The 7 success factors towards data transformation

The data economy continues to grow at an alarming rate. Every two days, organisations across the globe generate 5 exabytes of information: that’s the equivalent of all the words ever spoken by human beings since the beginning of time. As consumers, we’re all regular contributors to this booming data economy. According to recent calculations, Google now processes over 40,000 search queries every second, we touch our phones an average of 2,600 times a day and, in 2017, Netflix subscribers across the world watched 140 million hours of programming a day.

From Amazon to AirBnB, the most successful modern companies are agile and bold, driving industry transformation with their focus on customer experience and willingness to adjust their strategies based on data-driven insights.

However, when it comes to data, the real value doesn’t lie in volume; rather, it’s in the ability to find the nuggets of insight that reveal the true picture of business performance and to act on these to generate transformational growth.

The 7 factors below highlight the key areas to consider when working towards a successful data transformation.

1.    There is an Art & Science to Data

Data (Analytics) needs interpretation and creative thought. Data is truth but we need to understand that we get what we measure and all businesses have operational bias – what is cause and what is effect? We use science to code and describe our inputs and patterns. However, putting data to good use requires an understanding of the business and its bias in how it collects and codes. Businesses are “perfectly designed to deliver the business of today” – not necessarily tomorrow. 

The challenge in Data Science is not Big Data but Sparse Data. The universal truth is that we tend to know “a lot about a few” and “little about most” consumers or businesses. Winners in data transformation have learned how to unlock small changes in behaviour across millions of consumers rather than focus all efforts on the “best” customers.  

2.    Understanding the Difference between Functional & Emotional Engagement

Data science has emerged commercially over the last two decades (Clive Humby and I started it with Tesco Clubcard in 1995). All of this work and success was built on understanding Functional Loyalty – in other words, if you buy x, we will reward you with y points. But people don’t buy on price alone and they love many things much more than brands. So how do we understand the motivations and passions that drive behaviour and change perception?

Data reveals the truth about the reality of a business – and the truth can sometimes be unpleasant. Often different types of data sit in organisational silos, meaning that certain data sets are never overlaid with others, reducing the possibility for illuminating insights about the business and its customers. In achieve genuine data transformation, it’s essential to find a common direction that works across departments and to have internal stakeholders buy into that vision.

3.    Speed of Change

As my partner Clive Humby said, “Data is the new oil”, equally valuable and equally liable to change and shift as new customers, platforms and ways of working emerge. Most businesses have legacy systems and operational processes that are slow and ponderous. A constant flow of customer and data insight is critical; not for operational implementation, but for leadership to understand what is changing and the critical dynamics of that change. Systems don’t deliver that today. They are too slow and opaque.  

4.    Learning Culture

There is no shortage of new tools and technologies designed to help businesses get to grips with their data, meaning that a test-and-learn attitude is essential. A culture where stakeholders and business users are allowed to test and fail is counter-intuitive to many risk-averse organisations, particularly in industries such as financial services. However, if you’re not failing, you’re not improving. How can you set the right boundaries on this and create leadership permission to try new approaches? How do you create a culture of curiosity? Employees need to be unafraid to ask question and interrogate everything from the types of data available to the impact of new legislation. Only then will they be able to predict how the organisation’s needs will shift in the future and act accordingly.

5.    Education

Businesses are beginning to understand the importance of having the right kind of data, rather than focusing purely on volume, and are wising up to the fact that sparse data is the biggest challenge. How do you “fill in the gaps”? There are additional complicating factors at play; many organisations are suffering a skills gap, as they struggle to identify and hire people with the knowledge and experience necessary to take advantage of the data economy. As well as attracting new talent, leaders must encourage their current teams to develop to suit the organisation’s changing needs, combining numeracy and technical know-how with solid business and communication skills.  

6.    Top Down Buy-in & A Place on the Board

Owning the customer is not a junior or technical role; it’s one of the most important differentiators of future winners and losers. Having a leader who understands the science and art of the possible with data, both within and without the organisation, is critical. Breaking down internal silos and bias is important and requires evidence and persuasion. The customer champion needs to position where they fit; across product/service and owning data and technology. That’s a big remit! While getting to grips with the data itself may seem a difficult task, achieving a consensus regarding the value of data, as well as the cost of generating insight and data-driven decision-making, may be the biggest challenge.

7.    Goal Orientated

There is a danger that insight becomes analysis paralysis. Knowing that customers are different and have different needs doesn’t mean that the business will change its service or business processes/customer touchpoints accordingly. It is easier to hit pause and keep thinking. Moving to action is difficult and involves risk – the risk of getting it wrong and creating negative customer feedback. But if you don’t talk to customers and react to what they’re saying, then the gap between Amazon and Uber and ASOS becomes bigger and bigger. And who’s winning now?

Data transformation demands clearly-pronounced goals, strong engagement strategies and a willingness to move quickly and risk failure. These are not small asks and require solid, visionary leadership in order to inspire buy-in across an organisation.

In my world – the world of customer science -, we identify the value of getting millions of customers to buy one more product or visit one more time. That small move across the entire customer base leads to millions, if not billions, of pounds of sales improvement.

In order to embed a data culture in your business, you must commit to the veracity of data – to explore patterns and to present and reveal the truth, not to just back up a particular bias. Knowing what your customers really think, knowing how and where your supply chain is weak, knowing which stores don’t stock your best selling products, is the key to transformation. Once you know, you can set real measures and KPIs, store by store, brand by brand, customer segment by customer segment.

The truth can often be painful - for example, when you see your best or loyal customers leaving you - but if you have the courage and mindset to relentlessly explore weakness or failure, your business will start to improve performance.

Learn more about how we help organisations to embrace and utilise their data by visiting the Starcount website.