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So far in this series we’ve covered how many analytics strategies and technologies have not kept up with the rapid changes of the last several months, and how converged analytics can provide the necessary shift in dashboard capabilities.
In this post we’ll explore how these changes might impact the data analyst career and the rise of the so-called “citizen data scientist.”
Converged analytics and the “citizen data scientist”
The second post of this series noted that historically, the dashboards and BI tools of data analysts have operated independently from the predictive models of data scientists. For reasons explained in that post, converged analytics means organization knowledge becomes shared, not siloed. When that knowledge is shared, it is even better enabled to become “smarter,” as more devices and applications are interoperable, such as with the Internet of Things (IoT).
This new collaboration makes way for the so-called “citizen data scientist:” that is, a tech-savvy analyst or business user who, while not a full-time data scientist, still has considerable predictive power at their disposal through programming scripts, access to application programming interfaces (APIs), and trained models from the data science team.
Through this “AI-powered business intelligence,” this citizen data scientist can evaluate historical trends along with predictive models. They can save time on cleaning and preparing data through the use of automated technologies and spend more time assisting the business with insights.
Inquiring more deeply into the data
Traditionally, when a data analyst found a notable trend or relationship in the data, their ability to inquire and explore it was limited. Deeper dives into the data were performed by the data scientist or statistician, who used advanced statistical techniques and programming methods to validate findings. In this era, centralized IT also discouraged this first wave of self-service BI due to a lack of strong governance. But now, with governance greatly enhanced and woven into data architectures, there’s less reluctance and considerably more confidence to allow for democratized exploration.
With converged analytics, data analysts are able to more rigorously explore and confirm these relationships from right inside their BI tools using popular open source languages like R and Python. Confidence is instilled in self-service analytics when these embedded capabilities are highly governed. Previously difficult to incorporate into many analyst workflows, converged analytics makes it easy to implement the advanced data visualizations, statistical techniques and other capabilities of these languages.
As I wrote in the Modern Analytics Platforms ebook, “As more data is collected and put into production, the importance of a data governance process typically grows, describing who has authority over data and how that data should be used. Similar approaches are necessary to audit how models are put into production and how they work.” It’s not enough to let the data and models loose within the organization. People must remain in the loop to retrain, tweak and audit the results of these analytics products.
Tune into Dr. Spotfire for more
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To learn more about the future for data analysts and analytics, be sure to register for the upcoming Dr. Spotfire roundtable on Tues 12/6 at 12pm Eastern on LinkedIn Live. You’ll have the opportunity to chat with me and other industry experts on analytics trends for 2023, building data competencies in an organization, and more.
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