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A Smarter Planet

Smarter Computing

The IT infrastructure that enables a Smarter Planet

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Smarter computing: Designed for data

Every business—whether it's a small family business or an enterprise spanning multiple industries—faces the same challenge of how to extract value from all of its available data…so it can better serve its customers, anticipate trends, and improve processes. Better insight from data is a top priority for CEOs today, with an overwhelming eighty nine percent saying they want better insights through business intelligence and analytics. So it's no surprise that a smarter computing infrastructure is designed for data.

A smarter computing infrastructure can help manage the challenges of traditional databases and data warehouses to:

  • Reduce costs while improving service levels

  • Create a single version of the truth across many devices

  • Sustain a viable data lifecycle over time and technologies

  • Extract insights from data that yield tangible results

Today, we face even greater challenges in managing data. Eighty percent of the world's data is unstructured data residing in sources outside traditional databases and warehouses. And the amount is growing rapidly. Fortunately we now have tools to process a variety of data, extract business intelligence from huge volumes of data, and analyse high velocity streams of data as it arrives.

A smarter computing infrastructure helps you capture, analyse and generate insights from these new data streams coming into your business so you can integrate them with your traditional sources of data. You will gain more business intelligence to guide your business decisions.

Here are the challenges customers have identified and the Smarter Computing projects designed to address them.

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Solutions designed for data

Manage

Users of an information supply chain want to access data reliably and quickly, but you need to manage your costs and get the most out of your existing resources. You need to provide performance levels that meet the service level agreements. You also need to cut costs.

These are the challenges that cause many IT decision makers to follow the path of consolidating workloads—across servers, applications and databases, and through virtualisation of assets. Consolidation can also be a first step towards enabling your infrastructure for cloud computing.

Projects to address these challenges:

Consolidate database servers

Consolidate databases from multiple server machines into fewer DB2 instances running on IBM enterprise servers. 

Reduce the amount of data stored on disk

Compress or de-duplicate your data to reduce the amount of data.

Increase storage utilisation

Use IBM storage virtualisation hardware and software to create large pool(s) of storage shared across applications and systems to increase overall utilisation.

Automate storage tiering for better performance

Increase performance by putting the most-often accessed data on the fastest media (such as SSD), and put the least accessed onto slower and cheaper devices.

Upgrade to a scalable database

Upgrade to DB2 or add DB2 pureScale to current DB2.

Case study

Borçelik (US)

Borçelik reduces total cost of ownership for its SAP application environment by migrating to IBM DB2

Founded in 1990, Borçelik is Turkey's second-largest flat steel producer, focused on   product excellence and relentless cost reduction. The company has been using SAP ERP applications, initially with an Oracle database platform. Licensing and support costs were rising, so they looked for a more efficient solution to help optimise business processes.

Working with Compro, an IBM Business Partner, Borçelik migrated its SAP application environment to IBM DB2, running under IBM AIX on IBM Power servers. They gained:

  • About 25% in savings on software licensing and maintenance fees
  • More funding for strategic solutions

Integrate

With all the data sources and applications in a business, it is easy to get multiple versions of the same information (such as customer profiles) with conflicting data and mismatched formats. You can't leverage your data until you can integrate it from multiple sources, cleanse it, and ensure that you have a single version of the truth.

Projects to address these challenges:

Implement Master Data Management (MDM)

Create a master repository for key data such as customers or product info.

Integrate data, turning data silos into trusted information

Take data from different sources, check it for quality, cleanse it, re-format it and combine it with other data to gain business insights across the enterprise.

Case study

Suncorp-Metway Ltd (US)

Suncorp-Metway Ltd integrates data for smarter marketing

Suncorp-Metway Ltd., a diversified financial services group that offers general insurance, banking, life insurance and wealth management services based in Australia, integrated data from multiple, previously siloed sources for smarter marketing that limits internal conflict among brands and cuts costs.

They were able to:

  • Integrate data from 23 sources into a single master data hub
  • Consolidate 23 million source records into nine million unique accounts
  • Save $10 million per year in storage
  • Uncover new cross-selling opportunities

Govern

Good business dictates that you maintain the security and privacy of customer information and in many cases, laws and regulations require you to demonstrate that you have done it. You can't just let your data grow; you need to manage the data life cycle to keep current information accessible and store old information offline. And you need tools to continuously monitor and improve data quality.

Projects to address these challenges:

Automate database security auditing and reporting

Use IBM tooling to create policies and monitors to ensure data security and privacy.

Manage data lifecycle to comply with data retention policies, cut storage costs, and facilitate application retirement

Use IBM tooling to manage archiving of data from applications that have been retired, but keeping relevant, needed data online.

Continuously improve and monitor data quality

Use tools to check the data for errors, validity, format, and content to ensure analysis is being done against clean, accurate, usable data.

Case study

BlueCross BlueShield (US)

BlueCross BlueShield automates data management

BlueCross BlueShield of North Carolina is an insurance provider subject to HIPAA compliance. To manage an annual data growth rate of over 30%, they deployed IBM Optim Data Growth Solution.

They were able to automate management of member data, reducing staff and eliminating redundancies. Plus, they:

  • Reduced storage costs by 40-50%
  • Netted $2 million in annual savings

Analyse

The reason we collect data is to use it to make good business decisions. You need to ensure you have high performance and easy to maintain data warehouses and analytics tools. We have defined some actions you can take to ensure you can utilise all the data available to you.

Projects to address these challenges:

Deploy new analytics capabilities to support critical business processes

Get the basic business analytics in place using good data and operating efficiently on an IBM system.

Replace existing data warehouses with data-ready, flexible systems that deal with mixed analytical and transactional workloads

Create a warehouse using IBM Smarter Analytics Systems.

Replace existing data warehouses with data ready, low maintenance and high performance data warehouse appliances

Create a warehouse using IBM Netezza.

Analyse large streams of structured/ unstructured data in real time

Start using InfoSphere Streams to process large volumes of data in motion.

Analyse large volumes of structured/ unstructured data at rest

Use InfoSphere Big Insights to analyse huge volumes of data at rest.

Case study

Catalina Marketing

Catalina Marketing boosts coupon redemptions with predictive analytics

Catalina Marketing used Netezza for predictive in-database analytics to help retailers identify items that shoppers are likely to buy in future visits. Real-time analysis of the contents of shopping baskets triggers printouts of coupons handed to shoppers with their receipts at checkout. This resulted in 25% increase in coupon redemption rates.

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Meet the expert

Anjul Bhambhri

Vice President, Big Data Development

About Anjul

Anjul Bhambhri has 23 years of experience in the database industry with engineering and management positions at IBM, Informix and Sybase. She is currently IBM's Vice President of Big Data Products, overseeing product strategy and business partnerships. Previously at IBM, Anjul focused on application and data lifecycle management tools and spearheaded the development of XML capabilities in DB2 database server. In 2009, she received the YWCA of Silicon Valley's "Tribute to Women in Technology" Award.

Breakthrough IT economics

Explore the extraordinary economics of smarter computing. This paper uses case studies to illustrate the advantages of optimised systems, solutions built for data, and cloud delivery.

Resources for big data