Reveal meaningful insights with IBM business analytics for big data

Find patterns of meaning with big data models

Big data analytics is the perfect complement to big data environments, allowing a plethora of uses—from capturing and reporting web analytics to tracking and predicting data behavioral patterns to automate security detection and prevention policies. The IBM® business analytics solution is compatible across platforms, generating meaning and providing situational awareness in both basic and complex distributed computing environments. From CIO to systems administrator, IBM business analytics offers a far-reaching solution for the success of big data environments.

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Timothy Landers (landertr@universalinet.com), Consultant, Universalinet.com, LLC

Timothy LandersTimothy Landers, a principal at Universalinet.com, LLC, is a practice lead in an independent consultancy. He has an MBA in technology management and is a Project Management Institute-certified Project Management Professional with more than 15 years in increasingly more-responsible roles within the IT field. He has written more than 28 technical courses for corporate training, vocational training, and higher education, plus new product manuals, professional certification exams, and commercial sales catalogs (such as SkillSoft).



17 December 2013

Also available in Russian

To put our findings in perspective, the 6.4 x 1,018 instructions per second that humankind can carry out on its general-purpose computers in 2007 are in the same ballpark area as the maximum number of nerve impulses executed by one human brain per second.

Hilbert and Lopez

IBM business analytics equips big data environments with functional and effective insights into structured and unstructured data. The business intelligence of IBM business analytics also brings cost-effectiveness to businesses, allowing the analysis and manipulation of information from various sources to produce new insights. The result is resourceful, inventive, inspired, and imaginative knowledge that you can use to improve decision making and gain perspectives to better manage performance.

Technology should enhance your ability to remember, measure, collect, and examine big data for purposes of improving decision making and better managing performance. Who invented the Pascal programming language? What is the highest temperature ever recorded for Butte, Montana, in May? How many consumers have two or more credit cards? How often does the self-destructing palm tree flower? Why do solar flares affect power grids? Using massive amounts of unstructured and structured information, referred to as big data, IBM harnesses the power of searching and extracting valuable data sets in real time. To expound on a topic (for example, cross-referencing relational databases, finding news clippings on a scientific topic, researching scholarly expertise), you can relate different sets in many ways. The optimal value of IBM big data business analytics is in its use of models to qualitatively and quantitatively relate a set of variables to forecast outcomes for a scenario. For example, humans analyze possible outcomes by first ruling out a disconnected train of events (Figure 1).

Figure 1. Analytical train of events
Diagram of an analytical train of events with three scenarios that do not create the desired outcome

However, IBM big data business analytics can make extensive simulations, starting with a wanted outcome and finding the required train of events. See Figure 2.

Figure 2. Analytical outcomes
Diagram of three analytical outcomes. A train of events corresponds to one outcome.

Click to see larger image

Figure 2. Analytical outcomes

Diagram of three analytical outcomes. A train of events corresponds to one outcome.

For cause-and-effect testing purposes, a human computer operator might want to enter a train of events to find a resulting outcome, but the results might not be desirable. Ultimately, that operator benefits the most by entering a wanted outcome and allowing big data analytics to determine the required trains of events.

Big data models

A big data model is a combination of data blocks, schemas, and data maps for processing data streams and returning results sets. Big data models quantitatively or qualitatively apply a set of variables to disparate data sources to discover and communicate meaningful data patterns. In Figure 3, the big data environment uses the Data Manipulation Language (DML) to manipulate unstructured and structured information. When information that matches the search criteria is selected, the database management system (DMS) uses database schemas that are written in the Data Description Language (DDL) to produce search results sets.

Figure 3. Analytical outcomes from data model use
Image showing analytical outcomes from data model use

Figure 4 provides a closer look at how big data analytics uses the database schemas to produce new patterns of meanings.

Figure 4. How big data analytics uses database schemas
Image showing how big data analytics uses database schemas

In Figure 4, four data sources are interconnected by the DMS as a big data virtual or cloud environment by using clusters. The data types are matched based on predefined criteria to produce hybridized data results sets. IBM big data analytics can assimilate and combine meanings from disparate data sources to produce new, meaningful insights.

IBM had the foresight and analytics foundation to discover that one of the key areas for affecting organizations' bottom lines is financial planning and performance management systems. Ideally, the system that can extract specific patterns of meaning from the multipetabytes that are stored in big data environments can translate the proverbial picture worth a thousand words.


How does the IBM big data analytics solution work?

Offering potentially unlimited growth opportunities, IBM's big data analytics solution uses high-speed automations to aggregate, compile, and deliver a wide range of information reports directly to the user's desktop. The beauty of the IBM solution is that it can add big data financial processes to an organization's fundamental business analytics competencies. The solution's agility is adding dynamic, real-time financial, planning, and performance forecasts and allowing models to identify specific variances, alternatives, and scenarios.

The potential of IBM's big data analytics solutions covers every known application of big data and offers specialized features and functions for tackling the most aggressive big data objectives. Table 1 lists some of the more common objectives.

Table 1. Common big data objectives
Big data analytics initiativeIBM big data analytics solution
Big data analytics and embedded statisticsUses IBM solutions to discover opportunities from metrics and statistical inferences
Big data analytics for social networkingManages and scales to large datasets to extract valuable patterns of data by using IBM analytics services, applications, tools, and programming
Big data analytics for sales and marketingUses IBM analytics solutions as discovery-oriented services in performing profitability strengths, weaknesses, opportunities, and threats (SWOT) analyses for targeting profitability
Big data analytics for law enforcementUses IBM analytics solutions to predict crime from statistics, historical data, and metrics
Big data visual analytics for exploratory earth system simulation analysisUses IBM analytics functionality to perform advanced spatial analyses
Big data Analytics as a ServiceUses IBM analytics solutions across multiple platforms, from data centers to mobile device infrastructures to the IBM SmartCloud® and more
Big data analytics for mobile gaming Uses IBM analytics solutions to track data resulting from hundreds of millions of individual mobile game scores
Big data analytics for information securityIntegrates security access points with IBM analytics to improve the probability of detecting, isolating, preventing, and mitigating security threats such as data breaches and malicious attacks
Big data analytics for telecommunications innovationsIBM has seen growing interest in analytics technology as a tool for protecting cities in terms of both public safety and corporate security

Is it important that the big data environment be open source?

I answer a resounding "yes" to the importance of open source. The primary reason Apache Hadoop provides a premier big data environment is that it is open source software. Hadoop was created to be installed and operate simply, efficiently, and cost-effectively, which enables the IBM analytics solution to support the most advanced business sectors (for example, finance and retail) in accomplishing their big data initiatives. The finance industry alone provides petabytes of data at petaflop speeds, allowing hordes of computers to process quadrillions of floating-point operations per second in parallel. The retail industry accounts for more than USD$15 trillion in global revenue. With both industries positioned for strong growth, IBM's big data analytics solutions are poised to scale with the resulting big data to provide reliable, expedient information for capitalizing on business opportunities.


Humanizing big data

From point-of-sale peripheral devices to big data appliances that connect to collect big data in distributed computing environments, IBM big data analytics orchestrates distributed processing services to provide anywhere and anytime big data access. The best approach for real-time, dynamic delivery of big data is to embed the analytical process results for display and application to the ongoing decision-making process. Beyond automations of regular queries, the humanized big data experience strategically analyzes data to generate distinctive, meaningful insights.

Analytics is a methodology, one that can both derive the intended meanings of data and infer something from the information received. Analyzing massive amounts of data to generate predictions is a specialty of IBM analytics. Using the exploration of surveillance big data to unify disparate data speeds up problem solving while it adds just the right combination of visual-to-predictive Hadoop-based storage. IBM's analytics solution fully uses in-memory computing and allows human interaction to predefine the criteria for real-time queries.


In-memory analytical computing

A significant advantage of IBM analytics solutions is in enabling organizations to perform in-memory computing in addition to providing visual data exploration and predictive analytics. By unifying advanced technologies, businesses can realize greater profitability, productivity, and opportunities. The open source cost-effectiveness of Hadoop stems from its being inexpensive compared with commercial applications of a similar type. When combined with in-memory computing—an equally inexpensive solution—IBM big data analytics can provide what is perhaps the most competitively priced big data analytics solution available.

With in-memory computing, the Hadoop database environment uses each computer's main memory to manage data storage. IBM analytics can, therefore, unify its analytics platform for big data management across distributed computing environments.


Real-time decision making

Insights into data patterns create various types of analytics:

  • Market analytics develops and enables predictive insights into market segments, growth opportunities, and expansion into verticals markets.
  • Product analytics uses evidence-based decision making to shape and develop product lines, brand awareness, and services offered.
  • Customer analytics applies algorithms to customer behavior to target customer groups for better relationship management.
  • Performance analytics recognizes that operational excellence and innovation are driven by metrics that serve as key performance indicators to identify areas for improving productivity and profitability.
  • Information analytics in data-centric organizations (for example, news companies) aligns its IT solutions with its business objectives and improves forecasting to promote operations in every aspect of business.

Big data benefactors

Every organization must maintain relevance to its industry to remain competitive. With big data analytics, organizations gain a competitive advantage. They can support executives and other leaders in collaborative decision-making processes, produce new revenue streams, find new opportunities for business growth, and improve customer satisfaction levels.

Benefits to big data benefactors include:

  • Upper-level management (chief information officers, IT directors, professional consultants, and managers):
    • Optimized performance to excel in delivering meaningful insights at the automation and data visualization levels
    • Enhanced forecasts and projects for projects, operations, and programs to better manage budgets, procurements, and acquisitions
    • Continuous business process improvement in analyzing, examining, and scrutinizing portfolios of corporate holdings, projects, and processes
    • Identification of policy violations to ensure regulatory and business process compliance
  • Middle management (functional managers, deputy managers, professionals):
    • Developing relationships through social media analytics to gain vision and for situational awareness
    • Implementing compliance with industry best practices to realize market position, areas for improvement to operations, and using opportunities for the highest return on investment
    • Applying Lean Six Sigma data and formulae to assess and refine procedures that save time and money while they enhance quality
    • Predictive failure analyses and incident investigation reports to ensure uninterrupted productivity and deliverables
  • Customers:
    • Cost-effectiveness, use of open source design components, and cost-reduction features such as in-memory computing to meet objectives
    • New technological advancements in analytical data processing to produce information that is required to lead corporate initiatives

Innovating beyond traditional analytics means choosing a state-of-the-art solution that maintains productivity and profitability while it revolutionizes the use of big data for measuring and managing performance.

The key features of IBM's big data analytics offerings do not impose significant cost or technical constraints on how big data must be modeled for analysis. In fact, IBM is considered a world leader in providing big data analytics solutions (see Resources). Therefore, the IBM big data analytics solution can offer more options for optimizing big data results.


Conclusion

To remain competitive, organizations are continually faced with challenges to reduce product development time, improve product quality, and reduce operational costs and lead times. Increasingly, these challenges cannot be effectively met by isolated change to specific organizational units but instead depend critically on the investments that are made in the organization's technologies and human capital. The relationships and interdependencies among different organizations (or organizational units) depend on communicative expertise and relativity—an ability to translate big data and apply it as useful information to improve business operations and productivity.

With a movement toward a global market economy, companies are increasingly inclined toward specific, high-value-adding in-sourcing and outsourcing solutions. This movement, in turn, increasingly transforms big data challenges into problems of establishing and maintaining real-time data. IBM is rising to the occasion with its big data analytics solution, which is comprehensive, distributed, far reaching, and relevant to a vast list of disparate information platforms. The ongoing effectiveness of an organization is tied to the dynamics of its data-driven resources and supply chains. The recognition of this fact leads to considerable change in the way organizations traditionally interact with unstructured and structured data sources, external partners, and distributed environments.

IBM understands the critical issues and challenges that face big data applications and works as a committed partner to help organizations create innovative big data analytics solutions that produce meaningful insights.

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