How to Move From Data Reporting to AI-Driven Intelligence
3 min read ● Silk Team
For decades data analytics has been structured as follows: A business event occurs and data is collected and then 7 days or so after that a report arrives on a stakeholders desk. Descriptive analytics provides information as to what happened but does not provide much direction as to what should be done next.
In today’s fast-paced digital world, the traditional “rear view” approach to doing business is insufficient. In order to compete successfully organizations have shifted from the use of traditional reporting to the use of artificial intelligence driven intelligence. This shift is not simply based on a faster way to calculate numbers. It is also a move from looking at the rearview mirror to looking ahead to see what will happen.
The Transition From Hindsight To Insight
Historically, reporting has been based on manual interpretation to find patterns within rows and columns. AI-driven intelligence provides automation to the discovery process of data analysis.
- Reporting (The Past): Provides the answer to the question of “what happened?” (i.e. Sales decreased by 10% last month.)
- Intelligence (The Future): Provides answers to the questions of “why did it happen?” and “what will happen next?” (i.e. Sales declined because there was a supply chain lag; We project a recovery in 14 days if inventory is allocated properly.)
When using machine learning (ML) models in your data stack, you can take your analytics and change them from a static record of the past to a dynamic decision engine.
Three Key Technical Areas Of AI Enhanced Analytics
In order to make a successful transition to an intelligence-first model, you need to focus on the following three technical areas:
1. Automated Root Cause Analysis
Instead of taking hours of time for a data analyst to slice and dice a pivot table to determine the cause of an anomaly, AI algorithms can analyze millions of data points instantly and identify the contributing factors of the outcome and automatically surface the “why.”
2. Predictive And Prescriptive Models
While predictive analytics informs you as to what is probable to occur, prescriptive analytics advises you as to the best course of action. For example, an AI system could forecast a high churn rate for a particular customer segment and recommend a custom discount code to keep them.
3. Natural Language Processing (NLP) for Data Democratization
One of the largest barriers to developing a culture based upon data-driven decision making is the technical gap between the data scientist and the business user. AI closes this gap through NLP which enables the non-technical manager to ask questions in everyday language (for example “which regions are performing above average in Q3”) and obtain instant, visually-based answers.
Moving Beyond The “Data Silo” Barrier
The most common barrier to evolving to an intelligence-first model is fractured data. AI is only as good as the quality of the data it uses to make decisions. Therefore, to evolve to truly intelligent organizations must:
- Unify All Data Sources: Unite all of the silos that exist between marketing, sales and operations.
- Clean the Data: Use automated cleaning processes to eliminate “garbage in, garbage out” scenarios.
- Use Streaming Data: Replace batch processing with real-time data processing to enable instantaneous AI inference.
Summary: Intelligence Is The Competitive Advantage
Evolution from reporting to intelligence is a transformation from being reactive to being proactive. If an organization depends solely on traditional reporting they will always be a step behind the competition. Those that utilize AI to develop predictive insight will anticipate future market trends and be able to better allocate their resources, while acting while their competitors are still viewing yesterday’s news.
Organizations are no longer striving to merely have access to data, but to utilize that data as a completely autonomous competitive advantage.