Skip to main content

Guiding practices for dealing with IT Budget cuts


It is that time of the year when the CIOs, IT managers and financial analysts are trying to close on 2013 IT budgets while still managing the business needs and IT vendor costs. Add to this, the risk of unchartered Big Data and Analytics projects which promise a hidden treasure of $ but with an added rider of new investments.

Here are some guiding practices which you may want to consider while optimizing your project portfolio in 2013 and still carry on with your vision of Apache Hadoop projects.

1)      No short cuts please:

So while you may want to do away with frills and riders considering the cost cut, keep in mind that the standard compliance norms apply as much for your Hadoop and Big Data projects, as much they apply for other projects. So, you still have to ensure data security, access role segregation and purging policy as you apply for other data projects. Private Information (SPI/BPI) rules apply here as well. So, make sure, your Business Analyst and Architects have this ticked on your charts. Don’t leave it to the vendors or to the developers to ensure this all important compliance to avoid regulator and legal hassle later.

2)      Start with Basic(s):

Most of the Hadoop Distributions come with a Basic Edition which can be easily leveraged for your pilot projects. As you see business interest and technical team confidence in the project, you can order up the Enterprise stack. A decent level of due diligence is expected before you pick on a basic version as this is the distribution you may like to stick to when buying the Enterprise package.

3)      Cloud is here to stay:

As much as you would like to own your kingdom, it is a fact that basic upfront cost and lead time can be avoided with cloud provisioned hardware and software. The catch could be that small developer mistakes could lead to infinite processing loops and hundreds of $ added to your bill overnight. Make sure your set in system for monitoring cloud usage and wherever possible en-cash on free tiers and sign up incentives for your pilots.

4)      Agile is the way to go:

While business team have always harped on Agile for ages, your project portfolio may find it difficult to suddenly adopt a new delivery way. With volatile requirements and new discovery being a reality in Big Data, it is better to break projects into sprints or better use a Kanban Task based approach. Agile will also ensure you don’t invest in all resources up-front and refine your cost plan through the project prioritizing for each storyboard item.

5)      Invest in DevOps:

It is essential to ensure a smooth handshake between Dev and Ops.  While it is true that each portfolio has its own complexities, ensure that you enforce tools and standard operating procedures which make staging and deployment a cake walk. Better dev machines, automated code push and training could lead to a leap and bound improvement in productivity for the team resulting in a net lower cost of development.


The list could be bloated further to stress on partnerships, open source pilots and focus on revenue driven projects. However, as may have been evident to you each year, each CIO needs to reach a crucial mix of strategic moves along with cost cutting initiatives. Staff downsizing as has been stressed always should be last step even though it always catches the biggest attention due to it major cost pie percentage.  This list though written with Hadoop projects in mind may in fact hold true for other technology projects also.

Judicious use of each penny and still leveraging the benefit of cutting edge Hadoop and Big Data projects has to be exemplified as a CIO skill rather than a top driven mandate.  So, Mr.CIO, go out and make the most of the new tech – let the $ guys shrimp it with a shirk. 

------------------------------------------
Image source: rackspace.com, wikipedia.org

Comments

Popular posts from this blog

Offloading legacy with Hadoop

With most Fortune 500 organizations having invested in mainframes and other workload systems in the past, the rise of Big Data platforms poses newer integration challenges. The data integration and ETL players are finding fresh opportunities to solve business and IT problems within the Hadoop ecosystem.
To understand the context, challenges and opportunities, we asked a few questions to Syncsort CEO Lonne Jaffe. Syncsort provides fast, secure, enterprise-grade software spanning Big Data in Apache Hadoop to Big Iron on mainframes. At Syncsort, Lonne Jaffe is focusing on accelerating the growth of the company's high-performance Big Data offerings, both organically and through acquisition.
From mainframes to Hadoop and other platforms, Syncsort seems to have been evolving itself continuously. Where do you see Syncsort heading further?Lonne Jaffe: Syncsort is extraordinary in its ability to continuously reinvent itself. Today, we’re innovating around Apache Hadoop and other Big Data pla…

Data deduplication tactics with HDFS and MapReduce

As the amount of data continues to grow exponentially, there has been increased focus on stored data reduction methods. Data compression, single instance store and data deduplication are among the common techniques employed for stored data reduction.
Deduplication often refers to elimination of redundant subfiles (also known as chunks, blocks, or extents). Unlike compression, data is not changed and eliminates storage capacity for identical data. Data deduplication offers significant advantage in terms of reduction in storage, network bandwidth and promises increased scalability.
From a simplistic use case perspective, we can see application in removing duplicates in Call Detail Record (CDR) for a Telecom carrier. Similarly, we may apply the technique to optimize on network traffic carrying the same data packets.
Some of the common methods for data deduplication in storage architecture include hashing, binary comparison and delta differencing. In this post, we focus on how MapReduce and…

Top Big Data Influencers of 2015

2015 was an exciting year for big data and hadoop ecosystem. We saw hadoop becoming an essential part of data management strategy of almost all major enterprise organizations. There is cut throat competition among IT vendors now to help realize the vision of data hub, data lake and data warehouse with Hadoop and Spark.
As part of its annual assessment of big data and hadoop ecosystem, HadoopSphere publishes a list of top big data influencers each year. The list is derived based on a scientific methodology which involves assessing various parameters in each category of influencers. HadoopSphere Top Big Data Influencers list reflects the people, products, organizations and portals that exercised the most influence on big data and ecosystem in a particular year. The influencers have been listed in the following categories:

AnalystsSocial MediaOnline MediaProductsTechiesCoachThought LeadersClick here to read the methodology used.

Analysts:Doug HenschenIt might have been hard to miss Doug…