Data Democratization Isn’t Enough — Here’s Why

iGenius
May 27, 2022
4 min read
The Big Data narrative isn’t a new one — the more data the better. We’re certainly getting more data. We’ll be producing 163 trillion gigabytes a year by 2025, which is like 276 billion more people appearing on Facebook, or receiving 150,000 more emails a day. That’s a lot of spam.
But, will all this data make it better? Depends on how you use it. Huge amounts won’t amount to much if we don’t leverage it properly.
If we don’t firm up analytics structure to ensure the most people get the most benefit from their data pools. This is the very problem businesses are facing.

More data, more problems

There’re tons of data, and businesses are playing catch-up to get their systems up to scratch to manage it all.
According to a Gartner study, 87% of organizations suffer from “low BI and analytics maturity”.
This means they’re less prepared to modernize their BI capabilities, because of “primitive or aging” IT infrastructure, a big distance between IT and business users, and all the time lost by data preparation and modelling being left to specialized analytics teams.
Structures are often fragmented, and so are the data sets themselves — different teams use different BI tools to crunch data in different silos.
These big data analytics tools can be confusing too, meaning team members will be hesitant to use them.
This all compounds the fact that intel from data isn’t simple, easy-to-access or panoramic in its business coverage.
In other words, the insights aren’t as insightful as they could be.
Making systems efficient and unifying data siloes into one virtual lake can be the first step to helping everybody get value from their business data. To letting the average end user drive every decision with data.
To opening up data access. To giving the true data democratization meaning.

So, what about data literacy?

Let’s say that an organization’s data is in one place and accessible to every end user who needs it. That it’s democratized. Does that mean the end users will have the skills to interpret it properly and extract all that value? Probably not.
According to Gartner, poor data literacy and lack of relevant skills or staff are the second and third roadblocks to success for Chief Data Officers.
This was backed by a study by the University of Pennsylvania, that found only 24% of senior decision makers passed a data literacy test.
It isn’t a generation thing either — the same study found digital natives, like millennials, scored just 22% (take our survey to check your business’ data literacy).
With such a large data science skills gap, making data available isn’t enough — workforces need to be reskilled to know what to do with it. Or maybe they don’t.
Maybe the step after democratizing data access is democratizing access to the data’s value.
By that we mean reducing — or removing completely — the level of data literacy required to interpret data effectively and use it to drive better decisions.
That’s where AI comes in.

AI is the secret ingredient

AI can be the ultimate business data translator, when it has:
Augmented Analytics
Using Machine Learning (ML), Augmented Analytics does all the hard work behind the scenes to get data dashboard-ready quicker.
It automates time-consuming tasks like data preparation and manipulation, so you can get valuable insights from large amounts of raw data in a fraction of the time it would take normally.
As Gartner put it, Augmented Analytics allows businesses to spend “less time exploring data and more time acting on the most relevant insights”. In other words, there are less bottleneck processes so businesses can get what they need quicker, and data scientists can focus on more important tasks.
Combined with ML’s ability to learn from data over time, Augmented Analytics can both save time and make connections between data to serve up advice and powerful forecasting.
We wrote a white paper on Augmented Analytics by the way, check it out here.
💬 Conversational AI
Now that the data’s prepared and ready-to-go, it’s a question of how users interact with it. Traditional BI tools present it in graphs and charts, but people and data would surely work better together when they speak the same language.
That’s why conversational AI is ideal. It uses Natural Language Processing to understand and produce natural speech, so people can essentially talk to an extra colleague that knows their business data inside and out.
This way, the average end user won’t have to tackle complex graphs but instead simply ask.

Hello AI advisor

These are the two pillars of an AI advisor. A conversational platform that automatically extracts insights from data, and generates advice based on it. That not only informs users, but guides them through their data to make better decisions.
An AI advisor democratizes data but goes beyond data access — mitigating data literacy issues to deliver priceless insights to anyone who needs it.

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