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
— Rob Risany (@RobRisany) January 14, 2022
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
A1 a TRUE analytic company in 2030 will have full, secured, data access to their employees who have the skills and the tools in place for them to take action based on the data enabling them to execute at speed #saschat #iamintel
— Pat Richards (@patrichards) January 14, 2022
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
#SASchat A2 Speed, agility, not enough data scientists, or #AI #ML engineers. Truth, will never have enough in our workforce - having a platform where domain experts provide that insight for business and government - critical. It business #innovate beat competition. #IamIntel
— @gretchenstewart (@StewartGretchen) January 14, 2022
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
A4: They allow data scientists to use which ever language they are comfortable with (or multiple) and gives them the ability to exploit their output via API's. These platforms also serve as a motivating factor to its users. #SASChat #Analytics #Platform
— Warren Murray (@WarrenM4Life) January 14, 2022
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
A4: with the skills gap and resources scarcity that organizations have to deal with, allowing existing staff to be productive regardless of their skillsets and preferences is critical. Low-code, no-code, full-code, programming languages... You must be able to choose #SASchat https://t.co/vN86hnSms0
— Olivier Penel (@Olivier_Penel) January 14, 2022
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
— Ashutosh Kumar (@thoughts_Ash) January 14, 2022
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
... and these are all easier to achieve if you address the organisational issues at the same time: it's not just about the technology but how you share and deliver best practice and excellence
— Rob McManus (@RobMcManus2) January 14, 2022
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
A5 Its a people problem - training and tooling to effectively use all the data we are collecting. We can get all the data we want the big question is how do you create value from it #saschat #iamintel
— Pat Richards (@patrichards) January 14, 2022
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.