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8/25/2016 What's Holding Data Scientists Back? Upside https://upside.tdwi.org/articles/2016/08/25/whatsholdingdatascientistsback.aspx 1/5 What's Holding Data Scientists Back? By James E. Powell August 25, 2016 Data scientists are the darling of tech reporting this year, but not every enterprise that hires them knows how to support them in their work. Upside recently spoke to Kon Leong about the role of data scientists. Leong is the CEO and founder of ZL Technologies -- a supplier of content archiving software for large environments. Leong described how an enterprise can enable data scientists' best work by providing quality data management and encouraging collaboration. Upside: How would you dene the role of the data scientist? Kon Leong: People tend to think of data scientists as number-crunchers, but this generalization vastly understates the true value of a skilled data scientist. It's not where DATA means BUSINESS upside.tdwi.org DataDriven Biz Tech Talk Intersection BizIT Visualize It Subscribe Latest Articles Alpha Centauri, Here We Come Data Digest: Analytics for Data Storage, Structure for Data Scientists, Build Scalable Applications Is Collaboration the Critical Success Factor for Data Science? Data Stories: The Impact of Global War Related Articles Governed Data Discovery: Blending Self-Service and Standards Data Digest: Data SHARE WITH: What's Holding Data Scientists Back?
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8/25/2016 What's Holding Data Scientists Back? Upside
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What's Holding Data Scientists Back?By James E. PowellAugust 25, 2016
Data scientists are the darling of tech reporting this year,but not every enterprise that hires them knows how tosupport them in their work. Upside recently spoke to KonLeong about the role of data scientists. Leong is the CEOand founder of ZL Technologies -- a supplier of contentarchiving software for large environments.
Leong described how an enterprise can enable datascientists' best work by providing quality data managementand encouraging collaboration.
Upside: How would you de냩ne the role of the datascientist?
Kon Leong: People tend to think of data scientists asnumber-crunchers, but this generalization vastlyunderstates the true value of a skilled data scientist. It's not
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understates the true value of a skilled data scientist. It's notjust about running statistical analysis on data; it's aboutknowing -- or 냩guring out -- the right questions to ask.
A curiosity engine is what gives data science itshorsepower. Modern computing allows us to run incrediblycomplex calculations on incredibly large and diverse sets ofdata, but without the human capability to discern the rightquestions to ask, those computing capabilities are nearlyuseless.
In essence, the ideal data scientist is closer to a detectivethan a mathematician. The work is about collecting andprocessing data, discarding false leads, establishingmultiple lines of inquiry, and piecing together the mostplausible narrative.
What is holding data scientists back from executing therole that they were hired for?
We often see data scientists spending an inordinateproportion of their time simply trying to manage data:trying to make the data that's available into data that isactually useful and accurate. Data scientists need cleanseddata to extract insight, and all too often the data hasn'tbeen cleansed or managed before they arrive.
This is especially problematic in analysis of unstructureddata: the messy content of email, documents, media, andother communications. Despite the need to manage thiscontent for legal, compliance, and records management --or perhaps because of it -- unstructured data is oftenscattered across multiple disjointed data "silos" throughoutthe enterprise.
The problem today is that in the big data erathere aren't really important or unimportanttypes of content.
In theory, these isolated repositories allow speci냩c
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In theory, these isolated repositories allow speci냩cfunctions to be performed on certain subsets of data. Forexample, an enterprise content management platformmight allow granular life cycle policies to be applied tocertain "important" business records.
The problem today is that in the big data era there aren'treally important or unimportant types of content. Data issimply data in the eyes of the law, and the organization isresponsible for it. Isolated silos of content are preventingcontrol and hindering data scientists.
Someone needs to be in charge of pooling and managingdata so that it can be a resource rather than a burden. Thequestion, then, is who should lead the way.
Who (or what) in the organization should be handlingdata management?
Data management isn't a specialized function requiring justa single specialist to maintain it. It's an enterprisewideframework that structures and supports the entire weightof the organization's intellectual capital.
A brick-and-mortar o짋ㆅce space aims to maintain anorganized environment where employees have all the toolsthey need to complete their jobs. Similarly, acomprehensive information governance frameworkprovides easy access to all the human-created content thatindividuals need to conduct their communications andprojects.
That said, someone needs to be in charge or else theinitiative is likely to proceed at a glacial (or nonexistent)pace.
Many organizations in the early phases of a datamanagement initiative will elect a committee to head theproject, which is a step in the right direction. Often thisteam includes representatives from business units thatwork most frequently with human business content:records management, in-house counsel, compliance, and
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About the Author
James E. Powell is the editorial director of TDWI,including the Business Intelligence Journal and Upsidenewsletter.
records management, in-house counsel, compliance, andIT.
The problem with this committee approach is that there isoften no "captain" at the helm; all committee members areequally responsible, and they still maintain their full day-to-day workloads. With that structure, it's very di짋ㆅcult to moveforward.
Nearly everyone in the organization depends on datamanagement in one way or another. That's why it isabsolutely essential to involve interdisciplinary stakeholdersthroughout the process. Having a dedicated full-timeperson in charge ensures that progress is made, thatstakeholder disagreements don't end up in stalemates, andthat all business needs are considered in theenterprisewide data management infrastructure.
How can the C-suite better support data scientists intheir work?
Stereotypes have uncanny persistence. We tend to think ofscientists -- in the "data science" context or otherwise -- aslone masterminds rather than inseparable team players.This is a dangerous misconception for the enterprise.
Direct lines of communication are critical, yet data scientistsoften lack the support and collaboration that they need forsuccess. Data scientists are problem-solvers, not magicians,and they need interaction with other stakeholders tounderstand business needs, risks, and objectives.
Without regular exposure to a high-level perspective, theywill languish in the trenches with the data, missing the bigpicture.
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