Digital Analytics

Your stakeholder is (not) your father-in-law

Reading Time: 5 minutes

You arrive, slightly frazzled, a few minutes after the agreed time for lunch. At the door, you thrust the obligatory bottle of wine into your mother-in-law’s hand and say that you’re sorry you’re late; the traffic was terrible. “Which way did you come?” pipes up your father-in-law from the hallway, and you immediately realise that you have made a grave error.

As you stand, dazed, nodding along while nursing a room-temperature glass of Pinot Grigio, you find yourself thinking, “didn’t I just have to endure a conversation like this with the CMO this week?”

Nasty, brutish and short: The life of the modern CDO

Reading Time: 6 minutes

The 2010s were a big decade for Chief Data Officers: from a standing start ten years ago, CDO has risen to become an indispensable C-suite role, with almost two thirds of Fortune 500 organizations hiring one.

But the role of CDO, especially outside the US, is still poorly defined, and CDOs are frequently not set up for success within their organizations. Is the job a poisoned chalice?

Demystifying Data Science, Part V: AutoML

Reading Time: 7 minutes

As we’ve established earlier in this post series, Data Science is a process, with quite a lot of repetitive elements. Many Data Science projects involve a familiar set of tasks to identify, clean and prepare data, before finding the best model for the scenario at hand. And despite the mystique around the whole profession, many Data Scientists spend a lot of time complaining about all this repetitive work. But any repetitive process is ripe for automation, and Data Science is no exception. Enter the field of “AutoML”.

Read moreDemystifying Data Science, Part V: AutoML

Demystifying Data Science, Part IV: Models and Machine Learning

Reading Time: 9 minutes

As I mentioned in my first post in this series, the central purpose of Data Science is to find patterns in data and use these patterns to make useful predictions about the future. It’s this predictive part of Data Science which gives the discipline its mystique; even though Data Scientists actually only spend a relatively small fraction of their time on this area compared to the more workaday activities of loading, cleaning and understanding the data, it’s the step of building predictive models which unlocks the value hidden within the data.

Read moreDemystifying Data Science, Part IV: Models and Machine Learning

Demystifying Data Science, Part III: Data Wrangling

Reading Time: 6 minutes

Ask any Data Scientist and they will tell you that the process of ‘wrangling’ (loading, understanding and preparing) data represents the lion’s share of their workload – often up to as much as 80%. However, that number is not as alarming as it may at first seem. To understand why, let me tell you about my living room.

Read moreDemystifying Data Science, Part III: Data Wrangling

Demystifying Data Science, Part II: Data Science vs Analytics

Reading Time: 4 minutes

Ask an Analyst, particularly a Digital Analyst, how they’d like to develop their career, and they are quite likely to tell you that they want to get into Data Science. But in fact the two disciplines (if they can even be described as separate disciplines) overlap considerably – some would even say completely. So what is the difference between Analytics and Data Science?

Read moreDemystifying Data Science, Part II: Data Science vs Analytics

Demystifying Data Science, Part I: What is Data Science?

Reading Time: 5 minutes

There’s a lot of buzz about Data Science these days, and especially its super-cool subfield, Machine Learning. Data Scientists have become the unicorns of the tech industry, commanding astronomical salaries and an equal amount of awe (and envy) to go with them. Partly as a result of this, the field has developed something of a mystical aura – the sense that not only is it complex, it’s too complex to explain to mere mortals, such as managers or business stakeholders.

It’s true that mastery of Data Science involves many complex and specialized activities, but it’s by no means impossible for a non-Data Scientist to build a good understanding of the main building blocks of the field, and how they fit together.

Read moreDemystifying Data Science, Part I: What is Data Science?

The seven people you need on your data team

Reading Time: 8 minutes

Congratulations! You just got the call – you’ve been asked to start a data team to extract valuable customer insights from your product usage, improve your company’s marketing effectiveness, or make your boss look all “data-savvy” (hopefully not just the last one of these). And even better, you’ve been given carte blanche to go hire … Read more

Returning to the fold

Reading Time: 2 minutes

Five years ago, my worldly possessions gathered together in a knotted handkerchief on the end of a stick, I set off from the shire of Web Analytics to seek my fortune among the bright lights of online advertising. I didn’t exactly become Lord Mayor of London, but the move has been a good one for … Read more

Wading into the Google Secure Search fray

Reading Time: 3 minutes

There’s been quite the hullabaloo since Google announced last week that it was going to send signed-in users to Google Secure Search by default. Back when Google first announced Secure Search in May, there was some commentary about how it would reduce the amount of data available to web analytics tools. This is because browsers … Read more