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Big data requires extreme workloads

Read Using Big Data for Smarter Decision Making by Colin White.

Big data involves more than just the ability to handle large volumes of data. It also represents a wide range of new analytical technologies that opens up new business possibilities. But before reaping the rewards of big data analytics, there comes a set of challenges around deploying new technologies into existing data warehouse environments and providing systems that optimize computing performance for different workloads.

As I explored in my recent posts on smart consolidation, the data warehousing and analytics environment is more complex today than even just a few years ago. Many have found that mixing operational analytics and deep or advanced analytics on the same system brings significant challenges to performance and meeting SLAs. With operational analytics, business managers need continuous data ingest and fast access to standard reports with the ability to perform ad hoc queries that drill down into the data and provide new perspectives and insight. When a deep analytical query comes along that requires significant data volumes and extreme computing resources, operational query performance suffers. Big data adds yet another complexity around data sources, data quality, longevity of the data, and whether some of the big data should be integrated into the enterprise data warehouse for longer-term historical analysis.

The best way to handle these different types of workloads is to optimize systems to the workload, and combine these solutions with the enterprise data warehouse to create an “analytical environment”. We see many types of optimized systems in the market today – data warehouse appliances, data marts, noSQL systems, Hadoop-based systems, streaming data analytical systems, cloud-based solutions, etc., that complement (not replace) the enterprise data warehouse. Each system is optimized for a specific workload, and used together they can help streamline and provide fast response to a wide variety of business needs.

A majority of organizations today already understand this – really, optimizing computing resources to various types of data and associated workloads is nothing new. At some point in the data warehouse and analytical environment evolution, organizations reach a tipping point that drives separation of data and workloads. Data growth and new sources of (traditional) data, an increased number of users, increased complexity of queries, and “big data” are all drivers of this tipping point.

Colin White of BI Research wrote a white paper exploring new developments in data warehousing and analytics and the benefits that analyzing big data brings to the business. The paper also reinforces this notion of optimizing systems based on the types of data and workloads. The conclusion – integrating these systems together into a single analytics infrastructure drives smarter and faster business decisions. Read Using Big Data for Smarter Decision Making.

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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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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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Find out more: http://www.ibm.com/software/data/infosphere/warehouse/
Virtual Conference: https://events.unisfair.com/index.jsp?seid=33250&eid=556 

IBM InfoSphere Warehouse is a complete real-time data warehousing platform that delivers superior scalability and availability, design, build, and management tooling, and business analytics. It provides 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.

New and enhanced features of version 10:

One of the driving motivations behind this new version 10 is the desire to accelerate time-to-insight so that decision makers throughout an organization can make faster (and better) business decisions. Accelerating time-to-insight focuses on two key areas: 1) the latency of data ingest, and 2) the analytics latency – time to perform analytics and provide insights to decision makers.

Business value is realized when a decision is made or action taken.

Reducing Latency improves business value by being able to make decisions faster.

Continuous ingest

In more traditional warehouse environments, additional data is loaded once a day, once a week or perhaps even once a month. InfoSphere Warehouse continuously ingests data from multiple sources throughout the organization while optimizing resource utilization across all the data sources.

Continuous ingest of data optimizes loading of data – not once per day, not once per hour, but continuously. This ensures that the most up-to-date data is used all the time – no more waiting for a nightly ETL process and scheduled reports to be run the next morning. This is a significant feature for companies where being first is critical – ha, I can’t think of any company that wouldn’t want to analyze fresh data and take action before their competitors!

Multi-temperature data management

As warehouses grow in size, storage costs escalate. At the same time, service-level agreements (SLAs) for warehouse access are becoming tighter for certain mission-critical warehouse information. Multi-temperature data management enables warehouse data to be spread across multiple tiers of storage, defined by performance or cost. The optimizer and workload management features use information about data priority to enhance query performance for hot data while reducing resources for lower-priority queries that access archival or cold data.

Faster performance delivers insights faster. Pretty simple concept. Multi-storage data management puts high priority, frequently accessed data in the fastest (hot) storage available to the warehouse, and of course, less frequently accessed data in warm and cold storage. This accomplishes two things – 1) for end users, queries run faster, and 2) for IT, it optimizes performance, storage and costs.

Storage savings and enhanced performance through adaptive compression

Adaptive compression improves on the leading compression technology found in prior versions of InfoSphere Warehouse by adaptively determining the optimal compression between row- and page-based compression. Using InfoSphere Warehouse 10.1, organizations can improve the compression ratio by nearly 30 percent compared with prior versions. By combining InfoSphere Warehouse and the terabyte charge metric (in which organizations pay for only the post-compression data volume), organizations now can store more raw warehouse data with less storage while enjoying improved performance to query that data. Adaptive compression eliminates the need to completely rebuild compression dictionaries as data grows and reduces the requirement to periodically perform table reorganizations.

Time travel queries through Temporal Table support

The relevance of data within a warehouse often degrades significantly over time. For example, knowing the specific updates to a customer’s record throughout the day may provide valuable insight into a customer’s activity patterns. Two days later, however, it may no longer be necessary to store same-day activities data within the warehouse. Temporal Table support enables organizations to enhance table definitions to define the relevance of transitional changes within the rows stored in the table. After changes reach a predefined threshold, historical (temporal) changes are removed from the table. Organizations can use temporal analytics to gain new insights into the patterns and transitory activities of their business—insights that were previously too expensive to manage or store within the warehouse.

Improved efficiency and performance for BI queries

To improve the speed of BI queries, InfoSphere Warehouse provides industry-leading efficiency in collating multiple data formats, including unstructured data. The result is a 3x or better performance improvement for standard, star-schema types of complex BI queries. Intelligent analytic query optimization helps reduce I/O to minimize resource requirements for heavy workloads.

Advanced security through row and column access control

Advanced access control allows organizations to restrict data access to certain users or groups. Administrators can decide to present only the data that users are allowed to see, or they can mask certain data, such as all but the last four digits of credit card numbers. Quick setup and standardization of security features simplifies security, enabling organizations to control, monitor and help ensure enforcement of security by the database. These security policies help reduce complexity for organizations trying to manage these policies and procedures within applications and business groups. Fine-grain access control capabilities also enable organizations to host multitenant warehouses while limiting access to a single tenant’s information, thereby leveraging resources across multiple warehouse clients.

Real-time operational warehousing

Using the features and enhancements of InfoSphere Warehouse 10, organizations can realize the benefits of real-time data warehousing and deliver greater business value through faster access to time-critical data. Historical trend analysis using temporal tables allows finer insight into business and market trends. By maximizing the utilization and prioritization of resources, organizations can achieve those benefits at a lower total cost of ownership.

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Well, I have to admit. Using the retail sales of high heeled shoes to indicate growth or a downturn in the economy is bit out there. But after seeing IBM in the news this week, I decided to look into this a bit further.

And they are right; the height of high heels sold does provide an indicator of the current economic situation. In an economic downturn, data shows that the height of high heels goes up – evidently, women buy higher heels in an attempt to escape the reality of tough economic times. Surely, feeling better about yourself, feeling prettier, perhaps a bit of personal indulgence, does help one escape the feeling of being controlled (can’t escape a bad job), beaten down (unemployed), and even trodden upon financially (foreclosed).

I have spoken about retail analytics a number of times in recent months, and I have been using a graph to open my presentation that shows the rise and dramatic fall of U.S.consumer spending as a wake up call to those analytically inclined. Consumer spending is such a key part of the U.S.economy and the retail industry. And as I wrote about in one of my earliest blog posts, in good economic times, everyone makes money. But in these tough economic times with consumer spending at a similar level to 1997 (mind you, after a fairly significant increase from its recent low), it takes much more than luck to survive let alone prosper. It takes an “analytics-driven” attitude to survive and thrive.

“This time…something different is happening – perhaps a mood of long term austerity is evolving among consumers sparking a desire to reduce ostentation in everyday settings.”

So what will happen later this week on “Black Friday?” Will consumer spending be strong – hitting the $20 Billion mark on Black Friday as analysts at MasterCard predict, or will it limp along like a wounded duck? Is there enough pent-up demand after consumers have cut back so much in recent months, or is there a bright future on the horizon for retailers? Will the sales, promotions and advertising make a difference? Will hot ticket items like the new Kindle Fire be strong performers? What will happen to retailers that don’t sell hot ticket items?

There are a lot of questions here…. One thing, however, is certain. Many retailers that rely on gut feel may not make it. But those that mined their data, that used predictive analytics, and that extended themselves to analyze the “big data” of social media, well…they may hit the proverbial nail on the head.

Let’s see what shakes in the upcoming days. Stay tuned for more….

http://www-03.ibm.com/press/us/en/pressrelease/35985.wss

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