I’m me. My key skill is splicing together data from disparate sources into a compelling, graphical and actionable story that prioritises the way(s) to improve a business. When can I start? Eh, Hello, is anyone there??
One characteristic of the IT industry is its penchants for picking snappy sounding themes, usually illustrative of a future perceived need that their customers may wish to aspire to. And to keep buying stuff toward that destination. Two of these terms de rigueur at the moment are “Big Data” and “Analytics”. There are attached to many (vendor) job adverts and (vendor) materials, though many searching for the first green shoots of demand for most commercial organisations. Or at least a leap of faith that their technology will smooth the path to a future quantifiable outcome.
I’m sure there will be applications aplenty in the future. There are plenty of use cases where sensors will start dribbling out what becomes a tidal wave of raw information, be it on you personally, in your mobile handset, in lower energy bluetooth beacons, and indeed plugged into the “On Board Diagnostics Bus” in your car. And aggregated up from there. Or in the rare case that the company has enough data locked down in one place to get some useful insights already, and has the IT hardware to crack the nut.
I often see desired needs for “Hadoop”, but know of few companies who have the hardware to run it, let alone the Java software smarts to MapReduce anything effectively on a business problem with it. If you do press a vendor, you often end up with a use case for “Twitter sentiment analysis” (which, for most B2B and B2C companies, is a small single digit percentage of their customers), or of consolidating and analysing machine generated log files (which is what Splunk does, out of the box).
Historically, the real problem is data sitting in silos and an inability (for a largely non-IT literate user) to do efficient cross tabulations to eek a useful story out. Where they can, the normal result is locking in on a small number of priorities to make a fundamental difference to a business. Fortunately for me, that’s a thread that runs through a lot of the work i’ve done down the years. Usually in an environment where all hell is breaking loose, where everyone is working long hours, and high priority CEO or Customer initiated “fire drill” interruptions are legion. Excel, Text, SQLserver, MySQL or MongoDB resident data – no problem here. A few samples, mostly done using Tableau Desktop Professional:
- Mixing a years worth of Complex Quotes data with a Customer Sales database. Finding that one Sales Region was consuming 60% of the teams Cisco Configuration resources, while at the same time selling 10% of the associated products. Digging deeper, finding that one customer was routinely asking our experts to configure their needs, but their purchasing department buying all the products elsewhere. The Account Manager duly equipped to have a discussion and initiate corrective actions. Whichever way that went, we made more money and/or better efficiency.
- Joining data from Sales Transactions and from Accounts Receivable Query logs, producing daily updated graphs on Daily Sales Outstanding (DSO) debt for each sales region, by customer, by vendor product, and by invoices in priority order. The target was to reduce DSO from over 60 days to 30; each Internal Sales Manager had the data at their fingertips to prioritise their daily actions for maximum reduction – and to know when key potential icebergs were floating towards key due dates. Along the way, we also identified one customer who had instituted a policy of querying every single invoice, raising our cost to serve and extending DSO artificially. Again, Account Manager equipped to address this.
- I was given the Microsoft Business to manage at Metrologie, where we were transacting £1 million per month, not growing, but with 60% of the business through one retail customer, and overall margins of 1%. There are two key things you do in a price war (as learnt when i’d done John Winkler Pricing Strategy Training back in 1992), which need a quick run around customer and per product analyses. Having instituted staff licensing training, we made the appropriate adjustments to our go-to-market based on the Winkler work. Within four months, we were trading at £5 million/month and at the same time, doubled gross margins, without any growth from that largest customer.
- In several instances that demonstrated 7/8-figure Software revenue and profit growth, using a model to identify what the key challenges (or reasons for exceptional performance) were in the business. Every product and subscription business has four key components that, mapped over time, expose what is working and what is an area where corrections are needed. You then have the tools to ask the right questions, assign the right priorities and to ensure that the business delivers its objectives. This has worked from my time in DECdirect (0-$100m in 18 months), in Computacenter’s Software Business Units growth from £80-£250m in 3 years, and when asked to manage a team of 4, working with products from 1,072 different vendors (and delivering our profit goals consistently every quarter). In the latter case, our market share in our largest vendor of the 1,072 went from 7% UK share to 21% in 2 years, winning their Worldwide Solution Provider of the Year Award.
- Correlating Subscription Data at Demon against the list of people we’d sent Internet trial CDs to, per advertisement. Having found that the inbound phone people were randomly picking the first “this is where I saw the advert” choice on their logging system, we started using different 0800 numbers for each advert placement, and took the readings off the switch instead. Given that, we could track customer acquisition cost per publication, and spot trends; one was that ads in “The Sun” gave nominal low acquisition costs per customer up front, but were very high churn within 3 months. By regularly looking at this data – and feeding results to our external media buyers weekly to help their price negotiations – we managed to keep per retained customer landing costs at £30 each, versus £180 for our main competitor at the time.
I have many other examples. Mostly simple, and not in the same league as Hans Rosling or Edward Tufte examples i’ve seen. That said, the analysis and graphing was largely done out of hours during days filled with more customer focussed and internal management actions – to ensure our customer experience was as simple/consistent as possible, that the personal aspirations of the team members are fulfilled, and that we deliver all our revenue and profit objectives. I’m good at that stuff, too (ask any previous employer or employee).
With that, i’m off writing some Python code to extract some data ready ahead of my Google “Making Sense of Data” course next week. That to extend my 5 years of Tableau Desktop experience with use of some excellent looking Google hosted tools. And to agonise how to get to someone who’ll employ me to help them, without HR dissing my chances of interview airtime for my lack of practical Hadoop or MapR experience.
The related Business and People Management Smarts don’t appear to get onto most “Requirements” sheet. Yet. A savvy Manager is all I need air time with…