Observations about Big Data Application
Fragmentation of data and impact on data quality
As enterprises increasingly improve the sophistication of their data analytics tools and capabilities, many are recognising the importance of getting multiple stakeholders within the organisation to come together and agree on what they want to achieve from the data collected. Through my conversations with business and technical leaders, customer data is often fragmented across almost every function in the organisation from sales through to finance and HR.
Most organisations are still continuing to struggle with some of the fundamental building blocks required to move from data to insights, such as improving reliability and accessibility of data. The challenge lies in the complexity of data structures especially when using multiple systems with varied applications of the data. Companies are getting more advanced in data collection but they have big problems connecting them.
Intrinsic application of data analytics
Data quality and reliability are crucial in plotting real-time trends and mapping the customer journey using transactional data and heat maps. The next step is to make the information tangible for the business to improve user experience. This requires analysing multiple streams of data to extract actionable insights for the business. From my research, today’s leading organisations are now focusing on key themes such as how to bring analytics closer to the business, how to break down silos and encourage cross functional approaches, how to refocus their activity around strategic imperatives, how to prioritise what should and could by leveraging on data analytics.
I believe that the potential of data analytics lie in personalised marketing, prediction of future market trends and identification of new markets and revenue streams. Organisations which are deriving real results from using data analytics in marketing has been focused on using the information to create more tailored-made customer offers or to decide which services to launch so as to generate more cost savings and productivity.
Importance of stakeholder buy-in
Stakeholder engagement is important as the analytics and business functions have to be aligned on expectations of the value derived from data collected. Focus will need to be placed on creating better alignment between the business and analytics process which will allow IT and business leaders to better understand what improvements and capabilities required to support the business. This is possible through strong change management processes and executive support with a clear outline of the data measurement metrics e.g. revenue and sales projections
Thereafter, the respective stakeholders will need to have a common understanding to fully utilise and experiment with new forms of data and algorithms to automate decision making in order to increase customer satisfaction and expand organisational capability to better serve existing and future customers.
Lastly, the defining question for organisation leveraging on data analytics has to be the definition of what value they are looking for in the data — is it cost reduction? better management of risk? improved customer experience? All these targeted questions requires extensive collaboration and knowledge sharing across the organisation with focus and engagement from all stakeholders involved.