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Analyst Led Development Part 1:
Self-Service and Real-Time Data Analytics

Self-Service and Real-Time Data Analytics
Analyze, Predict and Intervene 

JS Irick
Associate Partner, TruQua, an IBM Company
Co-Director, TruLabs (TruQua’s Internal Innovation Group)

Improvements in self-service analytics and real-time data access allow analysts to take a leading role in the design and development of reporting and analytic tools.  In this session we discuss the impact of this paradigm shift, its benefits, and how organizations can enable this transformation.

Join us for Part 2, where JS Irick interviews TruQua’s Head of Financial Analytics, Chris Pauxtis, for his thoughts on how to best take advantage of cloud providers analytic offerings.



Video Transcript

JS Irick (00:01):

Hi, thanks so much for joining me today. My name is JS Irick. I work for TruQua, an IBM company, helping our customers with strategic, scenario-based planning as well as their analytics. I also am co-director of our internal innovation incubator, TruLabs.


Three quick facts about me, I love my big dogs. There’s Oliver on the left, Simon on the right. I love big Deltas, meaning change, changing how an organization runs, and helping people change their lives, through coaching and research, and I love big data, when used well.


So in talking about analyst-led-development, I think it’s easiest to go back and talk about, a really successful scenario, where this was put into practice. So I had been working with a customer, on their strategic plan, and in particular, I had been working with some of their analysts, and one of their analysts came to me with a very specific visualization they were looking to achieve and then made a couple of cuts at it in the system, I showed them a couple other ways that they could look at it, and we kept in touch about this analysis over email.


About six months later, I got a call from him, and he was very proud, that now this was included in the QBR, right? He’d created this very novel way of looking at product profitability, through the lens of their customer’s products, and now, it was used, not only to explain and monitor current performance, but also as a way to differentiate between different business decisions.


And then just last week, and where this presentation came from, I was looking through their most recent shareholder presentation, or earnings report, right? And low and behold, page five, there’s that same analysis. So over two years, this had become the defacto way, to explain performance, both internally and externally.


And this concept of analyst-led-development, really turns the traditional development lifecycle on its head, because we are utilizing two strengths, we’re utilizing stronger, easier-to-use, analytic tools, and we’re utilizing better access to data, real-time access to heterogeneous data sources across organization, and this is allowing us to design and visualize, on productive data, and then iterate very, very quickly. When we’re done with that, yes, absolutely, 100%, we’re still going through the traditional development life-cycle. We are hardening, doing security, doing the training, making it performant, or doing all those critical paths, but because this is analyst-led, we’re running up with a much stronger, more mature product, designed for our data, and for our business challenges.


And when I say, for our data, that actually is really important. Your business may have very unique customer segmentation, you may have different volatility challenges. The amount of historical data, for example, in predictive or ML scenarios, is very different across organizations.


So you’re building these tools for your business, not only in terms of the data, but then also, in the supporting data for root cause analysis, as well as understanding what needs to be done, from an interventional perspective.


I think if we look at this from a project-management perspective, it tends to be much less siloed and tends to look across the organization, when you let analysts lead design. Additionally, they tend to have stronger teamwork, and much fewer change management challenges, because we’re opting in, we’re really choosing to opt-in to something, as opposed to just looking at the new system.


So how do we encourage analyst-led-development? From the CFO seat we’re cultivating and challenging explorers, through open-ended questions, and then very importantly, we are rewarding and encouraging, those who are coming up with these innovations, and we’re spreading the innovations across the organization. It’s very trendy to talk about a virtuous cycle, but I’ll say, this is one, right? We are seeing what our colleagues are doing, and in seeing that innovation, we’re encouraged to innovate ourselves, and we’re encouraged to work across the organization, in a way that we maybe weren’t before. From the CIO, we’re looking to really build and encourage partnerships, and this is a major way to encourage, not only growth and success, but also job satisfaction.


When we are helping enable our teams to really partner with the business, and helping them do a lot more co-design, and a lot less designing to requirements. For the finance analysts that we’ve really seen grow, over the years, it’s a matter of challenging yourself. If I put on my scientist hat, and think back to working with doctors and PhD candidates, it’s building out that hypothesis about your business, and testing it. And in doing those tests and learning about the business, then broadening the vision, broadening it across your organization, and broadening your vision in terms of perspectives. That internal perspective of your business, as well as externally, how does the market or analysts view your business and your competitors?


And then for consultants, how do I encourage my team to enable our customers in this way? And the most important thing I encourage is to listen and to watch. If we take a very simple example, we have an Excel report, that comes out monthly, that we want real-time in an analytic system, the best thing you can do is sit with the key users and really understand how they’re interacting with this tool. What are they looking for? What are some of the subsequent analyses they do? If there’s a variance, how do they explain it? If there is some number that’s out of alignment, or not in expectation, how do they act next? What are the interventions they might do? Who might they contact? If you are getting that level of understanding, you’re going to build much stronger solutions, and you’re going to have a much better chance to delight your customers.


So we’re now actually going to switch over to an interview with one of my colleagues, Chris Pauxtis. If you do have any questions, want to discuss this further, you want to send me pictures of your pets, now that I’ve shared mine, you can see my email there. I try to tweet once every two or three years, so if you’re really worried about missing it, you can go ahead and follow me on Twitter, there.


I’m just going to leave the key takeaways there, and just say thank you so much for your time.

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