By Myles Suer
Published on July 30, 2021
CIOs seek salespeople who use plain language to describe complex business problems. In Key Takeaways from ‘The Qualified Sales Leader’, I shared that salespeople must sell product value over feature and function if they wish to excel.
The question I ask now is: What business problems does a data catalog solve for business customers? Fortunately, the #CIOChat has organically provided the answers over the last couple of months.
CIOs and CDOs are under the gun to deliver results faster, and this includes enabling a data-driven culture. A part of that journey often involves moving fragmented on-premises data to a cloud data warehouse. But how do you do so effectively and efficiently?
You clearly shouldn’t move everything from your on-premises data warehouses. Otherwise, you can end up with a data swamp. From a technical perspective, you want to quickly move only the right data to a cloud data warehouse and implement this migration in a cost-efficient manner.
Former CIO Tim McBreen says, “Cloud data warehouses and analytic data require effort to make sure they work and are delivered at their contracted cost per unit of data moved and stored. This goes back to knowing what you are doing and maintaining and evolving your catalog, architecture, and strategy.”
“Lifting and shifting poor practices will just move your problems to the cloud,” Hurwitz Analyst Dan Kirsch adds. Cloud Data Warehouses, therefore, are an opportunity to accelerate data driven decision-making — but only if they deliver the right data in an effective and efficient manner.
A data catalog empowers faster migration by spotlighting the best data, so you only migrate what matters. Put differently, you can migrate faster if you know the right data to move. Once migration is complete, your time to value is accelerated because you already know what you have in the cloud.
And in the era of Big Data, less is more. A curated collection of the most useful, trusted data empowers confident usage across all departments. Savings are a piece of this, too. By migrating only what matters, organizations no longer pay for peak compute, which means that spend can be invested in more targeted ways.
Good, trusted data is no accident; data governance is a crucial prerequisite. In fact, businesses are increasingly making data governance a first-step for analysis, finding that fueling analytics with well-governed data produces more reliable, trusted results.
As Tom Davenport argues in Analytics at Work, “You can’t be really good at analytics without really good data.” (23). Given that good and trustworthy data is the goal, the technical problem emerges: how do you implement data governance so data is ready for analysis?
“Data architecture represents a different kind of complexity,” former CIO Joanna Young asserts. “In fairness, a lot of organizations with all or most of their computing on premises do not have this well organized either. Ideally, IT organizations need to get this sorted before making a move to multicloud.” This is a painful lesson for many organizations, who have a lingering mess of data… and no real means to rationalize it.
“The key message is whatever is a mess on-premises will be a mess in multi-cloud,” Young continues. “Clean it up before or as part of switching and moving. Cloud in and of itself is not a cleansing agent! Instead it’s a different business model.” To make migration successful, leaders must “ensure there is a data governance process in place,” CIO Carrie Shumaker states. “This means… ensuring data is made available, not locked down unnecessarily.”
Without question, the COVID-19 Crisis has been a watershed event. It has made hybrid and remote work a reality for all employers, fundamentally changing the space-time continuum. This means that human band aids for business processes no longer work.
It also means that analyst productivity is a bigger and more pressing challenge than ever before. Analyst productivity was a problem before remote work became the new normal. Now that so many are remote indefinitely, how do you quickly supply people with the actionable data they need to do their jobs well?
In this way, the crisis has brought employee experience to the forefront. Leaders are focused on increasing remote employees’ productivity, and that involves making data and information more widely available. CIO Marin Davis says this includes fixing “outdated security models, inability to access key apps or data remotely, problems connecting, and the list goes on.” In this new remote-work era, former CIO Isaac Sacolick argues that IT teams need to do three things:
Catalog and label data (they need to know where it is, and the data’s sensitivity)
Identify data owners (even if they’re not ready to handle the responsibilities, and
Limit usage for BI, applications, etc. (until 1 and 2 are addressed).
Businesses have always wanted to minimize reputation loss because of its impact on brand equity. In his book Managing Brand Equity, David Aaker suggests that disasters can reduce the perceived value of a firm’s products or services.
Fortunately, CIOs are no longer lonely voices crying out in the wilderness. Recent hacks and ransomware requests have grabbed the attention of every business leader. The question all should be asking is: How do you actively protect the data that the bad guys want?.
While data catalogs don’t guarantee against a ransomware attack per se, they mitigate risk by discovering and protecting sensitive data. Indeed, the technical challenge here is discovering sensitive data, governing it, and protecting it. “You should make your data worthless during a cybersecurity breach,” advises Kirsch. “The first thing, therefore, in protecting sensitive data is to find it,” recommends CIO Carrie Shumaker.
Once you’ve found it, you’ll need to label it. “Cataloging data is the first step to bringing greater organizational awareness around the importance of protecting critical data assets,” points out Isaac Sacolick, President of StarCIO and bestselling author. “When data sensitivity is labeled, it enables IT to provide the highest protections to these assets.”
Clear labels in a catalog power smart governance and compliant usage post-migration. Kirsch says, ”One of the biggest challenges when thinking about cloud migration remains cloud security and compliance.”
You don’t know what you can’t find. The final business challenge in achieving value from available data is related to connecting users to data that they do not know about. The technical problem is that most firms lack the data intelligence to discover what is there.
This problem is what Sacolick calls dark data. “Dark data is data and content that exists and is stored but is not leveraged or analyzed for intelligence or used in forward looking decisions,” he points out. “Internally, dark data is already captured and stored by the enterprise but not leveraged to drive insights or decision making.
“This makes it a threat as well as a missed opportunity,” he continues. “The threat is if storing the data impacts the performance of a key business operation or contains sensitive information that should be better secured. The opportunity is to really figure out the value of retaining and processing this data.”
Modern data catalogs leverage machine learning to locate potential high-value data. Machine learning learns from human signals around data usage to spotlight valuable data sources for wider use. By making the best data sources discoverable, machine learning connects more experts to more actionable, useful data.
The GDPR and CCPA are fundamentally changing how businesses work with data. Businesses who disregard compliance best practices run the risk of incurring massive fines under these ever-evolving laws.Take Google as a cautionary tale. In March of 2020, France’s top court for administrative law upheld a penalty against the search giant for $56.6 million. Had Google provided more information to users about consent policies, and given them proper control over how their personal data is used, this penalty could have been avoided (source).
CIOs need software that supports compliant usage, even as those regulations evolve. This is not easy! Governance leaders are learning the hard way that external checklists simply don’t work because people don’t use them. This is why modern data catalog software is integrating compliance best practices into the UX design itself.
The above business problems clearly matter. The important thing is that CIOs and their vendors get focused upon business problems that matter. And several of these can be solved by an effective data catalog. It is clear from my discussions with CIOs that technology should never be the end. The end should always be about enabling the business to be more effective with customer facing business processes. The above problems, fortunately, meet this end.