Data science important component of stability and growth

Organizations are turning to AI and data science to use data to optimize business processes and better predict business outcomes, says Francois Ajenstat, Chief Product Officer, Tableau.

Now that we are in the post-Covid age, what are the tech trends that have become permanent and are there things that may return to the old normal?

The value of data is becoming more apparent and important. Data is not only helping us see and understand the complexities of our current situation, but the impact of our actions as we adjust to a post-COVID age. I’ve seen how companies worldwide are going beyond the realm of data science to change the face of business by putting users directly in touch with data to uncover insight on their own.

So instead of talking about returning to the old normal, I see data and data science continuing to be important components of stability and growth, especially in the areas of vaccine management and contact tracing. And data’s potential impact will only grow as increased automation, AI and forecasting models help us better predict and prepare for what’s ahead.

What was the rationale and need to come out with Business Science for the industry?

Francois Ajenstat, Chief Product Officer, Tableau

Today, in this all-digital world, people need to make better decisions faster. Speed, agility and empowerment is the difference between thriving and barely surviving. Last year fast-tracked companies’ digital transformations across the board. Businesses with carefully planned multi-year digital transformations found themselves scaling in days or weeks. In fact, McKinsey found that consumer and business digital adoption vaulted forward five years in just eight weeks.

In today’s digital economy, agility is the ultimate competitive advantage. Organizations that empower their people with a data culture, and with the technology they need to use data analytics for more things, make smarter, faster decisions. It also helps them rapidly iterate on ideas that can spur business growth.

This rapid digital transformation is causing a data explosion — even chaos — as organizations determine how to best process this influx of information.

Organizations are turning to AI and data science to optimize business processes and better predict business outcomes. Unfortunately, data science skills are in short supply resulting in massive backlogs of requests and many business problems unaddressed.

Data science is an important part of the analytics mix but it’s out of reach for most business users. It helps organizations extract powerful, precise insights using sophisticated models. But getting to those insights can be complex, expensive and it can take some time.

So, we set out to democratize data science with Tableau Business Science, a new class of AI-powered analytics that lowers the barrier to data science techniques.

Business Science puts data science techniques in the hands of business people, enabling them to make smarter decisions, faster. It helps business users automatically discover relevant patterns based on their data – without having to build sophisticated data models. AI, machine learning and other statistical methods helps people quickly sift through huge amounts of data to find important patterns and make accurate predictions.

What are the latest trends in data analytics and AI and what has been your role in driving business insights through predictive analytics?

The biggest and most obvious trend is how data-informed decision making has taken center stage in a world transformed by the pandemic. I’ll illustrate this through the lens of our India-based customer, TVS Credit. In response to COVID-19, the company needed to ensure the safety and wellbeing of its 14,000 employees and set up a dashboard in Tableau to visualize information related to the health and safety of employees. Immediately upon launching the dashboard, TVS Credit identified over 100 employees with urgent medical emergencies and was able to formulate a scheme to provide financial support where needed.

Another growing trend I’m seeing is how technology experts — data scientists and engineers — are no longer working on AI projects in silo. Instead, they are bringing domain experts into strategic planning conversations to ensure that plans for AI and ML align with the wider business strategy.

Taking a collaborative approach can unveil the parts of a business decision that are best suited for AI and the parts that need human intervention. Let’s say you’re opening a new retail store, but you’ve never opened a store like this before. To forecast expected sales for the store, an AI-powered predictive system might make a recommendation based on foot traffic or demographics in the area. But you would still need that human domain expertise to fill in the gaps around things like location visibility, competitor information, or parking availability.

Tableau helps customers apply advanced analysis to business problems — without having to write code or tune models. We also embrace the work of Data Scientists who are able to bring their own models directly into Tableau. So, you get the best of both worlds.

Business Science empowers people to make important decisions faster and with more rigor, while still leaning into their human judgment. It’s not about fine-tuning super precise models, but guiding people closest to the problem in the right direction. We help expand the impact of models and data science in addition to broadening the users that can perform predictive analytics.

Now when concepts like the democratization of technology are gaining acceptance, can you speak something on the democratization of data and analytics?

Democratizing data and analytics simply means to make data analysis accessible to everyone in an organization. It’s not about making the technology simplistic, it's about making it simple to use by everyone.

Democratizing data science on the other hand, is the next step in realizing the power of what self-service analytics can do. Business Science can transform the way we work by putting AI-powered analytics directly into the hands of skilled business people so they can make smarter, faster decisions with their data.

Equipping more people with governed, no-code AI, what-if scenario planning and guided model building, will help business teams do more analysis themselves and produce more applicable, real-world-ready models.

At Tableau, we’ve been democratizing analytics since the self-service revolution we led 18 years ago. Tableau fundamentally changed how people work by enabling anyone — from analysts to consumers — to see and understand their data. This self-service revolution helped people use data to answer questions at the speed of thought.

In this new world of billions of devices being connected by millions of data centres, how are we handling this absolute explosion of data and what are the challenges faced by data scientists?

The year of COVID-19 fast-tracked companies’ digital transformations across the board. This rapid transformation is causing a data explosion — even chaos — as organizations determine how to best process this influx of information. Organizations are turning to AI and data science to use data to optimize business processes and better predict business outcomes. Unfortunately, data science skills are in short supply resulting in massive backlogs of requests and many business problems unaddressed.

According to recruitment firm Michael Page’s 2021 India Talent Trends report, job opportunities and salaries in data science have spiked in India, with pandemic-driven digital transformation accelerating demand for ICT skills.

Data wrangling — preparing and cleaning data — still consumes the lion’s share of time in a typical data scientist’s day. Data scientists and analysts, in general, spend 80% of their time in data preparation. Regardless of the tool, data scientists need to understand data preparation tasks and how they relate to their data science workflows. Data prep tools like Tableau Prep Builder are user-friendly for all skill levels. It’s also important for data scientists to harness self-service analytics platforms to explore and share results with less-technical people for faster speed to insights.

What can India and the world do to sort out the problem of the shortage of data scientists?

It’s important to note that while Business Science solutions can help address the skills gap between business analysts and data scientists, it is not a replacement for data science. Data science professionals will continue to provide custom models, statistical analysis, etc., but more often, they will partner with business experts to validate the data being used in ML-powered models. This increased cross-team collaboration is critical to the success and performance of these solutions.

Data literacy will be critical to the future workforce. Training and upskilling will continue to play a big part in the war for talent. For example, organizations can start problem-solving initiatives like data competitions to propose new hypotheses that challenge established notions about how the business works.

The promise of analytics is that analysts will find business-altering insights in their data — discrepancies or opportunities for optimization, or time-savings. And so the democratization of advanced analytics becomes important to ensure talent continuity.

We can’t eliminate the talent crunch overnight but we can create a shared mission backed by leadership to promote data-driven decision making at every level of the organization.