The global analytics market will be worth $18.3 billion by the end of the year according to a recent Gartner. Despite this, many organizations are still failing to see return on investment following significant analytics infrastructure spending.
It is a problem I often come across. The explosion in the number of tools and platforms may have switched many organizations on to the idea of creating an analytical culture, but not many have achieved the maturity – in terms of skills, tools and data itself – to fully reap the benefits.
The Gartner report goes on to predict that investment in business intelligence platforms and tools will drop drastically over the next two years – perhaps as businesses focus on consolidating the tools they have at their disposal in order to properly get to grips with them, before encouraging further investment.
As Alation CEO Satyen Sangani tells me, “Look at the number of vendors when you go to Strata or somewhere like that. If you go up to any business person there and casually ask them ‘so which of these do you invest in?’, every time they will tell you ‘we’ve got one of each.’”
Gartner’s report forecasts that after jumping to $18.3bn at the end of 2017 A – representing an increase of 64% since 2015, it will grow just another 19% until 2020. It’s important to note that this definitely doesn’t mean less people will be doing analytics. A greater number of feet around the table as well as a consolidation of services available mean that a reduced price of entry will play its part too.
Key to tackling this issue could be the concept of the “curated data catalog”, according to Rita Sallam, who authored the report. Sallam says “By 2020, organizations that offer users access to a curated catalog of internal and external data will realize twice the business value from analytics than those that do not.”
I asked Sallam what challenges lie in wait for companies wanting to capitalize on this by adopting a curated catalog model for their own data. She told me “Just because we give data to users, doesn’t necessarily mean they know how to use it responsibly.
“The most critical challenge will be to make sure users are data literate, understand how to responsibly use data and know how to get value out of it.”
Another challenge is getting users to trust data – which is where the idea of curation comes in.
“Both of these challenges require organizations to invest in training and user enablement programs and to define collaboration and content promotion, and certification processes”, says Sallam.
It’s an approach which has already been adopted by leaders in the field. Walmart – one of the pioneers of data-driven analytics in retailing, recently unveiled its Data Café project, where anyone within the business with a data problem can come to gain insights from its 40 petabytes of data, with analysts on hand to help make sense of it all.
Overall the BI landscape has undertaken a dramatic shift in recent years, away from IT department-driven centralized reporting and towards the idea of self-service, “democratized” access to analytics. This paradigm shift has certainly caused some to flounder. But does it have to be an either-or issue? Walmart’s approach shows that a helping hand can often be the difference between a data initiative sinking or swimming.
In an increasingly automated world, however, is it not possible that this element of curation, or central oversight of data, could be handled by AI? Sangani thinks so, and this philosophy underpins the technology behind his platform.
“There are certain analogues in the consumer internet world, which effectively help solve these problems,” he tells me. “Yelp is a good example. It’s basically a catalog that people to use to go and find curated businesses in their area.
“LinkedIn is a curated network of professionals and Google is curated – the Pagerank algorithm suggests that a web page is good and important because everyone uses it. It is curated by the social activity that exists on the web. Every BI tool generates queries and every database accepts queries, so if we just look at all of these queries we can do Pagerank for data.”
AI-driven curation of this type certainly has the potential to broaden access to data-driven decision making within companies. One thing is clear though – fixing the BI market is not going to come about purely through an ever-increasing number of businesses marketing an ever-growing number of tools.
Continued efforts are needed to put the power of data and analytics into the hands of as many people as possible throughout an organization. If a department or business process is not measuring and making improvements, is it a problem of data, tools, skills, or all three?
Sangani says “It’s not just about investing in new ways of visualizing things – in some ways that’s a solved problem.
“It’s about allowing people who don’t have literacy with data, or the ability to understand it, to trust it and access it, because inside of an organization today there’re maybe 10% of the people who are using data when really we need there to be 90%.”
