Archive for category data integration

Looking for the Small in Big Data

While the buzz in the big data market is around massive amounts of data and what you can do with that data, in truth companies are more interested in the small stuff. Let me explain. One of the greatest benefits of getting hold of lots of data is that a company can see patterns and anomalies that would have gone undetected with a smaller set of data.

Companies are interested in big data not so much because of what that data represents in terms of volume, variety and velocity, but what the analysis of that data can do for the business. As business decision makers begin to recognize the potential of big data, they realize that the biggest insights and hidden gems of knowledge are found when big data actually becomes quite small. However, the analysis must start with very large volumes of data. Exploring the world of big data takes business leaders beyond the data found in their organization’s traditional databases. Researchers, data scientists, and marketers, are analyzing very large volumes of unstructured data from previously untapped sources such as e-mails, customer service records, sensor data, and security logs.

Now that companies have the ability to deal with so much more data, they are free to incorporate a greater variety of data into the mix. For example, they are incorporating social media, mobile phone location, traffic, and weather data into more traditional analysis. But what is the goal? Add more data and more elements so that those patterns emerge. Armed with the insight from analysis, business leaders can take a more precise set of data and compare it to data from a data warehouse or a system of record. As a result, the research becomes more targeted and directed to fit in with the context of the business.

Why do companies need to take this targeted approach to leveraging big data? In essence, companies want to use big data analysis to make a personalized offer that is just right for the customer when that customer is ready to buy. In some cases, the answer may be related not to selling but to diagnosing problems with a manufacturing system or a patient with an unexplained illness.

How do you move from big data analysis to small data insights and personalized action? You need to consider three elements: defining your business problem, defining and analyzing your data sources, and integrating and incorporating your big data analysis with your operational data.

Defining your business problem. Companies are beginning to ask the traditional questions about customers, products, and partners in new ways. For example, are you looking to manage your customer interactions armed with in-depth and customized knowledge about each individual customer? Companies with a focus on driving continuous improvement in customer service are asking, “How can I delight this customer and anticipate their specific needs?” These are important goals for businesses competing in today’s fast-paced, mobile-driven market.

Defining and analyzing your data sources. What information do you need to make the right offer to your buyer when he deciding on a purchase? What information can you glean from outside sources such as social media data? What big data sources do you have available internally that were previously underutilized? For example, can you use text analytics to gain new insight about customers from call center notes, emails, and voice recordings? One important goal with big data analytics is to look for patterns and relationships that apply to your business and narrow down the data set based on business context. Your big data analysis will help you find the small treasures of information in your big data.

Integrating and incorporating the analysis of you big data with your operational data.
After your big data analysis is complete, you need an approach that will allow you to integrate or incorporate the results of your big data analysis into your business process and real-time business actions. This will require some adjustment to the conventional notion of data integration. In order to bring your big data environments and enterprise data environments together, you will need to incorporate new methods of integration that support Hadoop and other nontraditional big data environments. In addition, if you want to incorporate the results of very fast streaming data into your business process, you will need advanced technology that enables you to make decisions in real-time.

Ultimately, if you want to make good decisions based on the results of your big data analysis you need to deliver information at the right time and with the right context. In order to make the results of your analysis actionable, you need to focus more on the small – targeted and personalized results of big data – than on the large data volumes.

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IBMs Vision for Analytics in the Midmarket: gaining deeper business insight

I recently attended an IBM analyst meeting focused on solutions for the midmarket.  What caught my attention was the focus on analytics as an important and growing revenue opportunity for IBM.  In fact, IBM mentioned during the meeting that 70 percent of midsized firms are looking for analytics solutions.  It is clear from this meeting that IBM wants to bring a comprehensive set of analytical tools to the midsize companies.  Unlike some of IBM’s packaging, analytics tools are being packaged specifically for the midmarket so that they can be more consumable and affordable.

Analytics is fast becoming a high priority for companies as a result of the explosion in the variety, velocity, and volume of data with a potential impact on business decision-making. Much of this data is unstructured, such as the text included in customer service records, customer sentiment data in social media, or streams of data from instrumented devices.  Making good business decisions  often requires analysis across multiple sources and types of data.  Companies often have independent systems designed to manage business processes ranging from order/inventory to point of sales, marketing research, and customer relationship management. The challenge for many of these companies is that answering the most urgent questions about the business requires analysis across all of these independent systems. Even a small company with a few hundred employees may have a dozen systems are are disconnected and keep the company from having a full picture of the business.

Therefore, it is not surprising that some midsize companies are finding they can benefit from business analytics solutions. Yet, while some midsize companies are finding ways to get the answers they want using analytics, the word needs to spread to other companies still struggling with manual spreadsheet analysis that doesn’t go deep enough.

IBM is going to market through its business partners that typically support midsize companies with a variety of solutions. These business partners are being asked by their clients in the midmarket to help them implement technology solutions that will enable them to make smarter business decisions. They want to find new ways to deeper their understanding of customer expectations and priorities. For example, a midsize retailer might be trying to figure out why certain products are returned while others sell well.  The analytics market offers huge opportunity for IBM and its partners.

The approach IBM is taking with analytics for the mid-market is to offer its partners a pre-configuration of hardware and software into a single system at a price point  that is both affordable for midsized companies, but also has enough of a margin to make it attractive to a partner channel.

However, the challenge for partners is to change the traditional way they have gone to market.  Many partners that have built successful businesses by specializing in hardware sales or a specific category of software such as IBM  Rational find that they need to meet a broader set of client requirements.  They now need to both learn the new analytics products and be ready to sell and implement solutions differently.  Selling analytics to the mid market requires much more than a technical sell. Partners need to have a thorough understanding of the business context in which the analytics will be used to help customer visualize the potential business value.

One of IBM’s offerings that partners should be looking at is  the IBM Smart Analytics System 5710, which is a database appliance for business intelligence and data analytics targeted at the SMB market. The IBM Smart Analytics System 5710 is based on IBM System x, runs Linux, and includes InfoSphere Warehouse Departmental Edition and Cognos 10 Business Intelligence Reporting and Query.  The system is designed to enable partners to get their clients up and running very quickly a broad set of  analytics and business intelligence capabilities. I expect that you will see a lot more of this type of packaging from IBM with collaboration from its solution business partners.

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