The business value of modern analytics platforms: Takeaways from our recent SAS chat


On 14 January, we held a SAS chat on the business value and importance of modern analytics platforms. Participants from SAS and partners, including Intel, joined us from across the globe. We checked in from London, Oxfordshire, Johannesburg and Istanbul, as well as Knoxville, Atlanta, Cary and Florida in the US, to share insights. Here are my top takeaways from the discussion.

Panellists agreed that most successful companies would be using analytics everywhere by 2030

Most panellists felt that only analytical companies would survive until 2030. Companies that are not using analytics will be a target for disruption. Data is the basis for almost all business services. The general consensus seemed to be that this would only become more true. 

True analytical companies will be driving all decisions using data and analytics

Pat Richards suggested that employees would have the necessary tools and skills to take action based on data insights and that companies could therefore deliver at speed. Analytics would no longer be an experiment, but something that added genuine value to the business. 

There are several needs driving a move towards analytics platforms

Speed to market, governance and transparency were crucial drivers of the use of analytics platforms. Speed to adoption, less custom plumbing and consistency are also factors. Participants agreed that other main drivers are the need for governance, productivity and reliability of results. A robust and intelligent platform could also help to compensate for a shortage of data scientists.

Platforms provide many benefits for data scientists 

The key benefits of a platform for data scientists are related to ease of use. For example, data scientists can use their programming language of choice, drawing on APIs for easy integration. Platforms also allow for the use of standard models, which users can assess quickly to look at weighting, accuracy and other key issues for the business. The use of model management, importing and exporting, and flexible data sourcing are also worth considering.

Platforms make data science easier – and free up data scientists’ time

One of the biggest benefits of using an analytics platform is that it makes life easier for data scientists. Platform adoption will free up data scientist time that can be spent on innovation. This, in turn, can support growth and expansion into new markets. In the era of "the great resignation," this is welcome support for team leaders and managers.

There can be an element of more haste, less speed in analytics

Platforms can be helpful in breaking down internal silos. However, Paul Jones noted that he had observed that a rushed move towards analytics could actually be instrumental in creating these silos. It was left to Rob Risany to add the final word to that particular conversation with my favourite expression of the day, talking about the risk of “jumping too far up the hypecycle” on analytics and getting “buried in buzzwords” as a result.  

As always, technology can only take you so far

It is easy to talk about the benefits of adopting an analytics platform. However, many of the benefits are actually easier to achieve through an organisational approach. Technology is not the answer: It is really about how you share and deliver best practices. The crucial question was not "what technology?" but "how do we organise ourselves around the technology?" 

The main challenges to adopting analytics platforms were likely to be people-related

Analytics is fundamentally a people problem. The issue of developing data literacy is often a matter of culture change, as well as skill development. We can now do amazing things, but we might need to change both organisations and our education system to take full advantage.


About Author

Gorkem Sevik

Senior Manager of SAS Customer Experience Practice

Gorkem Sevik is a Senior Manager of Customer Experience Practice at SAS. She is leading cloud, data, and analytics experts and architects in the EMEA region. She works with organizations to help them to get the most from their data through embedding the best data and analytics engineering practices to provide a solid foundation for analytics lifecycle. Her recent focus is on DataOps, DevOps, and modernization of analytics platforms.

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