4 Ways to Build a Robust Data Culture in Your Organisation
By Arijit Banerjee
Massive amounts of data continue to pour into organisations every day and CXOs are constantly looking to find new ways of generating value from it. According to Gartner, by 2020, more than 40% of data science tasks will be automated and 50% of new data transformation flows will integrate one or more machine learning (ML) algorithms. So, what are the implications of this trend?
Organisations looking to compete in a data-driven world will not only need superior data analytics capabilities but also the ability to manage data in multiple deployment models. This will lead to a steep surge in the demand for data analytics professionals. However, given the shortage of data analysts worldwide, building a robust data-driven culture can be a challenge. Nearly 50,000 analytics positions were open in 2017 in India alone.
Here are four ways, companies can create a sustainable data-driven culture by upskilling their existing workforce and changing the way they think about data:
Develop cutting-edge skills in ML and artificial intelligence (AI): According to the State of the Connected Customer report by Salesforce, by 2020, 57% of corporate buyers will depend on companies to know what they need before they ask for anything. This means, having solid data analysis capabilities underpinned by ML/AI will be the key to better engaging customers. Organisations that re-skill their employees in writing high quality software, working with large data sets, and building machine models will be able to create exceptional customer experiences - the number one competitive differentiator.
Develop abilities in IoT: Disney boosts customer experience and operational efficiency by analysing data collected by sensors and camera-toting robots. Acquiring the right capabilities in IoT analytics has helped the company create a more immersive and seamless experience for each customer. Companies that invest in IOT training that helps develop sound knowledge of sensors, edge, fog and cloud computing, and analytics on IoT data - will be better placed to create personalised and positive customer experiences.
Build expertise in cognitive technologies: Cognitive systems such as IBM Watson, Stanford’s Deepdive and Google’s Deepmind are enabling enterprises to make sense of unstructured data through natural language processing (NLP). In fact, Indian retail players such as Tata Croma, Aditya Birla Online Fashion, Titan Company and Metro Shoes are leveraging IBM Watson to gauge customer behaviour and make customer interactions more meaningful. However, to take advantage of these systems, it is imperative that employees acquire good working knowledge of deep learning and deployment of neural networks.
Polish skills in big data analytics: Shopper’s Stop, one of India’s leading fashion retailers, leveraged connected mobile experience to gain insights into customer behaviour to enhance in-store customer experience. Lesson to learn: ability to aggregate data to gain insights into individual customers helps deepen customer loyalty and boost sales. Up-skilling employees in big data analytics- such as prescriptive and predictive analytics - is a great way to gain more value from organisational data.
In the digital age, the data literacy level of employees can make or break a company. Reports and dashboards are integral to data-driven organisations, and data literacy ensures that employees can analyse the data themselves to make insightful decisions. However, it is important to note that it is not possible to build a data-driven culture overnight. It requires a multi-pronged approach that starts with providing enterprise wide access to clean data and is supplemented with good training to reinforce data literacy skills. Training in areas such as data science, data visualisation and inferential statistics can not only help enhance data literacy but also boost employee engagement and help businesses capture new opportunities.