2020 Data and Market Insight Trends

 In Newsletter, Newsletter Pro

Issue No. 124


2020 started off strong. Markets were at an all time high, business were in growth mode, by all accounts it was turning out to be a stellar year. And then Coronavirus took hold. In a matter of weeks 2020’s bright future dipped into a dense fog as US GDP shrank at a 4.8% rate in Q1


With a more dire economic outlook on the horizon, companies are making swift changes to conserve cash. The companies that will emerge from Covid-19 era strong have one thing in common—they are at the forefront of data analysis and BI within their industries.


According to Gartner, 87% of companies have low BI and analytics maturity. The 13% of companies that have developed processes and technology for their data have a significant competitive advantage. 


Why do companies have such a hard time actualizing the promise of data? 


  1. Slow to digitalization and shared datasets
  2. Knowledge gap – lack of technical expertise for data analysis
  3. Challenge of change management to improve outdated processes


For the companies that are leading this charge, we’ve noticed a few trends. 


Automation of Decision Support Using AI/ML – On Demand Forecasting


Businesses are looking more to automation to streamline their forecasting processes focusing on frequency and automation to assist with business decision support. This helps leaders lessen the decision cycle time, allowing even the most complex supply chains to be nimbler in the face of change. 


Focus on Forecast Accuracy – Measuring Error to Reduce Risk


The use of big data has long been a trend on the marketing side of the business. Now finance leaders are looking to enhance scenario planning and improve forecast accuracy using big data. This case study highlights the error rates for sales forecast accuracy as a way companies can benchmark their forecasting performance. We wrote about measuring forecast accuracy as the first step in mitigating risk. According the Institute of Business Forecast Planning, the demand planning process has two basic pillars: 

  • To create the most accurate demand forecast (best prediction of what is actually going to happen) with the lowest error.
  • To alleviate bias whenever possible.


Data Driven Process Without Human Intervention


AI Forecasting methods can incorporate a feedback loop of continual learning, so that overtime, forecast accuracy improves. We use about 30 different competing methodologies within our model. The forecasting method is built dynamically to account for the best methodology for consistent accuracy and low error. Our platform comprises ~15 billion data points and millions of learning algorithms that are updated continually in response to global events and market conditions to ensure accuracy. A key tenet of our platform is to have no human intervention in the analytic process, allowing the AI to function without imposition of bias, emotion or sentiment. As you can see in the image below, this accounts for amazing results when compared to non-AI, consensus forecast methods.



Want to learn more? Book a meeting with a member of our team.

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