Will self-service BI do the trick? - Why you cannot do without an Enterprise BI Platform

October 20, 2016

 

We need to talk about Enterprise BI and whether self-service BI is the sole component to cover enterprise readiness for Business Intelligence. I have been part of so many discussions lately on whether the popular self-service BI components do the trick when it comes to enterprise wide insights. Of course people representing the likes Tableau, Qlik or Ms PowerBI will say they do, but my customers are not so sure about that. Let's have a look and learn that enterprise business intelligence - having full 360° insights for all types of users - is more than just self-service BI.

 

I have been writing about self-service BI and how to use it to win your end users in this and this article quite extensively. Self-service BI definitely covers a wide audience of information consumers in a very powerful way. It talks to a very specific audience, doesn't it? ..... primarily to end-users who consume insights based upon governed data, and it talks to data-analysts who might both use governed, corporate data and blend that with other - corporate or non-corporate - data. But is it enough to cover all needs for insights in your enterprise? No, it is not when an enterprise requires to cover full insights to any type of users.

 

What do enterprise insights mean?

 

To explain you what full enterprise insights mean and stand for, I could go various explanations, but let me use the simple MAD framework here. It is not fully applicable anymore and a little old, but covers the bigger part: the MAD Framework stands for Monitor, Analyze and Drill to detail. The model explains us that an enteprise requires full insights on:

 

  1. Strategic level: here mentioned as the monitoring section. The strategic level applies to users overseeing business processes or even cross business processes. They require consolidated data presented in a highly visual way. Typicall KPI tiles, trend graphs, time comparisons and decisontree graphs. In the past the strategic level was about summarized data; today's in-memory platforms are so powerfull, we can present consolidated - and thus every single (transactional) detail - here. Often the visualizations here are in managed dashboard format, and allow to drill onto the tactical level. The strategic level is often deployed towards mobile devices too, allowing to have online follow up of the company's or processes' heartbeat. Users digest insights that are centrally created and governed, but require full flexibility with the insights they get. They require to be interactive and change data perspectives on the fly. Accuracy is real-time.

  2. Tactical level: the tactical level is about users who deploy a company's strategy onto concrete business follow up. The scope is within a process or cross-within a group of processes. Users require to interact with the data even at transactional level, and in on-line mode. They require status insights via bursted and scheduled reports. They require interactive and governed dashboards with alerting or even streaming data; they need to change the perspectives of the insight in online and real-time mode. They also require to answer ad-hoc queries so should be able to quickly enrich, blend and join with other data sources while still running in a governed mode. The latter is typically done by data analysts who need to be able to touch and analyze every single data element, but also require to access other (non-) corporate data.

  3. Operational level: this level aims at users who require (!) data insights to fulfill their day-to-day activities. Often these are operational activities. The insights are typically being provided by reports that are scheduled or bursted towards them. The user can interact with the report (filter, sort, comment, adjust etc) and might even be willing to print or mail it. But it is not only reports; think for example on real-time dashboards hanging on the ceilings of manufacturing plants indicating performance or alerting for system failure or other incidents. Another level of operational insights are the IT administrators monitoring data quality and using online BI tooling to browse and explore their core data sources.

 

We can summarize the above as per below model. All users act within this model and either require strategic, tactical or operational insights to get the enterprise moving. At any moment in time or in the process people can access the BI portal to either console or share insights. Any of these insights can be consumed, annotated or shared via mobile devices.

 

 

 

Self-service BI does not cover Enterprise BI

 

You all know I am a big fan of self-service analytics components. The articles on How To Win You End Users and Buffet or A La Carte say enough. But do they cover all required insights as we can expect from an enterprise business analytics platform? No, they definitely don't and I'll tell you why. Self-service BI tends to cover pieces of the strategic and tactical levels, yet leaves the operational level out plus skips some important functionality applying to all levels. If have indicated where self-service BI fits in the bigger MAD framework using the yellow triangle.

 

I have listed a generic(!) overview of things and capabilities not covered by self-service BI. Again, it is generic since some software vendors do have some functionality, yet miss others. The overview is in random order and certainly not exhaustive.

 

Insights required but not covered in self-service BI:

 

  • scheduled and bursted reports: many (!) users require scheduled reports providing specific insights that apply for their jobs. These reports could even be alert-based, meaning the only are sent once an alert-definition is met. Don't underestimate the volume of these kinds of insights. Especially in finance, supply chain, order management, IT and manufacturing I see lots of users requiring this functionality as absolute key. Bursting is another example of using one (!) report template that is automatically populated and sent to a list of users with data that applies to them personally. Think a bank sending the performance of a stock portfolio to each of their customer automatically each week. There is only one report for this that is re-created and re-processed for each individual customer.

  • public distributed insights: some enterprises - government is a good example - require to share insights to the public in either report or dashboard format. People accessing this insights can often interact with them by filtering or changing perspectives. To do so, the insights pass the governance layers and typically access a dedicated semantic layer. Semi-public content - i.e. distributed to the public only once registered - apply to the same model. Most self-service BI components are not capable of doing this. 

  • blind data discovery and monitoring: though self-service BI provides in data blending and mash up, there is a community of people that browses and explores corporate data at a real ad-hoc basis and typically on transactional level. Think of users within financial consolidation, order management or IT data stewards. To do so they require BI functionality to online browse systems and data on the fly; the skip from record to record, from system to system. They search data, filter it and highlight insights. Though this seems to be possible with self-service BI, day-to-day practice shows these are not sufficient to do the job given they often are not governed to access the core operational systems that apply here.

  • detailed report lay-outs: some insights will require pixel perfect detail or advanced lay-outs with headers, footers, groupings or sections. Think about operational and formal reporting to be sent to customers or the likes vendors. An example is a customer invoice or a SLA performance report for vendors. Receivers tend to print these reports. They need to be high-resolution (logo's, guidelines, specific rules etc) and can have a tremendous detailed lay-out with various sections and can be bursted. Especially institutions and governments require formal reporting that applies to a lot of detailed formatting specifications.

  • tile driven alerting: at the upper strategic and upper tactical levels, users require tile driven alerts, driving them to action upon in a certain process. I see these typically used on mobile devices allowing the user audience to be instantly informed the moment the trigger for an alert gets off. The tiles are interactive and quickly guide to a self-service or managed dashboard for further investigation.

  • integration with business planning: when applying the closed loop portfolio of analytics, lot's of self-service tools have to "throw the towel". Let me give you the example of integrating BI with business planning applications simply allowing the compare actuals with budgets. Doing this in an integrated way (cross-referencing to both applications), is only possible when using dedicated components that allow to not only integrate datasources but also have API's to the applications themselves

  • streaming data insights: For certain insights people require streaming datasources. Of course we have the IoT devices, but good examples are also manufacturing machines streaming data with what they produce or what incidents occur. Other samples are on social media feeds. To generate visualized insights for these streaming sources, we require components that can handle SDS (Streaming Data Source) components and allow for immediate alerting as part of the insights

  • Production applications: especially on the operational level, enterprises need insights on performance. Thinks of the large screens and monitors within a production plant or call center measuring the query lane, number of activities processed or alerts of incidents. This is typically handled by streaming washboarding applications that run in real-time mode. They run 24/7, are highly visual oriented and based on alerts or tresholds.

The above is by far from complete, but I just want to make the point of priority requirements for insights that you typically cannot find in self-service BI. As such my statement that self-service BI is only a subsection of enterprise analytics. It covers a whole lot of things users want, but it passes a whole lot too. I am not convinced at all that large and medium scaled enterprises can do with self-service BI solely; they'd miss half of what they should now.

 

Below is a little demo movie I made for you that highlights the work stream of a typical enterprise BI flow. It starts on strategic level with managed washboarding, and drills into tactical level where the user in this example requires to blend with non-corporate data. Our user also needs some operational reports for his insights. In the end the user generates a new insight based upon storytelling and shares this to all users via a portal and via mobile devices. Enjoy !

 

 

Enterprise BI: the SAP way

 

Traditionally SAP proofed to be very strong in delivering end-to-end enterprise business analytics insights with a platform built around BusinessObjects. Without going through the full platform (here is a good place to discover that), I'd like to highlight the positioning of some of their core BI components:

 

  • Design Studio: managed dash boarding component positioned to either strategic level and tactical level with high capabilities for self-service. They also heavily apply to the operational level for the above mentioned operational applications using streaming data. A third way where Design Studio is implemented is in the integrated business planning environments allowing to adjust budgets on the fly from within insights. Learn about Design Studio here. Find examples here and on this page

  • Analysis for Office: replace Bex Analyzer and is positioned on tactical and operational level to browse and explore every single detail in source data in online mode. Find some AfO examples here

  • Crystal Reports: programmable and highly sophisticated reporting developer tool positioned towards tactical and operational level. Crystal Reports is pixel perfect and allows fro tremendous lay-out functionality.

  • WebIntelligence: close-to-pixel-perfect web based report generated positioned towards all levels but typically used on tactical and operational level. Highly advanced lay-out capabilities, multi-tab and configurable towards scheduling, alerting and trigger based bursting. Samples are here.

  • Lumira: self-service oriented data mashup component that allows for quickly blending corporate and non-corporate data and bring this together in storyboards which can easily be shared on a BI portal or mobile device. In addition SAP Lumira aims at data analysts who require to analyze in either deep-dive level or to ad hoc queries. Lumira applies on strategic level (as an export from within Design Studio) and tactical level. A full list of best practices videos can be found here

  • BusinessObjects Cloud: BusinessObjects Cloud applies to the cloud analytics strategy of SAP and is positioned cross levels applying to operational, tactical and strategic levels. See examples here. BusinessObjects Cloud further applies to the closed loop portfolio covering both business intelligence, predictive analytics, collaboration and planning capabilities. See examples of BusinessObjects Cloud here.

  • Digital Boardroom: The Digital Boardroom (DiBo for intimi) is a special one since you might think it applies to strategic level only. Incorrect ! Digital Boardroom uses touch screens that present BusinessObjects Cloud driven content and has access to any transactional detail of the systems it is connected with. It allows for simulation (interactive planning) and also embeds the predictive analytics engine to anticipate on certain situations. Stunning ! Find examples of the Digital Boardroom at this page.

  • EPM Add-In: Users requiring to merge actuals data with budgeted information can use the EPM Add-In for Analyses for Office. It positions towards operational (financial consolidation) and tactical levels, and is sometimes part of strategic dashboards Find an example here