If rumors are true, it is sad to see pioneering BI on Demand provider LucidEra shutting down their service, however in attempt to recognize their value to the ongoing development of BI on Demand, let’s look at what we can learn from their demise.
LucidEra has been a strong evangelist for why, who and where BI on demand had a valuable role to play:
Why – on-demand can be a faster, lower cost, and more effective option
Who – especially small and mid-size, having a solution that can be rapidly deployed on a user by user basis may be the only affordable, and technologically viable choice.
Where – LucidEra also understood the significant value BI contributes at the front lines, such as the sales team and support centers.
Like any pioneer, the disadvantages are that the market has insufficient experience to determine what they want, what they need and how they want it delivered. In particular, BI customers want:
Fully featured BI – not simplified applications. Many early BI on Demand offerings were constrained by a standardized data model approach and standardized reports. This meant customers had to adapt their business problem to the LucidEra solution, rather than the ideal of the solution fitting their particular business needs.
Ability to Integrate Multiple Data Sources – To simplify deployments and deliver quickly, LucidEra started off focusing on Salesforce.com data only. However, many customers wanted to combine salesforce.com data with information from other systems, such as marketing, finance, and operational systems.
Power of true BI – a system is powerful enough to answer spontaneous business questions or identify and address unusual trends as they arise
Scalability – a system that will grow with them as their needs grow.
Did LucidEra make the mistake of assuming that customers would be willing to sacrifice power and flexibility for speed and low cost? If so, they are not alone, as many onDemand applications could be said to fall into this category.
In spite of the demise of LucidEra, this does not signal the downfall of BI on Demand applications. It merely serves to indicate as a painful way for one vendor to learn what the customer wants or needs before they build a service to meet that need. There are other BI on Demand vendors out there doing just that, having learnt from the experience of early BI pioneers and early BI adopters.
Just downloaded a very good whitepaper from Quantivo that provides an overview of how we can dig deeper below our normal web analytics to gain more insight into customer behaviour. Those of you who know me know how much a fan of cloud computing and business intelligence I am, and Quantivo has developed a great cloud SaaS application to enable all businesses to gain access to this valuable insight. I know this sounds like an ad [no, I am not getting paid to say this] – but I am just such a fan of making BI available for everyone.
Identify specific behaviour after:
Purchasing a product
Viewing a page or ad
Interacting with online content
Watching an online video
This helps you categorise each visitor as either “actively researching information, ready to purchase, or simply looking”
Take a quick look at this short video [4 mins]
The application also has an affinity database which provides relationship insight from relevant behavioral patterns to help you identify that big grey hole – the one where you don’t know what you don’t know!
Whether it is buzz, rants and raves, voice volume, sentiments or trends. When one delves into social media, there is the danger you may get lost and never find your way back out.
There are those who claim that influence cannot be measured, however much of ‘influence’ measurement depends upon the outcome the originator desires. If it is to create awareness or get market feedback, then a great toolset for providing that data is web based service application ScoutLabs.
Whilst I could find little evidence that SocialLabs provide real ‘analytics’ as claimed, they have come up with a great way for businesses to cut through the maze and locate the trails of impact that influence initiates.
Possibly a little pricey for small businesses – the lowest plan at $US99 a month to follow 5 search streams will possibly not be sufficient for most – yet the next plan, the Pro plan at $249 a month with 26 concurrent searches is a bit more than many SME’s can support. I would have liked to see something a bit more in between – say 12 searches for $125 a month. This would give more scope than 5 searches and remain more affordable.
But for corporates who pay more than this each month on getting market feedback via surveys and focus groups, SocialLabs offers a great service. Two plan features I do like is the unlimited user per workspace and the customisable template allowing agencies to offer clients access to their portal. Not sure how this works around restricting client views to certain search streams – but worth checking out.
And my condolensces on the passing of loved ScoutLabs team member Matt Ericson at such a young age.
PivotLink have a useful BI cost calculator to help you compare your current or potential BI costs against industry data published by TDWI.
Naturally, on-demand BI vendors PivotLink are keen to provide a comparison on the cost of on-demand BI compared to inhouse solutions, however I congratulate them on helping business and technical managers make this decision. As the author of The Logical Organization, I support anything that helps businesses make better decisions.
So check it out here - it is very easy to use, and you can quickly see how each key component of your BI solution impacts your bottom line, including:
BI software licensing costs
Database costs
Hardware costs
Staffing costs
Note: The 2008 TDWI BI Benchmark Report: Organization and Performance Metrics for BI Teams based on a web survey of 392 BI professionals found that the median capital budget spending on BI in 2008 was $260,000 while median BI maintenance costs were $235,000.
According to a recent Harris Poll, most Americans believe that advanced technology” will play a critical role in solving many of the country’s problems. The top 4 technology driven catalysts to recover from the current slump include:
Innovation and “technology sciences” in education – 73%
Investing in hybrids and alternative fuels - 71% – acting as a support for the failing U.S. auto industry.
The current climate has highlighted the need for more information, faster, and in a format that is instantly usable. As companies seek to avoid the trap of massive discounting they are turning to analytics to identify opportunities to instead increase value, through bundling products with smaller, incremental margins.
Analytic insights about customers, products, channels, partners and pricing is only half of the solution. The other half being the profitable and strategic application of the information revealed from the analyltics. Thus, decision making is not wholly handed off to analytics. In the four step decision making process: framing the issue, intelligence negotiating outcomes and ongoing learnings gains immensely when the intelligence part of the equation is from valid data, rather than relying on accurate recollection of personal memory.
Thus, analytics must be integrated into the conceptual framework of decision management. This framework encapsulates not only the process used for decision making, but the business rules applied to the process, and the outcomes from each stage of the process.
Adding analytical insight into the decision making process is providing better operational decisions and more integrated strategies.
For those of you who find it valuable to keep all your data at your fingertips, you may be interested to know that Salesforce.com has just released a free version of Salesforce CRM Mobile called Mobile Lite. The app works on the Apple iPhone, BlackBerry® and Windows Mobile® devices.
Mobile Lite is currently available to Professional, Enterprise, and Unlimited Edition subscribers, and supports:
The business environment is a complex interplay of internal and external forces that must be supported by agile, efficient and compliant technology. Business intelligence technology is the most affected capability by under-performing IT infrastructure and stagnant, poor quality information.
In an attempt to provide a quick fix to an organizations business intelligence needs, common errors come into play that not only prevent the speedy solution, but can plague a more robust BI program implementation.
The most common BI Program mistakes largely involve incorrect assumptions: Read the rest…
One of the main difficulties most businesses face when selecting software is separating the hype from the reality. This also applies to considering web-based on demand business intelligence analytics.
Gartner found that of the five commonly held assumptions – three are entirely false and the other two only partly true.
Businesses often don’t appreciate how quickly software can be improved after the initial launch. With such a competitive market, software development vendors are pressured to get products to market ASAP, and no longer wait for perfection in terms of either feature richness of functional performance. However, with feedback from early adopters, development continues vigorously and the applications can be almost transformed in the first 6-12 months. I think this is actually a really good process, as it helps to validate exactly what users do want, and not waste money developing performance or features that are not appreciated.
Of the five most commonly held assumptions about SaaS BI models:
1. SaaS is less expensive than on-premises software – TRUE
SaaS applications do not require large capital investment by businesses for licenses or support infrastructure. This can significantly reduce the total cost of ownership over the first two years. After this time, client site deployed applications can become more economical in terms of financial reporting [amortisation impact], however this is not necessarily true for an operational IT expense perspective.
2. SaaS is faster to implement than on-premises software – HALF TRUE
Speed of implementation for SaaS is faster for simple applications, however one must deliniate between initial implementation time and the additional time taken to deploy it to all users, which can take 2-3 times longer. As the complexity of processes and integration increase, the gap decreases. This is due to the larger percentage of the deployment time spent on customization, configuration and integration which can be equally difficult for both models.
3. SaaS is priced as a utility model, similar to electric companies – FALSE
Many vendors claim to charge on a usage basis, but in most cases they must commit to an agreed estimated usage independent of actual use.
4. SaaS does not integrate with on-premises applications and/or data sources – FALSE
Companies can integrate web based data using either batch synchronization, real-time integration using Web services or at the user-interface level through mashups.
5. SaaS is only for simple, basic requirements – FALSE
Whilst there are limits to customisation on SaaS models, the feature set of many applications rivals that of on-premise versions. SaaS vendors provide development platforms that enable high levels of configuration and the metadata level. The area of tightest constraint is in end-to-end processes requiring complexworkflow or business process management capabilities.
Microsoft has lagged in the business intelligence market, ranking fifth amongst main BI vendors last year in IDC rankings. In a strong push to gain more visibility in this rapidly growing market, Microsoft has announced two major projects to strengthen its position.
Project Gemini
Initial efforts from the desktop software giant were to more tightly integrate Excel with SQL Server and other back-end BI tools such as PerformancePoint Server or SharePoint Server. In it’s lastest move, Project Gemini will accelarate that effort with the aim of launching Excel based analytics mashup tools for Microsoft enthusiasts. Scheduled fro beta release late in 2009, the tools aim to provide capabilities such as in-memory, drag-and-drop, pivot-table-enabled dashboard and BI query access from Microsoft’s Dynamics 2009 ERP application. Sharing performance data will rely on browser based visualisation tools. Microsoft claims that the development will provide greater synthesis of Excel integration, ad-hoc self-service analytics authoring, collaborative publishing and sharing, and managed IT governance than currently provided by other major BI vendors such as SAP Business Objects, Oracle Hyperion, IBM Cognos and QlikTech – all of whom already offer BI/OLAP tools. A representative of Project Gemini, Kobielus claims “This is a game-changer for the BI and OLAP space, and will usher in the post-OLAP age of supremely versatile, deeply dimensional, user-developed analytics.”
Project Madison
Based on its recent acquisition of DATAllegro Microsoft is building a BI-focused version of SQL Server 2008, due for release n the first half of 2010. The preliminary version of Madison revealed at the BI Conference in Seattle last week, included a 150TB database running 24 separate instances of SQL Server 2008.
MS accepts it is playing catch up and is not aiming to compete with BI database giants such as Teradata, Oracle, IBM, Sybase, but with Excel being such a pervasive desktop tool, and the weakening economy driving companies to seek out cheaper and easier to roll out BI tools, the company seems confident they will have a market.
