From the Blogosphere
Five Questions Around Big Data
Data is the new currency of business and we are in the era of data-intensive computing
Feb. 6, 2013 09:00 AM
Data is the new currency of business and we are in the era of data-intensive computing. Much has been written on Big Data throughout 2012 and customers around the world are struggling to figure out its significance to their businesses. Someone said there are 3 I’s to Big Data
- Immediate (I must do something right away)
- Intimidating (what will happen if I don’t take advantage of Big Data)
- Ill-defined (the term is so broad that I’m not clear what it means).
In this blog post, I would like to pose five key questions that customers must find answers to with regards to Big Data. So here goes.
1. Do I understand my data and do I have a data strategy?
There are varieties of data – customer transaction data, operational data, documents/emails and other unstructured data, clickstream data, sensor data, audio streams, video streams, etc. Do I have a clear understanding the 3V’s of Big Data – Volume, Velocity, and Variety? What is data “in motion” vs. data “in rest”? Data in motion demands split-second decisions and do I have such tools? Every data source must be understood followed by their attributes and growth projections.
Customers must have an overall data strategy based on their business importance. For example, business critical data must be highly reliable, secure and of high performance. A data policy must be in place to take care of volume, growth, retention, security and compliance needs.
2. What are my reporting needs to transform my business and give me insights for growth?
Businesses are transforming to stay ahead of the competition. While we asked, “what happened” in the past, now it is “why did it happen and what is going to happen?”. From data collection, we have to move to data analysis. Instead of analyzing existing business, we must create new business. Therefore, the retail industry wants to give “today’s recommendation” on the fly to clients; internal IT needs operational intelligence to make it more efficient; customer service must provide customer insight; and fraud management must look at social profiles to reduce fraud. The list goes on…
Do you have a clear understanding of your reporting needs via data visualization on mobile devices like the iPad with touch interface? You will need a strategy of all the analytic tools for key employees/executives to make quick business-relevant decisions.
3. How do I drastically reduce my TCO of Data Warehousing and BI?
Many large enterprises are spending millions of dollars to move operational data to a data warehouse via ETL tools (Extraction, Transformation, Loading). This can be expensive and time consuming. Sears, for example, has a slogan “ETL must die”. By moving to Hadoop, they reduced the ETL time from 20 hours to 17 minutes. They claim serious cost reductions by moving from traditional ETL to direct loading of raw data to Hadoop servers. Today’s implementations must be studied for price-performance and newer technologies can bring down costs and improve processing time drastically. Would you like to develop reports in days rather than weeks?
4. How does Big Data co-exist with my current OLTP and DW data?
All enterprises have business-critical operational systems (OLTP). These are using traditional DBMS systems (such as Oracle, DB2, IMS, etc.). They also created separate Data Warehousing systems with BI tools for analysis. Now the new world of Internet data such as chatters from social networks and Web Log data (digital exhaust) are adding to the complexity. What is your approach to data integration of the legacy vs. new data?
5. What is the right technology for my needs?
I keep hearing so many new terms and vendor names – Hadoop, Cloudera, Hortonworks, Datameer, NoSQL, MongoDB, Map-reduce, Data Appliance, HBase, etc. It surely can be very confusing!
I need to know what is the right technology for my needs. If I have petabyte volumes data coming from various sources, what technology can I implement to efficiently handle that? Then, how do I get relevant information from that pile to help my business insights? I also need to know what skills I need to do that and the cost. I need an implementation roadmap for getting value from all the data that my business is coming up with.
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