Organizations have increasingly become more data-driven and more willing to embrace digital transformation strategies, which has only been heightened by the Covid-19 pandemic. Data is the essential ingredient to transforming any company. But such ideas can become wasted endeavours as businesses are not brave or honest enough to look at their current data resources – and acknowledge what they have been missing from the beginning: the correct data.
This misunderstanding of data -and not only what a company has, but also what it requires - is compromising numerous digital transformation initiatives, and leading businesses to squander months to years on projects that essentially, will never achieve their goals.
Alternatively, the ‘get started, learn first’ model is crucial for digital transformation success. Businesses must go through the data life cycle in order to find out what data a company has, what valuable insight can be drawn from this information, and then building on that foundation to accelerate the digital transformation journey.
A “data first” approach
An absolute lack of knowledge – or honesty – about current data resources is one of the largest issues facing businesses of all sizes. For example, they presume that specific data is being regularly collected, or that customer files are true and up-to-date. And this is not always the case. The quality of data that a company can survive on is much lower than the standard needed for digital transformation. Consequently, that is a fast track to becoming a wasted endeavour and undergoing costly mistakes.
Having a need to ‘change something’ is often the motivation for a company to begin its digital transformation journey. But after determining short, medium and long term business goals which could take weeks, months, or even years – it is only after that teams realise that the data needed to assist in this change has not been collated. In turn, companies' digital transformation journeys will stall before they can even start. Instead, a ‘data-first’ approach turns the model upside down. By acknowledging the primary data assets at the beginning, businesses can then drive significant change and unlock quick value. And only then will they be able to explore the opportunities they have to meet needs and understand their goals. Companies have to get the foundations right – having the right quality of data, and it being accessible at the right time.
Secondly, changes in workforce over time can put a stop to the digital transformation journey. Such initiatives can be driven by certain individuals from inside the business, but these cannot be sustained if those originally inspiring innovation are no longer at the company. To ensure the success of the digital transformation journey, organizations have got to start this journey rapidly to ensure that the same personnel with the same impetus are delivering the process, or else efforts will be useless. Prioritizing this journey will also ensure that the business can achieve change quicker, and in turn, influence wider business commitments by encouraging employees to acknowledge quality data as an essential contributor to the firm’s future success.
Overall, an alternative approach is vital for digital transformation to ensure organizations succeed. They have to endure the four stages of the data lifecycle to understand what data they have, how they can use it, and if needed, make the call to take corrective action on the data – rather than pressing forward towards inevitable failure.
Collect and quality control
It can seem easy to collect data but, as numerous businesses have found out, there is a huge difference between any data and the right data. Without the correct approach, organizations can end up either collecting too little (or too much) data or, in the worst cases, collecting the wrong data. Data quality is also crucial if workforces need to use the information to make significant business-led decisions. What is the point of collecting ‘free text’ information with unreliable data, from incorrect spellings to missing postcodes, for example? That data is promised to be of insufficient quality for use in a digital context.
Without collecting the correct, usable data from the beginning, organizations risk compromizing the whole data lifecycle – and ultimately derailing digital transformation initiatives. Strong data collection processes examine closely the ‘how, where and what’ to ensure the right data is being collected, and use expert data validation to decide the quality of data before moving forward in the data lifecycle.
Combination of sources
Businesses of all sizes are often data-rich, but insight-poor. There is a massive gap between creating a thorough data resource and actually unlocking real business value. Individual sources of information can be interesting, but the true business picture can only be revealed by combining various data resources.
What information is needed by the company? What data sources can be combined to reveal significant business understandings? And what is the best approach in merging data to ensure the desired information is produced? Combining data is a difficult process. There are a myriad of tools and solutions to hand, however, different data sources and different data structures make this a complicated process. Failure to understand the implications of different data constraints – such as inconsistent data – can undermine data confidence and again, derail the digital transformation process.
Following the collection and combination stages of the data lifecycle, the context stage is essential for business success, as well as making effective change happen. Data may have intrinsic value, but it's one true value to the business is the information it supplies. Therefore, contextualization is crucial in order to generate this information and produce actionable insights, in turn, allowing intelligent decision-making.
An effective data model and a clear vision is required in order to add context to the data, whether it is business or operational context. This can be particularly hard for small-medium businesses (SMBs), as this is an analytical journey that needs specific skills – skills that may be missing in-house. However, working with an independent data expert can support businesses to understand their data. Additionally, by applying algorithms derived from Artificial Intelligence and Machine Learning to produce insights, businesses can derive value from the data rapidly and benefit from the insights gained.
Repeat and change
The most important part of the data lifecycle is to understand that it is a ‘cycle,’ and not a finite process. While companies tackle each of these stages, changes may occur, or need to take place, to make the cycle, and end-results, more successful.
For example, if the organization is in need of additional data to understand how a particular operation is achieved, adjustments need to be made in the ‘data collection’ stage. It is crucial to remain agile and adaptable throughout the lifecycle, learning from business findings in each stage, and recognizing areas within the organization that need improvement. This is a repeatedly changing cycle, and companies need to adapt where necessary.
Data is the vital ingredient in the digital transformation process, and in order to be successful, it is necessary that businesses have a fitting game plan in place to get their data correct.
By going through the journey of the data lifecycle, making alternations where necessary, and benefitting from insights and analytics, organizations can become data-driven, making better informed decisions, which in turn, will act as a catalyst to accelerate the digital transformation journey.