Top 3 challenges of harnessing data

In the broader context of a digital innovation, technologies like Machine Learning, Artificial Intelligence are required to harness the digital breakthroughs. Companies have realized the potential of these technologies and are budgeting resources, however they unclear on the operational aspects to start their digital journey.

Companies currently face three major operational challenges in harnessing the data. Firstly, the technologies like machine learning need significant investments in terms of capital and talent and given the uncertainty surrounding the impact and the return on investment, major machine learning projects are stalled. The business line managers have an uphill task convincing the management on the effectiveness of the investment, killing majority of the machine learning initiatives even before they are started. Business line managers having difficulties even on convincing the management for pilot projects is a major pain point for the business lines.

Secondly, there is an acute shortage of talent in the industry and given the complexity and existing uncertainty around the robustness, training new candidates for the roles proves ineffective for the longer lead times and higher cost, impacting the timelines of the entire digital journey. The need of the industry currently is a plug and play tool with inbuilt models that can be used with basic understanding of the working algorithm.

Thirdly, companies struggle to identify a clearly defined path to deliver insights in a streamlined fashion. After days of working on the models, most companies nightmare is the “out-of-memory” error. Having the infrastructure to prevent this error is costly and under spending leads to further delays. In the context of a machine learning project, identifying the optimal infrastructure requirements is an herculean task, which most companies do not have necessary skillset or expertise to do.