Data Divide: Perceived or real?

Yes I do mean Data Divide! There is plenty conversation on digital divide but within the confines of our companies, data divide is a bigger monster. An apparent definition of Data Divide could be the ‘difference between the data haves and have nots’, but the real definition should focus on ‘use of data/insights with ease at the point of consumption.’ On an average, a company has about 30%+ (see Survey on LinkedIn ) of its people who could benefit from using data, but they don't or cant' because of friction in getting to data. Friction here means, users cannot find the right dashboard, are too lazy to click through, or are just not excited about using dashboards.

A true story:

Early October, I spoke with CRO of a super fast growing software company - well-funded, outstanding leadership team and very data driven. You would imagine data divide in this company would be non-existent. Think again, it is not about whether one has access to data or has been provisioned, it is about ease of access when needed.

The CRO admitted that her head of RevOps is super data driven, super conscious about data quality and first thing he did after joining was to standardize the KPIs with standard definitions and built extensive dashboards to track and communicate the same. This CRO, had just wrapped up a killer quarter in September, and started to think about year-end targets. She had a bunch of ideas to incentivize the sales team for Q4 and work up momentum for 2022. To support her thought process, she had a series of simple questions: how many reps were over 125% of their quota, how did the reps with less than six months tenure do, which reps had least Pipe to Quota attainment etc. She had to write an email with each of her questions bulleted, email to her head of RevOps, who forwarded it to the right BI analyst and it took over 48 hours for the round trip to happen before the CRO got her answers. That was the good news. The bad news, she had to start the same cycle, if she wanted next set of questions answered: which reps had least quota attainment QoQ for last three quarters, which rep added most to their pipe in Q3 etc.

Would you call this CRO as data-starved? Should it really take over 48 hours to get answers to these simple questions?

The data-divide is hidden:

The data divide is hidden behind the mask of ‘we have a data lake and a well-staffed BI team building dashboards.’ We, the data people and IT people, have done everything that we can to reduce/eliminate the data-divide. In fact, collectively, we spend over $25Bn/year on data initiatives in US alone. Hey, if you spending this much money, the divide should not exist – this is a fallacy, much like ‘spending big money will eliminate poverty’.

It is about data and insights available to user at their point of use at the time of their choosing - not about having them added as dashboard user. Think of IT leader preparing for lunch with one of her suppliers – s/he has three questions, 1. what was our total spend with this vendor over last three years, 2. What products did we buy by product category, 3. what are the current deals in the pipe and 4. what is the total outstanding payments. To get this information, the CIO can either 1. Call or email one of the analysts or 2. Go find the dashboard that has this information and study it. Most of the executives, like this CIO, are likely to call/email the analyst – not because that is the most efficient way, but because the other option is inefficient, time consuming, it just has too much friction.

Yes, this CIO, who owns the data and the whole Data and BI teams is data-starved! No executive ever looks forward to 10s of clicks though a forest of dashboards to get to information.

In closing:

The data teams and IT teams have truly done a tremendous amount of work to clean, transform and centralize data. We have governed the data, we can easily control who gets to see what. All we need is a smarter consumption solution – a better last mile of our data supply chain.

Today, we surf Google, rely on recommendations and do that seamlessly on laptops and mobile devices. Our users should be able to consume data and insights, just as seamlessly and get recommendations. Dashboards are great for analysts who love to spend 8 hours looking at data, but for the 30% of our data-starved business users, lets consider Quaeris.ai, which is AI model driven, natural language BI/data search platform.

Yes, data-divide exists within our companies, but this one, we can solve!