Why does Big Data literacy matter?
Businesses across Europe are facing huge challenges with Data literacy. A problem that has no linear solution yet critical to getting right should a business wish to maximise its investment in Big Data. We explore some of the systematic challenges facing businesses shifting towards big data and then set out the Digital Bucket Company concept to tackle the gap in data literacy.
What are the issues with data literacy?
As a child, I was always told “if you are going to do something, do it right.” More recently this has become a testament as an entrepreneur. I never really took the time to understand what it meant, until now. To me, it means if you are going to do something, you have to set your surroundings, environment, and mind on the subject matter. Half-heartedness just won’t cut it. The same half-heartedness seems true of organizations and companies approaches to the whole data literacy.
The current data literacy landscape in Europe isn’t great with people’s understanding of data being ‘fragmented’ and ‘lacking knowledge’. This problem goes beyond businesses and organisations with the general public not really understanding data and how these data collection mechanisms work is a big concern. The Lack of data literacy might even provide a clue to data bias in technologies such as facial recognition technology profiling certain ethnicities over others. It allows Privacy to be challenged with data collection by companies like Facebook and Amazon. And that’s just the tip of the iceberg.
Businesses investing in big data are now trying to work out how to get their staff to understand data. If staff doesn’t understand how to interpret the data or ask the right questions from the data it equates to bigger risks and mistakes.
How can businesses tackle Data literacy problems?
Making data part of the business culture
The solution to data literacy in Europe needs to be a joint effort between private and public organizations. Whilst businesses should make data part of their culture. Making data literacy accessible to everyone needs to go beyond just training and should be part of every decision-making process.
A business investing in big data will realize data is only effective if every part of the business understands data. This should include the value and ethical consideration of data. This means data should be part of the business culture. The starting point of any behavioral change is educating people about data. Yes, there are plenty of online tools and interactive software that can be used to do this.
My only observation with the available learning material is very tech jargon and technically written. This is perhaps the biggest criticism of any learning platform. It needs to be communicated in layman terms to the top and bottom of the organization. It should be focusing on training team members to understand what data is.
“We knew it was vital to change the mindset and for anyone joining the company they needed to understand data. That is why we spent a lot of time developing an academy system that was rich with training material that made someone with no understanding of data to developing them to understand the most sophisticated data models and processes” say Humayun Qureshi from the Digital Bucket Company.
The Digital Bucket Company academy provided the team with a sense of learning, with all sessions providing safety and well-being and creating a policy where everyone looks out for each other. The result from the academy at Digital Bucket Company was transformational, with 100% of students developing a baseline understanding of data.
Scrutinizing the data
This really follows on from setting a data culture. Part of that culture should be making staff question any data and understand how that result was achieved.
This requires a different team set up to the traditional top-down approach and goes beyond the agile working methodology used in software development. Whilst a top-down approach is necessary for decision-making and governance structure, a data model requires all aspects to be considered. This allows data to be thoroughly examined from multiple perspectives, and having a critical approach refines the result and algorithm.
The scrutinizing process requires data to be considered from ethical and operational understanding. Even though Big Data models should have a rigorous testing process. So essentially, the communication channels play a key role. By communicating with internal and external stakeholders with a feedback loop provides a much wider perspective and allows the team to formulate questions from a multi-stakeholder perspective.
By having a communication feedback approach the likelihood of bias and incorrect decisions is greatly reduced. This is where current data platforms on the market fall short with no real feedback or commentary on the data. It shifts data from being purely a management function to a cross-functioning asset to the business.