Posts Tagged ‘warehouse appliance’

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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No writing today, just  sharing a nice article on InformationWeek… http://www.informationweek.com/news/global-cio/interviews/showArticle.jhtml?articleID=229201238

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IBM’s Analytical Ways – Why Big Blue has invested $12 billion in data analytics.

Written at Forbes.com by Kym McNicholas, http://www.forbes.com/2010/11/05/data-analytics-ibm-technology-cio-network-jonas.html

In good economic climates, even a monkey on a rock can make money. In the tough times that we face today, you have to know your markets and your customers deeply in order to make better, faster and more confident decisions. This can only come from an integrated data warehouse based on trusted information, that is mined for trends, insights, opportunities and cost savings.

1 in 3 business leaders make decisions on information they do not have or do not trust, and 1 in 2 don’t have access to the information they need to do their jobs (IBM CIO Study 2010 – http://www-935.ibm.com/services/us/cio/ciostudy/executive-views.html).

So the New Year’s Resolution you must make, is to invest in providing your executives and Line of Business managers with the information and insights they need. And provide visual reports, scorecards and dashboards on key performance indicators.

You can bet that your competitors are fast at work doing this. Data warehousing and business analytics are more important in today’s global economy than ever before.

Once you have this insight, then and only then, can you take the right actions to optimize your business.

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Forbes.com BLOG – You can grow wealth in 2011 like Amazon, Apple, Priceline, Colgate – http://blogs.forbes.com/adamhartung/2010/12/24/you-can-grow-wealth-in-2011-like-amazon-apple-priceline-colgate/

This article lays out a great set of recommendations, but HOW do you execute these? For every company in every industry in every market, the key to accomplishing the recommendations in this article lies within the company’s information warehouse and business analytics functions. That is, in order to make decisions on each of the key points above, you must know your markets and your customers deeply – better than your competitors. But, 1 in 3 business leaders make decisions on information they do not have or do not trust, and 1 in 2 don’t have access to the information they need to do their jobs (IBM CIO Study 2010 – http://www-935.ibm.com/services/us/cio/ciostudy/executive-views.html).

So the New Year’s Resolution you must make, is to invest in providing your executives and Line of Business managers with the information and insights they need – actionable insights, based on trusted information. With sooo much data and information available today, to everyone, companies need to integrate it all together into a trusted data warehouse, mine that information to identify trends, patterns, business opportunities, cost savings, who your most profitable customers are, what your optimal marketing mix is, which business segment is most profitable, etc etc etc. And provide visual reports, scorecards and dashboards on key performance indicators.

You can bet that your competitors are fast at work doing this. Data warehousing and business analytics are more important in today’s global economy than ever before – when the economy is growing, even a monkey on a rock can make money, but it takes deep insights and better, faster decisions to make money in a tough economy.

Once you have this insight, then and only then, can you implement the suggestions laid out in this article.

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I think that a key reason why Business and IT Alignment is coming up again is that the two functions are now moving at significantly different paces. In years past, IT often lagged the business needs, taking some time to implement enterprise-wide initiatives. That was acceptable when business moved rather slowly. Now, business is moving faster and faster, and IT is struggling to keep pace.

This is one of the reasons that Microsoft SharePoint has gained so much ground as a departmental content management system – departments could not wait for IT to agree on and deploy an enterprise-wide ECM solution, so they ventured ahead with many (far too many, in most cases) SharePoint systems. Now they shot themselves in the foot, with far too many silos of disconnected information. And IT must regain control and pull everything back together.

Also, warehousing and business intelligence systems need to rely on a single source of trusted information on which Line of Business Managers and Executives make their daily business decisions. Again, the pace of what is possible in business has accelerated…there is so much data and information available today. Michael Porter’s “Competitive Advantage” plays a new role here – he who can make sense of all the information to better understand market and customer dynamics, and take action on that new insight, wins.

With so much information available, businesses want more and more insight, and they often struggle to state their requirements clearly. It’s been said that 1 in 3 business leaders make decisions on information they do not have or do not trust, and that 1 in 2 don’t have access to information they need to do their jobs. That is just plain scary! This is a key source of business and IT misalignment.

The issue is now doubly difficult – IT has to bring all the existing sources of information together, and they have to wrap in new sources that can be quite large (e.g. mining social media to understand market perceptions, or using social media as a marketing channel or at least supporter of marketing initiatives).

Telcos have massive data sources to understand customer churn. Banks, financial services organizations and insurers have tons of data to better evaluate risk and detect fraud. Healthcare providers can tap into new sources of data to better evaluate treatment options to improve patient outcomes. Police departments can dive into many new sources of local information to predict when and where crimes may occur. Retail organizations can better segment their customers/prospects to improve promotion response rates and better coordinate inventory levels with marketing campaigns. RFID tagging can track items, or shoppers, everywhere, providing retailers and supply chains with tons of information to improve efficiencies. The list of possibilities goes on and on and on….

And my closing statement which seems to resonate well with those I speak with… In good economic times, even a monkey on a rock can make money. But in today’s challenging times, it takes an agile, intelligent company to make sense of all the data and information at their fingertips, gain new insights and take decisive action. Companies that consistently invest in gaining new insights outperform their peers by a very significant margin. IBM’s “The Global CFO Study 2010” shows that very clearly. Data Warehousing & Business Analytics can provide that game-changing insight. Today, it’s the difference between survival and failure.

Ref:  http://www-935.ibm.com/services/us/gbs/bus/html/gbs-2010cfostudy.html

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‘Tis the season to be jolly….  That is, if your merchandise is selling!

What makes a successful holiday season for a retailer?  The National Retail Federation (NRF) is predicting a 2.3% rise in the U.S. over 2009 spending, certainly better than what we’ve seen in the past couple years.  http://www.nrf.com/modules.php?name=News&op=viewlive&sp_id=1010  At a macro level, that’s great…but what about an individual retailer?  How does an individual retailer get their piece of this macro pudding pie?

Let’s look at this question through a tale of two retailers – Larry’s Men’s Wear and Barbara’s Boutique (both fictitious).  Both businesses have been around for about the same amount of time and they each have 7 stores in similar locations and online shopping.  Both have had similar sales over the past few years and stock nearly the same level of inventory.  In other words, they are about as similar as you can get, except Larry’s store caters to men and Barbara’s store caters to women.

Both businesses suffered in the past two years, seeing their year-over-year sales drop 7% in 2008 and 1% in 2009.  Their 2008 season was 3% worse than the national decline of 4%, and their 2009 season was 1.5% worse.  They both trimmed staff, held less inventory, and utilized many different promotions to keep customers coming into their stores and shopping online.

There is one key difference, however, that started in early 2010.  The difference is that Barbara made a tough strategic decision to invest in upgrading her company’s data warehouse and business analytics system this year, where as Larry did not.  Barbara knew that she needed more facts and insight to run her company smarter, while Larry held back, hoping that this year’s sales will be good enough to fund his upgrades next year.  Let’s see how this one important decision is affecting their businesses now.

Larry has relied upon the same data model, the same data elements and the same analytics and reporting for the past couple years.  All daily transactions are processed, cleansed and loaded into his data warehouse on a nightly basis, and a set of standardized reports are run and sent out to himself, his VP of Merchandising, his CFO and COO, and each of the store managers.  He reviews these reports with his staff and store managers on a weekly basis, trying to spot trends in the data.  Among other items, they look at daily, weekly, monthly and quarterly sales status by store, top selling categories and items, pricing and incentives, margins and inventory levels, and customer segmentation.  This system has served Larry well, providing good insight into historical trends that enable his management team to make decisions on marketing campaigns, inventory purchases and management, pricing and promotions, etc.

Based on market and operational data, Larry and his team selected their holiday items carefully, as always, knowing that November and December “make or break” their year.  They were not lacking in traditional data, so they felt comfortable that their decisions, marketing campaigns, promotions and inventory levels would serve them well.

Well…they were mostly right.  Larry’s Men’s Wear is seeing an improvement over last year, but it’s not at the level that Larry was hoping for.  Through October, sales were up 2% over last year, but profit margins were down 3% due to deeper discounts and a few inventory issues – they were overstocked on two lines that they thought would sell better, and they had stock-out issues on several hot selling suits.  On Black Friday, they experienced similar issues, and a number of long standing repeat customers had to go elsewhere to find what they wanted.  It’s unclear yet what impact this will have on customer loyalty and 2011, but the outlook is just not as rosy as Larry would like.  Their November sales were flat compared to last year, and margins were 3% lower.

Barbara, on the other hand, started back in the Spring to upgrade their warehouse and business analytics system.  Her company tappedd into some financial reserves and credit lines to purchase one of these talked-about data warehouse and BI appliances – a complete software, hardware, storage and services package.

Barbara and her staff invested their time to think through what new data they wanted to capture, how they would update their data model, and what new standard (and ad hoc) reports they would want in order to gain deeper insights into market and retail trends as well as their store and online operations.  They laid out a set of goals to increase top-line sales, improve pricing and promotion effectiveness, increase marketing campaign response rates, reduce inventory stock-outs, drive larger margins and improve customer loyalty.

Rather than take gigantic leaps, they did their best to take reasonable steps forward, taking time to gather new data and ensure data quality, test their data model enhancements, and improve both the visual and interactive nature of their standard reports and ad hoc queries.  Once the technical side was up and running (took only weeks with this new appliance system, not months and months to piece together a multi-vendor system or do a build-your-own approach), they began to test new business assumptions, trends, price elasticity and more.

Within a short number of weeks, store managers were coming up with a ton of fresh ideas, suggesting some minor changes to store layout, tying multiple products together in new promotional ideas, and getting more granular and focused in their marketing campaigns.  At the same time, they were starting to do a much better job forecasting demand so that they could order the right amount of inventory – and not experience as many stock-outs nor be left with excess inventory that needed to be heavily discounted.

Although the fully operational system was not in place for the full year, Barbara’s Boutique saw 5% sales growth year-over-year up through the end of October.  Better yet, their margins improved 4% as they gained more and more confidence in their pricing and promotions.  Total inventory was down 2% and the number of stock-outs was cut in half.

Now, November results would give a glimpse of how the full year would shape up.  To Barbara’s excitement, Black Friday sales posted an 8% gain over last year, and in-store surveys were showing strongly positive customer satisfaction – customers felt that store marketing was more “personal” to them, they enjoyed getting x% off a pair of shoes when they bought a new dress (one example of a new cross-merchandize promotion), and they even felt like the store staff were more friendly and personal.  All good signs!

Larry sitting on a rock.

Looking forward at the start of this project, Barbara and her team were quite anxious, but now looking back they realized that this stress and anxiety was ill founded.  Morale and productivity are at their highest levels in years, thanks to empowering managers to make better decisions based on more complete information.  Barbara is thinking about taking her top store managers on a trip to the Bahamas in the Spring.

The moral of the “tale of two retailers” is that a monkey on a rock can make money in good economic times, but it takes deep market/customer and operations insights…good data and smart decisions…to make money in tough economic times.  Don’t be left holding the bag…get on with what you know you need.

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Key links for more info, straight from the source.

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