New year. New decade. Time to prepare for what comes next. As organizations continue modernizing and moving to the cloud, the analytics world we once knew continues to change. Here’s my annual list of predictions. I hope it helps you learn something new and embrace industry change as an opportunity for growth.
Analytics Trends in 2020
1. Shift to vertical industry solutions and expertise
Due to big tech market power, horizontal solution consolidation, and market segment overlaps, we will see growth opportunities for specialized vertical industry analytics apps and domain knowledge. Yellowfin’s CEO wrote a nice article about this trend.
2. Continued adoption of artificial intelligence and machine learning
Over the past three years, we’ve seen tremendous interest in artificial intelligence and machine learning. That trend will endure. As more organizations adopt and deploy machine learning models, proper governance, model management, and monitoring will elevate in importance. ML OPs solutions like Algorithmia, Domino Data Lab, MLflow and others in AutoML platforms such as DataRobot, H2O.ai, AWS Sagemaker will be used to apply DevOps to production ML services.
3. Democratization, embedded AI, and no-code citizen tools
Despite the emergence of easy to use AutoML tools for citizen data science, we’ve seen limited success in adoption. Steep data science learning curves and no-code deployment integration limitations persist. Data science today is still a programming paradise – SQL, Python, R, and REST APIs.
What is flourishing? No-code citizen workflow and app development tools. We also see much better embedded AI and personalization experiences within apps, BI platforms, and data visualization tools. Expect to see continued data science democratization within existing apps and analytics solutions.
4. Marketplaces for algorithms, data, and other analytics solutions
Data and knowledge monetization go mainstream. Need data or a solution template to get your project started quickly? If your existing vendors don’t already offer marketplaces like the AWS Data Exchange or Salesforce Marketing Cloud Data Studio, just do a simple Google search. Countless marketplaces already exist with more emerging at an accelerated pace.
5. Knowledge graphs, graph databases, and graph analytics
Organizations feast on information. Graph technology that easily queries and combines structured and unstructured data from many data sources is hot. Google’s Knowledge Graph and Microsoft Graph (Office 365) are two examples of this technology in action. Check out Diffbot, Attivio, Maana, ArangoDB, Datastax, Ontotext, and other specialty graph database, search and analytics offerings.
6. More business, process, and analytics automation
Automate or die in the digital era. Like it or not, reinvention and automation of processes will continue to provide a competitive advantage. There is a reason why AutoML is now widely adopted by data scientists. Look at how you can improve productivity. Automate busy work to deliver more value with brain work. Solutions to review include Trifacta, Paxata, Power Automate, Alteryx, UiPath, Automation Anywhere, Blue Prism, Zapier, Automate.io, IFTTT, and others.
7. Hybrid data fabric and microservices design patterns
Data fabric is an architecture that integrates data management across cloud and on-premises data sources and services. Familiarize yourself with the new data storage, data pipeline, and analytics patterns. Study data virtualization from TIBCO, AtScale, Denodo, Dremio or your data platform provider, and the growing sea of cloud analytics and microservices technologies in AWS, Azure, and Google Cloud.
8. Growth in IoT analytics, smart things, and edge AI
Unlike the consolidation we see in the business analytics market, the smart things market is exploding! According to IOT-Analytics latest market landscape report, there are over 620 vendors. Top vendors in that space grew 40+% YoY. The possibilities in the IoT landscape are endless. Therefore, edge analytics, gadgets, sensors, robotics, all of those areas bring fun new opportunities for analytics professionals to discover.
9. New and evolving data management regulations
As new digital era data-protection regulations such as GDPR and the California Consumer Privacy Act (CCPA) take shape, demand will increase for professionals and specialists who can successfully navigate the continually changing legal environment of data management. To keep up on these topics, review data protection resources, follow Data Protection Report, and ask your data management vendor for resources.
10. Data literacy takes front and center stage
Finally, as data-driven companies get more sophisticated, understanding data, storytelling, explainable AI, and decision modeling skills will rise in demand. Rather than tool-focused training, executives seek out practical outcome-based, analytics translation, and action-oriented curriculums. Context is key! Watch my Art of AI storytelling webinar, check out Qlik’s free data literacy program, and/or invest in related topic books or courses created by practitioners.