My 5 Predictions for Analytics in 2020
Ramesh Saketi, Global Leader for Data and Analytics Services at Sogeti, shares his top five analytics predictions for 2020.
Last year I predicted a growth in the data landscape, and this continues as a key trend for 2020. Indeed, I predict a minimum of 20% year-on-year growth in the market for data services. This is due to the need to modernize data landscapes in order to establish a platform for the analytics that will drive better, more intelligent business in the coming years.
1. Rise of automated data science and machine learning
Even in today’s highly automated IT landscape, data science still requires a lot of manual work. Storing, cleaning, visualizing, exploring and, finally, modelling data all remain manually intensive tasks. This manual work is just begging for automation and thus we have seen the rise of automated data science and machine learning (ML). Nearly every step of the data science pipeline has been, or will be, in the process of being automated. Cleaning big data in particular can take up most of a data scientist’s expensive time. Both start-ups and large software vendors are offering automation and tooling for data cleaning to reduce this workload.
The most significant automation is occurring with machine learning. AutoML, a method for automatic model design and training, has also boomed over 2019 as these automated models surpass the state-of-the-art. For smaller, less technical organizations, automation offers an opportunity to leverage and benefit from data science and ML with minimal investments and without the cost of a large team. H20, Data Robot, Google and IBM are just a few of the vendors who are heavily investing in AutoML. I predict more to follow in 2020.
2. Data consumers’ maturity is critical to success of analytics
As understanding of analytics, data science and artificial intelligence (AI) and their benefits grows, I predict greater readiness to experiment and drive further adoption. Initiatives by executive leadership teams in conducting programs to develop a data science culture is key. We will observe many organizations taking internal training programs to develop a data and insights-driven culture across the enterprise.
3. More industry-specific use cases for analytics
In our data-rich world, industry-specific use cases for analytics continue to emerge, including:
- Leading banks across the globe are providing banking on wearables, like Apple Watch and FitPay. I expect to see this trend on the rise due to an increase in solutions that process and analyze data collected through IoT devices on a real-time basis.
- With the combination of analytics and AI, insurance companies can analyze various types of actuarial data, driving data and persona history to gain insight into customers and to offer them personalized services.
- Analytics, AI and IoT combined will continue to disrupt the manufacturing and supply chain lifecycle. These technologies offer the benefit of automating processes from shop floor to supply chain and aid in the conceptualizing of products and parts.
- In today’s ‘HR economy’ we will see an increase in the application of HR analytics. This will be key to identifying talent in a reduced timeframe, minimizing attrition and tracking the health and security of employees on a real-time basis.
4. Data privacy, security and governance a key focus
While the business benefits of analytics are clear, we must not forget the need to safeguard the data on which analytics depends. Organizations must efficiently manage this data to ensure customer trust. To this end, they will continue to invest in technologies enabling data privacy and security. Further I expect to see more data governance structures being established, driven by executive leadership sponsors. This has become more pivotal with the emergence of legislation, such as GDPR and the California Privacy Act. These enforce a focus on data privacy and security with hefty penalties in the event of non-compliance.