Citizen Analytics

I would like talk about Citizen Analytics (a.k.a. Cognitive Analytics), and explain what it is and what it is not… from our point of view here at DataLingvo.

TL;DR Watch video here

Data Analytics

Let’s start from the beginning. What is Data Analytics? The term is so overused and so widely applied that many of us can’t really provide a concrete definition for it. It usually goes like, “Something to do with analyzing business data (which is a big data, of course) — right?”

Well, in a most basic sense Data Analytics simply means an ability to ask questions about data in your business and get reasonble answers. That’s all there’s to it.

Data Analytics simply means an ability to ask questions about your data.

Everything else — predictive analytics, big data analytics, ad-hoc analytics, business intelligence, actionable analytics or any other related marketing term — is more or less a variation on the same theme: an ability to ask a question about your business data and get an answer.

In fact, if you think about it, behind the most sophisticated data analysis that you may find in your organization there always lies a simple question that triggered that analysis in the first place.

Let’s look a an example. Here is an ad-hoc question that could just easily pop up in any marketing or sales meeting in your company:

Is there a correlation between sales in NY state for the last 2 months and the AdWord campaign we’ve run in the same period and the same region?

To understand the massive inefficiencies behind today’s data analytics systems and associated with them business processes — let’s see how an organization would answer this very question today.

Current Inefficiencies

Despite the perceived simplicity of this question the tedious process behind finding the answer to it is anything but simple or efficient.

First off, the data required to answer this question resides in two vastly different systems that are likely installed, maintained and managed by two different groups within your company:

  • Google Analytics where a company manages its AdWords campaign and generally collects web analytics data, and
  • Salesforce.com (or other sales CRM) where sales data gets collected

Because these systems are vastly different and incompatible (different user accounts, different interfaces, different data model, different query languages, etc.) it is very typical that they are managed by different people from different groups within a company.

Now, let’s deconstruct the process of answering our question in a typical small- to mid-sized (SMB) company:

  • Since it will involve two different groups to work together a project manager should be assigned (often a business or data analyst, or someone from the reporting department).
  • Project manager (or entire group) should spec out what a desired correlation really means (i.e. the essence of the question); it’s likely that number of AdWords clicks and bookings on a given date should be a strong indicator of correlation. For simplicity — we’ll leave out filtering out other influencing factors such as holidays, promotions, other marketing programs cross-pollination, etc.
  • Google Analytics (GA) person should now develop and run the report in GA user interface for the given time range that would include number of clicks per date for specified AdWords campaign and in specified geographical region.
  • Once GA report is complete it should be exported into CSV format and sent over to Project Manager.
  • Safesforce.com person should create (using SQL-like query language) and run a report that shows bookings per date for given date range and in specified geographical region.
  • Once Safesforce.com report is complete it should be exported into the same CSV format and sent over to Project Manager.
  • Once Project Manager received both reports she would combine them into one by importing both CSVs into SAS, Excel or other reporting tool, de-dup, clean up (date formats can be different, ensure that GEO regions are uniformly named, etc) and add correlation metric calculation as well as representative charts to visually show the correlation (or lack of thereof).
  • At the end, the result data tables and charts are exported into PDF or PowerPoint and sent back to people that had the original question.

Depending on agility of your company this unfortunately typical boondoggle can take hours, in most cases days, and often weeks to complete.

All just to answer a benign ad-hoc question that popped up in the meeting…

This example illustrates simple fact: companies found ways to effectively collect and store data but woefully inefficient in allowing thousands of average business users in their companies to access and explore that data. This is also the reason behind the fact that vast majority of business users don’t ask questions and therefore don’t rely on data in their decision making; who in the right mind would trigger such a costly process to answer just a simple question?!

In fact, the only people that have efficient access to that data are the people who don’t have any actual questions — your business and data analysts who only exist in organizations to find answers to other people’s questions…

Citizen Analytics

The catalyst behind Citizen Analytics is the desire to fix this problem. How do we empower millions of average business users to ask questions across dozens of completely different, isolated, incompatible systems that house our business data?

The answer is rather strikingly simple… use your natural language you already know and speak to ask these questions. Your natural language is the only “interface” that’s common across all these vastly different data systems and the one “interface” that everyone on your company already knows.

Scroll up and re-read the original question in our example… It’s simple and it’s understood universally. In fact, this questions can be easily asked by anyone who knows what “sales”, “adwords” and “marketing campaign” generally mean.

What if we had a system that could actually take the question asked just like that, freely in your native natural language — spoken or written — and give us an answer in seconds rather than days and weeks? This is what Cognitive Analytics is all about.

Citizen Analytics is a radically simplified interface to your data. An intersection of natural language processing (NLP), data analytics and artificial intelligence (AI) — all behind beautifully simple chat bot or web interface — providing the simplest, most intuitive way to interact with you business data.

That’s what we developed at DataLingvo:

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