I recently caught up with Kon Leong who is CEO and Founder at ZL Technologies, a specialized provider of electronic content archiving software for the most demanding large enterprise environments. Kon is responsible for managing all aspects of the business, including strategy, finance, sales and marketing. Earlier, Kon was co-founder and president of GigaLabs, a vendor of high speed networking switches. Kon earned an MBA with Distinction from the Wharton School and received an undergraduate degree in Computer Science from Concordia (Loyola) University, after completing a year at the Indian Institute of Technology. Here is what he had to say.
Daniel – Managing Editor, insideBIGDATA
insideBIGDATA: The problem with searching for a needle in a haystack is that the process, by nature, is inefficient. So why has it become a popular analogy for analytics efforts within the enterprise?
Kon Leong: The reason is because today’s analytics attempts – particularly for unstructured human data – are typically a mess. It often takes a ton of data and processing to get useful insight. Efforts are often ad hoc, and rely on incomplete or skewed sample sets. They commonly focus on only one narrowly-defined data type. So for each “needle” of insight, it seems that heaps of data are scattered and cast aside, often at the cost of subsequent business efficiency. When it comes to business content such as files, email, social media, IMs, calendar entries, images, and more, firms are failing to extract meaningful insight despite the potential wealth of information contained within.
insideBIGDATA: What is the root of the problem?
Kon Leong: The reason behind this is that data is poorly managed to begin with. Businesses are treating analytics as a separate business function from data governance, when it’s actually fundamentally dependent on it. Analysis occurs downstream from data management; so by neglecting initial information governance infrastructure and practices, the enterprise is essentially sampling tiny random buckets of data from a whitewater river of information.
insideBIGDATA: How should businesses overcome these challenges and better manage their data to derive value?
Kon Leong: Businesses don’t necessarily need to get rid of the extra data “hay” that these stacks are composed of; that would entail getting rid of potentially valuable content. They just need to completely re-think how the data itself is managed. No more data “haystacks” means no more disparate data sources, no more ad hoc sampling attempts, and no more dirty or duplicated data. Furthermore, it vastly reduces the compliance risk associated with data mismanagement; with consolidated control, policies for management and eventual disposal can be implemented centrally and securely.
insideBIGDATA: How should data governance be used to help businesses to succeed?
Kon Leong: Data governance is the necessary foundation of all successful data analysis. The statistics axiom of “garbage in, garbage out” is used ad nauseam for a reason: because it’s accurate and timelessly relevant.
Organizations should be working to build a data lake, pooling all available resources into a pre-cleaned, singular environment where content can be managed and analyzed in real time. Forward-leaning businesses are already making strides to achieve this, and they’re not doing it with flashy analytics tools – those can come later, once the foundation is built. In a cohesive governance environment, analytics should be brought TO the data, rather than data cumbersomely being sampled and brought TO analysis tools.
insideBIGDATA: What is the key takeaway for businesses aiming to improve their data management strategy?
Kon Leong: If large organizations want better analytics, they need to start with better information governance practices. Now, not later. Flashy tools currently on the market can distract organizations from longer-range goals of data management: particularly in the case of publically-held firms. But to neglect governance today means to sabotage future attempts at big data analysis… in a very big way.
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Excellent advice Every Company will manage records electronically sooner than later and time is running out for them to get an handle on data. More data can mean higher risk of litigation but properly managed you can turn data into an asset. Kon said ” If large organizations want better analytics, they need to start with better information governance practices. Now, not later. ” Adopt a set of principles and metrics so you know where you are so that you can establish goals and a roadmap to get data under control.