When we started Amalgam Insights, we oh-so-cleverly chose the AI initials with the understanding that artificial intelligence (the other AI…), data science, machine learning, programmatic automation, augmented analytics, and neural inputs would lead to the greatest advances in technology. At the same time, we sought to provide practical guidance for companies seeking to bridge the gaps between their current data and analytics environments and the future of AI. With that in mind, here are 8 predictions we’re providing for 2020 for Analytics Centers of Excellence and Chief Data Officers to keep in mind to stay ahead while remaining practical.
1. In 2020, AI becomes a $50 billion market, creating a digital divide between the haves and have nots prepared to algorithmically assess their work in real time. Retail, Financial Services, and Manufacturing will be over half of this market.
2. The data warehouse becomes less important as a single source of truth. Today’s single source replaces data aggregation and duplication with data linkages and late-binding of data sources to bring together the single source of truth on a real-time basis. This doesn’t mean that data warehouses aren’t still useful; it just means that the single source of truth can change on a real-time basis and corporate data structures need to support that reality. And it becomes increasingly important to conduct analytics on data, wherever the data may be, rather than be dependent on the need to replicate and transfer data back to a single warehouse.
3. Asking “What were our 2020 revenues?” will be an available option in every major BI solution by the end of 2020, with the biggest challenge then being how companies will need to upgrade and configure their solutions to support these searches. We have maxed out our ability to spread analytics through IT. To get beyond 25% analytics adoption in 2020, businesses will need to take advantage of natural language queries and searches are becoming a general capability for analytics, either as a native or partner-enabled capability.
4. 2020 will see an increased focus on integrating analytics with automation, process mapping, and direct collaboration. Robotic Process Automation is a sexy technology, but what makes the robots intelligent? Prioritized data, good business rules, and algorithmic feedback for constant improvement. When we talk about “augmented analytics” at Amalgam Insights, we think this means augmenting business processes with analytic and algorithmic logic, not just augmenting data management and analytic tasks.
5. By 2025, analytic model testing and Python will become standard data analyst and business analyst capabilities to handle models rather than specific data. Get started now in learning Python, statistics, an Auto Machine Learning method, and model testing. IT needs to level up from admins to architects. All aspects of IT are becoming more abstracted through cloud computing, process automation, and machine learning. Data and analytics are no exception. Specifically, Data analysts will start conducting the majority of “data science” tasks conducted in the enterprise, either as standalone or machine-guided tasks. If a business is dependent on a “unicorn” or a singular talent to conduct a business process, that process is not scalable and repeatable. As data science and machine learning projects start becoming part of the general IT portfolio, businesses will push down more data management, cleansing, and even modeling and testing tasks to the most dependable talent of the data ecosystem, the data analyst.
6. Amalgam Insights predicts that the biggest difference between high ROI and low ROI analytics in 2020 will come from data polishing, not data hoarding. – The days of data hoarding for value creation are over. True data champions will focus on cleansing, defining, prioritizing, and separating the 1% of data that truly matters from the 99% more suited to mandatory and compliance-based storage.
7. On a related note, Amalgam Insights believes the practice of data deletion will be greatly formalized by Chief Data Protection Officers in 2020. With the emergence of CCPA along with the continuance of GDPR, data ownership is now potentially risky for organizations holding the wrong data.
8. The accounting world will make progress on defining data as a tangible asset. My expectations: changes to the timeframes of depreciation and guidance on how to value specific contextually-specific data such as customer lists and business transactions. Currently, data cannot be formally capitalized, meaning asset. Now that companies are generally starting to realize that data may be their greatest assets outside of their talent, accountants will bring up more concerns for FASB Statements 141 and 142.
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