top 10 big data pain points
TRANSCRIPT
Big data is the flavor of the season, with companiescutting across sectors and size lining up to get on thebig data bandwagon.
However, all the potential and advantages of Big Datacome only if the pain points that come along with itare resolved.
Big Data- A matter of concern
1. Data Trapped in Silos
The first challenge that comes to any big dataanalyst worth her byte is consolidating acrossthe enterprise.
Data is not just spread across multiplerepositories, but is invariably trapped in silos,many of which are inaccessible or not online.
2. Data Overload
Data is growing at an exponential pace intoday’s highly digital world, and before anorganization knows it, they are submerged withmassive datasets.
Simply collecting every bit of informationsimply loads up the data warehouse andanalytical engine with large volumes of mostlyuseless data.
3. Data Interpretation
It is important to understand where each pieceof data came from, and how it may be bestused.
Data visualization, or presentation ofinformation in a graphical or pictorial formatmakes it easier to understand information.
4. Data Cleansing Raw data, or the data that comes in may
not have appropriate headers, might haveincorrect data types, or might containunknown or unwanted character encoding.
It is essential to modify the raw data to getrid of these discrepancies, for consistency.
5. Technical Challenges Related to the Processor
GPUs or graphics processor units do the jobwell than traditional CPUs that may simplynot be able to withstanding the load.
GPUs cost a lot less than CPUs in any case,but the pain point is the difficulty inprogramming GPUs
6. Handling Huge Data Volume in Less Time
Companies today require a resilient ITinfrastructure capable of reading the datafaster and delivering real-time insights.
Many standard commercial packages such asApache Hadoop IBM InfoSphereBigInsights,Cloudera, and Hortonworks are capable ofresolving such challenges
7. Scalability It is important to get the interaction
between storage and processingcorrectly.
Scaling multiple workloads howeverpose a challenge and at times, it maybe required to expand and distributestorage on a temporary basis.
8. Security
Big data inputs come in from multiplesources, and it is important to ensure that allthe data that comes in are secured.
Big data processing takes place in the cloud,and all the inherent security risks of datatheft are ever-present.
9. High Budget
Big data analytics is costly, and costs canvery easily overshoot estimates.
With the amount of resources and manpower required to set things up it alwaysbecomes a heavy budget project.
10. Selecting the Appropriate Tool for Data AnalysisDeciding on the approach taken to collect,
store, and analyze data is one thing, anddeploying suitable tools for analysis quiteanother.
Organizations need to spend considerabletime before selecting an appropriate tool foranalysis, for it is difficult to move anapplication from one tool to another.
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