Will they come?
This is a big fear for customers when planning their analytics projects. Sure, the data savvy Excel gurus will adopt the new tools with ease, but what about the rest of the team?
Data warehousing projects are notoriously risky and costly. Often they take 12+ months and still fail. But what if you do manage to deploy your enterprise analytics platform?
What if you discovered that only 30% of your team was accessing the new analytics solution?
Then imagine your CEO asking how your new 7 figure analytics project has progressed and you report that only a fraction of your users are logging in and accessing the dashboards your created over the last 12+ months of the project.
You user base and solution aren’t the exception. This is the reality. Build it and they will come is not what happens.
Global research shows:
“Pervasive business intelligence remains elusive, with BI and analytics adoption at about 32% of all employees.” – Cindi Howson, VP Research, Gartner
Its’ amazing, the best tools on the market are still only accessible by a select few in your organisation.
‘The problem with analytics isn’t the tools, it’s getting regular business people to use them.’
We find the main reason for low adoption rates in analytics is that the tech is still too difficult. Users must take their semi-vague business question and translate it to data query mode and then go hunting for the answer. The business question is a hypothesis – to be tested in the data. What happens in reality is that a good data analyst has to test many hypotheses to start extracting value from the data and crafting the insight. Lots of translating, then lots of hunting. The best tools make this process easy, buts it is overwhelming for new users.
Our view is that the leading tech in this space has been focused on data analysts, leaving the rest of the organisation behind. Our belief is that data, moreover, insights should be available to the whole organisation, personalized for the job role, and delivered at the right time in a compelling, relevant manner.
The Promised Land
We thought about this global problem where organisations invest millions in bringing data closer to their people.
What if your regular business people didn’t have to hunt through dozens of reports and dashboards to begin their analysis process?
We wondered if we could adapt technology to bridge the gap and unlock value in our customers’ data in a way that made it possible for the majority of the people in a company to engage with data.
Could we address this global problem in analytics and increase adoption rates far beyond the 32% rate that Gartner reports.
So we developed Luci, our AI-powered analytics portal, as the solution to increase adoption of analytics.
Luci senses who you are, your job context and the data you are most interested in.
One of Luci’s many abilities is to understand your role in the organisation, and therefore the data and insights most relevant to your job. She automatically generates insights based on who you are as the data changes. We call this type of insight: Contextual AI.
We thought that it would be possible to take these business questions or hypotheses and use robots to run all the hypotheses possible for you. Rather than hunting for the answer, we have our robots do it via a series of proprietary machine learning algorithms. Then we would rank the results and personalise it for the business user based on their role in the organisation.
That’s what Contextual AI is. It means you don’t have to go hunting for insights, we do the work for you automatically as your data changes.
‘Your data is there, it’s simply not being used and converted into real value for the business.’
Our breakthrough is on changing the paradigm of analytics altogether. With Contextual AI we challenge the modern-day paradigm of hunting for insights to one where Luci, via her Contextual AI algorithms, automatically serves us personalised, relevant, and timely insights directly to the user.
Dashboards and data visualisation is still important and a key part of a leading analytics platform. Our approach is focused on normal business users and capture their attention with automated insight that is:
- relevant for the user based on job context
- compelling based on AI-powered analysis,
and then have users understand and explore the insight with a rich visualisation.
In our next article on Contextual AI, we show you how this works in practice.