Liz Matthews, Head of Community and Education at Mango Solutions looks at the importance of building an analytic community to enable data-driven transformation from within.
The disciplines and practices around data science, big data and advanced analytics have established themselves as vital business tools, with organisations increasingly looking to analytics-led strategies and data-based decision-making for strategic business gain. The underlying business goals can vary dramatically – from client acquisition, reducing churn rate or optimising store locations, for example – but in each case, a data-driven approach can deliver critical competitive advantage.
However, many companies – particularly those with roots in the pre-digital era – struggle with the challenges of adopting and implementing data-led strategies while retaining market share in the face of sleeker, disruptive competitors for whom data is already part of their DNA.
This raises some big questions, particularly around how organisations can carry out their data-driven transformation process or project for maximum deliverable insight. How can they capitalise on what was learned to maximise opportunities for wider organisational effect? Equally important is which skills and team ethics are needed to build newer, bigger projects moving forward?
And here is one of the most important points: becoming data-driven requires more than the purchase of a technical solution or the hiring of data scientists. It requires that data is placed right at the core of an organisation’s strategy. This philosophy will enable the fundamental culture-shift required to realise the potential of the insights that data can generate and to create successful outcomes for the organisation.
That’s easier said than done and, typically, there are three key challenges that companies currently face in becoming data-driven:
1. They don’t understand what analytic skills they already have
Remember, analytics can encompass a wide range of practices, from expertise in Excel to the application of data science and advanced analytics. Potentially, each has a huge role to play in data-driven transformation projects, but understanding where these skills sit can be difficult, especially in large and complex organisations.
2. Their analytics skills are spread across the organisation
Without a genuine sense of community in existence, key analytics processes and approaches can vary considerably across an organisation. The problem is that this can not only create barriers for discussions around best practice, but can also lead to inefficiencies and missed opportunities to improve skills and learnings which could have made a positive impact on the business.
3. Their community is disconnected
While analytics is now a strategic priority for many organisations, a lack of community means that talent is disconnected and cannot be exploited as a whole. This is a major problem, as evidenced by a recent Women in Data UK survey, which found that more than half of all analytics professionals had no access to an analytic community in their workplace. This fragmentation of culture, experience and expertise makes it much more difficult to set objectives across the community and discuss ways to achieve them to best effect.
The importance of building an analytics community
So, what are the big advantages of building a strong analytics community within organisations focused on data-driven transformation? Firstly, connecting siloed analytic teams or individuals is paramount if a company is to adopt data-driven strategies. With analysts often using similar tools and techniques or approaching questions and problems in the same ways, it’s a classic case of increased communication helping bring minds together to grow together.
Then there’s the huge advantages communities offer to professional and skills development. The Women in Data survey confirmed that the appetite for professional development and learning new skills is high, and an important benefit of a connected analytics community is how it facilitates the sharing of knowledge and up-skilling of individuals.
The survey also revealed that every single respondent wants to improve their skillset. In particular, machine learning, deep learning and expertise in big data analytic technologies such as Spark, Storm and Flink were highlighted as being of most interest. The survey respondents also helped to uncover the barriers to learning these new skills, with time, money, managerial support and, tellingly, lack of access to an analytics community in their organisation all playing a role.
“Sharing ideas between industry sectors is what makes community interesting”
On the flipside, the growth in popularity of analytics community user group meetings and the number of people attending them is a clear signal that data science professionals want to network with peers and build their knowledge. In practise, the most useful user group meetings will include content such as a free workshop on a data science topic or methodology, followed by presentations from volunteers keen to share their own experiences and expertise.
It’s not uncommon to see a big variety of industries represented at these meetings. Members will often provide feedback that sharing ideas between industry sectors is what makes community interesting and worthwhile.
That’s all part of a picture that can help foster and retain scarce talent. Given the demand for data science expertise is greater than ever and with the average time in role for a data scientist being less than two years, it’s easy to see why employers want to minimise churn rates and retain their highly-skilled and knowledgeable data professionals.
Building an inhouse analytics community is a clear indication that an employer values the skills and contributions of its analytics personnel and is committed to providing them with opportunities for professional development and growth. It’s clear that for a business wishing to embed advanced analytics and adopt data-driven strategies, the creation and support of an internal analytics community can prove enormously beneficial in both the short- and long-term.
In part two of this article published in DataIQ, we will discuss the six key steps required to build and develop an analytics community.
Liz Matthews, head of community and education, Mango Solutions