Analyst First 101

So what is Analyst First all about?

In a nutshell, it is about making analytics cheaper, more relevant and appropriate to business (which can includes government, NGOs and any other folks actually using analytics to do something other than research for its own sake). It is also about presenting a radically different model of analytics to the one currently seen by most of the market.

Does this mean that it is not being done well already? Well… Let’s say that it could be done a whole lot better.

The biggest problem is:  most people think that analytics is about software, when it is actually about people.

What does this mean? It means that buying very expensive software that the buyers do not understand and do not have the staff to select appropriately – let alone use – is a lousy way to get going with analytics.

On the other hand, investing in people might just be the right idea. Investing in people does mean getting skilled analysts before software. Hence “Analyst First”.

But this is only the beginning, getting us to the first key principle of Analyst First:

Invest in Smarts: Build The Human Infrastructure First

This means getting highly skilled experts in analytics to advise, demonstrate and trial a range of techniques, mentoring the new team.

It means carefully building an appropriate team of analysts, business experts, communicators and data manipulators (yes, they are different skill sets).

More importantly it means establishing the right channels, expectations and incentives to gently educate executives about what they can ask of analytics, and what they need to provide to make it happen.

This may sound hard enough, but what does the team work with if there is no software? But there is:

Use Free, Commodity and Open Source Tools First

Tools on the desktop, such as MS Excel and Access, are more than enough for most analytics tasks attempted by beginners, or business areas trying out analytics.

If serious power is required, tools like R, Rapidminer, Knime etc will probably do. These tools are free, and industrial-strength enough for most applications. Certainly worth trying first, and perhaps sticking with.

Here is some evidence that this is catching on.

Here is some more.

In our experience, commodity and open source tools are good enough 95% of the time – 100% percent of the time for a new analytics unit. In the latter case, the unit is not entirely sure how analytics may be applied in their business, and their first job is to find out, capturing executive support in the process.

This is a big ask, made all the bigger if big $$$ have been spent on software, and a small number of mediocre staff are hired as an afterthought.

On the other hand, commodity and open source tools are a great alternative, allowing the money to be spent on human infrastructure.

Analyst First is not against buying expensive vendor tools, but it is against spending a cent on software until the buyer is an Educated Buyer, having used commodity and open source tools extensively, found their limitations, and seen a specific need for an expensive vendor solution.

Educated Buyers cut their teeth on readily available, inexpensive tools first, and invest their money in people: staff, skills, consultants, mentoring.

The Practice of Analytics: Exploration, Learning, and Making Mistakes

Analytics is not a linear process, like most engineering projects. Its end product is discovery: you cannot determine what will be discovered ahead of time. Thus the outcomes of analytics, and the decisions based on them, cannot be made before the analysis has been carried out.

Further, analytics is inherently exploratory in nature: data is a treacherous beast, and there may be many dead ends. Not all analytics exercises end in brilliant findings, accurate models or actionable insights. Nor should they. Mistakes are how we learn. The trick is to make them quickly, minimise their cost, learn from them and move on.

This organic, exploratory approach fits perfectly with a view that Analytics is an Intelligence activity.

Where it fits less well is with the view that analytics is IT…

Analytics is not IT

While accounting, graphic design, journalism and medicine are not part of the IT function, they all use a heck of a lot of IT.

Analytics done right is no different. While analysts require some very smart software to do their jobs, the software is not the star of the show. A strategic intelligence function does not belong  within IT.

The fact that analytics relies on highly sophisticated software should not serve to downplay the far greater role of people.

The exploratory, organic growth, human centric model outlined above is in stark contrast with the IT view of the world, which is all about pre-defined systems, known outcomes and large, top-down specified projects. An intelligence-based, human-centric model has no chance to flourish within such an environment.

Finally, most of the issues concerning IT in the context of analytics apply in the operational deployment of analytics results (eg campaign models), but apply far less in the context of analytics proper: the exploration, description and modelling of data.

Issues around security, real-time effectiveness, transfer bottlenecks etc are not real issues in most analytics contexts, though they are thrown around by IT departments.

More later.

Meanwhile, here is a video of the first A1 presentation, with slides.