This week’s Ask the Expert is answered by Nick Jewell. Nick is the Director of Product Strategy at Alteryx.
Ask the Expert: How can promoting the sharing of information between employees help build an organisation that’s truly driven by data?
In the recent past, data may, by necessity, have been handled by a core of specialised statisticians and data scientists using complex tools requiring a knowledge of coding. However, data analytics technology has advanced to a point where data is accessible for employees of all levels – and who don’t need to know how to code.
Giving the people who know their job and have a feel for their data the power to access it is a powerful force multiplier. One of the upshots of this accessibility is a new-found enthusiasm for data analysis amongst the non-data scientists in the business – which is to say, almost everyone from top to bottom. These newly empowered workers, together with the technically-adept data scientists, can drive forward the data agenda within an organisation – leading to a culture whereby data evolves from being a highly centralised resource to being one used to inform everyday decisions.
Whilst this culture is based on an enthusiasm for and the availability of insightful data, organisations can put into place internal structures that nurture this interest and maximise the utility of their new-found data superstars. By encouraging the sharing of data analysis workflows and properly vetted data sources, companies can ensure that the benefits of each individual’s hard work can be felt across the organisation – rather than being limited to, for example, that individual’s team.
Broadly speaking, there are three key approaches to building an effective data analytics community that organisations can incorporate into their internal structure:
A co-operative, adaptive and open analytics culture is a core element of organisations where self-service analytics, enterprise analytics, and advanced data science practices are complementary. By encouraging your new and existing data scientists to talk to one another, you can ensure that the everyday analysts learn from the best – improving your organisation’s performance as a whole.
It is common for an enterprise to have multiple groups and individuals performing data analysis, often with little knowledge of others within the company doing similar work. Recognise the many places throughout the enterprise where data analysis is performed and bring together these “pockets” of analytics to harness the power of analytics community. In this way, you can be sure that your teams don’t waste time reinventing the wheel.
The analytics community will struggle if highly centralised, and will be equally challenged if fully decentralised. Three levels of analytics services—self, shared, and central—must work together to build an inclusive and fully functioning community. Self-service supports local and autonomous work. Shared services promote reuse of analysis and workflows across lines of business. Central services support business-critical, enterprise-wide, and highly technical analytics. Once these three tiers are working together effectively, your organisation will see the results.
So long as the insight, workflow, or data source is useful, it deserves to be seen and heard widely. These data analytics communities, when properly co-ordinated, will help to promote this flow of data and will result in your employees being able to consistently make decisions based on data-based insights – and your now data-driven organisation will soon see the benefits of taking this informed approach to their day-to-day business practices.