In the impulsive, kinetic and interconnected world we now inhabit, people rarely make slow, linear decisions - whether at work, at play or voting in an election. This means that when it comes to business analytics, we’re always having the wrong conversation.
The most powerful example of this dynamic is in the consumer world where shoppers have not only outsmarted, but outpaced the companies they buy from. The instant someone gets an impulse to buy anything, they can search for it online before they are even sure whether or not it exists.
A kinetic commerce journey
It doesn’t take long for impulsive shopping to go kinetic and turn into a spree. Let’s say after a Friday evening session with my officemates involving craft beer and cheeses of the world, I decide on impulse during the cab ride home to Google “craft beer” and “cheese”. I stumble upon an annual beer and cheese festival in Somerset coming up next weekend that’s had great reviews on Yelp and it looks like sunny weather’s forecasted. Cool, I’m there! Now I’m thinking a couple of my old college pals in Cambridge might want to make a weekend of it. That converted Airbnb farmhouse outside Bath is only minutes from the venue and would be just the ticket - better book that ASAP. But hang on; I need to do something considerate for my wife to take the sting out of going away. She’s been saying that we need a new sofa and she’s obsessed with the colour of pomegranates lately. Hey, I wonder if someone actually sells pomegranate-coloured three-seater sofas. I could earn some serious brownie points. YESSSS - Amazon has one in stock and on sale! Oh that just reminded me, I’ve been meaning to buy some shares in Shopify. I think I’ll login and do that now while the NASDAQ is at a 60-day low. And at the end of this impulsive, kinetic commerce binge the feel-good neurons firing in my brain trigger the urge to broadcast my random purchases on Twitter and Facebook.
In a matter of minutes I have completed an elaborate “customer journey” that once would have taken days, weeks or months - in the unlikely event that it would have happened at all. And although this was about a series of impulse buys, each was informed by reliable data - peer reviews, weather, distances, availability and price comparisons - to help me the best possible informed choices.
That story might have seemed a bit over the top, but I KNOW you’ve been there!
What this means for analytics
The web isn’t just redefining how consumers engage with businesses; it is shortening everyone’s attention spans and radically altering their ‘modus operandi’. The implications for today’s business-aligned IT teams are enormous and daunting. Each new generation of worker that enters the corporate world becomes less tolerant about having to wait for information to support the micro-decisions they need to make daily, or just not having access to it at all. And with data volumes growing at a staggering pace, there’s more of it than ever to trawl through.
You don’t stand a hope in hell of fighting against this dynamic, so why not tap into it and make it work in your favour? The companies that do this successfully will architect analytics solutions that enable them to:
Predict, produce and serve at the speed of modern commerce - This is the one obviously tied to the opening story. How do companies producing goods and services notice all these consumer impulses flying through the ether and figure out how to respond to them competitively, profitably and IN TIME?
Serve up corporate data fast enough to improve your people’s day-to-day negotiating power - Whether it’s giving the office manager the data to make an informed decision about the best new caterer for the canteen; arming a sales rep with the ammo to sell a commercial property space; or giving the HR Director the info to minimize employment tribunals following a round of layoffs - the party who has the best, most convincing data on tap always has the upper hand in a negotiation.
Attract and retain new generations of workers - Do you honestly think the typical Millennial is going to want to work on your granddad’s SAS database? These days talented young employees choose to work in companies that use modern, fast, nimble IT systems and let them use their own devices. And Millennials are very goal- and results-oriented. They like to set and meet objectives and continually up their games. To do this, they need data at their fingertips.
You will disempower all these if you don’t deliver an analytics approach that helps your people be agile and above all, FAST in their decision-making. Worse case, your business could suffer the same fate of established players like Radio Shack, Nokia and Kodak - all of which failed to read the tea leaves in time to adapt to changing market forces.
It’s time to think differently about analytics
If the definition of insanity is doing the same thing over and over and expecting different results, it’s high time to think differently about analytics. The industry is allegedly at “Analytics 3.0” and yet we’re still treating data models as ‘pipelines.’ This paradigm adds time and cost to every stage of the data lifecycle:
- We spend significant money and time with the data analysts trying to understand precisely what information the business user needs to see. Most systems can only ingest small chunks of specific data through their pipelines at a time, so users need to know exactly what they’re looking for.
- It can take weeks or months to turn around an analytics request. Data has to be aggregated in the right way: If you want to see something quarterly, monthly, yearly it has to pre-aggregate all that. If you want to drill down to look at specific dimensions of the data, you need to know exactly what those are so you can make sure that data's there.
- Backlog of requests is extremely high due to constraints of system capacity.
- Any significant changes to existing products (metrics) can translate to material re-tooling of the data pipeline.
Not only is the whole process slow and expensive, but it’s just not how people work anymore. I started out by illustrating how consumers search for things they don’t even know exist yet. This is exactly how people now expect to use business analytics, but can’t.
The high opportunity cost of “data elitism”
One of the most stubborn legacies that keeps surviving through the enterprise software generations is the pricing model based on user, device, server and core. This model flies in the face of the ‘democratisation of data’ that so many vendors claim to deliver. The resulting ‘data elitism’ means that in most companies analytics are still mostly available to the executive ‘one percent’.
This data elitism is sheer madness! It’s the people on the front lines that need access to information minute-by-minute to make the best decisions for a company. There’s absolutely no point in giving executives pretty dashboards, only to view ugly sales figures and operating costs.
And before you conclude that data democracy is impossible, just consider previous distribution models in news, music and video publishing that were once so expensive that ordinary people couldn’t create or distribute content. Whatever you might think of “Fifty Shades of Grey,” the book was a self-publishing sensation, which made its previously unknown author EL James a multi-millionaire almost overnight.
Business analytics as a platform, not a pipeline
To support decision making in the new impulsive, kinetic and connected world, we can’t keep walking down the same legacy business analytics road. We need to blaze a new trail. To help the senior IT and data executives I work with to reframe, I challenge them to think of analytics models as platforms rather than pipelines. By changing to this perspective, all the old constraints disappear:
- No time is spent on defining data requirements
- Data consumers are creating data ‘products’ (analyses) themselves
- The universe of data consumers who can perform their own analyses is large and limitless
- Data consumers can share/refine products amongst themselves
Unlike pipelines, platforms generate value not by pushing raw materials (or data) through an assembly line, but by creating massive market efficiencies amplified by network effects. Value is created either by connecting large pools of users and consumers (think eBay, Uber, Apple) or directly connecting people (think Facebook, Instagram). Platforms don’t expend significant effort in physically creating products or services. They let the communities and networks focus on development, while they provide the tools for producers and consumers as well as the interface between the two.
In platform models we assume that consumers (business users in the case of analytics) are going to be both impulsive and kinetic in the way they go about defining their problem, exploring and making decisions. As IT and data professionals, we have to accept the fact we aren't going to be given completely thought-through, static requests - just like our shopper who didn't know he was going to buy a pomegranate couch that day. We’re going to have to let go and give each user the freedom and flexibility to go on journeys of data exploration.
Entering analytics’ Promised Land
Persuading senior executives to fundamentally change the way they think about analytics is not an easy mission, but one that never fails to get me out of bed in the morning. What really keeps me motivated is seeing how the platform approach to analytics is taking off and benefiting the early adopter companies I’ve worked with like Bed Bath and Beyond, CMC Markets, Miami Children’s Hospital, Nutanix, and Scotiabank - along with many others.
I’ll leave you with this final question: in our impulsive, kinetic and interconnected world can your company afford to slog down the same old business analytics path or are you ready to try a different approach?
Scott Holden, CMO, ThoughtSpot
Image Credit: Round Earth Consulting