Media & Marketing


Carlos Barge

Conventional (classical) BI is dead. ‘Classical BI; (reporting, query, etc) was a useful tool for tactical measurement of operational systems. Classical BI tools and techniques are very good at predicting the past. Classical BI rested atop relational data stores and mostly operational systems.

‘Neoclassical BI’ allowed us to drill a little deeper into root cause analysis and provided tools for estimation and budgeting. Analytic servers, cubes (MOLAP/ROLAP) with drill down/through ability. The manage by dashboard was minted here and the market was flooded with tools that supported visualization technology. Neoclassic architectures rested on star schemas, data warehouses and data marts. These systems require an enormous amount of care/feeding. Year-over-year TCO is high. Many of these systems are being dismantled today and replaced with Modern BI architectures due to the lack of agility and high cost/benefit.

Modern BI has emerged over the last years and leverages tools and techniques that allow us to mine and analyze structured and unstructured data in schema-less architectures. This era focuses on the analysis of large, rapidly changing datasets and is a byproduct of other technology movements like Social Media IoT and other SaaS platforms that creating massive amounts of data requiring analysis/decision making. The tools are good at consuming large data sets on top of big data platforms like Hadoop and Spark. This era introduced the Data Scientist and terms like machine learning and predictive analytics to mainstream IT. Much progress has been made in the democratization of data analytics and the creation of powerful self-service platforms. The modern BI era has also proven that most of the on premise analytic and data warehouse technologies once built on premise can be moved to cloud and consumption can occur on any devise. We are about to move into the next era.

Post Modern BI – We’ve entered the early stages of this era about two years ago and we straddle the Modern/Post Modern BI eras. The BI/Analytics that emerges over the next 5-7 years will be based on the addition of deep learning technologies layered atop the BI and data analytics process meta models. Post Modern BI will be characterized by:

a) Schema Driven AI – For example: a data model of key business entities and mapped onto an underlying dataset will be used by algorithms to autonomously analyze and report on interesting trends and relationships. The results of the analysis (descriptive stats and time series) will be graphed and/or presented tabularly for further review. The more insightful analytics can be saved for further monitoring and snapped into a dashboard for sharing/collaborative review. This feature currently exists in Microsoft Power BI. But it gets better.

b) Schema-less AI – The underlying AI has begun to identify the natural relationships (co variance/correlation) between data and with additional training uncovers important relationships that exist within structured and unstructured datasets. To some extent, IBM’s Watson does this today. There are a number of deep learning algorithms that can do this on narrow datasets.

c) Natural Language Query – on top of either aforementioned approach analysts will be able to submit natural language queries to the AI and with some additional training feedback answering spoken/typed questions related to aggregation, trends, discrete values (metrics/KPIs) and projections. Watson provides this capability in limited form today as evidenced by its success on the Jeopardy game show. Microsoft and SAP provide rudimentary natural language features on top of their schema driven meta models.

d) Analytic Construction Sets – tools for constructing deeper analytics experiments will get better with visual workbenches becoming more the norm than the exception. Look to IBM with SPSS and Microsoft with AZURE/ML to set the pace and provide more democratized access to deeper analytics. Watch as the results of these experiments are woven into the analytic fabric of the enterprise capable of being mined by an AI executing at the meta layer.

e) Experiment by AI – based on the underlying knowledge collected by our AI bots and armed with subject matter background our new model free, autonomous AI will be capable of devising, executing and summarizing the results of experiments.

Once the Post Modern era ends there will no longer be much need for our BI skills and depending on who’s philosophy you embrace we either move onto other analytics jobs incapable (for the time being) of being performed by an AI or we join many of our white collared brethren on the unemployment line… that is a worth topic for a discussion on another thread.

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