AI can only be as effective as the analytics behind it, and as analytical workloads increase, a comprehensive platform strategy is the best way to ensure success at scale
Nearly three-quarters of organisations (72 per cent) say analytics helps them generate valuable insight and 60 per cent say their analytics resources have made them more innovative, according to research commissioned by analytics company SAS.
That is despite only four in 10 (39 per cent) saying that analytics is core to their business strategy.
A third of the respondents (35 per cent) report that it is used for tactical projects only.
Despite acknowledged value – and most (65 per cent) can quantify this - businesses are not getting the most out of their analytics investments.
However, they are now pursuing rapid analytical insight as a priority as they push into emerging technologies like artificial intelligence (AI) and the internet of things (IoT).
The research, Here and Now: The need for an analytics platform, surveyed analytics experts, and IT and line-of-business professionals in a range of industries around the world.
The research, released at the Analytics Experience (#AnalyticsX) conference this week in Milan, found that analytics is changing the way companies do business, beyond operations.
It finds that analytics is also driving innovation - with more than a quarter (27 per cent) of respondents saying analytics has helped them launch new business models.
The respondents identified several benefits of an analytics platform, the most common being less time spent on data preparation (46 per cent), smarter and more confident decision-making (42 per cent) and faster time-to-insights (41 per cent).
“The findings show a strong desire in the business community to boost competitive insight and efficiency using analytics,” says Adrian Jones, director of global technology practice at SAS.
With AI now top-of-mind for many organisations, it’s more important than ever to have a powerful, streamlined analytics capability
“The majority recognises that effective analytics could benefit their organisations, particularly as they develop their ability to deploy cutting-edge AI. But the number of those effectively using analytics strategically across the organisation could be much higher.”
SAS says the research was based on in-depth interviews with professionals in 132 business and government organisations across EMEA. The findings from this phase then informed the second part of the research, an online global survey of 477 qualified participants.
The survey stressed a lack of alignment in the skills and leadership needed to maximise the potential of analytics.
It finds many companies struggle to manage multiple analytics tools and data management processes.
“If they are to achieve success, organisations must put analytics at the heart of strategic planning and empower analytics resources to drive innovation using a unified analytics platform,” says Jones.
Mind the gap: Platform readiness
In a recent blog, Jones points out the main benefit of a platform is that it can be used by many different people, for different purposes.
“It must therefore, almost by definition, be flexible,” he writes.
“It needs to work for both business users and those who program in code routinely. It must also allow a wide range of data sources and types of data, from traditional data warehouses to streaming data and social media. It has to work with existing architecture and processes, both data and analytic.”
He says the enterprise analytics platform needs to be easily scalable, so that complexity and volume can be ramped up or down on demand, and new and different types of algorithms can be added when they become available or needed.
Platforms also make model deployment significantly easier, meaning that more people can use and get value from the model much earlier.
This way, there is no need for recoding for different use cases, such as streaming, batch or on demand.
“The move from innovation to deployment is seamless and straightforward, and therefore saves huge amounts of time and energy. Instead of taking six months, value can be generated almost immediately – and because the model is more up to date, the value is greater. The key is having the appropriate integration points and the right processes to support this."
Tackling the AI and analytics challenge
“When we speak with business leaders who are scaling up to use analytics and AI strategically, challenges they commonly identify are the need for an enterprise analytics platform and access to talent with data science and analytics skills,” says Randy Guard, executive vice president and chief marketing officer at SAS.
“With AI now top-of-mind for many organisations, it’s more important than ever to have a powerful, streamlined analytics capability,” says Guard.
“AI can only be as effective as the analytics behind it, and as analytical workloads increase, a comprehensive platform strategy is the best way to ensure success at scale.”
The research finds differing views on the role of an analytics platform: most (61 per cent) believe it’s to extract insight and value from data, but many are split on its other purposes or benefits, such as better governance over data, predictive models and open source technology.
Fifty-nine per cent believe another role of an analytics platform is to have an integrated or centralised data framework, while 43 per cent believe it’s to provide modelling and algorithms for AI and machine learning.
The responses suggest companies know analytics can help them, but they lack a clear and common understanding of the benefits of using a platform approach across the enterprise and the analytics lifecycle.
SAS says this would explain why few organisations have a suitable platform in place according to results from its Enterprise AI Promise Study released at the Analytics Experience Amsterdam last year.
Organisations must put analytics at the heart of strategic planning and empower analytics resources to drive innovation using a unified analytics platform
The study revealed only a quarter (24 per cent) of businesses felt they had the right infrastructure in place for AI, while the majority (53 per cent) felt they either needed to update and adapt their current platform or had no specific platform in place to address AI.
The latest research finds confidence in the end results of analytics is high.
Respondents on average have 70 per cent confidence that they can derive business value from their data through analytics.
Those that invest in data science talent are more likely to see ROI: confidence rises to 72 per cent for those in analytics roles but drops to 65 per cent for standard IT teams.
The same is true when considering the future, according to the research.
Analytics teams are more confident (66 per cent) of their ability to scale to meet future analytics workloads, compared to those in standard IT roles (59 per cent).
Divina Paredes is attending the Analytics Experience conference in Milan as a guest of SAS
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