Industry Voice: The changing data needs of modern business

What are the changing data needs of modern businesses?

For many companies, now is the first time they are seeing all their most granular data in one place on modern cloud data platforms such as Snowflake and/or Databricks. This creates an opportunity to bring technical and business teams closer together to drive new use cases, build new types of automation and workflows, and simplify their operations.

At the same time, security and governance are taking prime position and organisations need to enable their people to leverage data in news ways, whilst balancing the need for control.

What are the biggest data challenges for businesses?

The single biggest challenge is how to fully realise the value of the significant investment in modern data platforms. The investment may have been transformational for technology teams, but has often had limited impact on the front line of the business.

Organisations tend to plug in their existing BI tools, which view modern data platforms as just another data source. This means there is no tangible value add for the business. In some cases the data has become even more inaccessible, now locked away behind distant process barriers.

What impact does this have on data-driven decision-making?

The same kind of issues continue to plague the business in terms of the following key areas:

Business teams hit an analysis concrete floor due to over-aggregated data, as previous generations of BI tools were only built for thousands of rows of data - this makes it hard to identify and act on transaction-specific issues
A continued reliance on central data and BI teams to build data models and dashboards to answer questions, due to the high skill barrier of existing BI tools
Business teams struggle to operationalise data, as existing BI tools do not support native write-back - business users still rely on ungoverned and error-prone Excel extracts to run business processes

How can firms address the inefficiencies of legacy BI systems?

Sigma was built for live query on cloud data, so the business can work with the most granular transactional data, allowing deeper, context-specific decisions.

With an estimated 1 billion users of spreadsheets globally, this is the universal language of data. Sigma embraces this reality and provides a spreadsheet-based user interface. Sigma lowers the skill barrier of adoption so that a wide range of users can work with data and quickly answer ad hoc questions without an overreliance on central BI teams.

Finally, Sigma provides native write-back to the cloud data platform. This allows business users to mix system generated data, along with user created content and collaborate live with colleagues to create new workflows and operationalise their data assets. Typical use cases include forecasting, planning, compliance and scenario modelling.

How does Sigma work with Snowflake & Databricks?

In essence, your cloud data platform (Databricks and/or Snowflake) is the compute and storage engine for Sigma.

This has a number of advantages:

Greater scalability and granularity as cloud data platforms have been built to work with millions/billions of rows of data
Improved security as the data never leaves your chosen cloud data platform
The data is always up-to-date, no more clunky batch extract processes
Lower total cost of ownership as there is no need to move and copy data from the cloud data platform to an analytics tool
Business users can easily leverage the built in AI & ML models available in your cloud data platform via Sigma

There are a lot of great customers like Mistral AI, Kleene, and others that increased productivity, identified new revenue streams, and drove adoption with Sigma, but I'll elaborate on HyperFinity's story.

HyperFinity is a decision intelligence platform for retail. They knew that using data to deliver personalised customer experiences can increase revenue, but only about 5% of retailers are benefitting from the promise of data and AI due to the cost-prohibitive nature of hiring and scaling data science teams.

HyperFinity leveraged Snowflake and Sigma together to make data accessible to technical and non-technical users alike. With Sigma's Input Tables feature, users can write new data back to their cloud data platform alongside the original dataset, allowing them to better plan for any scenario and scale effectively.

Retailers on their platform experienced a 20% increase in sales across their loyalty base and drastically reduced their time to deployment from months to days.