The power of data as a catalyst for collaboration - 3 min read
“Strength lies in differences, not in similarities” – Stephen Covey.
Today it is rare that a solitary data source is sufficient for analytics or decision-making in any organisation. Valuable insight is achieved through combining multiple data products to give better context – if you can understand why something has happened, then it is much easier to make the decisions that drive improvement.
“Organizations which design systems … are constrained to produce designs which are copies of the communication structures of these organizations.” – M. Conway
Collating internal systems and reference data is only part of the challenge and this in itself can be non-trivial. Conway described this over 50 years ago, and it is even more relevant today. As organisations change and grow then the combination of legacy systems, non-integrated departmental systems, cloud offerings, spreadsheets and small databases become a bewildering landscape. The daunting challenges facing any data consumer range from discovering what data is available, gaining access, bringing the data into the analytical tool environment and include gauging overall usefulness based on an assessment of data quality.
With data sourced outside of the organisation, the problems become more profound. In addition to the challenges above, there are even more layers of complexity and it is not uncommon for the same data products to be procured multiple times and for different teams to go through the same tortuous steps.
Today the challenge is not how much data you have, it’s how much data you can use effectively to make better decisions more quickly.
The old way of individual analysts working in silos; constrained by complex access to data and with a narrow view of the world, is slow and ineffective. It drives debate and argument higher up the organisation and fundamentally causes delay in actioning change. In this situation data is a drag and a cost to the organisation.
Analysts need to collaborate at the raw data level, in an ecosystem where data is acquired and profiled once. Where they can debate and iterate analysis as quickly as possible and establish an output that is a single version of the truth for others to trust.
A hidden benefit of this powerful shift is that serendipitous discoveries are far more likely to happen: conversations about the data spark curiosity not entrenchment and it is considerably easier for analysis to drive true innovation and effective decision making – data becomes the catalyst for meaningful collaboration.