The Art of the Possible with Business Analytics

It has been established beyond doubt that data and its analysis can have a huge impact on an organization’s top line and bottom line. Business Analytics helps organizations deliver better business performance in two ways – by optimizing business processes and by helping to innovate. Optimization helps organizations be efficient and effective by taking inefficiencies out of the business processes and focusing on the high impact opportunities. Innovation on the other hand helps organizations by uncovering new customer segments, new product categories, new markets, new business models etc.

The styles of analyzing data are many fold from answering questions like “what is going on?” to “why are the things the way they are?” to “what will happen if I do X or Y?” to “what does the future look like?” Broadly speaking the styles of analytics can be classified into three categories:

·         Exploratory Analysis: The objective of exploratory or investigative analysis is exploration and analysis of complex and varied data – whether structured or unstructured for information discovery.  This style of analysis is particularly useful when the questions aren’t well formed or the value and shape of the data isn’t well understood.

·         Descriptive Analytics: The objective of this style of analysis is to answer historical or current questions like what is going on. why are the things the way they are?. This is the most common style of analysis and here the questions as well as the value and shape of data are well understood.

·         Predictive Analysis: Predictive analysis aims at painting a picture of the future with some reasonable certainty.

So, what’s art of possible with business analytics? It’s the application of the above three styles of analytics to a business scenario for better insights, decisions and results. Let’s try and explain this with an example. Consider this scenario:

You are a Financial Services firm e.g. a large bank and are trying to improve profitability. You read Larry Seldon’s book titled “Angel Customers and Demon Customers” and agree with the findings that 20% of your top customers bring in 80% of the profits and would like to manage you business as a portfolio of customers as opposed to portfolio of products. So, how do you do that? The answer is business analytics.

You can start by using descriptive analytics techniques like operational reports, ad-hoc query, dashboards etc. on data collected from different sources like sales, customer service etc. to determine the profitability of each customer. You can then use predictive analysis techniques like data mining, statistical analysis to further enrich your customer data into profitability segments like high, medium, low and loss making customers. Finally, you can choose different customer service channels like personal banker, phone or ATM to cost effectively serve you customers e.g. a high profitability customer can be served by a personal banker free of charge but if the loss making customer wants a personal banker there will be a charge. Once you have implemented such programs you can use exploratory analysis to gauge the sentiment across social media channels like Facebook and Twitter to see if the programs are working as desired. Better yet you may come up with new innovative business models like mobile banking or online only banking to improve profitability.

That’s the art of possible powered by business analytics. Stay tuned, I intend to publish more examples from different industries to show the art of possible with business analytics.

Does your analytic solution tell you what questions to ask?

Analytic solutions exist to answer business questions. Conventional wisdom holds that if you can answer business questions quickly and accurately, you can take better business decisions and therefore achieve better business results and outperform the competition. Most business questions are well understood (read structured) so they are relatively easy to ask and answer. Questions like what were the revenues, cost of goods sold, margins, which regions and products outperformed/underperformed are relatively well understood and as a result most analytics solutions are well equipped to answer such questions.

Things get really interesting when you are looking for answers but you don’t know what questions to ask in the first place? That’s like an explorer looking to make new discoveries by exploration. An example of this scenario is the Center of Disease Control (CDC) in United States trying to find the vaccine for the latest strand of the swine flu virus. The researchers at CDC may try hundreds of options before finally discovering the vaccine. The exploration process is inherently messy and complex. The process is fraught with false starts, one question or a hunch leading to another and the final result may look entirely different from what was envisioned in the beginning. Speed and flexibility is the key; speed so the hundreds of possible options can be explored quickly and flexibility because almost everything about the problem, solutions and the process is unknown. 

Come to think of it, most organizations operate in an increasingly unknown or uncertain environment. Business Leaders have to take decisions based on a largely unknown view of the future. And since the value proposition of analytic solutions is to help the business leaders take better business decisions, for best results, consider adding information exploration and discovery capabilities to your analytic solution. Such exploratory analysis capabilities will help the business leaders perform even better by empowering them to refine their hunches, ask better questions and take better decisions. That’s your analytic system not only answering the questions but also suggesting what questions to ask in the first place.

Today, most leading analytic software vendors offer exploratory analysis products as part of their analytic solutions offerings. So, what characteristics should be top of mind while evaluating the various solutions? The answer is quite simply the same characteristics that are essential for exploration and analysis – speed & flexibility. Speed is required because the system inherently has to be agile to handle hundreds of different scenarios with large volumes of data across large user populations. Exploration happens at the speed of thought so make sure that you system is capable of operating at speed of thought. Flexibility is required because the exploration process from start to finish is full of unknowns; unknown questions, answers and hunches. So, make sure that the system is capable of managing and exploring all relevant data – structured or unstructured like databases, enterprise applications, tweets, social media updates, documents, texts, emails etc. and provides flexible Google like user interface to quickly explore all relevant data.

Getting Started

You can help business leaders become “Decision Masters” by augmenting your analytic solution with information discovery capabilities. For best results make sure that the solution you choose is enterprise class and allows advanced, yet intuitive, exploration and analysis of complex and varied data including structured, semi-structured and unstructured data.  You can learn more about Oracle’s exploratory analysis solutions by clicking here.

5 Facts that SAP won't tell you about HANA

SAP has been touting HANA as an innovative, breakthrough technology and the next “big thing”. They are aspiring to be the #2 database vendor riding on the HANA hype. Well, time to dig deeper and bring forward 5 facts that SAP won’t tell you about HANA.

#1: HANA is an In-Memory database. So, where’s the innovation?

SAP has positioned HANA as the newest, most innovative category defining offering. However, HANA is an in-memory database which may be a new category for SAP but has existed in the market for years. A quick Wikipedia search on in-memory databases reveals that in-memory databases have been around since the 1990s and today there are 40+ such independent offerings of which HANA is one. Oracle alone has 3 in-memory database offerings with successful products like TimesTen, Berkeley DB and MySQL.  Introduced in 1990’s, Oracle’s TimesTen remains an early innovator and a leader in this space. HANA, introduced in 2011 is the youngest member of the group.

#2: HANA adoption is growing rapidly. So, where’s the growth?

SAP will show numbers like FY 2011 revenues of $200M and 100+ customers to underscore HANA’s rapid customer adoption. Putting these numbers in perspective; Vertica, the largest independent in-memory database vendor before being acquired by HP in 2011, was on track to deliver revenues around $100M with 200+ customers and over 100% YoY growth rate. Oracle remains the leader in data warehouse platform market with FY 2010 revenues of close to $3B and thousands of customers. Given Oracle & Vertica’s impressive performance, HANA’s numbers while good are hardly “rapid”.

#3: HANA is enterprise ready. So, where’s the manageability and reliability?

It takes years to develop and perfect a complex product like database management system. Oracle database has been perfected over 30+ years and billions of dollars in R&D investment. TimesTen has been around for 15 years and is still being aggressively developed and perfected. SAP would like you to believe that HANA is enterprise ready from day one but dig deeper and you’ll find that HANA lacks basic features like clustering, high availability, file system persistence and ACID style transaction integrity support. HANA lacks referential integrity support so there is NO means to ensure the integrity of data stored in a HANA database. HANA’s support for locks and transaction isolation is primitive so multi user concurrency is an issue. Hopefully, you get the picture that HANA is an immature version 1 DBMS which is far from being ready to support mission critical enterprise applications.

#4: HANA is non disruptive. So, where’s plug and play?

HANA has limited support for standard ANSI SQL.  In fact, HANA requires applications to be custom written for it using non-standard SQL. In my view this is a major show stopper. In this day and age where every vendor is working diligently to support openness and application integration via support for services oriented architecture and web services in comes HANA with SAP’s age old vision of closed system with no access to underlying data structures. HANA takes vendor lock in to new levels by limiting your choice of applications, reporting & analysis tools to a few offered by SAP.

#5: HANA is an appliance. So, where’s ease and speed of deployment?

Wikipedia defines computer appliances as consisting hardware and software pre-integrated and pre-configured before delivery to customer, to provide a "turn-key" solution to a particular problem. Benefits of appliances include ease and speed of deployment with lower risk and faster time to value. With HANA you buy hardware, software, networking switches and storage from different vendors. There isn’t a single point of support and with different vendors having markedly different development and upgrade cycles it’s excruciatingly hard to test, configure, certify and update the joint solution.

In conclusion, SAP’s larger than life solution HANA definitely underscores the strategic importance of data management and analysis to organizations but due to limitations highlighted above HANA is far from being ready to support mission critical enterprise applications. Customers should consider mature technologies like TimesTen based Oracle Exalytics and Oracle Exadata for their in-memory analytics needs. 

Big Data Analytics – The Journey from Transactions to Interactions

Big Data Defined

Enterprise systems have long been designed around capturing, managing and analyzing business transactions e.g. marketing, sales, support activities etc. However, lately with the evolution of automation and Web 2.0 technologies like blogs, status updates, tweets etc. there has been an explosive growth in the arena of machine and consumer generated data. Defined as “Big Data”, this data is characterized by attributes like volume, variety, velocity and complexity and essentially represents machine and consumer interactions. 

Case for Big Data Analysis

Machine and consumer interaction data is forward looking in nature. This data available from sensors, web logs, chats, status updates, tweets etc. is a leading indicator of system and consumer behavior. Therefore this data is the best indicator of consumer’s decision process, intent, sentiments and system performance. Transactions on the other hand are lagging indicators of system or consumer behavior. By definition leading indicators are more speculative and less reliable compared to lagging indicators; however, to predict the future with any confidence a combination of both leading and lagging indicators is required. That’s where the value of big data analysis comes in, by combining system and consumer interactions and transactions, organizations can better predict the consumer decision process, intent sentiments and future system performance leading to revenue growth, lower costs, better profitability and better designed systems.

So, which business areas will benefit via big data analysis? Think of areas where decision-making under uncertainty is required. Areas like new product introduction, risk assessment, fraud detection, advertising and promotional campaigns, demand forecasting, inventory management and capital investments will particularly benefit by having a better read on the future.

Figure 1: Combination of big data and transactional data delivers better insights and business results

Big Data Analytics Lifecycle

The big data analytics lifecycle includes steps like acquire, organize and analyze. Big data or consumer interaction data is characterized by attributes like volume, velocity and variety and common sources of such data include web logs, status updates and tweets etc. The analytics process starts with data acquisition. The structure and content of big data can’t be known upfront and is subject to change in-flight so the data acquisition systems have to be designed for flexibility and variability; no predefined data structures, dynamic structures are a norm. The organization step entails moving the data in well defined structures so relationships can be established and the data across sources can be combined to get a complete picture. Finally the analysis step completes the lifecycle by providing rich business insights for revenue growth, lower costs and better profitability. Flexibility being the norm, the analysis systems should be discovery-oriented and explorative as opposed to prescriptive.

Getting Started

Oracle offers the broadest and most integrated portfolio of products to help you acquire and organize these diverse data sources and analyzes them alongside your existing data to find new insights and capitalize on hidden relationships. Learn how Oracle helps you acquire, organize, and analyze your big data by clicking here.

Figure 2: Oracle’s engineered system solution for big data analytics

Oracle Exalytics Pricing explained - a wonderful product at a wonderful price

Warren Buffet famously said and I quote (with some edits) “It's far better to buy a wonderful company (product) at a fair price than a fair company (product) at a wonderful price”. Conventional wisdom has it – quality doesn’t come cheap. Well in this day and age where conventions are broken every day - time to think differently. What-if the best analytics solution in the world was available at the bargain basement price.

Oracle recently announced the pricing for Exalytics, the industry’s first in-memory analytics machine and as conventional wisdom would have it a number of articles were published with Exalytics pricing in millions of dollars of range. But once again continuing with the glowing tradition of “WHY NOT “, Oracle is out to prove the conventional wisdom wrong. Drum rolls please….NOW YOU CAN GET EXALYTICS FOR MUCH LOWER THAN THE MILLION DOLLAR MARK. No gimmicks, no discounts, all based on the list price.

Exalytics includes 3 components – hardware, software and support.

Hardware Cost:

1)      The List price for Exalytics hardware is : $135,000

Software Cost:

Exalytics includes two software components:

2)      TimesTen In-Memory Database for Exalytics: Priced at $300 per named user/ 100 user minimum OR $34,500 per processor

3)      Oracle BI Foundation Suite: Priced at $3,675 per named user/100 user minimum OR $ 450,000 per processor       

Support Cost:

4)      Annual support cost includes support for Exalytics hardware ($ 29,700), TimesTen In-Memory Database for Exalytics ($66 per user OR $7,590 per processor), Oracle BI Foundation Suite ($808.50 per user OR $99,000 per processor).

So, what’s the total cost of deploying a analytic system with 100 users?

Exalytics Cost for a 100 user system = 1 + 100*2 + 100*3 + 4 = $135,000 + 100*$300 + 100* $3,675 + ($29,700 + 100*$66 + 100*808.50) = $135,000 + $30,000 + $367,500 + $117,150 = $649,650

Now, discounts of up to 50% are quite common in the software world. I don’t know how much Oracle discounts but assuming a conservative 50% discount rate we are looking at a 100 user system powered by 1 TB of RAM and 40 CPU cores and market leading BI and In-Memory database technology for under $300 K. That’s $3K per user. Compare this to reoccurring $3K per user per year Salesforce charges for their Sales Cloud and the $5K per month pricing offered by a cloud based BI provider.

In this day and age where we are moving away from “Whys” to “Why Nots”, I think Oracle Exalytics definitely proved the conventional wisdom wrong by delivering best value for best price. Well this would even make Warren Buffet revise his quote – “a wonderful company (product) at a wonderful price”.

Introducing the Industry's First Analytics Machine, Oracle Exalytics

Analytics is all about gaining insights from data for better decision making. The
business press is abuzz with examples of leading organizations across the world using
data-driven insights for strategic, financial and operational excellence. A recent study on
“data-driven decision making” conducted by researchers at MIT and Wharton provides
empirical evidence that “firms that adopt data-driven decision making have output and
productivity that is 5-6% higher than the competition”. The potential payoff for firms can
range from higher shareholder value to a market leadership position.

However, the vision of delivering fast, interactive, insightful analytics has remained elusive
for most organizations. Most enterprise IT organizations continue to struggle to deliver
actionable analytics due to time-sensitive, sprawling requirements and ever tightening
budgets. The issue is further exasperated by the fact that most enterprise analytics
solutions require dealing with a number of hardware, software, storage and networking
vendors and precious resources are wasted integrating the hardware and software
components to deliver a complete analytical solution.

Oracle Exalytics In-Memory Machine is the world‟s first engineered system specifically
designed to deliver high performance analysis, modeling and planning. Built using
industry-standard hardware, market-leading business intelligence software and in-memory
database technology, Oracle Exalytics is an optimized system that delivers answers to all
your business questions with unmatched speed, intelligence, simplicity and manageability.

Oracle Exalytics's unmatched speed, visualizations and scalability delivers extreme
performance for existing analytical and enterprise performance management applications
and enables a new class of intelligent applications like Yield Management, Revenue
Management, Demand Forecasting, Inventory Management, Pricing Optimization,
Profitability Management, Rolling Forecast and Virtual Close etc.

Requiring no application redesign, Oracle Exalytics can be deployed in existing IT
environments by itself or in conjunction with Oracle Exadata and/or Oracle Exalogic to
enable extreme performance and best in class user experience. Based on proven hardware,
software and in-memory technology, Oracle Exalytics lowers the total cost of ownership,
reduces operational risk and provides unprecedented analytical capability for workgroup,
departmental and enterprise wide deployments.

Click here to learn more about Oracle Exalytics.

Five ways Oracle Exalytics is “different” from SAP HANA

The social media platforms are abuzz with comparisons between Oracle Exalytics & SAP HANA. Some of our esteemed colleagues from the other side have tried hard but failed miserably to differentiate between Exalytics & HANA. Frankly, you don’t need to be a PHD or a wine connoisseur to understand the differences; all you need is to invest some time. Here are the top 5 ways (David Letterman style) of how HANA imitates to be Exalytics but fails miserably:

#5: HANA is an “appliance”, Exalytics is an engineered solution: Ok, I am being generous here by classifying HANA as an appliance. My definition of appliance is hardware & software put together for a specific purpose. So, may be HP & Microsoft joined forces to offer a BI appliance but a piece of software running on a bunch of supported hardware platforms without any specific purpose, which HANA is, doesn’t qualify as an appliance. Exalytics on the other hand is an engineered solution. Engineered solution is one which is purpose built to solve a specific problem, think dental braces vs. metal wires. Exalytics is hardware and software which is purpose built to best handle analytical workloads. Unlike an appliance, Exalytics’ software has been designed from the ground up to best exploit the underlying hardware. Features like in-memory, parallelization, automated intelligent cache management, compression etc.  deliver the best analytics performance and exploit the abundance of memory and processing capability available on Exalytics.

#4: HANA does everything but analytics and Exalytics is purpose built for analytics: HANA takes on a new purpose depending on the day of the week. HANA is an analytics database today. In the future, it will be a transactional database. Well there’s nothing wrong in being opportunistic and finding a new purpose every day, experimentation is good. I would certainly like my 4 year old to experiment and figure out if he wants to be an astronaut, a scientist or an artist but how would you feel about experimenting with your mission critical business systems. My advice, please don’t innovate for the sake of innovation.

Exalytics, on the other hand, does only one thing – it delivers unmatched performance and user experience for speed of thought analytics. It super charges your existing BI deployments and enables a new category of smart analytical applications like yield management, revenue management, real time forecasting, virtual financial close, dynamic pricing etc. It integrates transparently into your existing IT environment and requires no manual data movement or costly changes to your application code or behavior – now this is true innovation without disruption.

#3: HANA solves the problem which Exadata solved 2 years ago: HANA is supposedly a database query accelerator. It makes the database queries run faster. Dah, a ground breaking innovation from SAP, well SAP welcome to the party, you are just 2+ years late. Oracle solved the database query acceleration problem 2+ years ago with introduction of Exadata. We can debate the technical nuances like in-memory etc. but with 2TB of RAM and innovations around storage and data access, Exadata remains the fastest database machine on the planet.

#2: HANA is closed; Exalytics is an open solution: HANA is designed to work with SAP data and tools only. Now this is Database 101, you don’t design a database that is closed. The basic concept of a DBMS is to act as a data consolidation platform where data can be collected, stored, managed and accessed openly. OK, you can’t really fault the SAP development here; after all they are designing a version 0.1 of the product which Oracle has been investing billions for the last 30 years to perfect. Exalytics is a completely open middle-tier analytics platform. It connects to any or all commercially available databases like Oracle, DB2, SQL Server, Teradata, Netezza and even SAP HANA and delivers high speed reporting and analysis. Besides, over 40+ pre-built enterprise performance management, ERP and CRM analytical applications are already certified on Exalytics.

#1: No BI software included with HANA; Exalytics is a single stop BI solution: In order to make sense of data or as the former CEO of Business Objects, Bernard Liautaud aptly put it to derive intelligence out of data; BI tools like dashboards, reports, scorecards and ad-hoc query and analysis tools are required. Looks like our able colleagues at SAP forgot this minor detail and didn’t include any BI tools with HANA. Exalytics on the other hand comes preloaded with Oracle BI Foundation. Oracle BI Foundation delivers the widest and most robust set of reporting, ad hoc query and analysis, OLAP, dashboard, and scorecard functionality with a rich end user experience that includes visualization, collaboration, alerts and notifications, search and mobile access. So, all you do with Exalytics is plug it in, connect to a data source and you are on your way to delivering pre-packaged or custom analytical applications.

Hopefully the above provides insightful context on Exalytics and its imposter SAP HANA. Exalytics is and remains the industry’s first in-memory analytics machine. You can learn more about Oracle Exalytics here. 

Forrester Study - 986% ROI, $100+ million in additional revenues with Oracle's Decision Management Solution

Analytics is all about analyzing data for better insights and decisions. Intuitively, it seems that more data analysis should equal better business results like higher revenues, lower costs, higher profitability and productivity. A recent study on “data-driven decision making” conducted by researchers at MIT and Wharton provides empirical evidence that “that firms that adopt data-driven decision making have output and productivity that is 5-6% higher than the competition”.

With this background, Oracle recently commissioned a study through Forrester Research to determine the total economic impact and potential ROI for Oracle’s Real-Time Decisions (RTD) platform. This article presents some of the interesting facts from the Forrester study.

For people not familiar with RTD, Oracle's Real-Time Decisions (RTD) platform combines both rules and predictive analytics to power solutions for real-time enterprise decision management. It enables real-time intelligence to be instilled into any type of business process or customer interaction. RTD was chosen for the study precisely for its ability to influence business decisions based on defined rules and more importantly insights derived through real-time data analysis.

The customer chosen for this study, a large financial services company has offices in all 50 states with over 20,000 employees and millions of customers. This company derives a significant portion of revenues via the online channel and hence RTD plays a major role in influencing the customer acquisition and retention process.

The results – A three year risk adjusted ROI of 986% with over $100 million in additional revenue generation. The numbers are astonishing by any measure so let’s dive deeper to understand how the customer achieved such amazing benefits.

• Increased Closure rate & incremental deal size: Imagine a Financial Services provider doing business with millions of customers over the web. If only every interaction could be tailored to the right offering for the individual customer need, this is exactly what the provider did. Using RTD’s predictive analytics capabilities, in real-time, the financial services provider was able to tailor its offerings to best meet the customer needs. The results an approximately 0.8% lift in closure rate in Year 1 which increase to 1% in Year 2. Besides, by targeting the right offering to match the customer needs and not the cheapest offering the provider was able to increase the average deal size by $10 in Year 1 and by $12 in Year 2. These two initiatives yielded roughly $54.4 million and $41.1 million in additional revenues over 3 years.
• Post cart abandonment follow-up campaign revenue: Another RTD initiative focused on sending personalized follow-up emails to potential customers who asked for the quote but didn’t buy (abandoned their cart). Using RTD’s rule based and real-time predictive analytics models, the provider customized things like email subject, email body and even the time of the day the emails were send to better suit the individual prospect profile. The results – an astonishing 1% point lift in the conversation rate compared to the control group who received static message resulting in $56.4 million in additional revenue over 3 years.
• Another point worth noting is that the benefits like uplift in closure rate and higher average deal size continued to improve year over year. Obviously, some of this continuous improvement is based on better hypothesis testing and campaign refinement work undertaken by the decision management team. But more importantly, RTD’s predictive models are getting better with time i.e. the RTD system continues to learn and improve with time.

In conclusion, the Forrester study confirms beyond doubt the power of RTD in particular and data mining based automated decision management systems in general. Besides the Financial Services provides there are number of customers like a leading Credit Card provider and Ecommerce site of a large retailer who are using RTD to improve customer interactions and deliver additional revenues. We believe that the demand for solutions like RTD will continue to increase with trends like big data, automation and increased competition and as more and more businesses began to use analytics as a competitive differentiator. You can learn more about RTD here.

Are you underutilizing your most important corporate asset, data?

    It’s a fascinating time to be in the data management business. Pundits are using terms like “oil” and “soil” to describe the business value of data. The Economist magazine in a special report on managing information published in 2010 featured a quote from a computer expert describing the current times as the “industrial revolution of data”. The same report stated “data as becoming the new raw material of business: an economic input almost on a par with capital and labor”. To say data is important to running a business is an understatement, looks like storing, managing and analyzing the data could well be difference between success and failure.

          

    Pundits agree that across all the data available in an organization, 20% is structured and 80% unstructured. Structured data refers to the human generated data like orders, leads, support calls etc. which is generally well stored, managed and analyzed. Unstructured data refers to machine generated data like RFID sensors, web logs, application logs, emails, click streams etc. which is loosely stored and infrequently analyzed to drive business decisions. This is not to say that the unstructured data is not analyzed at all, it is generally used to drive technological decisions like improving applications and systems performance. So, in essence, organizations are using only 20% of their data to run the business. This means that organizations are leaving a lot of money on the table by under utilizing one of their most important corporate asset, data, which is comparable to other assets like capital and labor. Imagine running a business where 80% of your labor or capital is not utilized…how terribly unproductive, yet leading organizations around the world are doing just that day in and day out.

          

    Lot of businesses takes comfort in fact that they are really not data oriented business. Conventional wisdom states that structured and unstructured data is definitely relevant to online e-commerce businesses like Google, Facebook, eBay etc. but the unstructured data is of not much use to traditional businesses like manufacturing, utilities, consumer goods etc. Nothing could be further from the truth.

        

    So, how can a manufacturing business like automobile manufacturer benefit from analyzing both structured and unstructured data? Automobile buying just like any big ticket item purchase involves the traditional buying steps like need recognisition, information search, and alternative evaluation and purchase decision. The buyers spend a lot of time on the manufacturers or 3^rd party information provider websites generating unstructured data around making selections, gaining information and comparing alternatives. This user behavioral data can be constantly analyzed and combined with the structured “compare vehicle” section of the websites to make the comparative selection dynamic and based on user behavior vs. a static list. Similarly, the attitudinal data generated by the customers around a vehicle’s features can be used as an input to improve the vehicle design process.

     

    Another example is around achieving balance between mass customization and mass production with a service like NIKEid. NIKEiD is a service provided by Nike allowing customers to personalize and design their own Nike merchandise. NIKEiD offers online services as well as physical studios in different countries around the world. Mass customization provides personalization but without mass production the cost and lead time is prohibitive. NIKEid can use the unstructured user generated design data to identify the top selling merchandise and sell them as innovatively designed, semi-mass produced items at lower cost, with less lead time and at higher volumes generating better profits. E.g. NIKEiD’s unstructured personalization and design data can be used to identify major trends like demand for “shoes with a smaller carbon footprint” or “green shoes” and can be used to launch a new mass produced product line.

    

    These are just a few examples on how data can be used as a strategic asset to drive profitable business. In closing, as Rollin Ford, CIO of Wal-mart says “ Every day I wake up and ask, how do I flow, manage and analyze data better?”, data is your most strategic asset which could well be way underutilized.

Introducing Business Intelligence 2.0

Every technology undergoes innovation, evolution and experimentation to best meet the needs of changing times. It seems like the time has come to move beyond Business Intelligence (BI) 1.0 and start talking about BI 2.0. MIT-Sloan Management Review in a recently published survey titled “Analytics: the New Path to Value” identified the top 3 analytical techniques creating value for the organization evolving from historical trend analysis, standardized reporting and data visualization to simulations and scenario development, analytics applied within the business processes and data visualization. Other analyst firms like Gartner are validating this view and have identified one of the key future trends in business intelligence as a shift in the use of the BI system. According to Gartner, BI practitioners today are increasingly using their BI systems beyond measurement or reporting and more for the purposes of analysis, forecasting and optimization. In my view this is a “tipping point” in the evolution of BI technology and hence the coming of age of BI 2.0.

So, what does a BI 2.0 system look like? I think a BI 2.0 system is best described by outlining the top 5 capabilities that such a system exhibits. From my perspective, here are the top 5 BI 2.0 capabilities in no particular order:

•  Beyond measurement to scenario modeling, forecasting and optimization: BI 2.0 systems will include capabilities beyond standardized reporting, dashboards and ad-hoc query to scenario modeling and simulation capabilities like what-if analysis, forecasting and optimization. As organizations evolve and mature in the use of BI technologies, it’s only natural to move away from reactive to proactive mode of doing business and hence the shift in system capabilities from historical to forward looking predictive analysis.
•  In-business process analytics: As businesses realize the benefits of better insights, better decisions and faster actions; BI capabilities will be more pervasive and better integrated within the business processes. The next evolution of the ERP systems will be around “intelligent” process automation driving BI capabilities within business process. As organizations become efficient and learn to do more with less, BI will no longer be an afterthought and would be directly embedded with the business process making the process more efficient.
•  Support for “Big Data”: Businesses today are generating data at tremendous volume and speed. The so called “machine generated data” e.g. the data generated through web logs, RFID sensors etc. far out paces “human generated data”. A number of new technologies like Hadoop, columnar databases, NoSQL databases have come to age to solve the big data storage and analysis problem. The various data management technologies like relational, multidimensional, distributed file based data management systems solve specific business problems and would continue to thrive within the enterprise IT infrastructure. Big data presents interesting opportunities like analyzing the customer behavioral and attitudinal data to identify new revenue opportunities. BI 2.0 systems will provide ubiquitous access to multiple enterprise data sources via enterprise wide semantic layer ensuring conformance, single version of the truth and federation.
•  Insight to Action: BI 2.0 systems will deliver a closed loop analytics cycle by not only delivering the insights but also providing the ability to act on the insights. The BI and business process management technologies will come together delivering capabilities to initiate a business process directly from the BI dashboards. So, how does the Insight to Action Framework delivers value? Well it makes it easier and faster for your Business users to take the next step from gaining insights to acting on them. E.g. an accounts receivable manager might notice on his dashboard that DSO is trending up, and by drilling down, may see a late payment trend by few customers. By placing a credit hold on the problem customers he/she can take action on his/her DSO trending up problem. In this scenario, the account receivables manager never had to send out emails or call people or access different systems to figure out the next steps. The next steps are pre-build in the analytics system and just a click away.
• Always on, collaborative and consumerized BI: Personal tools and productivity devices like Facebook, Twitter, Google, Smart phones and Tablets are changing the way we consume information. Information is available 24*7, on the go and from the convenience of your mobile device. Gartner’s term “iphonesque” illustrates the simple, mobile and fun aspect desired by users off of their BI systems today. BI 2.0 systems will embrace the changing paradigm and will be simple, easy, mobile, collaborative and fun.

In conclusion, exciting times lie ahead for the BI community. BI 2.0 systems with some of the exciting new capabilities outlined above will change how businesses gain insights, take decisions and act faster. BI 2.0 offers new promises to better analyze disparate data sources and identify opportunities for top line and bottom line growth. Stay tuned as we continue to innovate and evolve.

New flash demo titled “Achieve Strategic Alignment with Oracle Scorecard and Strategy Management” is now available on Oracle.com

Want to learn about how to achieve strategic alignment with Oracle Scorecard and Strategy Management?

 If you are interested in Oracle Scorecard and Strategy Management and how it helps you define strategy, establish objectives, undertake initiatives, monitor performance, and take actions to achieve strategic alignment; check out the 3 minute flash demo posted here on Oracle.com

Event invitation: Oracle Business Intelligence Online Forum on December 15th 9:00-12:30 PST

Join us for the Oracle Business Intelligence Forum on Wednesday, December 15th at 9:00am (PST) where Oracle executives, customers, and industry analyst, Howard Dresner, come together to share what Business Intelligence can do for your business. Product experts will be available to chat live throughout the event.
Click here to register.

Closing the gap between strategy and execution with Oracle Business Intelligence 11g

Wikipedia defines strategy as a plan of action designed to achieve a particular goal. An example of this is General Electric’s acquisitions and divestiture strategy (plan) designed to propel GE to number 1 or 2 place (goal) in every business segment that it operated in. Execution on the other hand can be defined as the actions taken to getting things done. In GE’s case execution will be steps followed for mergers/acquisitions or divestiture. Business press has written extensively about the importance of both strategy and execution in achieving desired business objectives. Perhaps the quote from Thomas Edison says it best – “vision without execution is hallucination”. Conversely, it can be said that “execution without vision” is well may be “wishful thinking”.

Research overwhelmingly point towards the wide gap between strategy and execution. According to a published study, 49% of surveyed executives perceive a gap between their organizations’ ability to develop and communicate sound strategies and their ability to implement those strategies. Further, of these respondents, 64% don’t have full confidence that their companies will be able to close the gap.

Having established the severity and importance of the problem let’s talk about the reasons for the strategy-execution gap. The common reasons include:

–        Lack of clearly defined goals

–        Lack of consistent measure of success

–        Lack of ownership

–        Lack of alignment

–        Lack of communication

–        Lack of proper execution

–        Lack of monitoring

There are multiple approaches to solving the problem including organizational development practices, technology enablement etc. In most cases a combination of approaches is required to achieve the desired result. For the purposes of this discussion, I’ll focus on technology. 

Imagine an integrated closed loop technology platform that automates the entire management cycle from defining strategy to assigning ownership to communicating goals to achieving alignment to collaboration to taking actions to monitoring progress and achieving mid course corrections. Besides, for best ROI and lowest TCO such a system should also have characteristics like:

•  Complete

–        Full functionality

–        Rich end user access

• Open

–        Any data source

–        Any business application

–        Any technology stack

•  Integrated

–        Common metadata

–        Common security

–        Common system management

From a capabilities perspective the system should provide the following capabilities:

• Define

–        Strategy

–        Objectives

–        Ownership

–        KPI’s

• Communicate

–        Pervasive

–        Collaborative

–        Role based

–        Secure

• Execute

–        Integrated

–        Intuitive

–        Secure

–        Ubiquitous

• Monitor

–        Multiple styles and formats

–        Exception based

–        Push & Pull

Having talked about the business problem and outlined the blueprint for a technology solution, let’s talk about how Oracle Business Intelligence 11g can help. Oracle Business Intelligence is a comprehensive business intelligence solution for reporting, ad hoc query and analysis, OLAP, dashboards and scorecards. Oracle’s best in class BI platform is based on an architecturally integrated technology foundation that provides a unified end user experience and features a Common Enterprise Information Model, with common security, query request generation and optimization, and system management. The BI platform is

·         Complete – meaning it delivers all modes and styles of BI including reporting, ad hoc query and analysis, OLAP, dashboards and scorecards with a rich end user experience that includes visualization, collaboration, alerts and notifications, search and mobile access.

·         Open – meaning the BI platform integrates with any data source, ETL tool, business application, application server, security infrastructure, portal technology as well as any ODBC compliant third party analytical tool. The suite accesses data from multiple heterogeneous sources—including popular relational and multidimensional data sources and major ERP and CRM applications from Oracle and SAP.

·         Integrated – meaning the BI platform is based on an architecturally integrated technology foundation built on an open, standards based service oriented architecture.  The platform features a common enterprise information model, common security model and a common configuration, deployment and systems management framework.

To summarize, Oracle Business Intelligence is a comprehensive, integrated BI platform that lets you define strategy, identify objectives, assign ownership, define KPI’s, collaborate, take action, monitor, report and do course corrections all form a single interface and a single system. The platform’s integrated metadata model and task based design ensures that the entire workflow from defining strategy to execution to monitoring is completely integrated delivering end to end visibility, transparency and agility. Click here to learn more about Oracle BI 11g. 

Cloud ready business intelligence with Oracle Business Intelligence 11g

Business Intelligence (BI) on the cloud represents the coming together of two key information technology (IT) trends – evolution of the cloud computing architecture as a cost effective, quick and efficient computing platform and use of business intelligence technology to reduce cost, gain insight and improve the quality and speed of business decisions.  Leading analyst firms like Gartner and IDC are predicting high adoption rates for applications deployed on private and public clouds.

Oracle is committed to delivering hardware and software solutions that are complete, open and integrated. Cloud computing is driving a significant part of Oracle’s product development plans – from enterprise applications to middleware, business intelligence technology, databases, servers and storage devices, as well as cloud management systems. Taken together, these developments are building off Oracle’s decade long leadership in underlying technologies like grid computing, clustering, server virtualization and dynamic provisioning, SOA, identity management and large scale management automation.

Oracle is committed to delivering business intelligence solutions that can be deployed both on the cloud and non-cloud mode.  Oracle Business Intelligence 11g, Oracle’s market leading BI platform has been architected to support both cloud and non-cloud deployments. A web services based SOA architecture along with full BI functionality and high scalability and manageability makes Oracle BI 11g suitable for cloud type deployment.

I recently authored a whitepaper that provides an overview of cloud computing along with Oracle’s cloud computing strategy and significant features of Oracle BI 11g that make it well suited for cloud deployments. The whitepaper also presents examples of customers who have deployed Oracle BI on the cloud.

The whitepaper is available here. Registration is free so please register to download and let me know of your thoughts.

Business Analytics – The holy grail of running a successful business

Running a successful business is all about taking better and timely decisions. How can you improve the quality and speed of decision making? There are two ways. First you can rely on your gut or experience and be right 1 out of 10 times. Second you can analyze the huge volumes of data available to you and answer questions like – what is going on? Why are things the way they are? How can I improve performance? The odds in this case are being right 9 out of 10 times. Any reasonable, analytical person will choose the latter option unless you rely on your gut to take decisionsJ. Isn’t this what is taught at the business schools worldwide – Taking better decisions based on insights drawn from using analytical tools. So, in support of the title of this blog – running a successful business is all about taking better decisions and analytics can help you take better decisions hence analytics is the holy grail of running a successful business.

Now, what exactly is business analytics? Business Analytics answers a broad range of questions from what is going on in my business? To why are things the way they are? To how can I achieve desired business results? From a capability perspective analytics include broad capabilities from reporting which answers what is going on to ad-hoc query, statistical analysis, data mining which answer why are things the way they are to scorecards, budgeting, planning and financial analytics which answer how can I achieve desired results.  Sounds complex – no it’s not provided you take a balanced approach. Start small with something easy like reporting and grow and mature your analytical capabilities over time. Of course the trick here is to choose an analytical platform the various components of which fit together as Lego blocks.

This is where Oracle’s business analytics solutions can help. From BI Publisher for reporting to Oracle Business Intelligence Enterprise Edition for query, reporting and analysis to Oracle Essbase for OLAP, scenario modeling and forecasting to Oracle Real Time Decisions for real time decision management to Oracle Scorecard and Strategy Management for defining, communicating and measuring business objectives to Oracle database’s data mining and analytics capabilities to Oracle Exadata – Oracle’s database and data warehouse appliance with hardware and software engineered to work together, Oracle offers a complete portfolio of capabilities for end to end analytics. A tight metadata integration across the entire portfolio combined with scalability, reliability and manageability ensures that the platform delivers the best ROI for lowest TCO.

Net net, Oracle’s modular, Lego block like analytics platform can help you start small and grow as business grows to achieve the holy grail of running a successful business.