January 6, 2022

MIT Sloan’s 6 Trends in Data & AI

Cindi Howson, chief data strategy officer at ThoughtSpot, recently discussed six trends in data, analytics, and artificial intelligence at the MIT Chief Data Officer and Information Quality (CDOIQ) Symposium.  She acknowledges the gap between companies leading in data and AI innovation and those that are lagging is widening faster than ever before.


Here were Howson’s six trends:


  1. Companies continue bold momentum

Howson recognizes the COVID-19 pandemic forced many companies to react quickly to stay afloat.  The fast movers quick to embrace new strategies at the start of the pandemic were the most successful – restaurants that pivoted online quickly in a major way were the ones that came out ahead as a prime example.  Howson recommends three things to keep the momentum going: 1) accelerate cloud migration plans; 2) use this disruptive time to challenge the status quo; and 3) focus on the first 1% of change.


  1. Customer experience analytics take center stage

Understanding the customer from all touch points is critically important.  Bringing together data from multiple channels and sources is the key to forming the complete customer picture.  To gain this holistic view of the customer, leveraging data fabrics and data lakehouses could be valuable.


  1. Those who leverage external data outperform competitors by double digits

External data can provide valuable and additional context about what is occurring in the marketplace.  For example, Hershey’s used external data during the pandemic to predict growth in customers using chocolate bars for backyard s’mores and a decline in sales for smaller bars of candy for trick-or-treating.

Howson challenges companies to use imagination to best leverage the exploding number of data sources popping up each day.


  1. CDOs lead the charge toward a data-driven culture

Your people cannot be afraid of data.  Chief data officers need to champion people change management and understand the need for training and upskilling workers to take advantage of new technology and new data sources.


  1. Data science loses its luster

Some companies are finding data and machine learning projects have not delivered returns and are not as impactful.  Howson suggests learning from failed experiments.  “If you’re failing fast as part of experimentation, that’s good, you’re learning,” she said.  Howson goes on to say data scientists are typically trained with an emphasis on coding and math with less focus given to business context.  This needs to be rebalanced to increase impact to the business.


  1. Data exposes wide gaps in equity — and also empowers change

In 2020, Americans faced a reckoning surrounding topics of racism, inequality, and inequity.  Data and AI can either support improvement or reenforce these issues.  Major challenges include starting with biased data and lack of diversity in data and AI teams.  For example, Airbnb’s smart pricing algorithm had the intention of minimizing pricing disparities between black hosts and white hosts, but the data suggests it widened the gap by 20%.  A silver lining is organizations’ awareness and recognition that these biases are occurring and that there is a need to embrace transparency and change.


To read the full MIT article, click here.  If you’re interested in starting your graduate school application journey, explore Admitify’s services today!