Archive | May, 2017

Selling Data Science

9 May

Creating sales documents and pitches that list out all the shiny new things that our data science application can do is very tempting. We worked hard on those features and everyone will appreciate them, right?

Well, not really. For one, it’s very likely your target audience doesn’t have the technical ability to understand the point of what you’re selling. After all, if they had your technical skills, they wouldn’t be thinking of hiring a data science, they’d just be doing it themselves.

The next problem is that you can’t trust that the customer realises how your solution helps them out of their present predicament. Moreover, it’s disrespectful to get them to do your job for you. Hence, you need to make sure your pitch joins the dots between what you intend to do for the customer and how it’s going to make their life easier.

In sales parlance this is known as ‘selling the benefits’ – that is, making it clear to the potential customer how buying your product will improve their lives, and has been encapsulated in the phrase ‘nobody wants to buy a bed – they want a good night’s sleep’.The rub is that in most data science scenarios the problem that corresponds to the potential benefit is a business problem – such as reduced inventory or decreased cost of sales – rather than a human problem, such as a getting a good night’s sleep.

Therefore, being able to complete the journey from feature to benefit requires some knowledge of your customer’s business (whereas everyone knows the benefits of a good night sleep – and the horrors of not getting one – far fewer under the fine points of mattress springing and bed construction) and the ability to explain the links. This last is crucial, as the benefits of your work are too important to allow your customer an opportunity to miss them.

What all this means in the end is that the approach of inspecting data sets in the hope of finding ‘insights’ will often fail, and may border on being dangerous. Instead you need to start with what your customer is trying to achieve, what problems they are facing before seeing which problems correspond with data that can be used to build tools that can overcome the problem.

Timothy, Paul and Data Science

7 May

Like any other atheist who regularly attends an evangelical church, I often find myself wondering how to apply the sermon to my life. A recent example which seemed a little easier than other occasions was a sermon from a guest preacher on succession planning.

Part of the point for this preacher is that he’s a kind of mentor for a number of churches, so he traipses around Australia advising other pastors how to do things better – and also sees them failing, often for predictable reasons. Hence, when he spoke about succession, he was talking from experience.

Of course, succession planning isn’t specifically about churches. The phrase is more commonly heard in corporate settings. His solution to the problem – in the end a call to spread the Gospel, not all that surprisingly from an evangelical preacher – initially seemed one that had no application to the corporate world, but after a little reflection actually seemed very applicable.

By the pastor’s logic, the gospel was effectively the knowledge needed to participate in his religion. So, by extension, succession planning was about the transfer of knowledge. In a way, this is not a revolutionary idea – of course succession planning is about the transfer of knowledge of a working environment, customers, skills to get a job done.

But the emphasis is so often on the leader of an organisation and (too often, in both senses) his immediate reports. Hence the emphasis is on the knowledge that they will bring into the company, the skills in running a business they learnt elsewhere that they will aply to your company. It’s like judging future converts for the abilities they bring to a church from outside – their ability to speak in public, to be great fund raisers – rather than the pastor’s idea of succession planning through

The alternative is succession planning starting from the ground up – making succession planning being with the people make your product or provide your service, and the people who secure your customers. In a very real way they are your business. Certainly, in my own career in manufacturing, I’ve seen the results of failing to ensure knowledge is transferred from people who make the product to others. In short, when they retire, there are delays and defects as people attempt to re-discover the skills.

The previous blog post was about one way that skills can be transferred – the knowledge of a process can be converted into a computer program, with sensible commenting and documentation. Not a solution in itself, not the only alternative, but an extra tool that can be employed. From this perspective, the sermon was another way of seeing the big picture way that that tool can be employed in a Data Science setting.