of maturity of their data landscape definitions and the processes that actively manage these. If these are found to be wanting in any way, then a second step for the organisation should be to establish or improve the ongoing processes that create and maintain the organisation’s data definitions.
Only then can we ‘sit back, relax and enjoy the ride’.