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.

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.

Asking the right questions about information governance

I am looking forward to attending The Smart Governance Forum (23^rd meeting of the IBM Data Governance Council) in California on February 1-3, where I will be a panelist for a session on Smart Governance Analytics. As my panel group started to plan for the event, I did some background research on the Council to understand more about them. What kinds of questions were Council members asking about information governance when they began meeting in 2004 and how are things different today? Have they developed best practices that would be useful to other companies working to develop an information governance strategy?

Information governance refers to the methods, policies, and technology that your business deploys to ensure the quality, completeness, and safety of its information. Your approach to information governance must align with the policies, standards, regulations, and laws that you are legally required to follow. When a group of senior executives responsible for information security, risk, and compliance at IBM customer organizations began meeting in 2004, interest in IT governance was high, but there wasn’t as much attention focused specifically on information governance.

Books like “IT Governance: How Top Performers Manage IT Decision Rights for Superior Results” by Peter Weill and Jeanne W Ross  helped companies understand the benefit of aligning IT goals with the overall goals and objectives of the business. In addition, there were other publications at this time focused on how to take a balanced scorecard approach to managing business strategy and on best practices for implementing IT governance.   These approaches are of critical importance to business success, however there was also a need to develop a framework for understanding, monitoring, and securing the rapidly increasing supply of business data and content.

And that is what a group of IT information focused business leaders and IBM and business partner technology leaders decided to do. The amount of data they needed to collect, aggregate, process, analyze, share, change, store, and retire was growing larger every day. In addition to data stored  in traditional data bases and packaged applications like CRM (customer relationship management) systems, they were also concerned about information stored and shared in unstructured formats like documents, spreadsheets, and email.

Having more information about your companies customers, partners, and products creates great opportunity, but more information also means more risk if  you don’t manage your information with care. Council members asked each other lots of questions such as:

• How can we be sure that the right people get access to the right information at the right time?
• How can we make sure that the wrong people do not get access to our private information at any time?
• How can we overcome the risks to data quality, consistency, and security increased by the siloed approach to business data ownership that is so prevalent in our organizations?
• How can we create a benchmarking tool for information governance that will help our businesses to increase revenue, lower costs, and reduce risks?
• How can improve our ability to meet the security and protection standards of auditors and regulators?

As a result of its discussions, The Council developed a Maturity Model to help you assess your current state of information governance and provide guidance for developing a roadmap for the future. The Model identifies 11 categories of information governance.  The categories cover all the different elements of building an information security strategy such as understanding who in the business/IT is responsible for  what information, what policies do you follow to control the flow of your information in your company,  what are your methodologies for identifying and mitigating risk,  and how do you measure the value of your data and the effectiveness of governance. I read two IBM White Paper’s on the Model that add insight to the questions you need to ask to begin building a path to better information governance,  “The IBM Data Governance Council Maturity Model: Building a roadmap for effective data governance” and  “The IBM data governance blueprint: Leveraging best practices and proven technologies“.

So, what’s changed? FInancial crises, increasing regulation, high-profile incidents of stolen private data, cloud technology, and other factors have added substance and complexity to the questions you need to ask about information governance.  There is much to do.  One question we will explore at the conference next week is, How do you measure the effectiveness of your information governance strategy and what analytical measures are appropriate? For example, some companies are using analytical tools to look for patterns of email communication across the company and discover a greater level of insight into how information is flowing and what needs more review. Look for more on analytics and governance after the conference.

Why you need an information governance strategy for 2010

You say you already have a plan in place to guard your company’s data? Are you sure it has you adequately protected? While you certainly understand the need for data security – your sales challenges are tough enough without exposing your customer’s credit card information to a security breech, for example – the chances are good that in 2010 you will consider various options for improving the security of your data.  If you are going to protect your company’s most valuable asset — your data —  you will begin to view data security as a component of a more comprehensive information governance strategy.

The risks of internal or external threats to your company’s data are becoming more complex as the depth and breadth of your information expands rapidly and your data is shared with business partners, suppliers, and customers.  In addition, as companies begin to take advantage of cloud services for some of their workloads, additional complexity is added to the multitude of security concerns. Many companies have deployed a disjointed approach to securing, controlling, and managing its data making it hard to anticipate and prepare for constantly changing security risks. There are lots of different ways that unauthorized users may enter your network or otherwise steal your data.  Many companies typically have a distinct solution to combat each one individually and typcially can’t each of themprotect against all of them and. For example, access control, data encryption, network traffic monitoring, vulnerability testing, and auditing may all be monitored with independent applications.

There is a good reason why many companies find they need to deploy lots of different solutions to effectively govern its information. Some of the most innovative solutions have come from emerging companies who have built a niche around a particular vertical market or some segment of the information security market. So you deploy the best solution for you biggest challenges and move on. However, as you begin to think more holistically about your needs for information governance, you will want to ensure that information security solutions are well integrated. This is one reason why emerging companies with an information security solution have become desirable acquisition candidates for larger software vendors.

Guardium, a privately-held company based in Massachusetts, is one of the most recent examples of this trend. When IBM announced its acquisition of the company in the last week of November, Guardium moved from a fast growing startup to one of the pillars of the IBM information governance strategy.The company’s technology helps clients with some of the most challenging issues around unauthorized access to critical data. Their solutions provide secure access to enterprise data – across many different database environments such as IBM, Oracle, Microsoft, Teradata and others.  In addition, customers can reduce operational costs by automating regulatory compliance tasks. While many companies may have the ability to monitor one database at a time, Guardium brings added value by enabling companies with complex environments to monitor databases across their organization.

This acquisition aligns well with IBM’s strategy to provide customers with a well-integrated and comprehensive approach to information management. IBM has spent in the range of $12 Billion over the past five years to add software assets that will help companies to make more intelligent decisions and realize more business value from their information.

Do you have an analytics strategy and why should you care?

After just returning from IBM’s Information on Demand (IOD) Conference in Las Vegas, I would like to take this opportunity to virtually whisper just one word in the ear of a current day Benjamin Braddock, “analytics”. Many businesses have spent the past 25 years or so automating and streamlining business processes in order to drive improvements in efficiency and productivity.  But now, it is becoming apparent that these businesses expect their future success will increasingly depend on how skillfully they manage, govern, and analyze information. Businesses are applying analytical techniques to business information to help reduce risk and increase the certainty that they are making the right decisions.

IBM has, in fact, spent $12 Billion in software investments (both organic multiple acquisitions like SPSS, Cognos, Filenet, iPhrase, and Ascential Software, just to name a few) over the past 4-5 years to ensure it will be able to support its customers in their quest to unlock the business value of information. In addition, in April of 2009 IBM announced a new organization comprised of 4000 consultants focused on advanced business analytics and business optimization – teams with skills in applying business intelligence technologies like mathematical modeling, simulation, data analytics, and optimization techniques.

In an era of intense competition, tight credit, and cost concerns across global and vertical markets, this focus on getting the most value from the information you have makes a lot of sense. Companies find they are processing more information than ever before, but less of this information is being accurately and adequately used.  The quantity of available data that a business needs to manage and understand has skyrocketed along with the increase in instrumented and intelligent products. For example, RFID tags that are embedded in manufactured products,  plants and animals generate an enormous amount of data in efforts to control inventories and improve security and safety.  Trying to make decisions with inadequate,  inaccurate, or untimely  information is like driving a fast sports car down the highway with a very large blind spot impeding your view of the truck approaching on your side. You need to know about the obstacles that might appear in  your pathway before you try to make a “real-time” correction and steer your car (or your business) of a cliff.  So, students and business leaders alike please take note, I see some “analytics” in your future.