A confluence of technologies – including cloud, AI, ML, and IoT – and new business models disrupting the competitive dynamics of many industries are putting digital transformation at the top of boardroom discussions. While what digital transformation looks like may vary, one thread runs through all interpretations: the importance of data and data governance.
This puts the Chief Data Officer (CDO) front and center as companies digitally transform. Below are some tips that can help guide companies through the data strategy that must sit behind digital transformation.
1 – Improve access to data in the cloud to drive innovation
From a data management perspective, cloud architectures offer a variety of features–from multi-tenancy to content delivery networks–that enable easier access, re-use, and distribution of data. This can power software-defined automation as well as new DevOps approaches. Enabling the agile digital business to innovate, test, and scale quickly, while offering continuous uptime.
2 – Embrace AI and ML to analyze data at scale, while keeping an eye on transparency and the logic that underpins its analysis
By facilitating processes such as natural language processing, image and speech recognition, AI and ML have begun to automate tasks that previously could only have been performed by humans. Customer-facing applications such as Alexa, Google, Siri, and Cortana, achieve most instant-recognition, however, there is a myriad of use cases where AI/ML is used in the background to automate complex and/or large-scale data processing. Everything from automating insurance claims and optimizing financial models, to performing medical diagnoses.
It is vital that organizations using AI and ML techniques ensure tight oversight and an understanding of the data and logic underpinning that analysis. While neural networks were traditionally black boxes, the transparency of AI/ML decision-making is beginning to improve, which will be vital to support increased adoption.
3 – IoT is about to become bigger than ever; ensure your management of the data generated from these devices is robust and secure
The proliferation of data-gathering devices will provide an explosion of data to analyze, interpret, and ultimately guide business decisions–be prepared to manage this.
Just as IoT covers a wide variety of use cases, it also poses a broad range of data management challenges. One of the primary considerations should, therefore, be around how to ensure ‘things’ are protected from hackers. Equally, because of the distributed nature of some devices, network bandwidth considerations and communication protocols need to be properly evaluated.
4 – Data ethics is more than compliance with regulation; think about giving customers both transparency and value
At the very least, organizations will need to satisfy existing regulations relating to data privacy, which for multi-nationals involves complying with a complex matrix of obligations affecting different jurisdictions.
It is also about client consent and transparency, however. Customers are more likely to provide consent to organizations that they trust. That trust is often reinforced when a company can add value to the user’s experience. Modern digital titans (like Apple, Amazon, Netflix and Google) have done a good job at demonstrating value by carefully studying and modeling users’ behavior to present them with more relevant advertising, product,ac or viewing suggestions. A newer wave of companies (such as Airbnb and Uber) have used data and technology to connect new sources of supply (peoples’ spare rooms or those who want to earn money by providing rides) with demand for those services. Just as demonstrating value is of paramount importance to elicit consent, breaches of trust can be equally catastrophic.
5 – Get your organization behind you; responsibility may lie with you as the CDO but success will depend on company-wide change
True digital transformation requires cooperation from across the entire organization. A single person may be charged with defining a company’s digital strategy, but it will take agreement from across the organization to execute against that strategy.
Many digital transformation projects require organizations to break down data silos, which in turn will require support from business units that own those silos. Others may require setting up new businesses and/or adopting new practices, which could be seen as threats to established operations if not handled carefully. CDOs must do more than simply sprinkle data and analytics over an existing organizational structure.
