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Three Companies Driving Better Business Outcomes from Data Analytics

You’re unlikely to be able to build a business without data. But how can you use it effectively? There are so many ways you can use data in your business, from creating better products and services […]

  • 2 min read

You’re unlikely to be able to build a business without data.

But how can you use it effectively?

There are so many ways you can use data in your business, from creating better products and services for customers, to improving efficiency and reducing waste.

Enter data observability. Using agile development practices, companies can create, deliver, and optimize data products, quickly and cost-effectively. Organizations can easily identify a problem to solve and then break it down into smaller pieces. Each piece is then assigned to a team that breaks down the work to solve the problem into a defined set of time – usually called a sprint – that includes planning, work, deployment, and review.

Marion Shaw, Head of Data and Analytics at Chaucer Group, and Unravel’s Ben Cooper presented on transforming data analytics to build better products and services and on making data analytics more efficient, respectively, at a recent Chief Disruptor virtual event.

Following on from the presentation, members joined a roundtable discussion and took part in a number of polls, in order to share their experiences. Here are just some examples of how companies have used data analytics to drive innovation:

  • Improved payments. An influx of customer calls asking “where’s my money or payment?” prompted a company to introduce a “track payments” feature as a way of digitally understanding the payment status. As a result, the volume of callers decreased, while users of the new feature actually eclipsed the amount of original complaints, which proved there was a batch of customers who couldn’t be bothered to report problems but still found the feature useful. “If you make something easy for your customers, they will use it.“
  • Cost reduction and sustainability: Moving from plastics to paper cups improved cost reduction and sustainability for one company, showing how companies can use their own data to make business decisions.
  • New products: Using AI in drug discovery, data collaboration, to explore disease patterns can help pharmaceutical companies find new treatments for diseases with the potential for high returns. Cost of discovery is as expensive as using big data sets.

The key takeaways from the discussion were:

  • Make it simple. When you make an action easy for your customers, they will use it.
  • Lean on the data. If there isn’t data behind someone’s viewpoint, then it is simply an opinion.
  • Get buy-in. Data teams need to buy into the usage of data—just because a data person owns the data side of things does not mean that they are responsible for the benefits or failings of it.

Using data analytic effectively with data observability is key. Companies across all industries are using data observability to create better products and services, reduce waste, and to improve productivity.

But data observability is no longer just about data quality or observing the condition of the data itself. Today it encompasses much more, and you can’t “borrow” your software teams’ observability solution. Discover more in our report DataOps Observability: The Missing Link for Data Teams.