It is now widely acknowledged that the best way to utilize the wealth of data at organizations’ disposal is by democratizing it. By making it available to all employees in a format they can understand, you empower them to put it at the heart of their day-to-day decision making, allowing them to back up their hunches without having to go through the data science team. This means both quicker answers for business users and more time for data scientists to focus on more technical problems. This makes the organization agiler and data scientists' work more challenging, providing a better culture more likely to attract qualified talent over the competition.
At the upcoming Predictive Analytics Innovation Summit, Jason Perkins, Head of Data & Analytics architecture at BT, and Jason Howell, a Data Science IT unit lead at BT, will discuss how they successfully democratized data science at the telecom giant. Jason Perkins' role combines the business information aspects of a Chief Data Officer with the technical ability of an Enterprise Data & Analytics Architect, while Jason Howell's work primarily centers on data management and data warehousing, with the past 2 years spent launching a new data science unit. Between them, they have more than forty years at BT working in Data and Data Warehousing.
We sat down with them ahead of their presentations at the Predictive Analytics Innovation Summit, which takes place in London this March 21–22.
What first sparked your interest in analytics?
Initially drawn to data analysis as part of problem-solving, how we can better communicate to our customers and understanding more about our business. This was the start of a roller coaster ride as Analytics has continued to evolve. With the introduction of big data, data science and more, we are arguably in the most exciting times for the field.
How important is establishing a data-driven culture? What do you think is the most important thing companies can do to instil one?
There is a wealth of information demonstrating the importance of a data-driven culture and its impact on customer and business performance. Data leadership is important in championing the value of a data-informed culture. Also, as part of our ‘continuous learning’ culture at BT, we are leveraging our academy to create a community for our data citizens, including investment in both data career and learning pathways.
How do you see data scientist role changing in the future and how do you think machine learning will impact their role? Will data scientists themselves see their work automated?
Automation will play a part in improving the productivity of data scientists. However, much of the business data science role will remain inherently human - understanding the business scenario, the 'why' of the analytical model, how data science output can make a difference, etc.
Is there a skills gap in data science? If so, what can we do to fill it?
Data Science is a young field that continues to evolve across multiple dimensions including the data science methods and technical capabilities Our strategy is to democratize data science to more of our data citizens so they can use its capabilities to unlock the value in our data. In order to do this then we need to simplify data science and provide more support for our analyst community.
Is a data science team better centralized or decentralized? Why?
Data Science needs to support the business and therefore the right fit depends on the organisation. We have a federated model which combines keeping business data science (vertical satellites) close to the relevant teams where domain knowledge is important and technical data scientists (hub) in our IT and research teams who are our Centre of Excellence promoting best practice and emerging capabilities.
Do you think organizations put enough value on their external data, or too much?
For the majority of organizations, the most valuable and unique data they have access to is 1st party information they capture. External information can help enrich this view and its value is use-case dependent.
What new technologies and approaches to data strategy should we watch out for in data analytics in 2018?
We are excited about a range of technological capabilities and the maturation data science:
- Pervasive decisioning services with self-service to enable the business,
- Lightweight data science and machine learning being added to BI and Discovery toolsets
- Deep learning for deeper insights and interpretability for expert data science.
- Data Science automation for improved efficiency and productivity
- What if scenario modelling and simulation
What will you be discussing in your presentation?
In our presentation, we will take you through our journey to democratize data science in BT, the largest provider of fixed-line, mobile & broadband services in the UK. This includes the introduction of our big data service for data experimentation, building a forward deployed data science team, lesson learnt, and examples from along the way.
You can hear more from Jason and Jason, along with other industry-leading experts, at the Predictive Analytics Innovation Summit, which takes place in London this March 21–22. View the full agenda here