Agile business intelligence: 11 ideas to assess your self-service progress


The current excitement about self-service analytics centres around two main areas. The first is the rise of ‘citizen data scientists’, business users who are using self-service analytics to produce insights. They are drawing on a combination of their knowledge of the business, a level of comfort with analytical technology and maths, and a widening range of sophisticated but straightforward analytics tools. The second area is the availability of good self-service analytics tools—on the role of data scientists.

Over the past six months, SAS subject matter experts have expressed their views on these developments and how organisations can take advantage of them. We recommend them to anyone who wants to understand the spectrum of opportunities to drive better insights for business agility.

The rise of citizen data scientists

The widening spectrum of data science roles – a good introduction to the thinking behind the development of the role of citizen data scientists, and what it might involve. The article includes some suggestions for how this might change the role of data scientists themselves.

Are data scientists the chauffeurs of the 21st century? – citizen data scientists, supported by self-service analytics, may be the tool that allows analytics and data-driven decision-making to expand beyond an elite few companies that have access to qualified data scientists.

Citizen data scientists – would you like to correlate with me? – a response to criticisms that citizen data scientists are untrained and therefore dangerous. The author asserts that citizen data scientists have a role to play, because of their knowledge of how the business works, but that support from data scientists will continue to be necessary.

Developing self-service users – support for citizen data scientists

Self-service requires partnership – self-service analytics does not mean that data scientists should abandon business users to their own devices. They may need help in selecting the right data, and then working out what to do with it. Self-service is a partnership.

Getting visual analytics design right – more on the self-service partnership theme, this article explains that providing a great visual analytics package may not be enough. Citizen data scientists are likely to need to help to get started, including how to present data so that it tells the right story.

A powerful tool should not distract the purpose – a gentle reminder that it is possible to get carried away with the sheer prettiness of visualisations. Instead, data visualisation should always be about how to present the data in the clearest, most helpful way.

Specific situations where citizen data scientist skills may be needed

Why business controllers are becoming citizen data scientists – one specific role that now requires a more analytical approach is that of business controller. This article explores the skills required of a business controller, and suggests that these are similar to those for a citizen data scientist.

Can citizen data scientists improve risk management? – the rise in regulation in particular sectors, such as finance and banking, has meant that decisions must increasingly be evidence-based. The shortage of data scientists means that banks face a choice: citizen data scientists or non-compliance. There is, however, a balance to be struck between using data to gain insights (the purpose of data science) and controlling and protecting data (the purpose of data protection legislation).

Alternatives to self-service – buying in expertise

Fire propelled ancient intelligence – Results as a Service can light up analytics – this article uses the metaphor of the way that fire allowed our ancestors to change how they operated. It suggests that Results-as-a-Service—the provision of on-demand analytics bought in as a service—could have a similar effect on organisations.

Turbo-sharging self-service: Application programming interfaces(APIs)

The platform economy is an API economy – an introduction to the idea of application programming interfaces (APIs) and why these matter in a platform economy. This article demonstrates why platforms that use data will always win over those that believe data is not valuable, and therefore starts to make the link between APIs and data science.

How will APIs change the role of data scientists? – APIs will have an impact across the whole analytics lifecycle. This article suggests that APIs will accelerate the rise of citizen data scientists by making it easier to access advanced analytics. It also suggests that data scientists might either take on more complex analytical roles, or start to supervise the work of citizen data scientists.


About Author

Marieke Hilbers

The Internet of Things (IoT) is about connecting our cars, homes, gadgets and machines to the internet creating a network of connected objects. Today, IoT is a hot topic, encouraging a rush of excitement and possibilities that are endless. IoT is the future of technology that can make our lives more efficient. However, the real value of IoT lies in analytics of the mass of data that is collected. How can data streaming to and from all these objects be understood to benefit your organization, improve your daily routine or address society’s biggest challenges? At SAS, my role is to understand the needs and challenges that organizations may have as they adopt IoT. The Regonial Marketing Program aligns SAS resources to help customers explore IoT, make fast and confident decisions, be more effective or even create new business models.

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