The enormous impact of the Pandemic is obvious with wholesale changes to business ecosystems and fundamental changes to operating models. Less immediately obvious has been the dramatic impact on ongoing data science production setups.
Data, and models applied across broad business activities throughout the entire value chain became instantly less relevant. Models used for segmentation in all forms, and forecasting started to fail when traffic and shopping patterns changed, demand slowed, supply chains were interrupted, and transporting goods became ground to a halt as borders were locked down.
Needless to say Covid-19 has accelerated business innovation. Many companies who were already leveraging data science are transforming the way they operate with new contextualized, often real time insights.
Fundamental shifts in operating practices not only require an update of the data science process, but also revisions of the logic that underpin its original design. This can mean a new data science creation cycle:exploring data sources (new and lost) understanding and incorporating business knowledge as it transpires, and identifying, developing appropriate models.
In an A.D. Covid-World, culturally ready and well-resourced businesses are waking up to the wonders that they can achieve with Data Science, ML and Artificial Intelligence, getting ahead and winning. 2021 is a huge year for AI adoption. This year, the technology, experience, and talent involved in extracting true value from AI will reach critical mass. Having a short, medium and long-term data-driven strategy is vital. There is a huge and visible distinction between companies that have built the right strategy, teams, tools, and relationships with external vendors, and those that fail to adopt the approach of data science. Without data science businesses are severely disadvantaged.
An organizations’ analytical prowess is largely dependent on;
Over the past several years, technology has rapidly changed what enterprise analytics can do. There are a number of growing platforms enabling companies to increasingly take advantage of contextual information in their enterprise systems.
Despite this progress, it’s still difficult to use data and analytics to understand and predict many of the important phenomena in organizations.
Predictive models require a substantial amount of past data and a reasonable amount of expertise to create and use, which limits how and when they can be deployed.
Typically only big businesses have been able to attract, afford and retain the diverse data scientist expertise required to man a data lab and develop data product solutions. Attracting, rewarding and retaining highly sought, in demand data science talent is a challenge. Here are 10 strategies to help
Data Science as a Service, and associated solutions democratizes data science enabling All companies access to expertise and resources and advantages they would otherwise not be able to realize.
We are making cutting edge data science end-to-end available to every company.