Do you really need Data Scientists?

Data_Science_VDThis and many more common questions about Data Science are tackled by Instacart VP Data Science Jeremy Stanley, and former LinkedIn data leader Daniel Tunkelang. The term Data Science was only coined a decade or so ago but has gathered so much momentum that most business leaders now feel like they should have a Data Science team – even if they don’t know what they would do with them.

Jeremy and Daniel take us through some common misconceptions and recommended ways for thinking about finding real impact from Data Science. Some of my favourite lines from the article:

The above may sound a lot like data analytics, and indeed the difference between analytics and decision science isn’t always clear. Still, decision science should do more than produce reports and dashboards.

But collecting data isn’t enough. Data science only matters if data drives action.

Similarly, data-driven decision making requires a top-down commitment. From the CEO down, the organization has to commit to making decisions using data, rather than based on the highest paid person’s opinion ( or HiPPO).

Many people equate big data to data science, but size isn’t everything. Data science is about separating the signal in data from the noise.

Don’t hire a head of data or build a team until you have work for them to do. At the same time, ensure you’re collecting key data early on so that team can have an impact once you’re ready.

Build a company culture early that makes it a great place to practice data science, and you’ll reap dividends when they matter most.

Over time, the impact that a data science team has will be far higher if you build a diverse team with extremely different backgrounds, skill-sets, and world views.

Finally, focus early on hiring data scientists who reflect your company ideals. To be effective, data scientists must be trusted by their teams, the users of their products, and the decision makers they influence.

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Data Science meets Design: my visit to IDEO

Yesterday I was invited by David Webster to talk to the team at innovative design company IDEO. IDEO is a cutting edge digital and physical design studio in Palo Alto that has been leading creativity for over 30 years. I was lucky enough to have a tour by David through their workshop, engineering office, and toy lab.
ideoAfter the tour we had a joint Q&A with the whole team about how big data is used at Airbnb and how it might be used more in the design process at IDEO. Some key thoughts emerged:

  1. The world is moving towards more wearable sensory technology e.g. Google glasses, Apple watch, Fitbit. With this comes a wealth of feedback data on the user in the offline world. The internet of things (IoT) will make, for example, A/B testing in the offline (physical) world possible.
  2. For designers to be more data empowered, we first need the analytics and prediction tools to catch up. Currently it is easy to log data, cheap to store data and there are standardised tools to query data. However, no leader has emerged for extracting insights from data. This democratisation of insights needs to happen before data can permeate design.
  3. Data science works best with design when they collaborate early. At the start of a project it is easier to scope what data is necessary and easy to collect at the outset so that decisions can be informed and iterations can be faster.

The future for Data Science in Design is exciting and, when they start to overlap more, we will see changes in the world around around us accelerate even faster.

Data Scientists are more than just rebranded Software Engineers

Michael Li of the Data Incubator has written a timely article in VentureBeat on what a Data Scientist is not. In short a Data Scientist is:

  1. Not just a Business Analyst working on more data,
  2. Not just a rebranded Software Engineer,
  3. Not just a Machine Learning expert with no business knowledge.

A Data Scientist needs to be able to extract insights from datasets that are orders of magnitude larger than what they were 5 years ago. And they need to extract this insight carefully, with statistical significance and integrity. Moreover, the insight is only as useful as the business need it solves.

As a regular interviewer at Airbnb for junior and senior Data Scientists, attention to data cleaning and diligence in statistical analysis are fundamental for successful candidates. Moreover, we look for people that understand the ‘why’ of a problem and the business impact of a solution. This is what differentiates a really smart candidate from a hired candidate.

Forget Big Data, we need Big Insights

biginsights

A recent article in the UK Computing journal suggests that the new frontier in industry is extracting insights from big data. While a decade or more ago data ingestion was the greatest challenge to companies collecting large swathes of data, that problem has now largely been overcome.

Of 300 IT professionals interviewed, only 13% saw raw data access as the biggest challenge in their work. The majority were split between transforming raw data into useful data and extracting actionable insights from the useful data.

Database platforms such as SQL and Hadoop and other tools have largely standardised the warehousing and accessing of data across big data consumers. However, insight extraction is the next frontier and there is still no runaway leader or dominant player in this space. It is difficult to build a one size fits all solution to the problems a traditional business analyst might work on.

But it is likely we will see more and more contenders coming to the fore in this potentially lucrative space. It will be an exciting arms race to see play out in the data science world!