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Posts Tagged ‘Streaming analytics’


Many companies have found success in building data warehouses that meet basic needs, but are now finding they need to move beyond the back-office warehouse to leverage information on the front lines of decision making throughout their entire company. They need information on demand and need the ability to build systems that can deliver on those promises with real incremental returns.

For those who understand the power of an analytics-driven organization, this is a most exciting time. The opportunities are limitless: customers, prospects, suppliers and the business itself are creating endless geysers of data. Analytics tools are inexpensive, widely available and so easy to use that they make business sense in almost any situation.

To move forward, organizations need a strategy that delivers on several focused business requirements:
1) Operational management: Accelerate time-to-market to meet business SLAs for new and existing business processes, operational analytics and business intelligence (BI).
2) Big data: Leverage unstructured data, social media and other “big data” information sources to gain more insights from more data—without impacting the business SLAs.
3) Predictive analytics: Forecast future trends and analyze risks and potential outcomes.

Many IT organizations are adopting a strategy called smart consolidation that reconciles the need to simultaneously distribute data warehousing and analytics capabilities and infrastructure while centralizing management. Smart consolidation is a method for evolving an existing data warehouse architecture to meet today’s demanding analytic needs, such as big data, streaming data and unstructured data.

In a nutshell, it involves thinking beyond the traditional warehouse structures that have provided great success with structured data, basic reporting and analysis. Smart consolidation is driven by these four goals:

  1. Consolidate and govern enterprise data
  2. Optimize workloads for performance and SLAs
  3. Simplify the delivery of analytics by leveraging appliances
  4. Flexibly extend analytic capability as needed

The basis for smart consolidation is to completely optimize an analytics architecture by placing the right workload against the right data, in the right place, at the right cost and the right performance level.

Smart consolidation acknowledges that an organization requires different types of databases, analysis tools and data formats. It needs traditional data warehouses, data warehouse appliances and operational BI systems that can accommodate different types of workloads. It also needs systems based on advanced technologies that can efficiently handle data that is moving extremely quickly as well as large volumes of data that does not change frequently.

Single system? I think not

No single, data system could efficiently serve all these requirements and perform well for both transactional and analytical workloads. Under the smart consolidation strategy, multiple specialized elements use industry standards to communicate and join together to form a fluid, agile data ecosystem that delivers business insight, cross-organizational data governance and centralized IT resource management. By allowing many different elements to serve specialized needs, smart consolidation also enables organizations to accommodate the endless variety and rapidly growing ocean of semi-structured and unstructured data.

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Data will always lead information, always has, always will. Years ago, we created more data than we could analyze and understand at the time. Today, the same. Tomorrow, the same. The amount of data being created will always lead the ability to get information and understanding from it.

“Big data” is a leading edge description of having more data than can be processed into information, analyzed and understood. Many definitions of big data exist, let’s say 100TB or bigger for the sake of arguement. The volume, variety and velocity of data today is certainly accelerating, no question about that. But go back a couple of decades, and we could have made the same statements every year.

Leading companies in the big data space have solutions available today that can tap into an unprecedented amount of data. Petabyte-scale data warehouses, although not pervasive, are nothing new. Assembling the data is one thing, but analyzing it, presenting it and governing it is another. THE leading company has assembled a full “platform” covering the full breadth – operational analytics, deep / advanced analytics, predictive analytics, federated analytics, Hadoop analytics, streaming analytics… complete with end-to-end information governance.

Here is a sampling of big data use cases. Just skim through this and it’s sure to get your creative juices flowing on what CAN be done in your company. http://public.dhe.ibm.com/common/ssi/ecm/en/imc14715usen/IMC14715USEN.PDF 

Once you’ve skimmed through this, come back here and post your comments on 1) how you are currently using big data today, or 2) what you would like to use big data for.

And you know what? Years from now, the amount of data available will still outpace the ability to analyze it. At that time, will we call it “bigger data”?

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