What’s next? Sustaining your data fluency
What you’ll learn
In this concluding chapter we summarise the main contents of the book in the form of an ideal ‘data converser’ profile and their skills, attitudes and resources. The chapter highlights that it’s not just important how you talk about data, but also to whom you’re talking, and what their role is in the analytics field. We also point to emerging trends in analytics that you need to monitor – even as a generalist. This chapter also provides you with pointers to useful resources to keep your analytics skills current. Last but not least, we articulate a way forward in which you can now make the best use of what you have learned in this book and ensure your continued progress.
Congratulations! You have made it through the key concepts of statistics, analytics and data communication. You are now well equipped to talk competently, clearly and critically about data and analytics.
You have achieved a solid level of data fluency that boosts your employability and opens up many new options for your career or next ventures.
Being fluid in data is, however, not just a question of understanding or of skills. It is also dependent on your attitude and on the resources that you have available. Let’s look at these three elements of sustained data fluency in more detail and with the help of a final dialogue. This will assist us in consolidating our learnings on how to talk about data and brings them to life.
Joana is a successful, 32-year-old, enthusiastic project manager, working for a mid-size service organisation. Her background is in marketing, but she has recently developed a keen interest in information technology and analytics.
She realises that her projects (and marketing in general) are ever more affected by data and its analysis. She enjoys learning and interacting with people and thus talks to a colleague of hers in the IT department. She wonders how she might develop her analytics skills and catches the colleague during a coffee break:
Joana:Gordon, I know you are super busy, but do you have a minute for me?
Gordon:Sure thing. What’s on your mind Joana?
Joana:You know I’ve been working in marketing and project management for the last six years and I feel that my data skills are not where they should be. What’s your advice for me? What does it take to be fluent in data and analytics?
Gordon:You won’t like my answer, but I’ve found out the hard way that most of what we call data science today is actually statistics, with a bit of data management thrown in of course. But to talk about data, you first need to understand statistics.
Joana:Oh, I will have to revive that stuff from my university days then. And where do you think could somebody like me add value in the analytics realm?
Gordon:So many insights from analytics get lost in communication. That is why I believe the key skills are making data accessible, visualising data and handling data in group settings. I’ve seen that this often breaks down, especially among analysts and managers.
Joana:Got it. So I should focus on elements such as data storytelling right and ramping up my charting skills, right?
Gordon:Yes! But you know what: Being data competent isn’t just about skills. It’s also about attitude.
Joana:What do you mean?
Gordon:It’s about being critical about where the data has come from, if you can trust it, and whether it’s been analysed properly or not. There are so many potential biases that can affect or even distort analytics, so that a critical mindset is really key for working effectively with analytics.
Joana:Makes sense, data is not God-given. I’ll keep that in mind. But back to my original question: What role could I play in analytics in this organisation do you reckon?
Gordon:You could certainly evolve into an analytics project manager. They are key in bringing the business and data science sides together. You would have to manage not just data analysts, but also database architects first and then database administrators. There are many roles that revolve around analytics, you know. Here’s a diagram that shows you some of the key roles involved in an analytics initiative (see Figure 13.1). Think about whether you would like a job as an analytics manager Joana.
Joana:A fascinating perspective, thank you Gordon. Just one last question: Where can I get support? What resources could I use to keep learning about analytics?
Gordon:Find a young data analyst to have lunch with regularly, like a reverse mentoring. Enrol in an online course on advanced analytics, like those available on Coursera or Udemy. Follow analytics instructors on LinkedIn, such as Data Science Central. Why don’t you go ahead and create an informal business analytics interest group right here in our company? Sorry Joana, gotta run now.
As the dialogue above illustrates, the journey to data fluency is a never-ending one. Sharpening your skills, keeping a critical attitude towards data, and connecting with others are key in this endeavour.
As Gordon indicated in the dialogue, it is also important to understand that there are different roles in the analytics field that have different functions with regard to data (see figure). An analytics team only consisting of data scientist would not get very far. It needs to be supported by IT architects and engineers (especially for the infrastructure planning and setup phase), database professionals and administrators, and finally the business side to make sure the data and the way that it is delivered actually provide value.
Besides mentioning the importance of a critical data attitude, the dialogue also makes reference to resources that can help you in your journey to data fluency. So be resourceful when it comes to learning analytics and don’t just follow analytics experts on social media. Like Joana you can reach out to specialists in your own organisation, in your professional network, or among friends. You may even organise informal brown bag lunches in your department, where recent analytics trends are presented and discussed.
This book has covered the basics to understand and communicate data. Building on this, you can now delve into more advanced topics and trends that will shape the future of analytics, such as artificial intelligence, distributed analytics or maybe even quantum computing – a whole new IT paradigm. Monitoring these trends and translating them into business opportunities when the time is right, is an important part of data fluency. To whet your appetite, here are a few analytics topics that you should keep on your radar (Table 13.1).
You may notice that this list contains both enabling technology behind the curtains so to speak (such as quantum computing or edge analytics), as well as so-called frontend trends such as self-service analytics. To keep up to date with these trends, we recommend websites such as Gartner.com or following institutions like Data Central or outlets like Infoworld.com. Meetups are also a great way to get in contact with analytics professionals, as are LinkedIn online groups (find them at meetup.com and linkedin.com).
Next to these trends it is also important to keep up with the software tools used in analytics. For basic statistical analysis of data software packages such as IBM’s SPSS or even Microsoft’s Excel can do the job. Most analytics teams, however, work with programming software such as R, Python (also a programming language) or commercial (hence expensive) packages such as SAS or RapidMiner. Great and widely used tools for the analysis and visualisation of data are Tableau (now owned by Salesforce) and Microsoft’s Power BI. These two are often referred to as visual analytics packages, as they emphasise the graphic presentation and exploration of data. They are often business analysts’ first choice when presenting data as interactive dashboards (graphic compilations of key performance indicators).
Table 13.1 Analytics trends and what they mean.
This may now seem like a lot on your plate. Just take it step-by-step. Here is a simple, five step action plan that we recommend to ensure your data fluency stays relevant and current.
- 1. Talk with others about what you have learned in this book, share your learnings and ask probing questions.
- 2. Download trial versions of simple analytics packages (for example, at tableau.com) and play the sample files provided in that package to learn about their logic.
- 3. Immerse yourself in data projects and initiatives, perhaps first in a support role and act as a translator and connector.
- 4. Identify in which skill you still need to improve (perhaps the statistics, or the storytelling, or the bad news part) and try to improve step by step in that domain (for example, by enrolling in an evening, online, or weekend class).
- 5. Help bridge the IT-business divide by offering training (you understand best what you teach), presentations, or developing tools like tutorials or concise glossaries and analytics FAQs for your organisation.
Whatever the next step is that you take in your data fluency journey, we wish you much success and the best of luck. Here are your final take-aways and caveats.
Key take-aways
- ●Understand the organisational embedding of analytics in terms of team roles and responsibilities.
- ●Find a role that first allows you to speed up your learning curve and then gradually move into more central functions.
- ●Keep abreast of new developments in the analytics field, particularly the latest trends in artificial intelligence.
- ●Establish a set of useful resources for your analytics journey, including colleagues, online tutorials, or people and institutions to follow on social media.
- ●Become a translator among the different analytics roles and help them collaborate fruitfully.
Traps
- ●Never consider your data fluency journey finished. Keep on learning.
- ●Do respect people’s job roles and understand the scope of their work.
- ●Don’t deep dive into every analytics topic you encounter. Think about whether it fits your future profile or not.
- ●Do help others increase their data fluency by sharing your knowledge from this book, using simple and accessible language and illustrative examples.
Further resources
To keep your data fluency up to date, regularly check in with these premium outlets: