In my previous blog post, I shared examples of how data provides the foundation for a modern organization to understand and exceed customers’ expectations. However, the important role data occupies extends beyond customer experience and revenue, as it becomes increasingly central in optimizing internal processes for the long-term growth of an organization.
Collecting workforce data as a tool for talent management
A data-driven approach to talent management and development brings about greater transparency, reduced attrition and more effective training and enablement.
A 2020 retention report by the Work Institute revealed that over 42 million employees in the US left their jobs voluntarily in 2019, and this trend appeared to be increasing. The report classified employees’ reasons for leaving into six broad categories such as growth opportunity and job security, demonstrating the importance of using performance data, data collected from voluntary departures and historical data to reduce attrition for strong performers and enhance employees’ well-being.
Data driven training and enablement programs take the guesswork out of talent development plans, enabling organizations to educate and develop employees according to their specific development needs and preferred ways of learning. This is especially important for organizations to set and meet diversity, equity and inclusion targets to build well-rounded and successful teams.
A more data driven approach also leads to greater transparency and meritocracy when new opportunities and promotions are based on performance rather than politics, ensuring that top-talent is nurtured and rewarded.
Streamlining operations with advanced analytics to preempt issues
In a 2021 white paper titled “Data Excellence: Transforming manufacturing and supply systems“ written by the World Economic Forum and the Boston Consulting Group, it documented that 75% of executives interviewed believed that advanced analytics in manufacturing was more important today than three years ago. The report went on to say that in a post COVID landscape, manufacturing operations, in particular around intelligently managing supply chains, would be required to free up capital for future investment.
As an example of how data improves product reliability and quality, the paper mentions how Johnson and Johnson developed a real-time monitoring capability to exercise tighter control of process variations.
Citing another success story, Zoomlion, one of Cloudera’s manufacturing customers in China, increased the effective working time of its equipment by 20% and reduced manpower and maintenance costs by 30%. By using Cloudera’s big data platform to harness IoT data in real-time to drive predictive maintenance and improve operational efficiency, the company has realized about US$25 million annually in new profit resulting from better efficiency of working sites.
Data enables Innovation & Agility
For the past decade or more, successful organizations have relied on data for innovation or being agile in adapting to changing environmental conditions. Data could inform subtle improvements, like the shade of a button on the website, or how to influence a customer’s buying process on an e-commerce site.
Deciding what features or new products to build based on customer feedback and the associated monetary value, makes conversations with product management teams more objective and less about servicing the squeakiest wheel.
Exploring new possibilities to develop intelligent connected devices and services is made easier by creating digital twins,accurate models to predict behaviour. These models are then used for anomaly detection or inferences about what will happen.
A 2019 HBR article mentioned how organizational decisions backed by data have instilled more confidence and reduced risk. Strong and consistent enforcement of data governance and controls across multiple environments ensures that data lineage is clear, justifying insights from analysis or predictions from models.
One of the more obvious use cases of data’s role in reducing risk is insurance policies. Insurance companies like Metlife are using telemetry data from vehicles to assess risk and adjust premiums. This has created pathways to expose hidden risk correlations, such as driving habits with traffic and weather patterns. MetLife then reaches out to specific drivers to urge them to take precautions.
If we consider the financial services industry, there have been a number of high profile cases where not taking a strong data driven approach to risk management has resulted in breaches of regulations and the law. This typically incurs significant penalties from industry regulators that have been over $1B.
In addition to helping drive top-line growth, the examples in these two blog posts have shown how becoming increasingly data-driven can bring about positive change across many areas of the business and how technology is integral in supporting this. As organizations are met with expanding data volumes and workloads in today’s landscape, an enterprise data cloud will help customers manage the information in a secure environment while extracting the true value of their data throughout its life cycle.
If you are interested in learning about how a modern Enterprise Data Cloud can support the goal of being increasingly data-driven, please join me for my upcoming webinar .