Page 5 - Telecom Reseller JanFeb 2017

Don’t Let Your Data Lake Turn into a Swamp
Telecom Reseller 5 ATHERTON continued from page 3
continue to take market share. In the search for growth and market share, larger UC vendors will ponder the buy vs. build question and in the coming years many will choose to continue strategic acquisitions as a way to broaden their service o ering. With VC funding harder to come by in recent quarters, expect to see M&A become a real, viable exit opportunity for many fast-growing and disruptive startups focused on these areas. e harsh startup reality of building an enterprise salesforce to go against established and imbedded incumbents will only further drive these companies into the arms of those incumbents. We expect Uni ed Communications M&A activity to remain high as all players search for growth and technology di erentiation in this constantly changing market. n
January/February 2017
HELINSKI
built data stores to more generalized “data lakes” – a repository of raw data from many di erent sources, all gathered and stored in its native format.
One of the objectives in the move toward
a data lake architecture was to reduce or eliminate data silos and their associated costs, including expenditures related to building, maintaining, and linking siloes together to answer complex data questions. e promise of the data lake was one-stop-shopping – all of a company’s data needs in one place under one roof, satisfying an entire organization’s disparate data needs with a single system.
DATA SWAMPS
However, raw untreated data in its native format is not conducive to easily deriving answers to complex data questions. is approach puts the burden on the person
Purpose-built data stores provided end users the advantage of requiring a low understanding of data structures and relationships, while still being able to derive accurate and actionable insights from the data.
asking the question to rst understand the data, understand how it relates to other data, and understand how best to interpret the
data in a way that will yield meaningful, and more importantly, accurate results. As Gartner once noted, “without descriptive metadata and a mechanism to maintain it, the data lake risks turning into a data swamp. And without metadata, every subsequent use of data means analysts start from scratch.”
Purpose-built data stores provided end users the advantage of requiring a low understanding of data structures and relationships, while still being able to derive accurate and actionable insights from the data. End users could drill deep into the siloed data, but rarely very wide.
Data lakes promised to allow users to drill deep and wide with their data, but working
with untreated data requires a level of data knowledge and sophistication that is likely only present in a small subset of an organizations’ end users. erefore, unless each business unit is sta ed with data scientists it becomes extremely challenging for the typical end user to get the promised value from their data lake repository.
CLEANSING THE DATA
e solution to prevent a data lake from becoming a data swamp is to incorporate data treatment processes that enrich and “cleanse” the data. By taking a few extra steps up front
to treat the data in a structured, uniform,
and controlled way, those downstream data consumers can more easily derive the insights and answers to support their business units from a centralized data repository – and arrive at answers that are consistent and provide matching results across business units. In-lake data treatment provides organizations an opportunity to increase the value of their data lake while reducing costs to business units trying to extract intelligence from the data lake.
In telecommunications, proper data
treatment can provide marketers, customer care agents, network engineers, and others with greater insights into a susbcribers’ pain points and reveal new opportunities for revenue growth. Network tra c can be analyzed and categorized to better understand usages and trends, anticipate directional changes, and calculate quality of service and experience metrics.
Data lakes are bene cial, but they do need proper treatment to produce easily consumable and usable information. n
by David Helinski, Chief Operating O cer of Inovvo (www.inovvo.com)
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