Data is the key to transforming any business. Accelerated by COVID-19, businesses have started to become more data-driven and embrace digital transformation strategies. However, these often become wasted endeavours as organisations are not brave or honest enough to look at their existing data resources – and realise what they have been missing from the start: the right data.
This failure to understand data, what a business has, and what a business needs, is compromising far too many digital transformation plans, and leading businesses to waste years on projects that ultimately, will never deliver.
Instead, Peter Ruffley, CEO, Zizo, emphasises the importance of the ‘get started, learn fast’ model – by going through the data lifecycle in order to understand what data a business has today, what valuable insight can be immediately leveraged, and then building on that foundation to drive the digital transformation process.
Digital transformation paralysis
One of the biggest issues facing companies of all sizes is a complete lack of knowledge – or honesty – about current data resources. Don’t assume for example, that data is being regularly collected as stated; or that customer files are up-to-date and accurate. The quality of data that an organisation can function on is much lower than the standard required for digital transformation. Therefore, that is a fast track to expensive mistakes and wasted endeavour.
The catalyst for a business to embark on a digital transformation journey is having a desire to ‘change something.’ But after spending months, even years, to determine short, medium and long term business goals – it is only later when the teams discover that the data required to support this change has not been collected. Businesses' digital transformation journeys will fail before they begin.
A ‘data-first’ approach turns the model on its head. By understanding the existing data resources first, organisations can then drive effective change and unlock immediate value – only then will they be able to explore the real opportunities they have to meet needs and realise ambitions. Businesses need to get the foundations right – having the right quality of data, and it being available at the right time.
Additionally, changes in personnel over time can put a halt to the digital transformation journey. Such initiatives are often driven by specific individuals from within the organisation, but these cannot be sustained if those originally inspiring change are no longer within the business. To make a success of the digital transformation journey, businesses have got to start this process quickly to ensure that the same people with the same impetus are running the process, or else efforts will be wasted. This speed will also ensure that the business can achieve change quicker, and in turn, inspire broader business commitment by encouraging employees to recognise quality data as a vital contributor to the firm’s success.
A different approach is needed for digital transformation to ensure businesses succeed. They need to go through the four stages of the data lifecycle to understand what data they have, how they can use it, and if necessary, make the decision to take corrective action on the data – rather than pressing ahead towards inevitable failure.
It can appear simple to collect data but, as far too many companies have discovered, there is a huge difference between any data and the right data. Without the right approach, businesses can end up either collecting too much (or too little) data or, in the worst scenarios, collecting the wrong data. Data quality is also vital if business users are to trust the information to make key decisions. What is the point of collecting ‘free text’ information with inconsistent spelling or missing postcodes, for example? That data is guaranteed to be of insufficient quality to use in a digital context.
Without collecting the right, usable data from the outset, businesses risk compromising the entire data lifecycle – and derailing digital transformation initiatives as a result. Robust data collection processes look closely at the ‘how, where and what’ to ensure the correct data is in place, and uses expert data validation to determine the quality of data before moving to the next stage of the data lifecycle.
Organisations of all sizes are often data-rich, but insight-poor: there is a huge gap between creating an extensive data resource and actually unlocking real business value. Single sources of information can be interesting, but the true business picture can only be revealed by combining multiple data sources.
What information is required by the business? Which data sources can be combined to reveal vital business insights? And what is the best approach to combining data to ensure the right information is produced? Combining data is a complex process. There are a myriad of tools and solutions available, but different data sources, different data structures make this a complex process. Failure to understand the implications of different data constraints – such as inconsistent data – can, again, derail the process and undermine data confidence.
After the collection and combination stages of the data lifecycle, the context stage is fundamental for business growth and to make effective change happen. Data may have intrinsic value, but its only true value to the business is the information it provides. Therefore, contextualisation is crucial in order to create this information and deliver actionable insights, in turn, enabling intelligent decision-making.
Without an effective data model, there can’t be a clear vision of how to add that context, whether it is a business or operational context. The ability to present that data as information to the right people and deliver real insights from the data will not succeed. This can be particularly difficult for small-medium businesses (SMBs), because this is an analytical process that requires specific skills – skills that may be lacking in-house. Working with an independent data expert can help businesses to understand their data, and by applying algorithms derived from Machine Learning and Artificial Intelligence to produce insights, organisations can derive value from the data more quickly and benefit from the insights produced.
The most critical aspect of the data lifecycle (collect, combine, context, change) is to remember that it is a ‘cycle,’ and not a finite process. While businesses undertake each of these stages, changes may occur, or need to take place, to make the cycle, and end-results, more effective.
For example, if the business requires more data to understand how a particular operation is achieved, changes need to be made in the ‘data collection’ stage. It is important to remain agile and flexible throughout the process, learning from business findings in each stage, and identifying the business areas that need improvement. This is a continually evolving cycle, and businesses need to repeat and change where necessary.
Data is the essential ingredient in the digital transformation journey, and in order to be successful, it is crucial that businesses have an appropriate strategy in place to get their data right.
By going through the data lifecycle, making changes where necessary, and leveraging insights from new analytics, businesses can become data-driven, making better informed decisions, which in turn, will act as a catalyst to accelerate the digital transformation journey.