Why Spectio?
We care about driving value for our enterprise customers through better delivery of business intelligence. Spectio is built to democratize data collaboration, so that business decision makers and data teams can work more effectively together.
Give us a brief introduction about Spectio. What does it do and who is it for?
Spectio is a collaboration product that enables business stakeholders, analysts, and data engineers to more efficiently co-produce BI outputs like datasets and dashboards. More specifically, Spectio helps manage the inputs (such as requirements) and outputs (like a Tableau dashboard) of BI workflows by unifying communication, documentation, and prototyping. It is designed for analysts at enterprises who regularly work with business consumers of data and with engineers who deliver the data outputs.
Your career has been in working with data products and you have two degrees in Statistics. How did Spectio come into being? Did something from your career experience motivate you?
In working with data as a product, I kept running into the same problem, which is that there is often a translation gap between business stakeholders and data teams. So what? Well, this problem makes it difficult to deliver successful BI initiatives. In fact, this was a key point of discussion at the 2023 CDO Magazine Summit.
Business stakeholders have the challenge of communicating domain context and their data requirements in ways that are actionable by their technical counterparts. Similarly, data teams often do not have adequate domain context and business requirements to be able to deliver successful data outputs. And then there are the business analysts who often sit in the middle, acting as translators between the two functions — with the expectation of having specific functional domain specialization AND having to know a lot about a company’s underlying data capabilities. This is a really big ask for any one role.
Seeing customers struggle with this problem, discussing it with data leaders, and experiencing it directly, I decided to start Spectio to address this translation gap.
What are some of the biggest challenges that companies face today with BI?
It’s a multifaceted problem, I think there are 3 key challenges in BI that stand out to me: (1) Delivering ROI, (2) Driving data literacy, and (3) Governance. Delivering data ROI is one that seems elusive to many organizations. Successful BI relies on having the right data to support business use cases that move the needle because you never want to be spending company resources building a science project. It’s also equally important to clearly define, communicate, align, and evangelize KPIs for BI, analytics, and data initiatives.
Data literacy is another area that has made it difficult to deploy self service analytics as an example. There are a lot of BI tools out there with varying degrees of user friendliness (and many with AI enhancements), but one of the bottlenecks to adoption is people’s comfort with basic analytics, especially around interpreting results. So, even if you do take a company that has been able to deliver ROI and has enabled a more data literate workforce, then there’s the problem of governance. What do you do with the proliferation of redundant dashboards and datasets and tools? How do you speed up the delivery of data to consumers who now have more sophisticated BI needs? And this is where Spectio comes in.
Your website says “Cross Functional BI Collaboration." What does it mean for the business as a whole?
Gartner published their 2023 Magic Quadrant on Analytics & BI, in which they added an evaluation criteria for Collaboration. It’s defined as enabling a broad spectrum of users to co-produce analytics outputs and to enable organizations to make decisions with consensus. Spectio is about addressing the translation layer to enable business and data users to easily collaborate and align on requirements, definitions, and outputs.
How will Spectio work with existing BI tools and platforms?
Spectio is a companion product to any BI solution out there. It actually drives better adoption of those tools. Most BI tools solve for “the how,” but Spectio solves for communicating, documenting, and aligning on “the what” and the “the why.”
From a talent perspective, there are concerns about AI taking over jobs. Can you share some light on how AI is going to change the roles of business analysts? What advice do you have for them?
If AI is the rocket ship to the moon, and data is the fuel needed to get there, then business context is the map. AI excels at processing and presenting data, but it often falls short when it comes to interpreting results because good data driven business decisions require context. (AI models are limited by the data they are trained on.)
This is where domain expertise becomes a critical asset. Business analysts can (1) inform and guide the development of better data and better models, (2) contextualize data outputs through the lens of real time and real life business impact, and (3) follow up with the outcomes of data driven business decisions to iteratively inform future analyses and actions.
Business analysts can also repurpose their time and focus toward strategic value creation. This means they can become problem definers, rather than just being problem solvers. This requires deep understanding of business needs and opportunities, and in order to do that, analysts need to lean into cross functional collaboration to improve outcomes for data and analytics initiatives.
