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CIOs Must Beware Big Data Blindness By @ABridgwater | @BigDataExpo #BigData

Big Data is an oft mentioned and sometimes misunderstood subject

It's not hard to find technology trade press commentary on the subject of Big Data.

Variously defined (in non-technical terms) as the cluttered old shoebox of all data - and again (in more technical terms) as that amount of data that does not comfortably fit into a standard relational database for storage, processing and analytics within the normal constraints of processing, memory and data transport technologies - we can say that Big Data is an oft mentioned and sometimes misunderstood subject.

Three key Big Data control factors
Good advice for CIOs faced with this new planet of data types driven by everything from ecommerce to the Internet of Things (IoT) is to look for technologies that provide three key controlling factors and functions:

  • Management
  • Automation
  • Enhancement

Without management, automation and enhancement controls, Big Data starts to feel like blinding sun glare with no hint or promise of shade.

What CIOs need to move toward is a point where a more ‘governed' approach to Big Data ingestion starts to become possible. We need to know how trusted any particular data stream is before we combine it into our analysis engine - and that truth holds whether we are talking about structured, unstructured or semi-structured data (or a combination of all three).

We also need to be able to exert some sort of time-based intelligence upon our Big Data, especially as we start to move into the realm of real time databases and event processing. Then we need to know how trusted that data is too.

This is our third (above) bullet if you will, i.e., Big Data ‘enhancement' through additional intelligence. Almost like traditional metadata being defined as "information about the information," this is enhanced information about the Big Data information.

The Big Data selection pack
What we have to now realize is that Big Data comes in more than one shape across a spectrum of data architectures.

  • Data warehouses (in the traditional sense) - and these are good at holding onto prepared, structured data
  • Data lakes (a bigger broader consideration) which essentially sit at the other end of the spectrum to data warehouses - data lakes are good at acting as a repository for raw, unprocessed native data, i.e., this is the ‘big' in Big Data, the stuff we don't always know what to do with yet.
  • Data refineries (you might see other names for these, such as data factories or data processing plants) - this is the place where we look to transform raw data and incorporate data sources that are too varied or fast-moving to house in the data lake.

Getting to the point where we can create the new ‘blend' of Big Data from these now differentiated sources is where the modern CIO next focuses his or her efforts. Getting to the point where you can amalgamate and virtualize datasets on the fly as you also get additional insight by knowing the data lineage details of each chunk of data is an even better place to be.

There are no prescription sunglasses for Big Data blindness, but there is an awful lot of dazzle and distraction around - if in doubt, wear a hat, but keep your eyes wide open for a new rainbow of data definitions.

This post is sponsored by KPMG LLP and The CIO Agenda.

KPMG LLP is a Delaware limited liability partnership and is the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative ("KPMG International"), a Swiss entity. The KPMG name, logo and "cutting through complexity" are registered trademarks or trademarks of KPMG International. The views and opinions expressed herein are those of the authors and do not necessarily represent the views and opinions of KPMG LLP.

More Stories By Adrian Bridgwater

Adrian Bridgwater is a freelance journalist and corporate content creation specialist focusing on cross platform software application development as well as all related aspects software engineering, project management and technology as a whole.

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