A recent Gartner study pointed out the growing importance of Chief Data Officer in an organization. The percentage of Chief Data Officers reporting to CEO had doubled to 30% since last year. This has been possible through the impact that CDOs are bringing in their organizations. Below are a few notable points for a CDO, which could be the key principles of their day to day functioning:
- Measure your decisions taken through data: Asper hbr.org, 55% of the B2B companies quote the “ineptitude to combine data from disparate sources in a timely manner” as the biggest challenge in leveraging data to achieve their go-to-market goals. In SixthSenseData, we stress the aspect of decision making through data at various levels with all our conversations with Clients. Because the importance of IT spend is primarily 2 folds, one as an Infra enabler and other as Decision enabler. The decision enabler part is where Analytics, Insights and Innovation help to give incremental business benefits through the life cycle of the enterprise. So unless the Chief Data officer has a track for the business decisions enabled through data, the value proposition is not defined adequately.
- Prioritize your Data projects: An important day to day aspect for a Chief Data Officer is to prioritize the initiatives and engagements going on with the business in value. The business in value matrix along with a balanced scorecard approach helps outline the enterprise prerogatives and align with outcomes either for topline or bottom line. This will help the Chief Data officer pick up the projects which are of immediate importance to the enterprise while keeping an eye ahead on the roadmap.
- Automate your data: The amount of external and internal data for an enterprise getting generated daily is exceeding what was available in the last decade. With such volumes of data in command, disrupting the core business is tricky and the possibility of getting there first exists. In 2018, manual data preparation time would constitute up to 80% of a data-driven project time span and effort spent. This has repercussions on the cost of the data-driven projects and the number of decision making data projects that can be undertaken for the year. In order to disrupt the space, what is needed is automation of data sources and their integration to varied secondary systems.
- Machine Learning Ops: As the data sources are automated and integrated with decision making, the next step for Chief Data Officers is to establish the model for the data-driven decision making and run it as Machine Learning Ops. The machine learning ops require running the models and incremental revisions as necessary based on incremental features changes and delta data accumulated. The incremental revisions could be changes to the data model and effecting tracking of decision points with visualization.
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