There is data everywhere, but can it be used for effective decision-making? For my fifth article in "The future of the organisation" article series, I spoke with Nick Whitfeld about data driven organisational transformation in Telecoms Media Techonology (TMT) organisations. Nick is a partner who leads the data & analytics capability in KPMG’s finance transformation practice. He is passionate about helping clients by developing their information and data strategies, designing and implementing data transformation programmes and exploiting new technologies to drive value from data.

Firstly, it is important to understand why an organisation needs a data strategy. Organisations, particularly in the TMT space, can drive improved commercial offerings through better data. Secondly, there is a regulatory angle where data must be protected and controlled in the right way. Both are just as important.

We look at data strategy in terms of how data flows through the organisation. From our perspective, data flow has four layers:

• Data is created – this may be for new customers, new products or new services.
• Data is curated – this is where data is managed as an asset and prepared for analytics.
• Data is consumed using a variety of visualisation analytics tools to make sense of the data.
• Data is translated into a commercial outcome to serve customers in better ways and drive growth for the business.

The strategy, however you go about it, has to accommodate those four layers, and the strategy has to show that data will work together across those four layers.

Fundamentally, I do not think it differs greatly. Whether it is internal or external data, the data still needs to be created, curated, consumed and commercialised.

However, if there is one difference, internal data tends to be historic in nature and very structured. External data, wherever you get it from, may not have the same quality or controls around it and may not be structured. Therefore, how you manage and curate that data, and, specifically, the quality of the decisions that you take around it, have to be carefully governed.

We have recently conducted a detailed analysis and identified 25 different skills that are required around the management of data within a business. Where most people might point to data science, I want to be a little different, and I think there are three critical skills:

• Firstly, you need someone who can be the overall leader and evangelist. Someone who is going to promote the use of data into the different business functions. That is absolutely vital.
• Secondly, you need business analysts. People who can translate business needs into data requirements and can translate data outputs into business answers. It is also a critical role.
• Then, perhaps the third one, I would point to is data engineering. We spend, probably, only 20% of our time actually doing the data science and data analysis and 80%, in fact, is spent preparing the data and for that you need data engineers.

Technology cannot lead the data strategy. This has to be a business-led approach to the management and exploitation of data. Having said that, technology actually supports every single step in the lifecycle - whether it is the management of data at the point of creation, whether it is using data cloud technologies, and within those, data lineage and data asset management tools within them, through to analytics and visualisation tools, technology absolutely needs to be applied all the way through to make that data flow work.

First and foremost, we do not do data for the sake of it; there has to be a clear priority or business objective in mind when looking to manage and exploit data. Any strategy has to be business driven, business focused and answer what performance improvement am I trying to make. From that the data strategy will flow.

Secondly, it cannot be technology led. This is a business challenge and technology is a critically important enabler but it is there to support rather than drive.

The culture around data is also a very, very important point. It remains the case that garbage in equals garbage out. If the organisation does not understand that unless it takes responsibility for the quality of its data at the point that it’s created, then all investment and focus on analytics will be undermined. Therefore, an enterprise-wide focus on the need to drive better data and data quality is critical.

If you would like to explore in greater depth how organisations can maximise value from data, read Nick Whitfeld’s, Partner Management Consulting, KPMG in the UK, latest article “Enabling a data driven culture.”  

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