Data Confessional: Insights beget Insights
This is the last installment in a five-part series where we’re following a business analyst on a critical mission. His company is generally successful, but there’s a growing concern about customer loss. There’s still new business coming in, but the steady drumbeat of exiting clients is sure to get louder and become a serious problem. So Cliff is tasked with digging through the data to find answers: Why are customers who should provide ongoing revenue heading for the door, and what can be done to reverse this trend?
Think of it as a shopping adventure with only a vague notion of what you need. You have a perspective, a starting point, but you don’t know where it might take you. We’ve identified 12 steps, each representing a distinct purpose, along the way:
Step 1: Build a Business Glossary
Step 2: Establish Data Domain Models
Step 3: Define Policy and Reference Management
Step 4: Catalog your Assets
Step 5: Harvest Lineage and Use
Step 6: Refine with Profiling & Scoring
Step 7: Correlate using Data Matching
Step 8: Pivot to Idea Management
Step 9: Retrieve using Service Broker
Step 10: Protect with Access Management
Step 11: Perfect using Compositing, and…
Step 12 — Register to Data Marketplace: Enhance your Data Intelligence Graph by posting trustworthy, pre-defined analytical data sets that are both virtual and dynamic, such that it is both discoverable and executable by the organization at large.
In Part 4 of this series, Cliff was able to check out at the mere click of a button. This set off a workflow that included a Data Use Agreement (DUA), distributed and federated extraction of data sets, enforcement of identity and access policies and the rendering of 360-degree views of duplicate information. And Cliff selected how he wished to take delivery – borrow, lease or buy. With the selection of borrow as the preferred delivery option, Cliff receives notification when the requested data is available and secured in a virtualized container, not physically stored in a database or filesystem, for the duration in which he authorized from the DUA.
Cliff has countless options for how to view and work with this returned data set. If Cliff is partial to a specific Business Intelligence/Reporting platform such as Tableau, Looker, Power BI, etc., he may choose to visualize the raw and/or aggregate data through the dimensions, pivots, widgets, graphs, etc. that are native to his preferred platform. Independent of any assistance – technical or otherwise – operating at the speed of thought, and benefiting from the objective guidance that presents the greatest opportunity to use the best possible data for his analysis, Cliff is now prepared to learn why there’s a clear gap between customer wins and customer retention. This is a time when the company should be focused on new products aimed at new markets, not why is it losing existing business? The answer is probably not simple: The modern operating environment is often sprawling and complex, with a convoluted supply chain, regular compliance headaches, competition from upstarts and conglomerates alike, and so much more. But the data offers results that are likely unimpeachable, when it is trustworthy.
These learnings not only become the foundation of churn analysis but also serve as the building blocks for a business plan—a clear path forward, guided by intelligence and opportunity. It’s a shoppable asset that fits seamlessly into the data intelligence graph. This is the ‘brain’ powering Data Intelligence: It marries content to context and collates the collective wisdom of companywide perspectives to transform raw nuggets of information into relevant and trusted knowledge. More importantly, this knowledge is not hoarded – it is shared and repeatable.
Of course, even the most pinpoint findings can represent a snapshot in time. This is a business world defined by ongoing digital transformation with dynamic trends. The next business professional with the same credentials and access controls launching a data search with the same parameters may get the very same results…or very different results that are more relevant, and accurate, for that moment in time. This is the dynamic power of the Analytic Asset that Cliff created, and it can be further adapted for subsequent initiatives.
The 12 steps to Data Intelligence do seem to cast a wide net and cover a lot of ground. It seems deep and detailed, and it is. It suggests a significant use of time and resources…and it’s really not. The technologies to make this process possible exist today. Unfortunately, it requires extreme effort by organizations to select, integrate and maintain four or more best-of-breed technologies to even get close to a seamless, end-to-end data intelligence platform. Until now. Until Collibra. Join us on our journey as we deliver on this seamless, outcome-oriented service (yes, as-a-Service) to not only digitally transform your business but to become a data intelligent organization. You can hear more on how we’re delivering this in my session during Data Citizen’s 20: A Digital Experience on June 23.
There’s one other element of this journey that deserves attention. At Collibra, we’ve long maintained that Data Intelligence is not only about technology. We’ll get bigger databases and traverse the cloud to optimize performance and scale. We’ll commoditize many skills with agile apps and a scalable infrastructure. But ultimately, this is about people and processes. The goal is always to make it easier for people to find the right data, trust it, build on it and collaborate with it.
The real value of data is in the usage. Without that, we have yet more databases that are pristine and clean, and utterly useless. The business mission has failed. With proper access and usage, we open the door for data democracy.
Cliff is not a data scientist, he didn’t seek the permission of compliance counsel and never called on IT specialists. He built on their work—which had previously gone into establishing rock-solid processes to remove friction, limits and obstacles—to access the data he needed and build the plan he wanted. The data did its job, so that he can do his.
And with Data Intelligence, that’s the case with all of us.
We hope you have enjoyed this five-part series and we welcome your feedback. Stay tuned for additional Data Intelligence blogs – we have much more to share.
To read previous installments in this series, check out: Part 4, Part 3, Part 2 and Part 1.