“We are able to schedule the right set of classes – including formats, duration and intensity – which see the most demand at a particular time, and help customers become more active on the app and in our centres,” Tharwani says.

“As a four-year-old company that has seen rapid growth, we saw multiple small solutions adopted across different functions and business verticals. Each team designed their own data insights to address their own challenges and help them with the day-to-day decisions.”

Data foundation ensures business agility

Creating a data-driven enterprise requires the combination of technology, people and process – in equal measures – and for leaders to think of data as a strategic asset, says Herain Oberoi, a director at Amazon Web Services (AWS), a cloud-computing services subsidiary of the multinational tech company, Amazon.

Oberoi, who is in charge of AWS’ product marketing for analytics, databases and blockchain, says a data-driven organisation is one built on a strong data foundation, which includes a modern, cloud-based data infrastructure combined with a culture and process designed for farsighted strategies.

A strong data foundation comprises five key technology pillars: breaking free from legacy databases; moving to managed services; modernising data warehouses [that store data that is already structured, filtered and processed]; building modern apps with purpose-built databases; and turning data into insights.

Encapsulated in AWS’ Data Flywheel framework – which helps business and technology leaders to enable organisations to make the most of their data – this data foundation involves a self-reinforcing loop, which ensures companies can build long-term momentum.

Oberoi says being data-driven will enable organisations to remain agile.

“When companies move into a lakehouse architecture [allowing them to query and link data across their data warehouse and data lake – a store of raw data – and make the results of those queries accessible to other analytics services], they are able to perform different types of analytics on their data without being hindered by technology,” he says. “This lets them move and make decisions quickly.”

Cure.fit, for example, is able to use data to design its product offerings, as well as decide how these offerings are marketed.

“We analysed the ratings, feedback and also percentage of completion of classes by users to arrive at the ideal class duration for a user new to fitness and for one who has been working out [already]; this would not have been possible from subjective feedback alone,” he says

“During our twice yearly FitStart sales – where we promote and do a lot of marketing around the products – we monitor potential users’ reactions to the campaign using the tools and dashboards they had built.

“The data is used to create a more engaging concept and language to draw users. The sale happens over three to four days and the team reacts to the users’ responses in real time.”

The company’s data infrastructure is primarily an “AWS-based technology stack”, which relies exclusively on an AWS powered data lake built on Amazon S3 and Amazon Redshift for data warehousing, Tharwani says.

“We use AWS solutions for data warehousing, extracting data as well as for analytics. All of our key data for analytics purposes goes through that process – from reporting to the dashboard engine – to build our insights.”

Cure.fit also works closely with AWS architects to monitor its data warehouse and storage solutions. This collaboration – which has enabled multiple enhancements to Cure.fit’s data platform, including the optimisation of data lakes and prevention of storage spikes – has helped it cut its costs by 30 per cent, Tharwani says.

Its close partnership with AWS’ account team also ensures Cure.fit keeps its data infrastructure updated.

“If you are an organisation in a competitive space and you are not effectively leveraging data to drive revenue, innovate or reduce costs, then you will get out-competed,” Oberoi says.

“Businesses that are constantly looking for ways to differentiate themselves and view data as a strategic asset are the ones that end up being more effective.”

Building a data-driven culture

While tools and capabilities are important, so is building a data-driven culture.

Ishit Vachhrajani, an enterprise strategist at AWS, says: “Creating a data-driven culture across the enterprise is essential to move beyond just a few successful data initiatives and islands of excellence limited to certain business areas.

“It is important that data capabilities start with the business outcomes, rather than the data sources.”

Vachhrajani says this change needs to start right at the top. “The C-suite [executive-level managers] must go beyond sponsoring this culture change,” he says. “They need to be engaged and involved, visibly marrying data with good business instincts to make decisions.”

Cure.fit has benefited from this kind of top-down data-driven focus. Even before its launch in 2016, the company’s founders and leadership team were intent on building a data-driven culture.

“We’ve built the foundation on valuing any decision or recommendation made that was backed by relevant data,” Tharwani says. “This has cultivated our data-driven culture, which enables us to innovate for our customers.”

In July, Cure.fit launched a range of digital services, offering fitness, meditation and yoga sessions, in the United States – its first international market.

The company says the app has been downloaded 75,000 times in the US in three months and it has seen a 40 per cent month-over-month growth.

In the next quarter, Cure.fit plans to launch a paid-subscription product. “Getting to 100,000 true users who engage with the app at least 15 times a month will be the next milestone for us,” Tharwani says, adding that data will continue to be a key strategic asset as the business grows.

Although data analysis now plays an increasingly important role in people’s everyday lives, such tools can take us only so far.

“When it comes to the data, it is culture first and capability second,” Vachhrajani says.

“A data-driven culture embraces the use of data in decision-making. It treats data as a strategic asset of the company by making data widely available and accessible. It is a culture with a high level of data literacy and a belief that data helps everyone perform better.”

How data model predicts spread of pandemic

Leveraging data also has a significant impact on society. During the height of the Covid-19 pandemic, researchers at the University of Hong Kong developed a new method for tracking the distribution and intensity of the spread of the coronavirus disease, which is up to 96 per cent accurate.

The study, led by Dr Jayson Jia, associate professor of marketing at the university’s Faculty of Business and Economics, which used a Chinese telecommunications company’s nationwide population travel data, has resulted in a risk assessment model that can identify potential early-stage virus hotspots.

It offers global public health experts and policymakers a valuable risk detection toolkit to help them predict – and better control – outbreaks of infectious diseases, Jia says.

He and his team used the population flow data to create an “expected growth pattern”, based on the number of people arriving from the Chinese city of Wuhan, where the first Covid-19 cases were reported.

The resulting toolkit can use any nation’s available data “to make rapid and accurate risk assessments and plan the allocation of limited resources ahead of continuing disease outbreaks”, Jia says

“What is innovative about our approach is that it tells us how many cases we should expect, given [the] travel data, enabling us to accurately assess the level of community risk,” Jia says.