Friday 27 September 2013

Customer Science in Motor Retail: Keynote 2. How to Harvest Actionable Insights from Dealer Management Systems

Most motor retailers employ a Dealer Management System (DMS) to manage and administer all facets of their business operations.  The typical components of a Dealer Management System include Financials, Showroom and Customer Relationship Management (CRM), Parts, Service, Workshop Management, Point-of-Sale, Franchise Data, Vehicle Stock, Purchasing and Pricing Administration.Actionable Insights are those views of data that cause managers to ask new questions about how processes work and take action.  They differ from key performance measures and daily operating control (DOC) reports that focus on delivering a picture of progress against a strategic objective, operating budget or forecast.  What actionable insights can dealers expect to source from their Dealer Management Systems – and how do they source them?
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Many of the Dealer Management Systems that exist in the motor retail sector are proprietary systems and some of the technology employed dates back to the pre-noughties when the world had never heard of cloud computing and mobile phones were the size of a small brick.
The reason why the industry has been so slow to adopt change in IT has a lot to do with the complexity of the dealer information environment and the close synergies of the many operational areas that exist in a dealership.  As part of a tightly knit supply chain, dealers also need their systems to talk to the systems operated by the motor manufacturers.  These integrations are also complex, hard to create and adapt. Dealers ideally want to have a single information environment so to meet their need. This means software vendors have to invest hugely in attaining the ‘critical mass’ of capabilities the industry demands before they can hope to compete with incumbents.  This creates a natural barrier to new software vendor entrants hoping to enter the market.
The consequence of this market stagnation in viable competitive DMS options for major dealer groups means many are required to operate computer systems that are poor at sharing their data and exposing the actionable insights that dealer execs need.
In a market where competition is growing daily, dealers know they need to be much better at understanding the relationship with their customers, better at producing personalized offers that are event-driven, fine-grained and relevant, and better at finding more reasons to speak to customers that are ever more distant and only happy to engage with suppliers when it suits them.

Data Discovery from Dealer Management Systems
The quality and completeness of operational analytical tools found in Dealer Management Systems varies hugely.  Many of the younger software products include highly impressive reporting tools that are ideal for understanding the performance of pipelines and processes.  Where most dealer systems tend to fall down is their ability to source ‘holistic’ views of customer profiling, interaction and life-time value.
The essence of customer science is to gain a single page view of the customer that uncovers all aspects of the relationship including:
  • Profile: Affluence, location, family status, employment, habits etc.
  • Persona: What makes them ‘individual’
  • Communication: Communications history, activities and preferences
  • Driver behavior: How the customer drives their vehicle and the impact on vehicle depreciation and maintenance schedules
  • Buying behavior: What do they buy, how do they buy, likes and dislikes; what sort of relationship do they want to have with their suppliers?
  • Products: What vehicles do they have, like, have had previously?  What other products and services have they used, would they like?
  • Life-time value: What is the potential value of business derived from this individual
Uncovering these insights is valuable – useful - but it’s only when these many attributes can be connected that the eureka moments occur and marketers and their customer service colleagues uncover the gold dust that makes their campaigns more effective and raises the bar on their customer experience.
As a general rule, Dealer Management Systems are designed to support the many key processes that exist within a motor dealership.  By analyzing generic capabilities of motor dealerships we now know there are approximately 136 major processes that occur within a motor dealership.  These are supplemented by close to 200 common reports and approximately 150 primary referencing tables.  You get the picture – it’s complicated.
When software applications are built for a purpose the data models that underpin them take on that form.  In the case of Dealer Management Systems that means that most data models are poorly designed and include the same tables time and again in different parts of the data structure for different reasons.  For example, the field ‘Country’ will appear in customer and supplier addresses.  It will also appear in delivery locations and other records.  But will there be a single table for ‘Countries’?  Probably not.
So what?
The implication of this confused data structure is that execs that want to see customer views by activity, service used, spend (etc.) won’t be able to easily source these views because the data will be held in many separate silos and in the wrong structures too.  It also means that dealers, when they look at the data, they won’t necessarily know if they’re examining a list of vehicles from the CRM system, or a similar list of vehicles from their Vehicle Stock Book.

Advances in ‘Actionable Insights’ Data Harvesting
The good news is that cloud computing has introduced smarter ways of harvesting and creating new views of data that remove the hit and miss complexity of constantly trying to mash views on a spreadsheet.  How it basically works is this:
  1. Flat files reports (the type you produce on a spreadsheet with no pivot tables) are produced for each master process area.  These are designed to contain the magic fields that identify customers, vehicles, sales people, dealerships and other key identifiers.
  2. These flat files are uploaded to a ‘sorting’ engine that normalizes the data, uncovers errors and brings the various flat files together in the form of a relational database ‘in the cloud’.  This new data structure is sometimes called a ‘data mart’ – not to be confused with a ‘data warehouse’ which is a much more rigid, pre-planned data architecture for preprocessing views of data. 
  3. Once the data is onboarded, data connections are automatically generated between the variousmagic fields (i.e. primary key identifiers of the subject records) and it’s now possible to start performing queries on the data to create the actionable insights execs want to see.
The above summary ignores the fact that there’s a lot of data NOT on Dealer Management Systems that can enrich customer profiling systems and add fresh perspectives to analysis such as customer experience data, website clicks and interactions, finance company data, service plan data, SMS messaging inbound/outbound activity, mapping resources and consumer profiling data resources from third party vendors.
Federated operational insights systems like the one I describe are best placed to harvest data in this way and remove the complexity and cost of sourcing data from Dealer Management Systems.  A few words of warning however:
  • This type of system does not displace the need for effective operational reporting within the process-centric tools that run the business, though these are increasingly well catered for by vendors
  • Operational analytical systems are rarely able to feed data back into the Dealer Management System, although why you’d need to do this I struggle to understand.  I suspect most managers want a ‘single version of the truth’ for the data they look at but few people care if it resides on one system nowadays.

Key Technology Components
The good news is that cloud computing has introduced smarter ways of harvesting and creating new views of data that don’t require people to invest time manually authoring reports or playing with data in spreadsheets – and thank goodness because anyone that’s had to do it knows how boring and tiresome to task of manually aggregating and normalizing data can be! 
There are a few crucial technology innovations that have made this possible:
Web Server Apps
Popular platforms like the Microsoft Web Platform (comprising of modules including Microsoft SQL Server, Microsoft ASP.NET and Microsoft IIS) are able to publish applications in a format that means they can be viewed on a browser without requiring any downloads and plug-ins to the computer device.  That means you can view applications using mobile phones, tablets, PCs, laptops and smart digital televisions provided you have suitable User Permissions to login. Users are assigned to User Groups and their access and usability permissions are governed by the permissions assigned to the group, or groups, they belong to.  In a 24/7 world, users can experience the Martini moment of – anytime, anyplace and anywhere computing without having to fuss with onboard apps.
Private-Cloud Infrastructure
Deploying hosting web server apps on a private-cloud infrastructure means that buyers don’t need to fuss about installing web servers and managing them.  It removes the burden of installing hardware and software applications; and managing them.  Private-clouds can be configured for each account (so that each account has their own dedicated database and resources), or alternatively clouds can be multi-tenant environments where customers share the computing resources of the host web server.
Data Flow Augmentation and Data Extract, Transform and Load (ETL)
Something has to engineer the automated uploading, normalization, transformation and workflow of data to the cloud environment.  The tooling to do this now is relatively common-place.  What this automated routine needs to do is to watch a folder and wait for reports to turn up, or automatically kick into life at a scheduled time, to then audit the content and coax the data into the technology equivalent of a food blender that will spit out ‘good data that fulfils the upload criteria’. 
One of the clever features of this ‘blob’ of technology is that it needs to be capable of dealing with iterative changes to successive uploads of the same reports.  Let me have a go at explaining this:
When you upload several reports to the data crunching engine in the cloud for the first time, all is good with the world because there’s nothing else up there.  Do it again and the data crunching engine needs to sift through the fresh upload of data and work out: (1) What data exists and hasn’t changed? (2) What data exists but has been updated? and (3) What new records have been added that need to be included?
This is a non-trivial challenge and is an important feature of the data crunching engine.  Otherwise, the next time data is uploaded it will result in duplicates and lots of confusion (not good!).
Operational Analytics Software
There are many different sorts of operational analytics software (see my summary article on http://www.business2community.com/business-intelligence/operational-analytics-best-software-for-sourcing-actionable-insights-0560328 for more details) but some kind of data visualization and reporting tool-kit is needed to make the most of the harvested data.
Combine these four attributes – web apps, private-cloud deployment, data flow augmentation/ETL software and operational analytics software – and you have the essential ingredients of an Actionable Insights system that can harvest data from your DMS without have to work hard to do it!

Purchasing Options
There is a wide spectrum of technology vendors able to provide either components or complete platforms for authoring operational insights systems capable of extracting data painlessly from a DMS. There are also companies out there that provide marketing, data cleansing and customer science services targeted towards serving the specific needs of the dealer market. 
When it comes to the computing platform itself, as I summarize above, any solution needs to manage data crunching (to get the data in the right shape and form) together with data visualization and reporting tooling to make sense of it.  These ingredients with then need to reside on a server somewhere; more commonly now on a private-cloud to prevent the need to manually install hardware and software.
Buyers have a range of sourcing options to consider and these include:
1. Build your own system by using a highly dexterous and enterprise scalable cloud platform
There are too many of these to cover all of them but examples include the obvious BIG names likeMicrosoft Azurecloud.google.com/appengineForce.com (from Salesforce.com), WebSphereClouds orAmazon Cloud.  These big cloud tool-kits make development much easier than it used to be and all of the major vendors are on-point when it comes to scalability, security and resilience.  Don’t expect to have a quick solution though because it takes time and effort to get solutions ‘tuned’ to suit your needs.  Nevertheless, once you’re done you should have a first-class system.
In the last few years we’ve also seen a proliferation of smaller, expert cloud app platform vendors likeCloudFoundryCloudAppStudio and ActiveState that make it easier to design and publish custom applications to secure cloud environments, often demanding lower skills overheads.  The challenge facing these vendors is to ensure they can offer sufficiently complete platform capabilities.  They need to satisfy customers that they’re able to go the last mile on developments: The last thing customers want is to find, after months of a development, that some capabilities are missing and they have to start over on a bigger platform like IBM WebSphereAmazon Cloud Web Services or Microsoft Azure that profit from the many millions of R&D dollars that bigger vendors have at their disposal.
2. Buy breed tool-kits
Getting a solution up-and-running faster can be achieved by selecting best-of-breed components. Two will be necessary to ‘fast-track your development;
  1. A platform for designing the apps you need and managing the cloud data crunching using platforms like InterneerEncanvas and OutSystems that offer out-of-the-box tooling to meet the majority of needs.  All of these providers provide ‘low or no’ coding overhead to the task of authoring applications.  They also possess rich data connectivity and workflow features.
  2. An overlaid expert analytics tool-kit like QlikviewTibco SpotfireTableauYellowfinBirst,iDashboardsJedox and Jaspersoft.  These providers offer data visualization, dashboarding and reporting tools.
Of course, the nature of competition means that if you ask any of these vendors they will probably say they can offer a complete solution ;-)
3. Pay someone to build it for you
There are many companies out there like CSCCACICanonUS Tech SolutionsWiproTCSPA Consulting and countless smaller industry expert companies like DealerSolutionsNybble and Ambridge Consulting that specialize in the data centric custom applications authoring arena.  These companies will provide fixed price projects to deliver solutions to buyer specifications.
4. Buy a ready-made service
I have no doubt that, like NDMC Consulting and introhive, there are other companies have entered the automotive market in the past few years thanks to the available of suitable tools to provide ready-made online services that deliver the outcomes needed without requiring dealers and motor manufacturers to build their own solutions.  The challenge facing providers is that each and every system will require heavy customization and tailoring.  Encanvas Remote[Spaces] is an example of a cloud architecture that enables every customer to enjoy their own individual and private cloud service.  Competing platforms like Interneer,Tibco SpotfireForce.com and OutSystems (to mention a few) are no doubt able to offer a similar capability.
Summary
Cloud-based operational analytics solutions are growing in popularity as a way to extend the life of Dealer Management Systems investments.  They lever value from sleepy data held in administrative systems by finding smarter ways to turn data connections into new reasons to engage customers in dialogue.  These solutions do create a single version of the truth but in a different way; not by seeking to install another system, but instead by adding a value extracting capability in the cloud that gives dealer executives the actionable insights they need without the pain of yet another major IT project.

About This Series of KeyNote Articles on Customer Science in Motor Retail
How does Motor Retail benefit from applying operational analytical tools to source actionable insights?  In this series of keynote articles I explore how the coming together of mobile computing, innovations in customer science and advances in big data analysis are creating the perfect storm for marketers in motor retail.

Thursday 26 September 2013

Customer Science in Motor Retail: 5 Actionable Insights That Every Dealer Should Have


How does Motor Retail benefit from applying operational analytical tools to source actionable insights? In this series of keynote articles I explore how the coming together of mobile computing, innovations in customer science and advances in big data analysis are creating the perfect storm for marketers in motor retail.
Keynote 3.
5 Actionable Insights That Every Dealer Should Have
Actionable Insights are those views of data that cause managers to ask new questions about how processes work and take action.  They differ from key performance measures and daily operating control (DOC) reports that focus on delivering a picture of progress against a strategic objective, operating budget or forecast.  What are the top 5 actionable insights that every motor dealer should have?
Motor Dealerships are a complex multi-disciplinary business operation.  The aspects of activity are so many and varied that managing performance and growth requires a clear head and clarity over the objectives that really matter.  Every dealership has a different combination of capabilities and franchises that add an extra layer of ‘uniqueness’ to the reporting systems required.  So is it possible to pinpoint just five actionable insights that all dealership exec teams should have access to?  I believe it is.  In this article I suggest my top 5 list of actionable insights together with an explanation of why they’re important.
1. Consolidated Performance Scorecard
A scorecard is a dashboard that incorporates a series of objectives that an organization is striving to achieve.  In most organizations the number of objectives defined in a scorecard is in the order of 12-16 as any more becomes unmanageable.  A consolidated performance scorecard provides a topline view of progress towards business outcomes.  It’s essential for any organization to know how it defines success and seek to achieve it.  Scorecards are therefore valuable and useful management tool to articulate and communicate progress towards strategic outcomes – BUT… the point about objectives, and the key performance indicators used to measure progress towards them, is they should be ‘stuff’ that managers already know to be important.  This type of reporting is therefore ‘handy’ but it would be difficult to argue KPIs are ‘actionable insights’ as they rarely encourage managers to take action in themselves.  What starts the journey of objectives and KPIs becoming ‘actionable’ insights occurs when managers can drill-down into the data that is the source of the KPI.  When managers drill into data and ‘become curious’ the magical and mysterious kapow! explosion happens that leads to processes being improved, new initiatives tabled and the like.   A Consolidated Performance Scorecard gives the entire management team a snapshot of how THEY are performing to meet strategic outcomes.  It’s my FIRST actionable insight because it STEERS the ship.
2. Consolidated DOC Side-by-Side Dealership Analysis
The Daily Operating Control is a report that summarizes how each revenue generator and discipline of a motor dealership is performing.  What normally gets covered are the revenue generating pipelines of the dealership including MOTs, parts sales, red and amber Vehicle Health Check (VHC) / deferred works, service hours, warranty repairs, oil, tyres, bodyshop repairs and more.  Most dealers I know favor a side-by-side analysis of branch performance because it encourages competition – and the comparison between franchises and dealerships makes for interesting reading (to understand which business units are performing well, which are performing poorly, all judged on the same scale given that organizations commonly struggle to compare performance between business units when managers seek to qualify ‘what good looks like’ using their own scale.  The Consolidated DOC gives managers across the business a daily progress report and a month-to-date summary that provides opportunity to correct sub-optimal performance before it’s too late.  It is the epitome of ‘actionable insights’ in the operational reporting world and becomes my SECOND ‘must-have’ actionable insight for dealerships.
3. Customer Persona Profiling Summary
It’s always been important to manage customer relationships one customer-at-a-time as Don Peppers andMartha Rodgers put it but as customers become ever more distant and empowered, the need to build digital personas has moved on to a new level of priority.  I’ve struggled to understand why the Motor Retail industry has been so slow to adopt customer profiling and persona building when retailers such as Tesco with their Clubcard system and online companies like Amazon have so clearly demonstrated the business rewards, but I do see this initial reticence is changing as more dealers become ever more aware of the potential to boost growth by simply harvesting their most important asset – their customer relationships. There are many sides of a customer relationship – communications preferences, activity history, contact points, location, buyer behavior, life-time value, future life-time value, affluence… - and it’s when you combine these views that exciting things happen.  There are many ways to build personas and through them, marketers and sales leaders see patterns of likes, spend habits and relationship characteristics that lead to new campaigns and better ways of sourcing customer value.  A customer profiling persona summary presents a one-page view of the many different facets of your customer community.  It covers not just ONE customers’ characteristics but overlays ALL of your customers in one ‘map’ of your customer universe.  It makes for very interesting reading and becomes my THIRD actionable insight.
4. Customer Happiness Index
Customer happiness matters because it’s the lion in the cave that comes out to bite your business unless you keep an eye on it.  There are many ways organizations can measure and monitor customer happiness but dealers can still struggle to place a ‘happiness index’ on their business that produces a snapshot of happiness and trending.  In addition to manufacturer sponsored analysis, many dealers are creatively sourcing happiness data by using simple tools on their websites and paying third party.   I’m also a big fan of soft-measures that systems like www.tag-check.com are excellent at sourcing.  You can’t hope to buildLoyalty Beyond Reason when your customers are unhappy; it’s even a big call to ask for respect(sometimes described as love in plain clothes!).  Installing a customer happiness index is my fourth actionable insight.
5. Mapping Customers and Qualifying Addressable (Local) Market
Understanding the opportunity of a dealer within their locality helps to qualify how well a dealer is doing compared to how well IT COULD be.  It’s not difficult to plot customers on a map.  In countries like the UK and the Netherlands mapping customers is made easier by the quality of the postcode system.  In other countries like the United States and South Africa, postal codes are not very accurate and it takes street address details to accurately plot customers (far less convenient!).  Once customers are plotted it’s relatively easy to assign customer attributes to records so map views can be filtered by dealership, sales team, sales person, affluence, persona, outstanding MOT reminders – whatever.  If your customer records include a CAMEO (‘affluence’) profile, companies like www.improvemydata.com  make it pretty easy to obtain a listing of similar customers in your region.  This makes it possible to understand your MARKET SHARE! How cool is that?  Maps are great!! I’ve never produced a mapping application that hasn’t shownsomething interesting for dealers to see.  That’s why creating a map view of the customer landscape and qualifying the addressable local market is my fifth actionable insight that every dealer should have access to.

The list above is MY top five.  If you have other opinions or examples I’d be very interested to hear about them!

Wednesday 4 September 2013

Real-Time Data Diagnostics - How Encanvas Takes Agile To A New Level


Situational Applications EyePC Image

Have you ever created an application only to find it doesn’t work?  

Sounds crass doesn’t it.  The fact is that most manually programmed applications have errors in them.  But modern codeless applications design environments like Encanvas overcome the majority of programming ‘hick-ups’ by auto-generating the code that underpins authored applications through the use of drag-and-drop, point-and-click and wizard based interfaces.   That means almost no testing, tuning or re-working costs for business IT projects.

This is important to the software and data integration industry that has a pretty sour track-record of project delivery.  The article ‘Why Your IT Project May Be Riskier Than You Think’ by HBR (November 2011), that followed a survey of 1,471 IT projects with an average spend of $167m, found that:

  • The average overrun was 27%
  • One in six of the projects studied was a black swan, with a cost overrun of 200%.
  • Almost 70% of black swan projects also overrun their schedules.
One area that has remained to challenge the software development industry has been how to overcome data integration errors on deployed applications.  No matter HOW a design environment links to data sources, such is the complexity of the task that it becomes almost inevitable that some data integration errors will creep in when developing a complex database-centric application.  For example, a field in a table could be re-named or deleted in the source database resulting in no data appearing on the published site page because the database is unable to find the requested data.  Sometimes it’s quite easy to track down these issues; other times – with complex applications – it takes a pretty neat understanding of the application and how the data hangs together in order to overcome the issue.  When applications designers have spent hours working on a great application, the very last thing they need is to fall at the last hurdle and find ‘the app doesn’t work’.

Thankfully, Encanvas now overcomes this problem area too with its new real-time data diagnostics technology.

Encanvas Create Design Studio Image
Those familiar with Encanvas will know that the design of applications all takes place in ENCANVAS CREATE DESIGN STUDIO in a codeless environment.  It offers design elements, data linking and action linking features to build content and logic into applications using an easy to learn design interface that has the look and feel of a desktop application. What it actually produces are cloud deployed ASP.NET portals that present data in HTML so they can be used on desktop, tablet. Mobile and digital TV platforms.  While it adheres to Microsoft Enterprise Platform standards of security, performance tuning and scalability, it removes the use of programming and means application can be authored in workshops with users and stakeholders.

When applications are published to the cloud, Encanvas’s new visual diagnostic mode allows designers to review the data connections employed in applications using an innovative visual referencing system.

Each field is audited for its data connections and the results are presented on the screen.
Encanvas Real-Time Data Diagnostic Image

Full details of connections are presented when clicking on the associated icon. Using the live data diagnostic features of Encanvas removes the last major hurdle in applications engineering for authors of business applications.
Encanvas Real-time Data Diagnostic image

The roll call of applications authoring productivity features in Encanvas now includes:

  • Codeless platform and server installation
  • Codeless private cloud creation (and replication)
  • Codeless data connectors
  • Codeless information flow automation
  • Codeless data mashup and automated data structure creation
  • Codeless design elements
  • Codeless logic link creation
  • Codeless action layer automation
  • Codeless KPI creation
  • Codeless map creation
  • Codeless upload/ETL automation
  • Codeless download (export) with formatted reporting/printing
  • Codeless social network creation
  • Codeless graphing (and graphing logic) creation
  • Codeless email workflow creation
  • Codeless publishing
  • Codeless UI design 
  • Codeless federated user identity management augmentation
  • Codeless site structure design
  • Codeless and real-time data diagnostics
  • Automated design element creation from data structures
  • Automated database creation from designed data structures
  • Automated lookup table generation
  • Automated form creation

If you’ve yet to try Encanvas, it’s easy to arrange. Simply email sayhello@encanvas.com and request a secure private-cloud Remote[Space] to have a go at codeless situational applications authoring for yourself.

Tuesday 3 September 2013

Big Data Training Workshop 1: Introducing Situational Applications

Situational Applications Introduction Article (Image)

BIG DATA TRAINING WORKSHOP 1: INTRODUCING SITUATIONAL APPLICATIONS

Situational Applications are any software applications designed to serve the long-tail of demand from individual information workers (or small communities of workers) for ‘better ways to analyze data, capture it and act on it’.  They may be created for a moment and then discarded, or later adopted and absorbed into enterprise systems.  Sometimes they are unkindly described as throw-away or experimental applications but I prefer to think of them as the first step on the accelerated business innovation journey.  They are key to the world of BIG DATA because they are the tool-kit to apply learning lessons from data and turn it into better ways of working.

I’ve had such overwhelming feedback and interest on the Big Data Situational Applications articles I’ve penned that it’s encouraged me to write a few more that walk through how we use Encanvas to assimilate data from various sources, analyze it and act on what matters.  Of course there are a wide variety of Encanvas partners and solutions providers that offer ‘off-the-shelf’ industry solutions so in this series of articles I’m assuming that the application is something ‘custom’ or new that hasn’t been addressed before.

In this series of articles I plan to include the following topics:
  1. Introduction to situational applications 
  2. Data assimilation: what it is and how to do it
  3. Operational Analytics: making sense of data with useful tools
  4. ACT: designing apps to iterate processes one canvas at a time
The obvious article that’s missing is a training course on HOARDING technologies like Hadoop, Microsoft Business Intelligence, SAP hana etc.  The reason I’ve missed this step is because (a) I don’t know anything about it and, (b) In many cases you don’t need to hoard data and organize it before creating situational applications.

Just a few brief introductions before we start…
If you’ve not encountered Encanvas software before, it’s basically a business computing platform designed to exploit data held in different places and different formats by designing situational applications to analyze data and apply learning lessons. Unlike business intelligence systems and other platforms that HOARD data and create a data warehouse before you can do anything, Encanvas connects to, and organizes data into new structures by exposing data to designers in a consistent format to enable ‘drag and drop’ selection and formation of new data structures.  It’s really easy to use for business analysts, and it produces results quick, so it’s realistic to develop situational applications in workshops with stakeholders, users and sponsors present (yes really!).

Companies like USTech Solutions, NDMC Consulting, Ambridge Consulting, Nybble, SovereignTech and others use Encanvas as a tool-kit to author situational applications as a service.


Everything you wanted to know about situational applications but were afraid to ask
The first ever situational application platform was the spreadsheet.  Times are changing.  Information workers need to deal with much bigger data sets and IT leaders must ensure that data is organized, always-secure and any software application must adhere to necessary standards of governance and compliance.  The new generation of situational applications platforms like Encanvas are engineered to meet ‘IT hygiene standards’ of security, performance and scalability, yet they provide unrivalled self-service tooling to enable web workers to fully exploit accessible data resources.

History
Encanvas was conceived in 2002 by the founders of NDMC Consulting.  It was initially an idea (perhaps a belief) that in future, business users would seek to work in social groups that would span across and beyond enterprise boundaries and, having experienced this new form of business workplace, key knowledge workers and creative contributors to business processes would seek to be able to author applications for their communities in a form purposely sculptured to the needs of the community of use.

This idea of socially-centric, built-for-purpose and potentially thrown-away software was unknowingly endorsed by technology thought-leader Clay Shirky in his essay ‘Situated Software’ published in March 2004 when he wrote, “Part of the future I believe I'm seeing is a change in the software ecosystem which, for the moment, I'm calling situated software. This is software designed in and for a particular social situation or context. This way of making software is in contrast with what I'll call the Web School (the paradigm I learned to program in), where scalability, generality, and completeness were the key virtues.”

In August 2007, Luba Cherbakov and a team from IBM wrote the first of two articles on what they described as ‘Situational Applications’.  In their paper titled ‘SOA meets situational applications, Part 1: Changing computing in the enterprise’, Cherbakov and her colleagues defined the attributes of Situational Applications, stating, “The loosely accepted term situational applications describe applications built to address a particular situation, problem, or challenge. The development life cycle of these types of applications is quite different from the traditional IT-developed, SOA-based solution. SAs are usually built by casual programmers using short, iterative development life cycles that often are measured in days or weeks, not months or years. As the requirements of a small team using the application change, the SA often continues to evolve to accommodate these changes. Significant changes in requirements may lead to an abandonment of the used application altogether; in some cases it's just easier to develop a new one than to update the one in use. The idea of end-user computing in the enterprise is not new. Development of applications by amateur programmers using IBM Lotus® Notes®, Microsoft® Excel spreadsheets in conjunction with Microsoft Access, or other tools is widespread. What's new in this mix is the impressive growth of community-based computing coupled with an overall increase in computer skills, the introduction of new technologies, and an increased need for business agility. The emergence of Asynchronous JavaScript + XML (Ajax)—which leverages easy access to Web-based data and rich user interface (UI) controls—combined with the Representational State Transfer (REST) architectural style of Web services offers an accessible palette for the assembly of highly interactive browser-based applications.”

Today
Situational applications remain a largely misunderstood concept in enterprise computing due to pre-conceived notions about how IT works that are now proven to be fatally flawed.  These include:

  • Applications that can be designed cheaply enough to ‘throw-away’ can’t possibly be expected to meet enterprise data integration, security, performance and tuning expectations.
  • It’s not possible to create the critical-mass of building blocks and tooling required to remove the majority of programming overheads.
  • Organizations are better off buying best of breed solutions, to then mackle them together.
  • There is no competitive advantage to be gained from IT as companies now use the same platforms. 
  • If players like Microsoft, Google, Oracle and IBM haven’t made it work then it isn’t possible!

In many ways the demand for situational applications has never gone away but, until the emergence of the BIG DATA story, the message of Situational Applications has struggled to find a voice within organizations as it is a genre of technology designed to meet the many and varied ‘small’ needs of a variety of communities. It has lacked any understandable clarity of purpose in IT architectures.  Instead of sourcing expert tools, CIOs with other things to think about have instead done their best with 'approved enterprise tools' – Content Management applications like Microsoft SharePoint and Enterprise Applications Platforms like IBM Websphere and Oracle Weblogic.

BIG DATA has led to the re-discovery of the important role of expert situational applications tooling as organizations acknowledge the need to equip analysts and middle-managers with self-service, community-centric applications to assimilate, analyze and act on the actionable insights they’re surfacing.  Technologies designed support the creation of Situational Applications are by necessity ‘codeless’ which means the authoring of applications is done using drag and drop, point and click or wizard based interfaces that automatically generate the programming code rather than it being authored ‘manually’.  This reduces errors in code.  It also means that applications can be authored in near real-time; very often within a workshop environment.  Having stakeholders directly engage in the authoring process creates better applications in a shorter time.

In the next article in this series, read about the first stage in Accelerated Business Innovation: Data assimilation: what it is and how to do it.