Alistair Dorans gives us his top tips for creating a successful analytics team.
Alistair has recently moved on from the ABC after just over 3 years, during his time there Alistair was integral in creating the Data Science function and championing advanced analytics. His strong background in commercial and marketing strategy has allowed Alistair to utilise advanced analytics to solve key business problems.
1. Get the sales pitch right
There is no shortage or hype surrounding Big Data, Data Science and Advanced Analytics, and the expectations of business sponsors often need a good dose of realism. Hype won’t secure you that budget, it's important to realise there is always a need to take on an internal sales role and demonstrate quantifiable benefits.
It goes without saying that people in your Analytics or Data Science team are a critical factor in success. The mix of statistical capability, data engineering skills and domain knowledge needs to be right for your organisation and this may change as you progress on your analytics journey.
Data Scientists can do little without data, your organisations citizen data scientist’s with lots of domain knowledge and little data understanding exist in a danger zone, producing potentially misleading analytics from unreliable data.
Foster innovation, realise that analytics is a rapidly evolving technology ecosystem, encourage the team to continuously up skill and share knowledge.
3. Cloud and agnostic technology
In a world of big data and advanced analytics you need flexibility, scalable storage and compute capacity. When it comes to tools, don't lock into one option. I have had success with small open source systems like Snowplow Analytics as well as mega vendors such as Power BI. The important factor is to make your data accessible to the people that need it, allow data consumers to use tools they are familiar with.
What value have you been able to deliver to the business?
Creating a data analytics function in an organisation with a limited analytics capability can give almost instant success. At the ABC when we first started on the digital analytics journey we were able to answer simple questions quickly. Tracking our in App menu selections provided invaluable insights for our Product Managers and Producers who were able to see the numbers of people watching particular content categories.
The value of the insights will vary depending on the organisation and market, considerable value is achievable from bringing together disparate data sets into a basic model. As you move further up the analytics "value chain" from descriptive to predictive and prescriptive, it becomes harder when demonstrating value, time-boxing analytical experiments can become tricky and having to accept that experiments can fail is an obstacle to overcome. This however should not discourage you as when it comes off the payload is big.
What advice would you give to others looking to build their data capability?
Data is a strategic asset. It creates competitive advantage and new product possibilities. The role of the analytics leaders in your organisation is to bridge the gap between data capability and business capability, a failure to do this can prove to be a costly and unproductive exercise. Identify the areas where existing data sets and analytical techniques can be utilised to solve complex problems and build a data roadmap to support the organisations strategy.