Any quick search indicates that there’s no shortage of analytics technologies to extract meaning from Big Data. Yet, a review of the recent activity of Lavastorm Analytics reveals that its Lavastorm Analytics Engine may be one of the more viable.

At the end of April 2013, the company partnered with Datawatch Corporation to include its engine in the latter’s Information Optimization Platform, enabling customers to create analytics applications for a variety of unstructured and structured data significantly faster than before. At the beginning of April, Lavastorm collaborated with Cyfeon Solutions to include its analytics engine in Cyfeon’s Answer Factory solution, which also utilizes MongoDB, Apache Solr, and Hadoop, to increase the speed of analytics and capacity for optimization, while processing massive quantities of data.

In February, Lavastorm unveiled its Lavastorm Analytics Engine 4.6 with updates that increase support to QlikTech QlikView, VMWare 5 virtual machines, and data visualization tools for Excel. More importantly, the company is currently giving out 14 day trials of the Professional Plus desktop version of its analytics engine, as well as a desktop public edition for free. A third version, the Professional Edition, is available for purchase only.

Professional Plus Versus Public

Although the desktop version of this visually-based, incrementally agile analytics tool is designed for single users, there are limited reasons to download the Public Edition. Users can get a feel for the tool and how it works, but will be severely restricted in its capabilities. Whereas the Professional Plus processes up to 10 million rows via databases and Excel, delimited files, and supports a range of XML, HTML, image files, and interfaces for Java, SQL, Python, and SQL; the Public Edition is merely good for 100,000 rows of Excel, delimited files – and supports none of the other aforementioned functions.

The Professional Plus enables users to access a variety of advanced features such as Lavastorm’s vaunted InFlow reporting and visualization for analytics, collaborative library nodes for custom controls, access to the Lavastorm Analytics Engine server, and fee-based, on-site training. The Public Edition only comes with basic collaboration and analytics capabilities, although it hints at the speed and full capacity of the engine. The Professional Edition only lacks the advanced collaboration library controls and the programming interface potential of the Professional Plus; it has a maximum of a million rows.

Engine Diagnostic

The value of the Lavastorm Analytics Engine lies in its ability to allow users to perform data discovery, automated controls, and ad-hoc analytics within the same environment. Its highly scalable architecture (especially with the Professional Plus Edition) also integrates a multitude of data types from disparate sources, so that organizations can preserve legacy silos and still gain a unified picture of their data – without an explicit warehouse. Users can run continuous analytic models by automating process and freely sift through different types of data with discovery tools due to the visual nature of Lavastorm’s analytics. Schema is not required, allowing business users to combine data sources without using code.

The engine works by granting users access to over 100 different analytics nodes in the Lavastorm Analytics Library. Each node is pre-packaged to serve a different purpose, such as to gather information about metadata, data acquisition, qualitative or distributive patterns of data, correlations and more. Since each node already comes ready to perform a specific task, users spend less time programming and can simply deploy analytics on the fly or in automated processes, enabling them to spend more time actually analyzing data.

Professionals can supplement their libraries by downloading additional nodes from Lavastorm that are designed for certain types of functions and frequently-used business systems. Recent packs of nodes include those for R Analytics and for Advanced Analytics. The contents of the May 7^th Enhanced Analytics Node Pack include nodes for statistics such as “Quick Stats”, which provides averages, maximums, minimums, and null counts, as well as nodes for encryption, decryption, and for interfacing URL requests to HTTP servers.  Nodes can also be modified by IT for more specific deployments.

Another distinct advantage of using Lavastorm’s analytic engine is its InFlow Reporting, which offers graphical outputs for specific points within a node and helps users to gain insight via visual representation. This feature is not only essential for ascertaining quick information through discovery tools, but it also assists with the reliability of information gleaned from data. The result is improved data transparency and a clear auditing trail from decisions to the information that substantiated them. This sort of self-documentation by visual representation is easily repeated and helps to optimize data use. Users get a clearer understanding of where information from data comes from, enabling them to rely on data more frequently and accurately.

The relative ease in which discovery tools and ad-hoc analytics are performed is ideal for an Agile work environment. Lavastorm Analytics Engine’s schema-less approach permits users to create iterations before developing formal tables, which increases the celerity in which they can be performed. Additionally, the visual representations of analytics indicate the precise point in which future iterations should occur by displaying anomalies. Users can streamline processes via automation, enabling greater efficiency and continuous analysis of data types. Results can be published in a variety of application environments utilizing conventional BI tools, ERP, data warehouses, and other data management systems.

Lavastorm Analytics Platform

In addition to working in conjunction with other solutions or on its own, Lavastorm also offers a more comprehensive analytics platform in which its analytics engine functions as the central component. When used in conjunction with the other components in the Lavastorm Analytics Platform, the analytics engine serves as a means of federating data from five different components and applying user-tailored analytics capabilities to all of them.

Other than the analytics engine, the most integral component of the Lavastorm Analytics Platform is a transaction warehouse that provides automated analyses of data from transactions. The warehouse is extremely scalable, can process billions of records in almost real time, and can aggregate a variety of data sources such as CRM, crash data retrieval systems, billing events, and IP networks. Its processing expedience enables users to discern patterns and trends almost as soon as they take place, which is valuable for detecting fraud and other threats.

The platform also comes with a resolution center that provides a central dashboard with reporting and visualization tools for case management, which serves as an environment to process concerns found in the transaction warehouse and analytics engine. Lavastorm Bill Analyzer functions as a practical extension of the analytics engine and displays combined views of services and accounts to customers through various billing systems. The Lavastorm Data Acquisition module is optional and utilizes metadata to process structured data without using typical ETL systems or code. Lavastorm has a desktop version of its platform for single users which enables them to deploy rules-based analytics for ad-hoc projects.

Final Thought

As its recent partnerships with Datawatch Corporation and Cyfeon Solutions indicate, Lavastorm’s analytics engine is capable of the type of analytics to transform Big Data into valued information. Its key assets are its extreme scalability and data integration utility. The ease of use of its node library allows for virtually anyone to create analytics to their liking while minimizing the involvement of IT departments – which can still enhance analytics options as needed.

Users have the capacity to integrate, analyze, and optimize Big Data, increasing its viability and importance in business and organizational processes. The highly visual nature of the analytics engine facilitates a degree of traceability which enhances data lineage and agility, allowing users to generate continuous automated analytics as readily as those for ad-hoc purposes.

A new article out of the company reports that, “Oversight Systems will demonstrate its operational analysis software platform which helps businesses and government agencies evolve from exploring big data to quickly generating and delivering operational insights for greater business success, at the DATA (Digital Accountability and Transparency Act) Demonstration Day Hosted by the House Committee on Oversight and Government Reform on May 16, 2013 in Washington D.C. This marks the second annual DATA Demonstration Day, designed to provide Members of Congress, Congressional staff and the public an opportunity to learn how data technologies could help the U.S. federal government cut waste, streamline reporting processes, and improve public accountability if federal spending data were fully standardized and published, as would be required by the proposed DATA Act. The DATA Act, originally introduced in 2011, is expected to be re-introduced in both houses of Congress.”

The article adds, “Across industry and government, Oversight analyzes over $1.75 trillion of transactions to deliver insights that recover revenue and deliver stronger compliance for customers worldwide. The Defense Finance and Accounting Service (DFAS), the U.S. Navy, the U.S. Department of Education and the U.S. Bureau of the Census all rely on Oversight’s platform to prevent over $2 billion in annual improper payments and ensure the accurate reconciliation of expenditures.”

Read more here.

photo credit: Oversight Systems

Tony Cosentino of Information Management writes, “Microsoft has been steadily pouring money into big data and business intelligence. The company of course owns the most widely used analytical tool in the world, Microsoft Excel, which our benchmark research into Spreadsheets in the Enterprise shows is not going away soon. User resistance (cited by 56% of participants) and lack of a business case (50%) are the most common reasons that spreadsheets are not being replaced in the enterprise.  The challenge is ensuring the spreadsheets are not just personally used but connected and secured into the enterprise for a range of consistency and potential errors that all add up to more work and maintenance as my colleague has pointed out recently.”

He continues, “Along with Microsoft SQL and SharePoint, Excel is at the heart of the company’s BI strategy. In particular, PowerPivot, originally introduced as an add-on for Excel 2010 and built into Excel 2013, is a discovery tool that enables exploratory analytics and data mashups. PowerPivot uses an in-memory, column store approach similar to other tools in the market. Its ability to access multiple data sources including from third parties and government through Microsoft’s Azure Marketplace, enables a robust analytical experience.”

Read more here.

photo credit: Microsoft

David Booth of MarketingDaily recently wrote, “IBM’s recent study, ‘Marketing Science: From descriptive to prescriptive’ quantified what most marketers intuitively know: while virtually everyone wants to use data to drive marketing decisions, most marketers (82 percent) still rely largely on hunches and experience. Although experience should never be understated in the art of marketing, adding the science of data and analysis to the mix can unlock enormous potential. Here are four things to consider as you shift toward the cutting edge of data.”

He continues, “The first step to gaining data/analytics maturity as an organization is wanting to change. Building a data-driven culture is difficult and takes time and energy — and you can’t get there by simply buying the latest or greatest technology. Data becomes valuable when it becomes useful, so investing in the people, the processes, and the organizational structure is essential in building a solid foundation on which to grow. Instituting small, consistent, and realistic steps with buy-in from all of your stakeholders will be your key to positive change.”

Read more here.

Erin Carlyle of Forbes reports, “Angus Davis is kicking off Swipely ‘s weekly management meeting, and he wants to hear just one thing: Quick wins. ‘Matt?’ Davis gestures at the startup’s VP of sales, Matt Oley, who doesn’t miss a beat. The sales team is on track to meet this month’s goal, Oley reports. ‘Raise the goal!’ Davis shouts hoarsely. The five department heads laugh. Davis points his rolled-up piece of paper quickly around the square table, at the heads of business development, engineering, products and finance. One by one they tell him about the smooth deployment of a product feature, new partnership opportunities, a promising potential hire. ‘Do you need us to do our show-the-love e-mail?’ Davis asks about the candidate, who’s on the fence about taking the job. That’s the all-hands-on-deck e-mail bomb that Swipely employees lob at potential hires to get them to accept. ‘It’s actually surprisingly effective,’ Davis says, grinning broadly.”

Carlyle continues, “So is starting each meeting with quick wins, a move he borrowed from LinkedIn CEO Jeff Weiner. Setting the right tone matters immensely to the 35-year-old founder because Swipely, which processes credit card transactions for shops and businesses, will need every ounce of optimism it can muster to meet its ambitious goal of reaching $1 billion in annualized transactions by the third quarter of this year, up from a run rate of $500 million now. There are lots of payment services out there, but Swipely’s selling point is helping merchants better understand their customers. Its cloud servers crunch the data left by card swipes, strip out personally identifying factors for security, and turn the data into nicely designed customer dashboards showing which card numbers bought what and when and insights such as how a rainy Tuesday affects profits. Swipely synchs with a merchant’s social accounts so that owners can see how campaigns on Facebook or reviews on Yelp affect business in their restaurants or stores.”

Read more here.

photo credit: Swipely

According to a new article out of Accenture, “Four in 10 top marketing executives say they are not well prepared to meet their objectives, citing a lack of funding and inefficient business practices as the main impediments to improved performance, according to global research by Accenture. The research also reveals a growing commitment to digital marketing and analytics capabilities to tackle an increasingly complex customer environment. According to the Accenture Interactive report, Turbulence for the CMO, 70 percent of the executives believe that corporate marketing will undergo a dramatic overhaul within the next five years and that their organizations must create a digital direction that will help them achieve higher revenue and increase market share.”

Brian Whipple, global managing director of Accenture Interactive said, “Marketing executives are growing increasingly concerned that tight budgets and the lack of a clear strategy for implementing digital technologies – are hurting their company’s ability to compete in the digital age. There is a clear performance gap between the demands of the marketplace and the ability of marketing organizations to apply the digital technology talent required to be more effective.”

The article goes on, “The report shows that the need for change is being driven by increasingly demanding customers who expect more relevant interactions with brands. According to survey respondents, consumers’ expectations for relevant, targeted experiences are having the greatest long-term impact on marketing strategy (65 percent).”

Read more here.

photo credit: Accenture

A new article out of RPI states, “To prepare students and career professionals for the expanding scope of careers requiring Big Data and analytics skills, IBM and Rensselaer Polytechnic Institute are combining forces to offer a new, one-year Lally School of Management and Technology graduate degree program in fall 2013: the Master of Science in Business Analytics. Nearly two million information technology jobs will be created by 2015 in the U.S. to support Big Data, according to research firm Gartner Inc. Analytics skills will be a key differentiator for candidates seeking to fill those jobs.”

It continues, “The news today underscores IBM’s efforts to help students and career professionals enter and succeed in the growing, high-demand analytics workforce. In addition to collaborating with Rensselaer on the new degree program, IBM has also recently donated a Watson system to the school in order to help faculty and students explore new uses for cognitive computing and expand their understanding of Big Data and analytics. The new Master of Science in Business Analytics degree is a one-year, 30-credit graduate program offered by the Lally School. The program will provide students and career professionals with the hands-on experience and knowledge required to succeed in analytics jobs spanning a range of industries, from the data scientist who helps chief residents make sense of millions of medical records, to the marketing analytics specialist who helps chief marketing officers personalize consumer brand campaigns.”

Read more here.

photo credit: RPI

Teradata has long aspired to provide an environment in which it can readily analyze and integrate all forms of data for the enterprise.

On April 15, 2013 it revealed a number of technologies that have made the dream a reality.

The prominent data analytics solutions provider substantially bolstered its Unified Data Architecture (UDA) with the Teradata Enterprise Access for Hadoop technology and its fabric-based computing system, Mellanox’s Infiniband. Both technologies revolutionize the ease of accessing and running analytics for Big Data through Hadoop. Teradata also announced the release of an updated Teradata Active Enterprise Data Warehouse 6700 and a smaller version, the Teradata Data Mart Appliance 670, a departmental warehouse designed for testing and development.

Fabric-Based Computing

The company’s commitment to fabric-based computing is essential for carrying out its goal of providing universal analytics through UDA. Infiniband operates as the common backing through which users can move data seamlessly between UDA’s principle components, the Teradata Integrated Data Warehouse and the Aster Discovery Platform. Enterprise Access for Hadoop includes Hadoop in the free exchange and analysis of data, which UDA expands to conventional data marts and analytical archives.

According to Teradata’s strategic deployment of Big Data solutions head Tasso Argyros – who founded Aster Data before Teradata acquired it in 2011:

“The reason this is important is because one size doesn’t fit all anymore. You can’t build your data architecture on only one data source. There’s a huge advantage to using best of breed technologies working together. Having a monitoring infrastructure that allows you to view all sources from one place is very important for operation efficiency.”

UDA’s “best of breed” technologies not only include Big Data access through Hadoop and Apache developments like HCatalog, but also those of a variety of products such as Hortonworks Data Platform, Intel Xeon Processors, and Linux’s enterprise server operating system. UDA users can monitor and manage data in any location with Viewpoint, while InfiniBand provides the hardware for Teradata’s BYNET V5 software for massive parallel processing broadcast functions.

Infiniband provides the foundation for Teradata’s fabric-based computing, and is considered a highly scalable, swift means of enabling connectivity between analytic and reporting tools, and transferring of data between sources. Its reliability, speed, and scalability are largely enhanced by BYNET, which boosts the capacity of Teradata’s Enterprise Data Warehouse to 61 petabytes and works best when moving data between dual networks. The result is that users can perform real-time in-query data sorting in an environment that is designed to optimize the speed and performance of business intelligence and analytics – which is crucial for integrating various types of structured and unstructured data under tight time constraints. BYNET also increases network fail-over capability.

Enterprise Access for Hadoop

The ultimate benefit of fabric-based computing is the uniformed analytics it makes possible through shifting the data into various sources, which is facilitated through the Teradata Enterprise Access for Hadoop when Big Data is involved. The most important features of this release are Teradata’s SQL-H (as in Hadoop) and Teradata’s Smart Loader for Hadoop. The latter enables analysts and laymen to manipulate and move data from Hadoop to Teradata’s secure, proprietorial integrated data warehouse. It is able to do so through the power of the former, which allows users to formulate queries and issue reports on Big Data using SQL, the impact of which Argyros says should not be taken lightly:

“Now, all the SQL analysts that most enterprises already have can do SQL analytics on Big Data without knowing anything about Hadoop. That’s one of the reasons SQL-H has been so successful so far, because instead of enterprises having to go and hire 30 Hadoop data scientists, they can utilize the 25 SQL analysts they already have and, with SQL-H, only hire five more people.”

SQL-H also mitigates security concerns about accessing data in Hadoop (which is open source), since it allows users to move data into their own data warehouses. Thanks to InfiniBand and BYNET, analysts can access Big Data in real time and issue queries and reports without code or script. This self-service aspect of SQL-H encourages operations, business, and executive use for either ad-hoc or planned analysis. Organizations can still extract information from Big Data sources utilizing the conventional architecture and methods for BI that they’re already acquainted with, without extensive overhead costs for hiring and training in No-SQL.

SQL-H integrates with Hortonworks Data Platform and Apache HCatalog to facilitate intelligent data across a multitude of systems. The latter enables users to minimize replication and data movement costs by only moving data into Teradata’s data integration warehouse that is required for a query. The combination approach of integrating and performing analytics on data from virtually all sources is the basis for Teradata’s claim for UDA. Users can choose between Cloudera Distribution and Hortonworks Data Platform for commercial distribution of Hadoop, while Teradata’s integration warehouse grants numerous users simultaneous access.

Teradata Studio with Smart Loader for Hadoop simplifies the Hadoop browsing experience by presenting data in tables (with table properties) for an easy, point-and-click experience. Bi-directional table copies create maps of data by type between Teradata and Hadoop sources for ready comparison. Other features include transfer status and history functions for users to track statuses of loads.

6700

The Teradata Active Enterprise Data Warehouse (EDW) 700 provides operational and strategic intelligence with real-time updates. Its speed is due in part to running BYNET on Infiniband, as is the extreme scalability it offers. The most recent version of the Active EDW Platform is available in two different models, the 6700C and the 6700H. The 6700H has more memory, storage capacity, and a higher Teradata performance per node. One of the central differences between the two is that the 6700H comes with a hybrid storage architecture that utilizes both Solid State Drive (SSD) and Hard Disk Drive (HDD) technologies; the 6700C comes with HDD and can be upgraded to include SDD. One of the primary benefits of this platform is the fact that more regularly used “hot” data is placed in SSD for expedient access, whereas less frequently used data is relegated to HDD. Teradata’s Virtual Storage allows users to specify in which technology they would like data placed.

The primary distinction between the recently released Teradata Active EDW Platform and its predecessor is that the updated version incorporates an Eight Core Intel Xeon Processor and high performance computing nodes that, when combined with recent fabric-based computing technologies, makes it significantly faster. It utilizes Viewpoint for convenient monitoring of data and supports subsequent and prior platform generations to increase investment protection and encourage sustainability. The Data Mart Appliance 670 also features an Intel Xeon Processor and high performing computer nodes and is available in both HDD or hybrid versions, yet has substantially less storage than the 6700. Argyros commented:

“We have products that are very cost effective and geared toward point problems all the way to high end products like the 6700. That allows you to integrate structured data from across the enterprise scaled to many terabytes, and it supports hundreds of thousands of users.”

Unified

Ultimately, Terradata representatives base the validity of UDA’s viability and comprehensive data analytics on the strength of its integrated data warehouse, which utilizes Hadoop’s Big Data and Aster’s discovery tools to unlock its full potential. When one considers all of the other data sources that can integrate with it, Teradata’s claim for offering unified analytics appears convincing. Argyros reflected on the process of UDA’s development:

“We were looking for how we could unify the analytics and the processing trail. In order to do so you need to be able to move data from Hadoop and Teradata into Aster, and from Aster and Teradata into Hadoop. And you ideally want to make sure that analytics frame data from Aster, Teradata and Hadoop at the same time. That’s kind of the holy grail of software integration, and we’ve done that.”

In an effort to leverage the knowledge of several of the top minds in the Data Management industry, DATAVERSITY™ has been conducting a series of interviews on some of the most relevant topics in the field today. Recently, we interviewed Patricia Klauer, the Founder & Enterprise Information Architect of Eclipse Data Systems.

Patricia will be giving a presentation at the Enterprise Data World 2013 Conference in San Diego, CA from April 28-May 2, 2013. The presentation is titled “Front End First: A Fresh Approach to Building BI Solutions.”

The Speaker Spotlight Column (and its parallel venture the Sponsor Spotlight Column) is an ongoing project that focuses on highlighting several of the central issues represented at the many Data Management conferences produced by DATAVERSITY.

The primary emphasis of the interview was to question Patricia Klauer on her work and history within the industry, with particular importance on her presentation at the upcoming conference:

DATAVERSITY (DV): Please tell us a little about yourself and your history in the industry e.g role at company (as opposed to job title), past experience and how you got started in the data profession?

Patricia Klauer (PK): I was a chiropractor, phobic about computers and had never typed. I moved to Boston in 1985, 2 years after graduating and getting my California state license. I had to qualify for Massachusetts by taking another exam. In the meantime all my friends were in to computers and programming. I took a class out of curiosity and learned to program. I naively answered an ad in the paper for a system programmer at MIT (without knowing what it meant). They hired me! So began my computer career. I was given responsibility to monitor all backups and a few small projects to work on. One project entailed learning how to install and load a relational database (DB2) and how to write queries. This changed my life forever. Still, never intending for this to be my career, I took jobs at major companies just to pay my bills and student loans, increasing my database knowledge. I wound up in NYC working on large DB2 project implementations for the financial industry in the late 80’s.  Bill Inmon was still talking about OLTP back then. I took jobs as a data modeler and eventually gave up my chiropractic career. I grew proficient while working with large companies in North America and Europe. In 1997 I co-authored ‘Building Data Warehouses for Decision Support’. Since then I have continued to build many data warehouse environments.

DV: What’s the focus of the work do you currently do within your organization?

PK: Currently I provide strategic assessments, architecture, roadmaps and implement projects as an Information Architect as well as support organizations as a Strategic Advisor for BI Initiatives.

DV: What is the biggest change going on in your particular area of the industry at this time?

PK: I believe new data discovery technologies using compression and in memory strategies enable us to approach data warehousing in a completely different way.

DV: How does such a change affect your job?

PK: Traditional data warehouse architecture and methodology can be augmented to take advantage of new technologies for data discovery and integrating Big Data.  I like to work with clients to understand their business needs and develop an architecture and methodology that is responsive to business, facilitates collaboration and ensures more effective use of their data warehouse and BI environment.

DV: What are you going to discuss during your session at Enterprise Data World and what will the audience gain from attending your talk? (Please be specific about one or two issues you’ll be addressing, and the benefits the audience will obtain).

PK: I am going to make the case for a new approach to development in the BI environment that takes advantage of data discovery tools. Adding a ‘Discovery’ stream into the process methodology, enabling business users to have early access to data facilitates collaboration, identifies data quality issues upfront, uncovers business rules and process issues reduces rework. This makes for much happier business users and a more effective BI environment.

DV: How has your job, and/or the work you’re doing at your organization, changed in the past 12 months?  How do you expect it to change in the next 1-2 years?

PK: Business users are more engaged in the process rather than being the recipients of the final product they are taking ownership.  This is an exciting opportunity to create a collaborative, vital, competitive, responsive BI environment that pays for the company’s investment over and over again.

DV: More broadly speaking, what do you believe is the most significant change happening in Enterprise Data at this time?

PK: Data Discovery capabilities and bringing together structured and unstructured data.

DV: How is Big Data going to affect your job (in your organization) in future?

PK: The business need to parse and take action on unstructured data will present another capability to include the right tools and methods into the organization.

DV: What is something noteworthy about yourself that you would like to tell the conference attendees and our readers that they may not know?

PK: I teach a course on focused awareness and mindfulness called ‘Walk in the World’ to professionals and entrepreneurs through my company Alpha-i.

If you are interested in attending Patricia’s presentation at EDW2013, please see the conference schedule at: http://edw2013.dataversity.net/agenda.cfm?confid=72&scheduleDay=PRINT

The presentation is on Wednesday, May 1, at 11:30am.

About Enterprise Data World:

Enterprise Data World is the business world’s most comprehensive educational event about data and information management. Over five days, EDW presents a diverse schedule of programming that addresses every level of proficiency, including keynotes, workshops, tutorials, case studies, and discussions.

Mobile Business Intelligence (BI) extends the decision-making and analytics capabilities of conventional BI beyond the office. The ubiquity of smartphones and tablet devices (such as iPads), in addition to a growing number of vendors, mobile platforms, and applications, has rendered this form of BI one of the most viable means of accessing and extracting value from data today. Depending on which app is selected and integrated with which particular device, Mobile BI offers all of the capabilities and features of traditional BI, plus additional benefits such as displaying analytics on other portable and desktop devices in any location.

However, the nature of Mobile BI presents a set of considerations that are distinct from enterprise versions. The principle concern is to select an appropriate app and mobile device that best integrates with existing BI software. All of the traditional concerns for mobile BI, such as issues of security and platform extensions with current BI tools, have largely been addressed. Users are still responsible for determining what sorts of data will be analyzed most frequently via Mobile BI, while evaluating platforms and devices to determine which is most compatible. Although all forms of BI are accessible via mobile devices, some data is better visualized than others, depending on the portable device and app selection.

Modernizing Mobile BI

Around the turn of the millennium, Mobile BI was severely limited in its utility. It required a dedicated server and was difficult to integrate with existing tools. With the development of tablet devices and smart phones in more recent years however, BI vendors began supporting mobile devices so that they served as extensions of conventional tools on the same server. Querying, reporting, visualizations, and animations are shared between conventional and Mobile BI.

Other than the proliferation of mobile devices in recent years, the most significant factor to influence the rapid rate of adoption of Mobile BI is the virtual elimination of security concerns for it. Most forms of Mobile BI offer security at three respective levels: the device, the network, and at the point of transmission. Handsets have security features such as firewall and antivirus software, full disk encryption and passcodes. Secure socket layers and virtual private networks help fortify security at the network level, while the same security clearances required for BI tools in the office are required for mobile devices as well.

Operational and Business Value

Aside from the convenience of accessing BI as needed in any location mobile devices are supported, Mobile BI can assist real-time decision-making for those who need it most in the field – such as as sales representatives. Access to information from remote locations helps to complete transactions more expediently, increase productivity, and decrease administrative and material costs. Mobile BI is ideally suited for informing short-term decisions, which makes it valuable for analyzing operational data and flexible metrics related to pricing.

Although Mobile BI analyzes the same data that traditional BI does, it comes with a variety of features that significantly enhances its use. In addition to providing filters and alerts, Mobile BI has become increasingly characterized by intuitive graphical user interfaces that can accommodate sophisticated levels of visualization. Whereas early Mobile BI apps could only access previously canned reports, current tools can generate new queries. Particularly competitive apps like those from Logi Analytics (LogiXML, Logi Info, Logi Ad Hoc) enable users to do so without code, simplifying the querying process, and eliminating input from IT. Apps integrate with all of the conventional sources of data such as relational databases, warehouses, CRM and ERP, and provide a bevy of dashboards, reports, graphs and grids which can be rapidly deployed many times.

Other features found in products like MicroStrategy Mobile and others specifically pertain to mobile devices, such as integration possibilities with email, calendars, phone lists, and other apps on the mobile unit itself. Those that incorporate HTML 5 have a plethora of offline capabilities, which may be enhanced by IT personnel. The sensor-based querying feature is a distinct advantage over traditional BI, with which users can scan barcodes to generate and change queries. Queries can also be facilitated via convenient voice to text and voice recognition functions. Several solutions accommodate ad hoc querying and reporting, which can be updated via GPS. Mobile platforms can support a variety of Mobile BI apps, while key stroke shortcuts and multi-touch gestures facilitate ease of use.

Selectivity Concerns

Mobile BI utilizes web browsers on portable devices to access conventional BI tools. Costs typically revolve around purchasing devices, a BI solution, mobile apps, and whatever training and design assistance is required. Most BI vendors have their own mobile apps that extend enterprise service remotely via interfaces that are similar to the desktop version. There may be additional licensing fees for desktop users to access Mobile BI. Other apps have been designed to integrate with a variety of BI platforms. Although not all apps can integrate with existing BI tools, popular BI vendors are supported by a number of different apps. There are more limitations on the type of mobile device that a particular app can work with, as some are designed only for particular manufacturers or for certain smartphone operating systems.

Other selectivity concerns relate to the design of Mobile BI, which is considerably different than that for the desktop version. Limitations related to screen size, memory capacity, and processing speed/capacity of mobile devices influence the way reports will look – which affects their overall utility. Too much information may appear clustered on smaller devices. Mobile BI design should ideally limit the number of objects on the screen or dashboard to increase usability. A chief determinant in achieving this objective is the process of data categorization, in which organizations specify which types of data will be accessible to which individuals and design tools for apps accordingly.

Although Mobile BI can handle all of the functions of desktop versions, the presentation of data on individual handsets factors heavily into what data is most advantageous to base designs around. There is a direct correlation between the type of mobile device an app supports and the type of data that it works best with. The primary goal in selecting Mobile BI solutions is to standardize data, yet the particular form of data analysis most frequently used factors into what sort of device is desirable. Lengthier data mining processes tend to work better on bigger tablets, while smartphones are ideal for scanning data and making quick decisions relating to real-time information for pricing and operations.

Facilitating Simplification

Adoption rates of Mobile BI are projected to increase in the very near future. How rapidly they do so largely depends on the effectiveness of implementing these platforms with current BI solutions. Vendor support on both ends (from the mobile and enterprise community) is already there. Most tablet devices present functionality and usability similar to desktops, while certain uses of smartphones (including scanning and GPS incorporation) make them viable options as well.

In that respect, the trend towards Mobile BI merely reflects the larger movement towards the simplification of BI. Cloud-based Data-as-a-Service options are another integral component in moving BI out of the realm of IT and into the daily world of operations and business professionals. Many of the features of Mobile BI – alerts, ad hoc tools, trends related to key performance indicators – increase usability, particularly in products in which code is not required for design.

Although such platforms and applications are increasing, the primary challenge in utilizing Mobile BI today lies in facilitating a design that optimizes visualization, reporting, and other key tools on the mobile device a particular solution supports. As more solutions allow such tools to be manipulated without the need of an IT team, the variety of data types which can be presented optimally (per device) should only increase, further spurring adoption rates. The potential for enhancing decision-making and incorporating data to generate business value will become significantly more accessible.