One of the key benefits of structured data is that it is typically organized, predictable and defined.

I define ‘Dynamic Data Transformation’ as the process by which structured data can be remapped dynamically in the application layer and therefore temporarily transformed from one value to another in a managed way.

By transforming data dynamically, data can be remapped to better interface with other data sets or client processes. Now this sounds quite technical but what does this actually mean in real terms?

Simply put, as a business you want data presented to you in line with your business process. Therefore, you want to have the data presented how you require it not how someone else defines it.

Data must have a purpose to have value

I firmly believe that for data to have business value it must have a purpose, and that purpose must enable a process that supports a business outcome.

The value is in the outcome. This is the important thing to remember.

Every day I hear clients are frustrated at their inability to be able to use and analyze data in a way that fits their business process resulting in many days of man effort to force data into a form that aligns with their process.

Is the data itself valuable? It may be unique, and of a high quality but for it to be valuable it must fit in with your process.

Take for example the system that shows sales made across Europe. If another client uses the same system but instead wants to see European sales with slightly different constituent countries, selecting Europe as a country set doesn’t work for that client. Instead they typically have to output all the country data and then use Excel or another system to build new aggregates views.

Why can’t I, as a client, load my own country or field definitions and have data presented to me in line with my process?

This is what I call Dynamic Data Transformation, the ability to change data sets dynamically within the application layer so that they transform from one form to another as needed.

What if I want to see all my sales figures in different currencies? Why can’t I normalize all my currencies, taking account of different exchanges rates at the time of sale, so they are presented in a form that fits my process? That is Dynamic Data Transformation.

Standardization is greatest barrier to dynamic data transformation

The greatest barrier to transforming data dynamically is the ability to standardize data so that it can be consistently transformed. This makes dynamic data better suited to well defined structured data. Requiring sets of well thought through data taxonomies that will standardize data values. There are many standards in the markets today such as standards for countries, cities, addresses, entities, technologies, currencies, exchanges – I could go on and on.

Embracing Dynamic Data Transformation requires a commitment to standards, a relentless focus on structure and a disciplined culture that challenges existing data structures in order to find opportunities for standardization and transformation.

Commitment to dynamic data

As discussed, Dynamic Data Transformation happens in the application layer but to enable it data must be structured, well defined, standardized and clean. The commitment to dynamic data requires an investment at every level of an intelligence provider. The problem for many companies is their data archives are vast and this data is continually in use. Therefore, the process to change data structures against this backdrop requires vision, a major financial commitment and a strategic mandate. Sadly too many intelligence companies are happy to force customers down their view of the world without making this commitment.

Addressing this anomaly, HG Insights has from day one laid down a strategic intent to challenge and standardize where possible every data asset that it builds via the mapping of all data to a common standard data format which can interface with many more data sets. Thus, Dynamic Data Transformation allows HG Insights to dynamically provide data to clients in a structured way that suits their existing process, allowing timelier and more accurate analysis that uncovers better and deeper insights.

Simply put, HG Insights’ application layers empowers the client to decide how the data should be presented to fit their process.

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About the author
Tim Royston-Webb is passionate about helping providers and enterprises benefit from big data insights that drive real value into their businesses. His groundbreaking whitepaper, Propensity Modelling for Business (2017), has helped shape the use of data analytics in the technology sector, particularly the use of propensity scoring to identify customer opportunity and minimize risk.

A big data thinker with an analytical mindset, his focus currently is on redefining technology intelligence and establishing HG Insights as the global thought leader in actionable insights. Tim holds an MBA from Cumbria Business School and is a founder member of the Data Science Foundation.