About Data Science

An Emerging New Economy

As economies across the globe reopen and organizations learn to operate in the new normal, COVID-19 continues to subdue consumer confidence and alter buying preferences - even altering buyer segments.

Emerging themes in customer behaviors are represented by a shift to value and essentials, a flight to digital and omnichannel, shocks to prior loyalties, the health and “caring” economy, and the homebody economy.

In the U.S., visible safety practices and care for employees and customers has become even more important, with the reliance on online business now surpassing 10% in many retail categories according to Mckinsey.

Prior to the Coronovirus, a whopping 85% of purchases occurred at physical stores,, emphasizing the value that consumers found in an in-store shopping experience. Now, contactless retail is a major use case for omnichannel delivery including curbside pickup, delivery and drive through services.

COVID-19 is the most serious health crisis of our lifetime and has disrupted economies all over the world. As the economies begin to reopen, there is no greater need than to reimagine sustainable business models that can survive additional disruptions.

Companies and employees need to innovate at a serious pace to stay relevant in the marketplace. Many of these challenges are tactical operational challenges and while AI isn’t going to answer all challenges, automation and intelligent decision support would be used at any point to improve speed and safety of execution.

Analytics has seen significant adoption and business sponsorship in the past decade. Data Scientists are being hired to build analytics models—largely machine learning and, increasingly, deep learning—capable of turning vast amounts of data into insights.

Business leaders continue to be invested in the concepts, but are not without concerns around cost and results. The need for integration of analytics solutions in business can’t be overstated as a way to drive end value.

Success with analytics requires data scientists and an upstream team of data engineers, data architects and downstream teams of software developers working in agile scrums to implement their frameworks and solutions.

As a result of this dynamic, an analytics initiative can easily take on its own life and become a significant cost center. It is therefore critical to ensure that analytics initiatives consistently show business value and business outcomes are attributed to these specific initiatives.

This is where a need for a more operational role emerges to help shepherd these initiatives to implementation -- the analytics translator and interpreter.

Small & Medium Business Enterprises (SMBs) are typically characterized by having less than 500 employees. Forbes publishes an impressive list of these ‘small giants’, who showcase the best in American entrepreneurial spirit.

COVID-19 has disrupted the SMB sector and as the US begins to reopen, the US Chamber of commerce playbook recommends analyzing your sales data, customer behavior and ROI in order to understand what’s working and what you may need to adjust.

In many cases, the pre COVID-19 business model no longer works and a new value proposition needs to be evolved. Kickstarting analytics may be a competitive advantage for small enterprises -including non-profits, as the economy recovers.

In order to adopt and jump start analytics, here are a few key factors that need to be considered:

COVID-19 Is changing analytics spending, with 97% of companies pursuing cloud-based analytics and collaboration technologies for the growing number of analytics professionals now working remotely.

Today, businesses expect analytics to be fast, scalable and easy to use. The key to achieve this is to empower business users and reduce reliance on centralized shared services. The primary challenge remains that only “80% of time [is] spent in cleansing and prepping data and 20% on actual analysis”, increasing the lead to deliver insights and reducing ability to action quickly.

An optimized analytics architecture helps reduce the degree of separation between data and business to bridge this gap. Gartner defines this as self-service analytics.

Here are key levers to optimizing self-service analytics to improve time to decision.

The 2012 article ‘Data Scientist: The Sexiest Job of the 21st Century’ talks about the emergence of Data Science, with LinkedIn as a case study for how to get started.  Since then, many more companies have realized the value of Data Science, driving up the demand for talent.

The US was projected to need 2,720,000 data professionals by 2020, Until Q1 2020 Data Science roles showed year-on-year growth of 32%.

With the COVID-19 slowdown, companies need to do more with less and ensure that Data Scientists are engaged to predict the unforeseen while maintaining business continuity in face of possible recession.