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Posts Tagged ‘Data Warehousing’


Oh no! The next “big data” project is coming!

Your IT infrastructure has grown and evolved over many years and is the heart and soul of how your company operates. You’ve invested years to get it to where it is today – it’s running smoothly, and you consider your IT staff to be the very best at what they do. But deep in your heart you have an uneasy feeling…you know what’s coming next.

There is a backlog of projects on your plate. Important projects that will improve your company’s bottom line. First-mover projects that will tap into “big data” and empower your line of business managers to pursue new markets and get a jump on your competitors. But you know that your infrastructure can’t handle much more and that your staff can’t keep up with performance tuning and the few projects that are currently in the works.

The CEO just requested a meeting for next week. You know another significant big data project is coming, and this is just the tip of the iceberg for what’s coming later this year. On your drive home you ask yourself, “How can I possibly take on more projects, and more data? How can I change my infrastructure so I can deploy new applications faster? How can I shift my staff from tuning and maintenance to focus on higher value work?”

Workload optimization with expert integrated systems

As more servers, storage and software components have been brought into the data center, complexity has risen to the point of being almost unmanageable. General purpose systems have been forced to handle multiple workloads, and teams of database, application and system administrators spend a great deal of time and effort configuring, tuning and maintaining the systems for top performance and efficiency. With such a complex infrastructure, reliability often suffers and system downtime becomes a serious business risk.

At the crux of the issue is that different applications have different data workload characteristics, placing often conflicting requirements on the hardware, storage and software. Transaction and analytic processing tasks constitute very different workloads. Unless your data workloads are modest with respect to characteristics like data volume, number of users and analytics complexity, you need systems optimized in different ways to efficiently meet big data challenges.

Typically, IT organizations purchase general purpose systems that are not optimized for any workload – systems that are general purpose in nature. They tune these systems for one workload or the other, and spend considerable time and effort keeping the system tuned.

But what is good tuning for one type of workload is not good for another. Data retrieval optimizations that benefit one access path are likely to penalize alternative paths. The structural elements that were optimized for transactions, for example – indexes, shared memory, locks, caches, etc. – all impose performance and complexity penalties in an analytic environment, where unpredictable (“against the grain”) access paths and patterns are the rule.  Systems that are optimized to handle structured data are different than those that handle a wide variety of unstructured or structured data.

Separating transaction processing and analytic processing onto separate, workload-optimized systems helps ensure that overall performance is optimized. Data transaction systems can process large numbers of simple look-ups, while analytic systems execute complex queries on massive volumes of data.

There is a great opportunity to improve system performance and efficiency, and to accelerate solution deployment, by using expert integrated systems that come from the factory already optimized for specific workloads. And this is why IBM designed and built the PureData System with different models that are specifically optimized for different transaction processing and analytic workloads.

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A modern data warehousing and analytics architecture

Consider the example of a credit card company.When a customer applies for a credit card, the sales department collects the customer’s details and financial history, and the compares it to historical data from third-party reporting agencies to determine the customer’s ability to manage and repay debts. The customer data flows to the marketing department, where it is analyzed for trends and compared with opinion content collected from the Internet to make decisions on promotional campaigns.

Eventually, the customer might request a credit-line increase, at which time the customer service system will recommend up-sell opportunities and the lending department uses the customer’s payment history to evaluate the request. Meanwhile, the company’s online transaction processing (OLTP) systems are fielding millions of transaction authorization requests per minute. Real-time analytics systems are looking for anomalies that may indicate fraud by comparing the streams of transaction data to patterns developed by analyzing customers’ purchasing histories. As all this data ages and becomes more static, it shifts to archival systems and is stored using specialized technologies like Apache Hadoop—yet it remains available for instant auditing and long-term trend analysis.

At the same time, the marketing department is investigating a new customer segmentation model to use in an upcoming product launch. Marketing has been busy analyzing their complete customer database to determine online banking trends as well as smart phone and mobile banking adoption rates. After many iterations of their segmentation model, they believe they have identified the data elements and customer behaviors that define a financially sophisticated and technologically savvy customer segment. Now, several months prior to the launch, the product manager is running predictive models to test the business case on combinations of marketing messages and user adoption rates. The team is free to test and retest their assumptions, even though their queries take a long time to execute, because they are running on an analytics-optimized data warehouse appliance—not the primary operational analytics system.

The credit card company is taking advantage of distributed data and a distributed workload architecture. By intelligently separating workloads, it is able to creatively analyze data to identify new business models, test assumptions for new paid services and optimize launch and execution plans without impacting the daily, hourly and up-to-the-minute operational needs of its core business.

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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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Big Data

Big Data (Photo credit: Kevin Krejci)

In the case of big data, “Build it and they will come.” The building of big data is the explosion of information, partly made possible by the Internet, partly by massive storage at low cost, etc etc etc, these factors are quite well known and discussed.

But in the ever-competitive business climate out there, aggressive and agile line of business managers have developed a passionate thirst to mine all that data for the next competitive advantage – be it a new business model, tapping into emerging markets, finer customer segmentation, real-time marketing offers, reducing customer churn, fraud detection, customer sentiment analysis…the list goes on, bounded only by the creative juices of business leaders that want to succeed and win…meaning, there is no bound to the potential uses of big data.

What is exciting right now is the “perfect storm of big data.” It is here, and whipping up little tornados all over the place. It is an exciting time to be in this space!!!

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Find out more: http://www-01.ibm.com/software/data/infosphere/warehouse/
Find out more: http://www-01.ibm.com/software/data/db2-warehouse-10/

Scenario: A retail store is now able to predict and replenish its merchandize stock by operationalizing an automated process flow. The retailer can analyze inventory data and product stock levels in real time. By deploying IBM InfoSphere Warehouse to reduce decision latency, the store’s accelerated decision-making capability now effortlessly keeps pace with the speed of demand.

Key benefits of InfoSphere Warehouse v10:
Faster Insights

  • Real-time Business Intelligence as operational data is continuously being feed into the warehouse.
  • Faster, accurate decision making, turnaround times.

Improved Cost Efficiencies

  • Adaptive Data Compression provides on average 30% improvement (up to 75%) over IBM’s existing Deep Compression.
  • Multi-Temperature Storage allows you to optimize data storage cost-efficiency.

Higher Performance

  • Star Schema optimization delivery for quicker response times – delivering 3x performance on BI workloads.
  • Continuous Ingest of data optimizes loading of data leading to faster decision making.
  • High Availability Operational access concurrent with analytics.

Increased Team Productivity

  • Built-In Time Travel query enabling faster historical and trend analytical queries.
  • Row and Column access controls to support multiple tenant operational warehouses.
  • Native Bi-Temporal support improves developer and DBA productivity.

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Find out more: http://www-01.ibm.com/software/data/infosphere/warehouse/

Facing the limitations that can hamper access to insight

Many existing data warehousing solutions have limitations, such as a lack of real-time operational data, that make it difficult for organizations to fully leverage the power of information. 

  • Limited reach: Business users need to access and analyze a broad array of information, from the unstructured information in call center notes, emails and blogs to the structured information in databases, spreadsheets and other data sources. Yet current infrastructures often have limited reach. The proliferation of transaction systems and the emergence of new data sources available outside the business force users to cast a wide net to gather the information they need to support better decision making.
  • Limited access: Enterprises must deliver useful and timely information to more people as part of everyday business processes to drive innovations and reduce costs. Business intelligence (BI) and analytics tools can help, but only if they are widely available rather than limited to high-level decision makers and specialized analysts.
  • Limited depth: Many existing information infrastructures also have limited depth. Often business users are unable to get answers to complex ad hoc questions. IT teams need flexible tools that can capture and deliver more types of information in the way that users need it, when they need it, where they need it and how they need it.
  • Poor flexibility: Older BI systems may restrict the type and quantity of data that certain users can access. Organizations need ways to support a large numbers of diverse users and enable those users to quickly and easily customize how they receive information based on their specific needs.
  • Poor responsiveness: In many cases, existing information systems lack the responsiveness required by today’s business landscape. Organizations can gain a significant advantage over competitors if they can quickly analyze a wide range of information and deliver actionable insights to executives and frontline decision makers. They need solutions that can be implemented swiftly and then deliver rapid results.
  • Excessive costs and complexity: Some BI solutions can be costly to acquire, difficult to deploy and complex to manage. Organizations need solutions that can be implemented quickly and include capabilities that streamline administration and lower the overall cost of ownership.
  • Lack of real-time operational data: Without the availability of accurate, real-time operational data, business decision making can stall and organizations may miss identifying opportunities and insights. Several factors contribute to a lack of real-time operational data. For example, archaic extract, transform and load (ETL) processes can lead to delays in capturing operational data into the data warehouse. Performance overheads in handling heavy workloads and complex queries from the data warehouse for analytics processing can also cause problems. Within the data warehouse, non-responsive resource allocation according to the priority and criticality of data can slow analytic results.

Organizations must address these challenges and work to achieve on-demand access to insight. If they succeed, they can target smaller customer segments and communicate with them about their individual needs and wants. They can identify and capitalize on even the smallest trends, attaining competitive advantages normally only realized by more flexible and dynamic businesses. They can detect small behavior patterns that can have a significant influence and impact on business in terms of revenue, expenses and growth. Most important, they can build competitive strategies around data-driven insights and generate impressive business results.

Gaining insight without boundaries using IBM InfoSphere Warehouse

Through dynamic warehousing, IBM helps organizations extract insight from virtually any type of data—helping to deliver the right information at the right time and in the right context so business leaders can make the right decisions quickly. IBM dynamic warehousing solutions integrate data warehousing and business analytics to help define an organization’s central business concepts and the data required to support those concepts. These solutions allow organizations to pull data from a variety of enterprise and source systems that traditional BI and data warehousing solutions have not been able to access in the past. As a result, IT organizations can better support business requirements for actionable information. This information is not just raw data but data backed by intelligence that can help people take action and make sound business decisions.

IBM InfoSphere Warehouse is a complete, multipurpose environment that allows organizations to access, analyze and act on operational and historical information, whether structured or unstructured. With InfoSphere Warehouse, organizations can gain the insight and agility they need to generate new opportunities, contain costs and satisfy customers. Unlike traditional data warehouse and BI solutions, which may be complex and inflexible, InfoSphere Warehouse simplifies the processes of selecting, deploying and maintaining an information management infrastructure while offering the flexibility for dynamically integrating and transforming data into actionable business insights. It enables organizations to centrally, accurately and securely analyze and deliver information as part of their operational and strategic business applications.

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