In the wake of the long-running massive industry consolidation in the Enterprise Software industry that reached its zenith with the acquisitions of Business Intelligence market leaders Hyperion, Cognos, and Business Objects in 2007, one could certainly have been forgiven for being less than optimistic about the prospects of innovation in the Analytics, Business Intelligence, and Performance Management markets. This is especially true given the dozens of innovative companies that each of these large best of breed vendors themselves had acquired before being acquired in turn. While the pace of innovation has slowed to a crawl as the large vendors are midway through digesting the former best of breed market leaders, thankfully for the health of the industry, nothing could be further from the truth in the market overall. This market has in fact shown itself to be very vibrant, with a resurgence of innovative offerings springing up in the wake of the fall of the largest best of breed vendors.
So what are the trends and where do I see the industry evolving to? Few of these are mutually exclusive, but in order to provide some categorization to the discussion, they have been broken down as follows:
1. We will witness the emergence of packaged strategy-driven execution applications. As we discussed in Driven to Perform: Risk-Aware Performance Management From Strategy Through Execution (Nenshad Bardoliwalla, Stephanie Buscemi, and Denise Broady, New York, NY, Evolved Technologist Press, 2009), the end state for next-generation business applications is not merely to align the transactional execution processes contained in applications like ERP, CRM, and SCM with the strategic analytics of performance and risk management of the organization, but for those strategic analytics to literally drive execution. We called this “Strategy-Driven Execution”, the complete fusion of goals, initiatives, plans, forecasts, risks, controls, performance monitoring, and optimization with transactional processes. Visionary applications such as those provided by Workday and SalesForce.com with embedded real-time contextual reporting available directly in the application (not as a bolt-on), and Oracle’s entire Fusion suite layering Essbase and OBIEE capabilities tightly into the applications' logic, clearly portend the increasing fusion of analytic and transactional capability in the context of business processes and this will only increase.
2. The holy grail of the predictive, real-time enterprise will start to deliver on its promises. While classic analytic tools and applications have always done a good job of helping users understand what has happened and then analyze the root causes behind this performance, the value of this information is often stale before it reaches its intended audience. The holy grail of analytic technologies has always been the promise of being able to predict future outcomes by sensing and responding, with minimal latency between event and decision point. This has become manifested in the resurgence of interest in event-driven architectures that leverage a technology known as Complex Event Processing and predictive analytics. The predictive capabilities appear to be on their way to break out market acceptance IBM’s significant investment in setting up their Business Analytics and Optimization practice with 4000 dedicated consultants, combined with the massive product portfolio of the Cognos and recently acquired SPSS assets. Similarly, Complex Event Processing capabilities, a staple of extremely data-intensive, algorithmically-sophisticated industries such as financial services, have also become interesting to a number of other industries that can not deal with the amount of real-time data being generated and need to be able to capture value and decide instantaneously. Combining these capabilities will lead to new classes of applications for business management that were unimaginable a decade ago.
3. The industry will put reporting and slice-and-dice capabilities in their appropriate places and return to its decision-centric roots with a healthy dose of Web 2.0 style collaboration. It was clear to the pioneers of this industry, beginning as early as H.P. Luhn's brilliant visionary piece A Business Intelligence System from 1958, that the goal of these technologies was to support business decision-making activities, and we can trace the roots of modern analytics, business intelligence, and performance management to the decision-support notion of decades earlier. But somewhere along the way, business intelligence became synonymous with reporting and slicing-and-dicing, which is a metaphor that suits analysts, but not the average end-user. This has contributed to the paltry BI adoption rates of approximately 25% bandied about in the industry, despite the fact that investment in BI and its priority for companies has never been higher over the last five years. Making report production cheaper to the point of nearly being free, something SaaS BI is poised to do (see above), is still unlikely to improve this situation much. Instead, we will see a resurgence in collaborative decision-centric business intelligence offerings that make decisions the central focus of the offerings. From an operational perspective, this is certainly in evidence with the proliferation of rules-based approaches that can automate thousands of operational decisions with little human intervention. However, for more tactical and strategic decisions, mash-ups will allow users to assemble all of the relevant data for making a decision, social capabilities will allow users to discuss this relevant data to generate “crowdsourced” wisdom, and explicit decisions, along with automated inferences, will be captured and correlated against outcomes. This will allow decision-centric business intelligence to make recommendations within process contexts for what the appropriate next action should be, along with confidence intervals for the expected outcome, as well as being able to tell the user what the risks of her decisions are and how it will impact both the company’s and her own personal performance.
3. The industry will put reporting and slice-and-dice capabilities in their appropriate places and return to its decision-centric roots with a healthy dose of Web 2.0 style collaboration. It was clear to the pioneers of this industry, beginning as early as H.P. Luhn's brilliant visionary piece A Business Intelligence System from 1958, that the goal of these technologies was to support business decision-making activities, and we can trace the roots of modern analytics, business intelligence, and performance management to the decision-support notion of decades earlier. But somewhere along the way, business intelligence became synonymous with reporting and slicing-and-dicing, which is a metaphor that suits analysts, but not the average end-user. This has contributed to the paltry BI adoption rates of approximately 25% bandied about in the industry, despite the fact that investment in BI and its priority for companies has never been higher over the last five years. Making report production cheaper to the point of nearly being free, something SaaS BI is poised to do (see above), is still unlikely to improve this situation much. Instead, we will see a resurgence in collaborative decision-centric business intelligence offerings that make decisions the central focus of the offerings. From an operational perspective, this is certainly in evidence with the proliferation of rules-based approaches that can automate thousands of operational decisions with little human intervention. However, for more tactical and strategic decisions, mash-ups will allow users to assemble all of the relevant data for making a decision, social capabilities will allow users to discuss this relevant data to generate “crowdsourced” wisdom, and explicit decisions, along with automated inferences, will be captured and correlated against outcomes. This will allow decision-centric business intelligence to make recommendations within process contexts for what the appropriate next action should be, along with confidence intervals for the expected outcome, as well as being able to tell the user what the risks of her decisions are and how it will impact both the company’s and her own personal performance.
4. Performance, risk, and compliance management will continue to become unified in a process-based framework and make the leap out of the CFO’s office. The disciplines of performance, risk, and compliance management have been considered separate for a long time, but the walls are breaking down, as we documented thoroughly in Driven to Perform. Performance management begins with the goals that the organization is trying to achieve, and as risk management has evolved from its siloed roots into Enterprise Risk Management, it has become clear that risks must be identified and assessed in light of this same goal context. Similarly, in the wake of Sarbanes-Oxley, as compliance has become an extremely thorny and expensive issue for companies of all sizes, modern approaches suggest that compliance is ineffective when cast as a process of signing off on thousand of individual item checklists, but rather should be based on an organization’s risks. All three of these disciplines need to become unified in a process-based framework that allows for effective organizational governance. And while financial performance, risk, and compliance management are clearly the areas of most significant investment for most companies, it is clear that these concerns are now finally becoming enterprise-level plays that are escaping the confines of the Office of the CFO. We will continue to witness significant investment in sales and marketing performance management, as vendors like Right90 continuing to gain traction in improving the sales forecasting process and vendors like Varicent receive hefty $35 million venture rounds this year, no doubt thanks to experiencing over 100% year over year growth in the burgeoning Sales Performance Management category. My former Siebel colleague, Bruce Cleveland, now a partner at Interwest, makes the case for this market expansion of performance management into the front-office rather convincingly and has invested correspondingly.
5. SaaS / Cloud BI Tools will steal significant revenue from on-premise vendors but also fight for limited oxygen amongst themselves. From many accounts, this was the year that SaaS-based offerings hit the mainstream due to their numerous advantages over on-premise offerings, and this certainly was in evidence with the significant uptick in investment and market visibility of SaaS BI vendors. Although much was made of the folding of LucidEra, one of the original pioneers in the space, and while other vendors like BlinkLogic folded as well, vendors like Birst, PivotLink, Good Data, Indicee and others continue to announce wins at a fair clip along with innovations at a fraction of the cost of their on-premise brethren. From a functionality perspective, these tools offer great usability, some collaboration features, strong visualization capabilities, and an ease-of-use not seen with their on-premise equivalents whereby users are able to manage the system in a self-sufficient fashion devoid of the need for significant IT involvement. I have long argued that basic reporting and analysis is now a commodity, so there is little reason for any customer to invest in on-premise capabilities at the price/performance ratio that the SaaS vendors are offering (see BI SaaS Vendors Are Not Created Equal ) . We should thus expect to see continued dimunition of the on-premise vendors BI revenue streams as the SaaS BI value proposition goes mainstream, although it wouldn’t be surprising to see acquisitions by the large vendors to stem the tide. However, with so many small players in the market offering largely similar capabilities, the SaaS BI tools vendors may wind up starving themselves for oxygen as they put price pressure on each other to gain new customers. Only vendors whose offerings were designed from the beginning for cloud-scale architecture and thus whose marginal cost per additional user approaches zero will succeed in such a commodity pricing environment, although alternatively these vendors can pursue going upstream and try to compete in the enterprise, where the risks and rewards of competition are much higher. On the other hand, packaged SaaS BI Applications such as those offered by Host Analytics, Adaptive Planning, and new entrant Anaplan, while showing promising growth, have yet to mature to mainstream adoption, but are poised to do so in the coming years. As with all SaaS applications, addressing key integration and security concerns will remain crucial to driving adoption.
6. The undeniable arrival of the era of big data will lead to further proliferation in data management alternatives. While analytic-centric OLAP databases have been around for decades such as Oracle Express, Hyperion Essbase, and Microsoft Analysis Services, they have never held the same dominant market share from an applications consumption perspective that the RDBMS vendors have enjoyed over the last few decades. No matter what the application type, the RDBMS seemed to be the answer. However, we have witnessed an explosion of exciting data management offerings in the last few years that have reinvigorated the information management sector of the industry. The largest web players such as Google (BigTable),Yahoo (Hadoop), Amazon (Dynamo), Facebook (Cassandra) have built their own solutions to handle their own incredible data volumes, with the open source Hadoop ecosystem and commercial offerings like CloudEra leading the charge in broad awareness. Additionally, a whole new industry of DBMSs dedicated to Analytic workloads have sprung up, with flagship vendors like Netezza, Greenplum, Vertica, Aster Data, and the like with significant innovations in in-memory processing, exploiting parallelism, columnar storage options, and more. We already starting to see hybrid approaches between the Hadoop players and the ADBMS players, and even the largest vendors like Oracle with their Exadata offering are excited enough to make significant investments in this space. Additionally, significant opportunities to push application processing into the databases themselves are manifesting themselves. There has never been the plethora of choices available as new entrants to the market seem to crop up weekly. Visionary applications of this technology in areas like metereological forecasting and genomic sequencing with massive data volumes will become possible at hitherto unimaginable price points.
7. Advanced Visualization will continue to increase in depth and relevance to broader audiences. Visionary vendors like Tableau, QlikTech, and Spotfire (now Tibco) made their mark by providing significantly differentiated visualization capabilities compared with the trite bar and pie charts of most BI players' reporting tools. The latest advances in state-of-the-art UI technologies such as Microsoft’s SilverLight, Adobe Flex, and AJAX via frameworks like Google’s Web Toolkit augur the era of a revolution in state-of-the art visualization capabilities. With consumers broadly aware of the power of capabilities like Google Maps or the tactile manipulations possible on the iPhone, these capabilities will find their way into enterprise offerings at a rapid speed lest the gap between the consumer and enterprise realms become too large and lead to large scale adoption revolts as a younger generation begins to enter the workforce having never known the green screens of yore.
6. The undeniable arrival of the era of big data will lead to further proliferation in data management alternatives. While analytic-centric OLAP databases have been around for decades such as Oracle Express, Hyperion Essbase, and Microsoft Analysis Services, they have never held the same dominant market share from an applications consumption perspective that the RDBMS vendors have enjoyed over the last few decades. No matter what the application type, the RDBMS seemed to be the answer. However, we have witnessed an explosion of exciting data management offerings in the last few years that have reinvigorated the information management sector of the industry. The largest web players such as Google (BigTable),Yahoo (Hadoop), Amazon (Dynamo), Facebook (Cassandra) have built their own solutions to handle their own incredible data volumes, with the open source Hadoop ecosystem and commercial offerings like CloudEra leading the charge in broad awareness. Additionally, a whole new industry of DBMSs dedicated to Analytic workloads have sprung up, with flagship vendors like Netezza, Greenplum, Vertica, Aster Data, and the like with significant innovations in in-memory processing, exploiting parallelism, columnar storage options, and more. We already starting to see hybrid approaches between the Hadoop players and the ADBMS players, and even the largest vendors like Oracle with their Exadata offering are excited enough to make significant investments in this space. Additionally, significant opportunities to push application processing into the databases themselves are manifesting themselves. There has never been the plethora of choices available as new entrants to the market seem to crop up weekly. Visionary applications of this technology in areas like metereological forecasting and genomic sequencing with massive data volumes will become possible at hitherto unimaginable price points.
7. Advanced Visualization will continue to increase in depth and relevance to broader audiences. Visionary vendors like Tableau, QlikTech, and Spotfire (now Tibco) made their mark by providing significantly differentiated visualization capabilities compared with the trite bar and pie charts of most BI players' reporting tools. The latest advances in state-of-the-art UI technologies such as Microsoft’s SilverLight, Adobe Flex, and AJAX via frameworks like Google’s Web Toolkit augur the era of a revolution in state-of-the art visualization capabilities. With consumers broadly aware of the power of capabilities like Google Maps or the tactile manipulations possible on the iPhone, these capabilities will find their way into enterprise offerings at a rapid speed lest the gap between the consumer and enterprise realms become too large and lead to large scale adoption revolts as a younger generation begins to enter the workforce having never known the green screens of yore.
9. Data Quality, Data Integration, and Data Virtualization will merge with Master Data Management to form a unified Information Management Platform for structured and unstructured data. Data quality has been the bain of information systems for as long as they have existed, causing many an IT analyst to obsess over it, and data quality issues contribute to significant losses in system adoption, productivity, and time spent addressing them. Increasingly, data quality and data integration will be interlocked hand-in-hand to ensure the right, cleansed data is moved to downstream sources by attacking the problem at its root. Vendors including SAP BusinessObjects, SAS, Informatica, and Talend are all providing these capabilities to some degree today. Of course, with the amount of relevant data sources exploding in the enterprise and no way to integrate all the data sources into a single physical location while maintaining agility, vendors like Composite Software are providing data virtualization capabilities, whereby canonical information models can be overlayed on top information assets regardless of where they are located, capable of addressing the federation of batch, real-time and event data sources. These disparate data soures will need to be harmonized by strong Master Data Management capabilities, whereby the definitions of key entities in the enterprise like customers, suppliers, products, etc. can be used to provide semantic unification over these distributed data sources. Finally, structured, semi-structured, and unstructured information will all be able to be extracted, transformed, loaded, and queried from this ubiquitious information management platform by leveraging the capabilities of text analytics capabilities that continue to grow in importance and combining them with data virtualization capabilities.
10. Excel will continue to provide the dominant paradigm for end-user BI consumption. For Excel specifically, the number one analytic tool by far with a home on hundreds of millions of personal desktops, Microsoft has invested significantly in ensuring its continued viability as we move past its second decade of existence, and its adoption shows absolutely no sign of abating any time soon. With Excel 2010's arrival, this includes significantly enhanced charting capabilities, a server-based mode first released in 2007 called Excel Services, being a first-class citizen in SharePoint, and the biggest disruptor, the launch of PowerPivot, an extremely fast, scalable, in-memory analytic engine that can allow Excel analysis on millions of rows of data at sub-second speeds. While many vendors have tried in vain to displace Excel from the desktops of the business user for more than two decades, none will be any closer to succeeding any time soon. Microsoft will continue to make sure of that.
10. Excel will continue to provide the dominant paradigm for end-user BI consumption. For Excel specifically, the number one analytic tool by far with a home on hundreds of millions of personal desktops, Microsoft has invested significantly in ensuring its continued viability as we move past its second decade of existence, and its adoption shows absolutely no sign of abating any time soon. With Excel 2010's arrival, this includes significantly enhanced charting capabilities, a server-based mode first released in 2007 called Excel Services, being a first-class citizen in SharePoint, and the biggest disruptor, the launch of PowerPivot, an extremely fast, scalable, in-memory analytic engine that can allow Excel analysis on millions of rows of data at sub-second speeds. While many vendors have tried in vain to displace Excel from the desktops of the business user for more than two decades, none will be any closer to succeeding any time soon. Microsoft will continue to make sure of that.
And so ends my list of prognostications for Analytics, Business Intelligence, and Performance Management in 2010! What are yours? I welcome your feedback on this list and look forward to hearing your own views on the topic.
Posts
Good post, Nenshad. How do you envision #9 happening in the near future, though, since no vendor to my knowledge has even touched the subject of integrating metadata for structured data and unstructured content?
ReplyDeleteNenshad,
ReplyDeleteMaybe due to my past in predictive analytics, I find market predictions to be fundamentally flawed. I'd like to see a confidence level associated with each of these. I'd also like to see an analysis of past years' predictions to understand the likelihood that they come true.
BTW, for several years now, I've tracked BI and PM market predictions. There's rarely much commonality: http://alignment.wordpress.com/2009/01/26/predictions-for-2009/
Hi Nenshad,
ReplyDeleteWhile I agree with most of these for large organizations and the upper end of the mid-market, I think its a different story for midsized companies and small businesses.
Just a couple of examples.
2) Predictive, real time - SMBs don't have the skill sets or money to hire consultants to build predictive models.
The same goes for CEP.
Who is going to build the event model?
Who is going to build the semantic layer - controlled vocabulary, taxonomy, ontology?
Who is going to build the business rules that map the thousands of events going across an enterprise bus to the semantic layer?
Its just way to much for a SMB, which is why the early adopters have been large financial institutions doing algorithmic trading and governments do security related applications.
Until vendors build solutions that solve industry specific needs I just don't see much uptake in SMB.
4) Performance, Risk and Compliance Unification
Many SMBs are just trying to get a handle on data access, reporting and analysis.
Siloed purchasing of budgeting and planning apps by business users is still occuring.
And while regulatory requirements are driving adoption of software like Access Controls, SMBs see risk management as some big, nebulous vision that beyond there resources.
In addition SMBs in general don't have an overarching plan or framework for BI, EPM, and GRC.
8) Open Source
The primary inhibitor to BI adoption in SMB is lack of technical resources. While open source lowers license and maintenance cost you still need an IT resource to install, configure, develop reports, administer, etc.
And lets be honest. Pentaho and JasperSoft tout the number of downloads but they don't really know how many people are actively using the products. In my opinion the sweet spot for open source BI has been with consulting firms using it to increase their margins.
Don't get me wrong, I definitely believe there is a place for open source and that there is bottom up adoption.
But are they making a significant impact in regards to market share? Absolutely not.
They are probably around $10 million in revenue a year in a $6.2 billion dollar market in 2008.
The things I think will be hot for SMBs
SaaS BI - The next generation from SAP BusinessObjects provides a single interface to explore, monitor and share information.
https://create.ondemand.com
Visualization - Although I would not put QlikTech in the same category as Tableau and Spotfire. Maybe I have not worked with QlikTech enough but I found it to be more of a dashboard building tool like Xcelsius, than a ad-hoc visualization tool like Tableau, Spotfire or SAP Business Objects Explorer
In memory analytics - Most people seem to focus on the fact that you can scan through millions of records in a very short period of time. But for SMBs I think the value is in the flexibility of analysis. You don't have to have IT prebuild data models, you can use any element as a fact or dimension on the fly. This in my opinion is the real strength of QlikTech
Excel - The research I have seen recently estimates 45% of SMBs still use Excel as their primary BI tool. Lets face it we will probably always have the love hate relationship with Excel.
Automated Data Discovery - Maybe this would be included in your number 6 or 9, as some of the functionality is available in data profiling and cleansing tools. But some is not for example there are vendors who's software can scan laptops, network drives, SharePoint, document management systems to discover all the spreadsheets in a company, and capture information about data, calculations, links, references etc.
Well I guess I have said enough for now.
Best Regards,
Dan Everett
Nenshad,
ReplyDeleteVery detailed and great post.
I wrote a one-sentence summary of your #6 in my sneak peek of issues to worry about now. I concur massively with you.
I am concerned no the politics involved in #4, agree that is necessary -- but this may be another time when reality hits planning with a clue-by-four and stops it dead on its tracks. time will tell.
I am going to politely say that my most sincere hope is that #9 is truly going to happen, but we will need lots of discussion on that before I can concur in actually happening. Then again, if the semantic web does start next year, then we have a much easier discussion. I think that this is a 10-12 year issue, not a one-year issue -- but I most sincerely would love to see some advances in 2010. This is not a time will tell, rather an enlightenment-will-tell story. If organizations and people get it, then we will see some movement. Hoping for it...
#10 makes me smile. yeah, i agree on that (if you want to add access, then you have the whole "BI + Analytics Suite from MSFT" cornered).
Nice summary, lots of big important stuff in here -- is this just 2010 or 2010 through 2015?
How do you see the market developing products/services for information security risk; an area of IT that has no clear metrics or system of measurement.
ReplyDeleteNenshad;
ReplyDeletePlenty substance here to ponder on.
I see #3 having the most influencial and pragmatic over all the trends and has the most momentum going into 2010 and beyond.
SaaS/BI will be under the scope in terms of #3 in how each vendor differentiates thenselves from the traditional on-premise vendors. Capabilities in these solutions will be designed to service the masses; amd success will be measured at breaking the 25% barrier?
As #6 and the big data and alternative data management continues to grow; I see that having an impact on how the providers of #9 respond and grow their offerings. The data governance/data integration solutions will need to respond to the semantic requirements and the ability to handle these big data volumes.
As the clouds start to part and the blue sky appears; the big data trend will converge at some point with the SaaS BI offering as a usable presentation layer is required. I see this building over time most likely beyond 2010.
Nenshad, wonderful and thought provoking piece.
ReplyDeleteI agree with you on all counts but please allow me to retort (hats off to Samuel L Jackson for that line)
http://www.youtube.com/watch?v=4DL1yfQFa4o
1. Packaged Strategy Driven Execution:
The people deploying this will need to be careful as driving this inappropriately will be like waves crashing against rock. The waves will win eventually, but initially they will smash into serious resistance unless handled correctly.
Milind Govinkar said it well at the Gartner conference, Metrics make people to good thing and bad things.
In addition when the "wave" of strategy driven execution hits the "rock" of Enterprise IT, a lot of garbage will be (thankfully) washed away, but there will be a lot of reckoning as to how this impacts IT implementation and execution.
don't get me wrong, the Shift report from Deloitte makes it clear that (with a 75% decline in Return on Asset since 1965) US Public companies are filled with dead wood that deservedly needs to be washed to sea. But it will be undoubtedly interesting to see the proverbial irresistable force of business hitting the immovable object of IT.
You can argue that deep evolution of business and continuous improvement has a critical path that lies outside of Enterprise IT. But I don't think it will, and if it does it will rapidly commoditize.
I agree with the rest but want to comment on number 7. I dont think you go nearly far enough with visualization, citing what I consider to be some fairly traditional approaches. We are at the early days of exploiting inbuilt pattern recognition abilities of the human nervous system for decision systems and radically new user interfaces will spring up, particularly as technology transfers from gaming, energy exploration and military applications.
I think that we're entering a very exciting time in computer-aided decision support, particularly in the areas of open source sollutions in all areas of data management and analytics, and the explosion of innovation in database management systems.
ReplyDeleteWe've been working with F/LOSS BI/DW related software since projects started popping up at the beginning of this decade, such as Jetstream (now defunct) for ETL and Mondrian for OLAP. In 2005, the VCs took notice, and companies such as Pentaho and Jaspersoft took off. EIIspa in Italy took notice as well and added F/LOSS BI tools to their Spago framework, which has continued to spin-off solutions to become Spagoworld. We currently list over 60 open source projects related to data warehousing, reporting, OLAP, ETL, data services, and related areas.
One thing that has truly amazed me this year, is that decades after Codd's famous paper outlining relational algebra, and Oracle's implementation of RDBMS, we are seeing wondrous new developments from over 45 vendors extending the power of the RDBMS into in-database analytics, real-time analysis, predictives and more.
In 1979, my first Bayesian algorithms were transformed into FORTRAN programs. Using tools like DynamoDB/LucidDB, Mondrian, SQLStream and R, I'm still doing Bayesian inference and prediction, but in splendid new ways.
The next decade is going to be fascinating.
See you on Twitter
- @JAdP
Nenshad,
ReplyDeleteMost commentaries I read about on this subject seem to consistently miss an imporatant business element in the grand picture of business intelligence. My perspective is from the asset intensive companies such as refiners, chemical companies, pharmaceutical manufacturers, and power & energy producers. In those businesses, there is nothing to analyze if the pumps aren't spinning or the gears aren't turning. Availability, utilization, and performance metrics of PRODUCTION assets are rarely, if ever, considered in the tapestry of performance management and risk analysis.
So, if you're a big oil company, and one of your refineries has expereienced a mechanical failure that has cost your business three million barrels of production, what kinds of business analytics could have been put in place to prevent that mechanical failure from occurring?
Or, if you're running a coal-fired power plant, and you've lost seven days of power generation, not only do you lost the revenue from the lost generation, you need to BUY power to meet your commitment. You've lost a few million in revenue and incurred a few million more in cost. Why? Because a forced air fan had dirty oil and the journal bearing failed because of it. What part of the business intelligence tapestry does production equipment (plant) availability occupy?
Production equipment is the basic limiting factor. You could comb over all of the BI you have until you have analysis paralysis but its the production machinery that makes the product that brings in the revenue. Might not be sexy, but its pretty basic economics.
Makes me think of Apollo 13...
(Apollo 13)
"Power is everything"
"What do you mean?"
"Without it they don't talk to us; they don't correct their trajectory; they don't turn the heat shield around. We gotta turn everything off"
"What do you mean 'everything'?"
"With everything on the LEM draws 60 amps. At that rate, in 16 hours, the batteries are dead. We gotta get them down to 12 amps"
"Twelve amps! How many? You can't run a vacuum cleaner on 12 amps, John!"
The attribution of R is wrong. R itself was and is being developed by the R Core Development Team (plus many contributors) and the primary distribution is from their web site (which you can find by searching for the single letter R in google).
ReplyDelete