Six tips to building your data analytics dream team
The days where having data analytics capability was seen as luxury are fast receding. Organisations in all industries, from banking to retail, from energy to healthcare, private sector and governments, all have a need for insights.
According to a 2019 report by Burning Glass Technologies, commissioned by the Royal Society, the demand for data scientists and data engineers tripled over the previous five years, while the US Bureau of Labor Statistics forecasts the data science field will grow nearly 30% through 2026.
The challenge for many organisations tasked with establishing analytics capability can seem quite daunting. Where do you start – especially when faced with identifying and attracting talent in such a fluid, competitive job market.
Here are a few points of advice to help cut time and cost of creating a world-class data capability.
- First, understand your data analytics needs. Businesses and organisations generate data all the time. But all this information in its unexploited raw form is of little use to grow profits or increase bottom line. Data analytics, done effectively, can sharpen your organisation’s competitive edge, simply by giving business development and sales teams more and better insight into trends and customer behaviours. If you are thinking about how to build data analytics capacity from scratch do have clear ideas and objectives about what needs to be achieved. These could be in relation to increasing sales, growing customer acquisitions, reducing churn, identifying highest spending customers, or seeing the impact of new product launches.
- Know what you want to achieve with data analytics capability: The degree to which the analytics is adopted by the wider organisation and the ability of teams to put analytics insight into action is key. Therefore, be clear about what you want to achieve to inform your data analytics needs: is it past (descriptive analytics) why (diagnostic analytics), future (predictive analytics), course of action (prescriptive analytics), or some combination of all four?
- Search for data analytics talent within: Job roles with data in the title (data scientist, data engineer, data translator) can seem a bit “out there”. Start by looking within your team, or wider organisation, because there may be one or two who are already doing data analytics in some form, or who have the skillset or aptitude that can be developed further. It might be colleagues from a finance, economics or engineering discipline, or someone who is highly IT literate. Some transferable skills into data analytics:
- Capable of working with quantitative and qualitative data
- Strong research skills
- Familiarity and ease with different types of software
- Experience of programming or coding
- Good communication skills to convey technical topics to non-technical colleagues Don’t under-estimate the value of experience and domain knowledge, these are far harder to attain than tech skills. Consider investing in cross training existing staff, who know the domain and know your business augmenting with experts as required. Pair the analytics team with customer-facing colleagues who have deep knowledge about the end-user customer base when necessary.
- Recruitment route: If you are looking to grow your data analytics team, think about where your gaps and opportunities lie. Are you lacking leadership, experienced hires or junior staff? For leadership and experienced hires, relevance of experience is hugely important. Analytics and data science is an enormous field that can require deep technical expertise that isn’t always directly transferable from one problem space to another. Similarly, don’t be in awe of an impressive list of academic qualifications. They have their place but relevance is everything. If you’re growing your analytics capability for the long term, consider investing in graduates with data analytics/data scientist qualifications and upskilling with domain knowledge.. Although the market is fiercely competitive, I’ve had success with LinkedIn to advertise for various graduate roles in the data disciplines. Think about job advertising sites too like Indeed.com, as these will be searched via google putting the role in front of many potential candidates. Also check out postgraduate courses or recruitment organisations that can put employers in touch with graduates, or create links with Higher education departments to try and source talent at source.
- Get the right tools Skillsets is one side of the equation of building a data analytics team. Toolset is the other and we are spoiled for choice with software for enabling data analytics. Knowing what your data analytics needs are is key to informing software selection among many different products, ranging from good old Microsoft Excel, SQL, Python and Tableau, through to whole ecosystems from the big cloud providers, AWS, GCP, Azure and to emerging platforms like Databricks. Spend time reviewing and researching various options. The whole data technology landscape is advancing at an incredible rate, so plan and be prepared for technology upgrades as business as usual. Develop a culture of technology curiosity and invest in R&D as a matter of course.Remember, when we think about data analytics, we are talking about insights that need to be shared among non-technical colleagues who want to take away clear actions without all the jargon and so investing in data analytics tools that visualise can be essential. The last mile delivery of complex analysis is often the difference between winning and losing.
- Partnering: If you are working to a tight timetable where you need data analytics capability, like yesterday, outsourcing to the experts can be a shrewd move. Again, if you have done points one and two, this can help guide your engagement with a third-party provider of analytics capability. If you haven’t, working with an expert partner can address your data analytics needs from scratch and may even uncover trends to boost your business that you never knew existed. Hiring full-time data analytics staff can be cost-prohibitive, while outsourcing this capability can be done according to different budgets. If data analytics is unchartered territory for you then working with a partner that can flexibly meet your needs is important. Whether as a six-month project or on a rolling basis, a data analytics partner can deliver insights for your organisation to act upon and excel at what it does best, which is meeting the needs of your customers more effectively than your competitors.
Head of Data Science
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