Today’s Holy Grail is the data-driven organization. Like the Grail, nobody knows what it looks like, though many are on the difficult quest to find it. Becoming data-driven is hard, and many obstacles prevent organizations from reaching this goal. Two major obstacles include data inaccessibility and limited collaboration. The SaaS duo of Slack and Panoptez offers a shortcut around these challenges, getting you faster to data-driven bliss.
The Promise of Being Data-Driven
Data-driven organisations offer numerous advantages over traditional businesses. The promise is that data is a strategic asset that can “inform decision-making processes and drive actionable results” (IBM). When everyone has access to organizational data and analytical tools, “data empowers people to make decisions without having to consult managers three levels up” (VentureBeat). Taking advantage of data means that hunches can be replaced by hard data enabling even “junior employees to make decisions” (VentureBeat). Consequently, employees are empowered to make decisions and react faster to the market.
Competitive edge is often a byproduct of superior information. While all organizations are producing reams of data around their business and operational processes, data-driven organizations are capturing this data to make it usable. By quantifying processes and collecting this data, inefficiencies can be rooted out and new opportunities discovered. This is usually easy within an organizational silo, but it gets increasingly complicated as you span silos. Paradoxically, these insights typically have the most power. Examples include:
- How do social media impressions affect sales inquiries?
- Does content marketing engagement boost registrations?
- How does lead velocity compare with expectations?
- How do meetings affect productivity?
- How do software releases affect customer support volume?
- Do managers actually matter? (via Google)
Obstacles Along The Way
Most of the benefits of a data-driven organization are only possible when data is transparent and accessible to everyone in the organization. When data is not accessible, it’s near impossible to conduct a holistic analysis. Requests can easily be lost in a swamp of bureaucracy leading to lost opportunities. Furthermore, it creates bottlenecks in the decision-making process when only a handful of people can provide specific datasets.
But why is it so hard to become data-driven? Most IT systems were not designed for interoperability and data sharing, so getting data out of these systems is difficult. According to McKinsey, “existing IT architectures may prevent the integration of siloed information, and managing unstructured data often remains beyond traditional IT capabilities.”
The traditional solution to this problem is to embark on a strategic IT project to integrate data together. While strategic initiatives can benefit a company in the long-term, you can’t hold your breath and wait for these projects to be completed. It’s quite often that “fully resolving these issues often takes years” (McKinsey). Protracted timelines are anathema to data-driven organizations — who has time to wait years for an answer?
What we really need is to quickly conduct an ad hoc analysis: immediate answers to immediate questions. In finance, desk quants served this purpose, answering complex analytical questions in near real-time. Not everyone needs the fire power of a quant but many do need immediate answers with the help of analytics.
When IT involvement is not an option, many people resort to spreadsheets as a way to get quick answers. Nowadays it’s fairly easy to collaborate on spreadsheets and get data into them. However, not all data is easily accessible. Operational data is largely buried in legacy systems lacking friendly APIs. Getting data out of spreadsheets for use in other analyses can also be tricky. Dashboards are similarly flawed since data is not easily shared across reports. Interactivity is also typically limited to drill-downs. But what if you want to quickly explore the relationship of one variable with another that might not be in the report? Now you have to ask the analyst that created the dashboard, which again creates bottlenecks!
Slack as a Data Hub
Thanks to Slack service integrations, all sorts of operational data are now appearing in Slack. Spanning all silos of an organization, this operational data feed in from sales, marketing, customer service, product development, etc. The key is that all these operational events transform Slack into a de facto data hub. Hence, data accessibility no longer requires a strategic initiative: any Slack user has access to organizational data immediately.
If an organization is using Slack, then a lot of attention is already in Slack making it a great canvas for collaboration. What if it were possible to conduct an analysis and visualize it directly in Slack? Then your colleagues don’t need to switch apps nor download anything because it’s right there. Now imagine if your colleague has an idea on how to improve the analysis. What if she could modify your analysis straight from Slack?
Panoptez for Collaborative Analytics
Slack can make data accessible to all, but to truly democratize data, it needs to be usable. Enter Panoptez. Panoptez is a collaborative analytics environment that integrates with messaging systems like Slack. As part of the offering, Panoptez automatically parses messages from service bots and transforms them into data structures. Without ceremony nor strategic initiatives, you get your operational data in usable form — as they are created.
And since it’s all accessible in your Panoptez environment, you can instantly conduct cross-silo analyses. Want to know how social media marketing campaigns affect customer service inquiries? How do customer support requests affect the velocity of development? Which projects are most tightly coupled? Are development priorities aligned with marketing messages? How much do meetings affect development velocity?
Here’s an example that compares the size of our Kanban Review queue, with the number of commits to bitbucket. The idea is that high commit activity might indicate hastily written code or bug fixes.
At the beginning and end of this series there appears to be outsized commit activity versus the change in the Review queue. Perhaps something in the Active queue is causing this issue? We can add that series to the plot and render it.
The other half of being data-driven is collaboration. Most analytics platforms claim collaboration but what they mean is presentation. Dashboards, videos, narratives are for presentation. Collaboration is about working together to arrive at a solution. Panoptez moves beyond comments and annotations on a report. Instead, business users and analysts can share actual data and functions so they can collaboratively conduct an analysis.
In our example, if a product manager wanted to dig deeper into the data, it’s right there in Slack. This statement gets the last few events on our Trello engineering board and has links to the cards in question.
In short, not only is your visualization instantly available to any user in your Slack channel, so are the commands and data to create the visualization. Panoptez democratizes your data and your analytics tools, so everyone within your organization is empowered to make decisions. With Panoptez, becoming data-driven doesn’t have to be an epic journey.
This post originally appeared on the Panoptez blog