Back around the breakfast table talking shop, Ammonite hosted our regular ‘Leaders in Data Breakfast’ event held at Riding House Café, Fitzrovia.
Attendees for the event.
Hosts– Keith Robinson & Phil Marks, Directors @ Ammonite Group.
Chair –Janet Bastiman, Chief Science Officer @ StoryStream.
Guests – Hilla Pedramparsi, CDO Consultant, Ex-CDO Gamesys.
Rob Heffernan, Head of Data, Tandem Bank
Dan Hunt, CDO, Bibliu
Douglas Penna, Lead Data Scientist, BBC.
Paul Schaack, Product Analytics Manager, Facebook
Matt Doltis, Head of Data, yulife
Premal Desai, Data Director, Consultant, The GymGroup
Matt Whiteley, Head of Analytics, Mobkoi
The 2 topics selected for the round-table discussion were;
- How best to innovate & stay cutting edge on limited resources?
- How to measure success – KPIs?
As always, we’ve briefly summarised the output from the lively debate on these subjects and bulleted the top takeaways.
- How best to innovate & stay cutting edge on limited resources?
Everyone acknowledged that this is a difficult balancing act. Managing the expectations of the business, developing a culture of creativity and innovation for employees whilst delivering enhanced capabilities as well as BAU is difficult enough. Achieving that with limited resources against a backdrop of financial targets is extremely testing. Whilst a consistent problem for all businesses round the table, there were distinctly different approaches and mindsets;
a) There are various definitions of innovation, even within companies.
‘Innovation’ is a pretty hard to define term when we discuss how it actually applies to a business, processes and outcomes. Does it mean doing things slightly differently or building a completely new revenue stream? Definitions varied across attendees depending on company / team size, culture, level of sophistication in data & resources.
b) Innovation needs space & time …
Whilst difficult, time must be carved out to foster innovation. A culture of innovation doesn’t happen by accident. It’s a top down mindset: experimentation and mistakes should be allowed. There must be time factored in to allow ideas to be explored. How that is implemented is the key question.
c) ….but innovation should ultimately be linked to ROI.
Innovation can create tech debt (or ‘data debt’) which can produce problems down the line. So, although innovation must be given space it needs to have a clear link with the big picture and ROI. It requires a flexible and mature mindset, where idea creation and research are given some time to develop without the requirement of immediate ROI, but there must be limitations on time invested and attachment of ROI as soon as sensible. This will depend on the resources available to the company, the OKRs set and the more immediate demands of BAU. It was also noted that early hires in a start-up are often the innovators, but they tend to lack the rigour of best practice and big picture plans which can build up problems down the line.
d) Get creative with ideas to motivate individuals and the business to be innovative.
There were a variety of really interesting ideas about how to foster and incentivise innovation.
Using company-wide gamification: write the many different business challenges on cards, spread them on a table, give everyone in the company an amount of poker chips to put on the challenges to rank their importance in order to determine where the focus of innovation should be.
Linking financial incentives to innovation, for example as part of the 6 month’s review of individual
performance with innovation directly linked to their bonus.
Innovation and creativity being part of a yearly review, but critically also assessing applied
knowledge, e.g. was your innovation / creative idea applied to the business? It was noted that there is a need to balance that out for less creative individuals, so they are not penalised inappropriately.
Building cross functional ‘mission teams’ to explore customer challenges, giving them time in the
roadmap to research solutions.
Hackathons (or ‘data derbies’??) and sprint planning were also on the list of ideas briefly discussed.
2. How to measure success – KPIs?
An age-old question and made more difficult by the fact that many organisations don’t fully understand data or necessarily have a data strategy to measure against. So how are leaders in data being measured and judged? How is data success perceived across the wider business?
Summarising the argument, the art of data leadership is getting the link right between building quality data products and profitability to the business. Measuring success should be directly linked to business goals and outcomes but also take in to account softer targets. KPIs implemented poorly can have devasting consequences for any business, but not achieving ROI in an acceptable form for any data operation will eventually prove fatal to the data team and possibly the business. So, what was the feeling around the table?
a) Horses for courses.
A different approach and method are required depending on sector, maturity, size and need of the business. For example, a B2B start up business with annual customer renewal cycles has different requirements for measuring success then a B2C social media platform with millions of users. For smaller businesses with lower volume of sales, it is often difficult to measure the building of quality data products with direct sales revenue.
b) Fixed KPIs in a fluid environment
The KPIs themselves aren’t the problem necessarily. They could be right at the time but not over an extended period of time as the needs of the business change over the year(s) … but often the KPIs remain as they were. Also, the way they are implemented, and the culture of the company can determine if they prove to be positive or detrimental to the growth of the company.; an intelligent, responsive employee might do what is right for the business but won’t hit their targets because the KPIs don’t reflect the changing needs of the business. An alternative approach is for a company to use KPIs as one of several feedback mechanisms to iteratively improve performance but not be dogmatically tied to them.
c) Everything rises and falls on leadership.
To borrow a quote from John Maxwell: everything rises & falls on leadership. Measuring overall success doesn’t lie on the shoulders of the people on the front lines; data scientists, engineers, analysts. Putting effective metrics in place is the responsibility of the C-suite, in conjunction with their leadership team, setting the long-term objectives of the entire business, communicating that effectively down to all and being iterative with those metrics if the key business goals change. Having clarity and understanding at the top of the business makes measuring success of the data team so much more manageable as OKRs can be aligned top line objectives.
An engaging and interesting discussion for all involved, a big thank you to all the attendees and to
our venue Riding House Café with their mind-blowing pancakes.
Get in touch now for info on our next breakfast roundtable for data leaders. Be part of the conversation.