Natalie Jakomis, Global Director of Data Science and Analytics, Coats
The data revolution is changing businesses and industries in permanent ways. Revolutions seldom move backward; they continue marching ahead. The scale at which parts of the revolution advance may differ, but it is hard to ignore the movement. And it is quite the same with data. For example, the growing propagation of the data culture signals the age and maturity of data science.
Today, organisations and businesses are increasingly dependent on data to understand trends, drive insight and help streamline efforts to make sound business decisions that can be transformed into the right actions to help achieve business outcomes that make all the difference, especially when navigating times of uncertainty and change.
Companies that succeed in their data-driven efforts look for patterns everywhere; tie decisions back to the data and continually discover and learn. They understand that creating a data culture is a diligent quest and magic bullets do not deliver results. To adapt a famous quote from Thomas Edison, becoming data-driven is 1% inspiration and 99% perspiration.
The following are five lessons that future leaders need to understand to be successful with data and analytics and sustain a culture with data at its core.
Data-driven culture starts at the top companies with strong data-driven cultures, set an expectation that it’s not an exception for decisions to be tied to data, it is standard. And these practices propagate downwards. The example set at the top of the business stimulates significant shifts in company-wide norms.
Companies need to embody data in their DNA and ingrain data fundamentals and robust processes to evolve data from a raw ingredient to a finished good. This requires the right focus, commitment, and investments to follow through on commitments. As well as an understanding of what data can be used for value creation.
• Remove friction
When developing and implementing a data culture or a transformation agenda it is imperative to stay true to the business problem. Focus on the business objectives and outcomes and then look at the landscape of data and what insight is required. Quickly act on it, whilst maintaining quality, iterate, and deliver the analytics back to the customer. Use the feedback as an accelerator for improving the capability and/or service of the data product to make better decisions more often.
A good starting point is to look at places across the business where people are attempting to make decisions. Review their processes and attempt to identify gaps, for example, time to obtain the data, the effort to evaluate the data, find insight, or make a decision. Put simply, start by attempting to remove friction from an existing decision-making process.
• Data Yin and Yang
Introduction of good data management and data governance – master data management, data dictionaries, transparent logic and rules, data health metrics are all examples of fundamental data tools to help enable data-driven businesses. The other side – ‘Data Yang’ is more of the data analysis, data science, and innovation. If you don’t have a strong footing, you can’t use the data. If you have a solid foundation but are not being ingenious with the data, you’re not growing. The successful blending of these two is a key challenge for any industry. You must combine both, it’s a pre-requisite for data success.
• High impact learning
A learning culture is what enables some companies to identify problems in their products and fix them quickly. It is what enables some companies to ‘out-innovate’ their competitors. It is what enables other companies to grow at faster rates. And a lack of learning culture prevented many now non-operational companies from embracing changes in their markets and evolving their products.
High impact learning requires experimentation, which involves accepting positive and negative results. It is important for leaders in companies to keep an open mind, learn from the experiences of others, their failures, and successes — and look beyond the four walls for inspiration and success models.
• Failure is an option
Leveraging data is both aspirational and risky; if you’re not failing, then you’re not pushing the boundaries enough.
At the beginning of a data science initiative, there is no guarantee your project will be successful. Iterative trial-and-error is part of the data science project lifecycle. Science is messy and often many people do not appreciate the ratio of failures to successes. Sometimes developing an idea through to completion can be unsuccessful. The model does not work, or the idea is incorrect and needs to change. Data Science is more about experimentation and iteration than building up solutions from paper to production.
By only knowing what doesn’t work and why, can we determine what will work? Failing in a controlled way as part of an experiment creates value. If this isn’t acknowledged it can become new friction and damage any innovation or experimentation.
To conclude culture can be a multifaceted problem or a multifaceted solution. When an organisation’s data mission is detached from the business strategy and core operations, it should come as no surprise that the results of analytical initiatives may fail to meet expectations. But when excitement about data analytics permeates throughout the entire organisation, it becomes a source of energy, drive, and momentum.
Ultimately, today’s technology is incredible. Imagine how far it can go with a data culture to match.