Managing Data: Building Data Capability In Organisations
We live in the era of big data.
Organisations are looking at data to unlock opportunities for business growth and operational efficiency. They are using data as the fuel to drive large scale decision making.
Insights-Driven Businesses are growing at an average of 30% annually and are on track to earn $1.8 trillion by 2021
To become what Forrester refers to as an “insights driven business”, organisations must focus on building strong data capabilities.
To understand how to build strong data capabilities, we hosted a panel discussion with an independent group of four data experts from varying backgrounds.
They all have had pivotal roles in the successful embedment of data capabilities within organisations. This panellists group of experts is not affiliated with Morgan McKinley.
Founder & Director
This innovative business is leading the way in combining data, behavioural psychology and artificial intelligence to unlock real human insights for their clients in actionable ways. For more than two decades, he has led best-in-class digital and data-driven initiatives for businesses including Coca-Cola, Pfizer, Google, Gilead, KFC, Philips and OPSM.
Emma Lo Russo
Emma Lo Russo is CEO of Digivizer, Australia’s leading digital marketing technology and activations company, which she co-founded in 2010. Digivizer helps businesses understand, optimise, and make better decisions about their investment in digital marketing, across organic, earned and paid social, search and website media.
Executive Director, Data Insights & Transformation
NSW Data Analytics Centre
Simon has been instrumental in the transformation of the NSW Data Analytics Centre to an agile culture. He and his team have built an advanced analytics service in the commercial cloud which delivers the necessary capability and scalability to support the Customer Service department and NSW Government as a whole.
Leanne (Lee) Ward
Partners for the Future
Leanne has worked with customers to deploy cognitive solutions that address business challenges and create new insights. She has worked across many industry sectors including Finance and Banking, IT&T, Federal and State Government and Infrastructure, maintenance and facilities management and commercial small business.
1. Starting Your Data Journey and How to Prioritise
Start by asking the right questions
- What is your business trying to do?
- What problems do you need to solve?
- How can the data help you solve these problems?
- What data do you have?
- What data do you need to get to help you solve the business problem?
- How do you compliment your data with humans to make sure you can identify and remove bias?
Align your data & business strategy
Your data strategy should be a cascade of the business strategy to really ensure your data strategy aligns with your overall business strategy. Where is it that you want to be in 5, 10 or 20 years? If done correctly, your data strategy will give you a step by step guide as to how to get there.
Visualise your data insights
Having asked the right questions and knowing where to find valuable insights lays the foundation of your analytics initiative.
You then want to make sure you choose the right way of presenting your data by enhancing visualisation capabilities to engage users.
Use data visualisation tools to help tell stories by putting rows of data into a format that will illustrate the highlights and trends.
Have the right data governance
Data and governance go hand in hand. When starting your data journey, it’s crucial you have the right security process in place, the right ethical documentation for your customers and an agreed definition of the data across the business.
2. Building sponsorship to secure funding
Stakeholder management plays a crucial role in building sponsorship to secure funding for your data capability. You need to find stories that will resonate with the stakeholders you are talking to.
It’s crucial to provide stakeholders with actionable business insights: Give the business stakeholders something they didn’t know, which will allow them to then make a change in the way they are delivering a service or delivering their business that is going to have a positive outcome.
“You need to make sure that once you deliver that insight, that you can actually determine what that benefit was because that will directly link back to your ability to gain sponsorship”
Simon Herbert, NSW Government
Board members don't need to know the process, the syntax or the model, but they need to be presented with the idea of how this will positively impact their business and customer.
3. Key skills you need in your team
When building your data capabilities, you need to hire the right people with the right skills for your team. Below are the skills and competencies you should look for when hiring your next Data Analytics professional.
- Storytelling: Data Scientists should be able to tell a data story and be able to communicate the data back to the business.
“Employers are looking for people with an ability to derive actionable insight from their analyses. This means being able to form what businesses call the Data Narrative. “
Sam King, Principal Consultant - Data Analytics at Morgan McKinley
- Data Steward: A Data Steward is someone who is responsible and accountable for all the data that sits on your platform.
- Analytical mindset: People on your Data Team should have an analytical mindset; they should be able to “sell” data. Data only holds its true value when we have analysts at the helm who can go beyond the "what" and start to ask the "what next.”
- Data Journalism: You need talented people on your team who are able to bring together behavioural sciences and data.
If you are at the beginning of your data journey and not sure who you need to hire, you can start building your data capability by hiring data engineers and analysts.
When it comes to transferable skill sets, maths, stats and computer science are extremely valuable, but there is also a significant place for other commercially analytical based disciplines such as law, accounting and business.
4. Hiring in Data - How to attract the right talent?
One thing all expert tips have in common is the importance of having the right people on your team. How then do you ensure you attract the right talent? Our data analytics recruitment specialists share their insights:
The trend we're seeing as a recruitment consultancy is that all businesses, big and small across all industries are now truly looking to enhance their data capabilities.
Tapping into good candidates can be difficult enough but the real question is how to tap in to the best candidates with an inviting, exciting and engaging proposition.
Job Descriptions & Job Ads
As a starting point, job descriptions are key and this is also the part of our service we're getting asked a lot more about now. We have seen huge success in partnering with key hiring managers to spend quality time writing job adverts and job descriptions with them, on site.
It's a consultative, insightful and detailed approach which has given hiring managers a true window into what we know will attract untapped Data Talent, who wouldn't normally respond to adverts on LinkedIn or Seek.
While many also think people mainly move because of things like money and location and for a step up (all important hygiene factors), we know from speaking to hundreds of highly in demand candidates that top Data Talent responds to three main drivers, or pull factors:
1. What is the level of sponsorship of data from the board?
2. Who will I be working for and reporting in to?
3. What is the data maturity of the business?
1. Investment in data
Firstly, and unsurprisingly, top talent are drawn to businesses where data is backed up by a significant want or need to invest.
Most companies say they want to, but we often see people mis-sold or pulled in other directions once in the business, which leads to resentment and unhappiness. Being presented with a definitive 'data roadmap' will do a lot to attract the best talent in data. Coupled with example pipeline projects and a single, key business deliverable question, e.g. "We aim to be the first wholly data driven financial services business in APAC" or "Our goal is to be the first data science led, business decision retailer in Australia" will pack a powerful punch!
2. Team members and reporting lines
Secondly, the surrounding team and, most importantly, the reporting line is equally key.
From extensive candidate interviews, we can confidently say that people leave jobs when they have no technical or commercial mentor.
In the case of Data this weighs even more so on the technical side because analysts want continual development and challenges.
For them to be involved in key business initiatives with a reporting manager who can lead by practical example and experience is critical.
“On a top level, hiring a CDO with demonstrable data analytics and programming experience as opposed to a CDO from a purely commercial background will speak volumes about the type of data function you are presenting to the rest of the market.”
Sam King, Principal Consultant - Data Analytics at Morgan McKinley
3. Business data maturity
Thirdly, the data maturity of a business will vary dramatically from large scale financial services and banking teams of several hundred right through to those who are beginning their journey from the ground up.
Both established and greenfield projects can be equally exciting to top talent and both will depend on the type of personality you would like to build your team around.
Transferable skills are key to this, but most candidates are drawn in by the desire to grow and succeed via exciting projects answering the businesses key critical questions. It's not a one-size fits all of course, but many data professionals will overlook a "secure" or established, more well-known data team, in light of a compelling growth plan and mentor with pedigree and experience.
Whatever your data vision, the key is to start small.
Work from the business question down and organically grow from there. Hiring the right CDO will put you in a significantly more powerful position to see a huge return on investment for your business.
Don't hire a data scientist with a PhD in machine learning and algorithm development if your data isn't mature enough yet - they will get bored and leave.
Don't hire a reporting analyst if your data is too big to handle and you don't know what to do with it - you need someone who can deep-dive and start to pose questions you didn't know you had.
Be realistic about what you need and be honest as to what you are offering. Starting small, identifying gaps one at a time and scaling outward will offer a realistic and achievable goal of scaling your data business while also attracting and retaining the best possible untapped talent in the market. The potential is huge and it's a great time to be looking for top data talent.