The Future of Information Systems: Design from the Data

This third post in a series of three on BI programme management looks at a new way of designing systems for both transaction  and decision support to improve the organisation’s effectiveness further. I will examine the concept of BI architecture further and give hints of how BI programme management can evolve towards an ideal architecture which merges transaction and decision support systems in a powerful ensemble, ready for the new economic challenges.

I propose an “Idealtyp” knowing that no existing organisation can achieve this in less than a decade for reasons like sunk cost fallacies, the dialectics of progress and simply resistance to change.

But new organisations and innovators who can make the change will notice that the rewards of this approach are immense. They will combine architectural rigidity with business agility and improve their competitive power with an order of magnitude.

Why a BI Architecture is Necessary

I am a fan of Max Weber’s definition of “Idealtyp”[i], which has direct links with architecture in information technology. BI architecture is an abstraction of reality, and as such an instrument to better understand a complex organisation of hardware, network topologies, software, data objects, business processes, key people and organisational units. All these components interact in –what appears to outsiders- in a chaotic way. An architectural framework brings order to the chaos and provides meaning to all the contributors to the system.

Architecture is used as a benchmark, a to be situation by which the present state of nature can be measured. It is a more crisp and more manageable concept than CMM-like models which express maturity sometimes in rather esoteric terms. For a quick scan, this will do but for in-depth managing of the above mentioned BI assets, an architectural framework is better for BI environments.

CMM Level	BI symptoms	Principal risks
Initial	A serious case of “spreadsheetitis”: every decision maker has its own set of spreadsheet files to support him in his battles with the other owners of spreadsheets. Everyday tugs of war over who has the correct figures.	Your project may never take off because of political infighting and if it does, there will be a pressing need for change management of the highest quality and huge efforts will have to be invested in adoption tracks.
Repeatable	The organisation uses some form of project management, in most cases inherited or even a carbon a copy of systems or application development	The project management method may be totally inadequate for a BI project leading to expensive rework and potential project failure in case everybody remains on his position.
Defined	The organisation has a standard procedure for the production of certified reports. These can connect with one or more source systems in a standardised way: direct connection to the source tables, import of flat files, or some form of a data warehouse.	Resistance to change. This depends on the way the organisation has implemented the data warehouse concept and how reversible the previous efforts are in a migration scenario.
Managed	The development processes are standardised and monitored using key performance indicators and a PDCA cycle.	The iterative and explorative approach of BI project management may frighten the waterfall and RAD fans in the organisation. Make sure you communicate well about the specifics of a BI development track.
Optimising	The development processes only need fine-tuning.	Analysis paralysis and infighting over details may hamper the project’s progress.

Table 2 Example of the BI version of the Capability Maturity Model as described in Business Analysis for Business Intelligence on page 202. In the book, it is positioned as a tool to help the BA with identifying broad project management issues

Why this "Idealtyp" is not Easy to Achieve

Proposing an ideal BI architecture is one thing, achieving it, another. I will only mention three serious roadblocks on the path towards this ideal BI architecture that unifies transaction systems and decision support systems: the sunk cost fallacy, the dialectics of progress and resistance to change.

The sunk cost fallacy is a powerful driver in maintaining the status quo; organisations suffering from this irrational behaviour consider they have invested so much effort, money, hardware, training, user acceptance and other irretrievable costs that they should continue to throw good money at bad money.  And sometimes the problem is compounded when the costs were spent on technology from market leaders.

No one ever got fired for buying… (fill in any market leader’s name)

No matter what industry you look at, market leaders fulfil their basic marketing promise: provide stability, predictable behaviour and a very high degree of CYA (google it) to the buyer. But that doesn’t mean the purchase decision is the best possible decision for future use. Market leaders in IT are also very keen on “providing” vendor lock-in, disallowing the client to adapt to changing requirements.

As a footnote: today, buyers are more looking at the market cap or the private equity of the Big Data technology providers than at their actual technical performance and their fit with the organisation’s requirements. Yes, people keep making the same mistakes over and over…

At the other end of the spectrum are the dialectics of progress:  this law was discovered by the Dutch journalist Jan Romein who noticed that gas lights were still used in London when other European capitals already used electricity.  This law suggests-and I quote an article on Wikipedia-  that making progress in a particular area often creates circumstances in which stimuli are lacking to strive for further progress. This results in the individual or group that started out ahead eventually being overtaken by others. In the terminology of the law, the head start, initially an advantage, subsequently becomes a handicap.

An explanation for why the phenomenon occurs is that when a society dedicates itself to certain standards, and those standards change, it is harder for them to adapt. Conversely, a society that has not committed itself yet will not have this problem. Thus, a society that at one point has a head start over other societies, may, at a later time, be stuck with obsolete technology or ideas that get in the way of further progress. One consequence of this is that what is considered to be the state of the art in a certain field can be seen as "jumping" from place to place, as each leader soon becomes a victim of the handicap. 
(From:  https://en.wikipedia.org/wiki/Law_of_the_handicap_of_a_head_start)

As always, resistance to change plays its role. New tools and new architectures require new skills to be trained, new ways of working to adopt and if one human species has trouble adapting to new technologies it is… the tech people. I can produce COBOL programmers who will explain to you that COBOL is good enough for object oriented programming or IMS specialists who see nothing new in the Big Data phenomenon…

What is BI Architecture?

Here’s architecture explained in an image. Imagine Christopher Wren would have disposed of modern building technologies. Then either the cathedral, based on the architecture “as is” would have looked completely different, with higher arches, bigger windows, etc… Or,… the architecture could have evolved as modern technology would have influenced Wren’s vision on buildings.

Exactly this is what happens in BI architecture  and BI programme management.

Figure 5 On the left: architecture, right: a realisation of architecture as illustrated by Wren’s Saint-Paul’s Cathedral

Architecture descriptions are formal descriptions of an information system, organized in a way:

• that supports reasoning about the structural and behavioural properties of the system and its evolution.
• These descriptions define the components or building blocks that make up the overall information system, and
• They provide a plan from which products can be procured, and subsystems developed,
• that will work together to implement the overall system.
• It thus enables you to manage your overall IT investment in a way that meets the needs of your business.

It is also the interaction between structure, which is requirements based, and principles applicable to any component of the structure.

What is the Function of BI Architecture? 

BI Architecture should reflect how the BI requirements are realized by services, processes, and software applications in the day-to-day operations. Therefore, the quality of the architecture is largely determined by the ability to capture and analyse the relevant goals and requirements, the extent to which they can be realized by the architecture, and the ease with which goal and requirements can be changed. 

Figure 6 The Open Group Architecture Framework puts requirements management at the centre of the lifecycle management. The connection with business analysis for business intelligence is obvious. 

Reality Check: the Two Worlds of Doing and Thinking

Now we have established a common view on BI architecture and programme management, it is time to address the murky reality of everyday practice.

Although Frederick Taylor and Henri Fayol’s ideas of separation between doing and thinking have been proven inadequate for modern organisations, our information systems still reflect these early 20^th Century paradigms. You have the transaction systems where the scope is simply: execute one step after another in one business process and make sure you comply with the requirements of the system. This is the world of doing and not thinking. Separated from the world of doing is the world of thinking and not doing: decision support systems. The business looks at reports, cubes and analytical results extracted from transaction and external data and then makes decisions which the doers can execute.

What if the new economy were changing all this in a rapid pace? What if doing and thinking came together in one flow? That’s exactly what the Internet is creating, and I am afraid the majority of organisations are simply not ready for this (r)evolution. Already in 1999, Bill Gates and Collins Hemingway[ii] wrote about empowering people in the digital age when they gave us the following business lessons:

• q  The more line workers understand the inner workings of production systems, the more intelligently they can run those systems.
• q  Real-time data on production systems enables you to schedule maintenance before something breaks.
• q  Tying compensation to improved quality will work only with real-time feedback of quality problems.
• q  Task workers will go away. Their jobs will be automated or combined into bigger tasks requiring knowledge work.
• q  Look into how portable devices and wireless networks can extend your information systems into the factory, warehouse and other areas.

I am afraid this advice still needs implementation in many organisations. The good news is that contemporary technologies can support the integration of doing and thinking. But it will require new architectures, new organisational and technological skills to reap maximum benefits from the technology.

The major and most relevant BI programme management decision criterion will be the answer to the question: “Which quality data yield the highest return in terms of competitive advantage?”

Bringing IT Together: Design from the Data

What if we considered business processes as something that can change in 24 hours if the customer or the supplier wants it? Or if competitive pressure forces us to change the process? What if information systems would have no problem supporting changing business processes because the true cornerstone, surviving any business process is data? This could be a real game changer for industries that still consider data as a product of a business process instead of the objective of that process.

The schema below describes a generic architecture integrating transaction and decision support systems in one architectural vision. Let’s read it from left to right.

Any organisation has a number of business drivers, for example as described by Michael Porter’s generic strategies: be the cost leader, differentiate from the competition or focus on a niche. Parallel with the business drivers are decision making motives such as: “I want complete customer and product insight” and finally, the less concrete but very present knowledge discovery driver to make sure organisations are always in the lookout for unpredictable changes in the competitive environment. These three drivers define a number of business objects, both static and dynamic. And these entities can be endogenous to the organisation (like customer, channel, product, etc..) or they can be external like weather data, currency data, etc…. These business objects need to be translated into data objects suitable for transaction and decision support

Figure 7 This is the (condensed) target architecture of an integrated  “Big Data Warehouse”: combining batch and stream processing using low latency for operational intelligence and aggregate data for tactical and strategic decision making. Built from the ground up using data in stead of business processes as the analytic cornerstone.

Conclusion: an integrated view on transactions and decision making will improve BI programme management supported by this architectural vision. The major and most relevant BI programme management decision criterion will be the answer to the question: “Which quality data yield the highest return in terms of competitive advantage?” And thus, which project (whether on the transaction or decision support systems need the highest priority in allocation of resources? 

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[i] According to the excellent website http://plato.stanford.edu/entries/weber/  this is the best description of Max Weber’s definition:

“The methodology of “ideal type” (Idealtypus) is another testimony to such a broadly ethical intention of Weber. According to Weber's definition, “an ideal type is formed by the one-sided accentuation of one or more points of view” according to which “concrete individual phenomena … are arranged into a unified analytical construct” (Gedankenbild); in its purely fictional nature, it is a methodological “utopia [that] cannot be found empirically anywhere in reality”. Keenly aware of its fictional nature, the ideal type never seeks to claim its validity in terms of a reproduction of or a correspondence with reality. Its validity can be ascertained only in terms of adequacy, which is too conveniently ignored by the proponents of positivism. This does not mean, however, that objectivity, limited as it is, can be gained by “weighing the various evaluations against one another and making a ‘statesman-like’ compromise among them”, which is often proposed as a solution by those sharing Weber's kind of methodological perspectivism. Such a practice, which Weber calls “syncretism,” is not only impossible but also unethical, for it avoids “the practical duty to stand up for our own ideals”.”

What is less known is that Weber used the concept also in decision making theory when he analysed the outcome of the Battle of Köninggratz, where Von Moltke defeated the Austrian-Bavarian coalition against Prussia and its allies in 1866, an important phase in the unification of Germany.

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[ii] “Business at the Speed of Thought” Bill Gates and Collins Hemingway, Penguin Books, London England, 1999 pp 293 -294

The Eternal Business Intelligence Conundrum

Finding an Optimum between a Manageable BI Architecture and Business Agility

This is the second post in a series of three about programme management in Business Intelligence (BI). In the previous post we positioned project- and programme management in BI and the latter’s relationship with BI architecture.

In this post, we discuss the universal and eternal problem, conflict, dialectics,… (call it what you want) between the business who wants a decision support solution here and now, no matter what the consequences for the IT department are and the IT guys who want to steer the team and the infrastructure into calm waters. “Calm waters” meaning a strict architectural, TOGAF based approach to managing the BI assets. 

Head for the Cloud!

I won’t describe the situations where the IT guys –according to the business- waste time with the introduction of new tools and the business strike a direct deal with a vendor, using the tool completely outside the managed environment. 

Figure 2 The data mining tool Data Maestro is an example of a powerful cloud based tool

Needless to say that many cloud based solutions offer a solution with a small IT footprint: all the business needs is a browser. Well, that’s what the business thinks. Issues like data quality, data governance and data security are not always handled according to corporate standards and legislation on data privacy and data security is becoming stricter and more repressive every so many years.

What Business Stakeholders Need

As I pointed out in the section “Managing Strategy” (Business Analysis for Business Intelligence p. 66 – 71) business stakeholders need decision support for their intended strategy as well as emergent strategies (note the plural in the latter). To support analysis and monitoring of intended strategies (i.e. the overall business plan or a functional strategy as described in a marketing-, HR- or finance plan for example) a balanced scorecard (BSC) does the job. If well done, a BSC aligns all parties concerned around a well-designed causal model breaking down strategic priorities into critical success factors and key performance indicators as well as a project plan, a data model and an impact study on the existing analytics architecture. But to capture, evaluate, monitor and measure the impact of emergent strategies is a different ball game. The business intelligence infrastructure needs an agile approach to produce insights on the fly. Some vendors will suggest that all can be solved with in-memory analytics. Others suggest the silver bullet is called “Self Service BI” (SSBI) Yet even the most powerful hard- and software is a blunt and ineffective weapon if the data architecture and the data quality are in shambles.

Sometimes new tools emerge, producing solutions for niches in finance, marketing or production management which cause the business to urge IT for adopting these tools. This ends with either a mega vendor acquiring the niche player or the niche player broadening its offerings and competing head on with the established vendors. In any case, if the IT department happens to have standardised on the “wrong” technology partner, there will be bridges to cross for both…

Other than issues with new software “interfering” with IT’s priorities, most of the troubles are found in the data architecture. The reason is simple: if not all BI projects are backed by an enterprise wide data architecture that is connected with BI programme management, new information stovepipes will emerge. This is quite ironic as the initial reason for data warehousing was to avoid the analytic stovepipes on transaction systems. So here’s my advice to the business:

Whatever business you are in, make sure you have an enterprise view on the major information objects for your analytic projects

Without it, you are destined to waste money on rework, on incomplete and even false information.

What IT Stakeholders Need

IT management has many constraints do deal with. Keeping up with business requirements, while getting the biggest bang for their buck means pooling skills, facilities and technology components to optimise license cost, education and training and hard- and software performance. The final objective is to provide high service levels and keeping their customers happy at a reasonable budget. But if ”happy customer” means: acquiring new, exotic software, training new skills and insourcing expensive tech consultants from the vendor to explore new terrain without experience or knowledge of best practices, then IT management may be at the short end of the stick.

Take the example of data visualisation ten years back. Business had a point that the existing vendors weren’t paying too much attention to good visualisation to produce better insights in data. Even the most common tools had problems creating a histogram, let alone sophisticated heat maps or network diagrams. Then came along vendors like Tableau Software selling end user desktop licenses at affordable rates, educating the business to enjoy the benefits of visual exploration of data. The next step in this “camel’s nose” or “puppy dog approach” is getting the organisation to acquire the server for better management, performance and enterprise wide benefits of the technology.

So here’s my advice for IT Management:

If a new technology becomes available, it will be used. Make sure it is used in a managed and governed way instead of contributing to information chaos.

Don’t fight business intelligence trends that have a pertinent business case, fight BI fads only.

A Governance Decision Model for Conflicting Interests

I don’t like dogmatic thinking in management but when it comes to governance in BI, I will defend this dogma till the bitter end: only duopolistic governance will produce the best results in analytics.

That a business monopoly won’t work was clear after a consulting mission where I found a data warehouse with no less than six (6!) time dimensions. This extreme situation can only be explained by what I call “the waiter business analysis model”. Without any discussion, counterarguments nor suggestions, the analyst-waiter brings the ordered tables, cubes and reports to please the business. If the business funds the projects solely, then accidents will happen.

Figure 3 The BI Waiter Model: don't argue with the customer, bring him what he wants, no matter what...

But IT monopolies also are a recipe for failure in BI. At another client’s site, the IT department repeats over and over “x unless…” (x is a well-known BI tool provider). As it happens, this tool provider is lagging seriously in data mining and visualisation functionality so the business is wasting money on external service providers who do the analytics off line. Another source of waste are business managers installing software on their private PC to explore new ways of analytics at home.

In a duopolistic governance model, decision makers from both sides have to consider five key governance decisions. This will result in a better mutual understanding of each other’s concerns and priorities as well as provide a roadmap towards a managed analytical environment.

The Five Key BI Governance Decisions

(from my book Business Analysis for Business Intelligence, page 300 -301)

1.       BI Principles decisions:

a.       In what measure do we value data quality in the transaction systems?

b.      If we have a trade off between security issues and potential gains from better distribution of information, which direction do we choose?

c.       Do we choose a proactive or a reactive attitude towards our BI users, i.e. do we deliver only the required information or do we make suggestions for enhancements?

2.       BI Architecture decisions

a.       Do we follow the general architecture policies or is there a compelling reason to choose an alternative route?

b.      If we need alternatives, where will they be of importance: in databases, ETL tools, BI server(s), client software,…?

3.       BI infrastructure decisions

a.       What are the shared IT services the data warehouse will use?

b.       What part of the infrastructure will be organised per department or business unit?

c.      What are the access methods for the information consumers: local client PC, PDA, web based, VPN,…?

4.       Business Application needs

a.      Specify the business need

b.      Specify the urgency

c.      Present alternative solutions

5.       Prioritisation of investments in BI

  a.        How will we evaluate the priorities?

  b.        Who will handle conflicting interests?

  c.        Which user profiles will be served first?

  d.        Which subject areas will be tackled first?

Figure 4 More on BI Governance in this book, available in all major bookstores

In the next post I will have a look into the next generation of information design and architecture. Comments are welcome!

Business Intelligence Programme Management: Optimising Time to Market with a Manageable Architecture

In this series of three posts, I will address some typical aspects of programme management for decision support. The first post will define programme and project management in Business Intelligence (BI) and its relationship with IT architecture.

The second post will deal with some issues in governance, the tensions that arise between business and IT and how to deal with them.

The third post will propose a new way of designing systems for both transaction  and decision support to improve the organisation’s effectiveness further.

A project holds the middle ground between routine and improvisation to produce a product

This is the essence of project management: managing unknown and unfamiliar risks and uncertainties using experience with proxies and lessons learnt to produce something within a time frame, a budget and within a certain quality range while delivering correct management information for the steering committee about the progress and the resources needed.

In Business Intelligence, this product orientation sometimes leads to overly focussing on the deliverables while ignoring valuable opportunities along the way. Many BI projects are described in terms of delivering x reports on y KPIs or delivering OLAP cubes and reports on x sources to describe what I call “stocks and flows” of business processes. I am not arguing against this approach but project steering committees should be aware that a tunnel vision can be a costly liability in BI.

Already in 1994, Séan Kelly (not the Irish cyclist nor the politician, but the datawarehouse manager from Eireann Telecom) stated that the business user can’t formulate correct requirements at the start of the BI project and changes these requirements during the project’s lifecycle. Twenty years later, many BI project teams haven’t learned anything from Kelly’s observation. Either they stick to the initial product description or they reiterate the analysis –design –build cycle at extra cost and throughput time. The major reasons are a lack of BI maturity in the organisation and the lack of an overall BI programme vision, at best an incomplete one.  In the next section I describe BI programme management and how this contributes to more effective BI projects.

Projects deliver products, programmes have outcomes

There is certainly some truth in this dictum on one condition: BI programme management should focus on favourable outcomes for decision making. The BI programmes can focus on improving decision making processes, on information objects like customer, product, channel or on broader outcomes like knowledge sharing, improved positioning, improved competitive capabilities etc.

The decision on the programme scope is not trivial. Sometimes programmes can define too broad an objective for the organisation’s maturity. Not everyone sees the relationship between a new column store and improved competitive positioning.

Some authors and practitioners see a BI programme as a collection of projects, a higher hierarchical level from tasks via projects to programmes. I strongly disagree. In my experience, BI programmes have links with other programmes on HR, IT infrastructure, marketing and sales, and other functional or strategic endeavours to produce a better outcome for the BI project portfolio.

The matrix below shows a few examples of how BI programme management interacts with other business programmes in the organisation.

Functional strategies and programmes	BI Programme interaction	Dependencies outside the BI programme
Marketing: improving customer knowledge	CRM and ERP systems deliver data to the customer analytics programme	The organisational question of customer ownership needs to be addressed when multiple divisions deal with the same customer.
Marketing: improving direct communication response rates	A customer MDM programme is initiated to improve data quality for BI and CRM processes	The customer logging processes,( e.g. in the contact centre) needs improvement initiatives
Finance: reducing days receivable outstanding	Within the customer analytics programme, a data mining project is initiated to predict payment behaviour	The invoicing process needs updating
HR: reducing absenteeism	HR and ERP systems deliver data about potential influencers on absenteeism as well as customer analytics to examine the impact of customer behaviour on absenteeism	The organisation’s management style needs adjustment for the new economy

Table 1 How BI Programmes interact with other business programmes

From these simple examples you can see clearly that the outcome of a BI programme affects other programmes and in its turn is affected by other programmes. In the case of BI projects we can also distinguish dependencies with other projects but these dependencies are usually smaller in scope and easier to manage in the steering committee.  You will probably recognize some of these quotes:

I can’t get the data from the HR department, it’s classified information,

They say I have to take the matter to the architecture board,

The infrastructure needs upgrading,

The license negotiations are slowing down the project, etc…

Now that the link with external programmes is clear it is high time to look what is inside a BI programme. This is where the link with architecture becomes clear.

BI programmes should have an overarching view, vision, business case and target setting for the principal contributors to better decision making.

In Kimball terms: the conformed dimensions, in Linstedt terms: the data vault’s hubs and satellites. No matter which solution approach you choose, a vision on the principal information objects is quintessential to the success of individual BI projects. 

From Information Objects to BI Architecture

Thinking about information objects like customer, channel, product, location, is thinking about the way they are created, stored, updated and deleted in the various information systems of the organisation. It also relates to the business processes using these information objects to produce context for transaction registrations like information request, complaint, order, payment etc..

Thus, answering the what, where, why and how questions as the Zachman Framework does is talking architecture. The illustration below is a classical BI situation. With the advent of complex event processing and big data technology on Hadoop, things are changing but for 99% of the organisations, classical BI is still the modus operandi.

Figure 1 A generic architecture of contemporary information systems: transaction systems and decision support systems are separated systems where information objects pass from the transaction systems to the decision support systems via an Extract Transform and Load Process.

Some comments with the above schema in Archimate modelling language:

Business drivers such as “end to end support for the order to pay process” define various business processes which in turn are supported by transaction registration systems. These systems create transaction lines like order, order confirmation, bill of material, manufacturing data, shipping bill, delivery note, customer receipt registration, invoice,  customer payment registration etc…

All these transactions relate to information objects like date and time, product, customer, shipment mode, etc..

Via the Extract Transform and Load process these objects are scrubbed, normalised and made ready for publishing in analytical environments and reports. That’s when they become decisional data objects: facts and dimensions are always the end result, no matter what intermediate storage you use: a data vault, an anchor model or an enterprise data warehouse in the third normal form. The facts are the measures in the reports and the dimensions are the perspectives on these measures. Usually you read the facts per dimension, i.e. the sales per region, per outlet, per account manager,…

In data mining projects the facts and dimensions will be flattened in a matrix for offline analysis and combined with semi structured and unstructured data in Hadoop files systems. In streaming analytics temporary snapshots are compared with the scoring model which is derived from the facts and dimensions as well as semi structured and unstructured data in Hadoop file systems.

With new Big Data technologies, new architectures will emerge but that is for another post.

Suffice it to conclude that managing enterprise wide facts and dimensions as well as semi structured and unstructured data is both the task of programme management and BI architecture.

BI Architecture and Programme Management: See the Picture

Programme managers need to see the entire picture to set priorities, look for synergy between projects and initiate data management projects to fill the gaps between the BI projects as required by the business.  An example can make this clear.

Let’s take the first example from table 1: “Improving Customer Knowledge” driving a programme to consolidate all static and dynamic information about customers and their behaviour.

Conferring with the enterprise architecture board, the programme manager discovers that the geographical coordinates of each customer are valuable information for the logistics department to optimise delivery schedules. Though it is outside the scope of marketing, the programme manager will initiate a project to add geolocation data to the customer dimension. Later in the process, the marketing manager discovers the potential of geomarketing.

Of course, the interaction can also work the other way around: the BI architecture review board evaluates programmes and projects and readjusts priorities and project scope on the basis of availability and cost of data capture. Sometimes the replacement or adjustment of a source system can impact the BI programme heavily. It always boils down to defining a business case that sticks. Excuse me for hitting the nail over and over but it all starts with business analysis for business intelligence that sets the scene. Too many projects and programmes have failed in BI because of a gung ho approach of the designers and builders who cannot wait till the specs are thought through after thorough analysis of the strategy process and the data landscape.

The Stockholm Papers on Self-Service BI

I had the pleasure of moderating a peculiar kind of brainstorm session in Stockholm called speed geeking, a process which will remain unrevealed to those who weren't present. Never mind the "how". Let's talk about the "what". The "what" is a set of interesting replies to the three theme questions on Self- Service Business Intelligence (SSBI)

The three theme questions discussed were:

• Why do you use SSBI?
• What are the major problems encountered?
• Will IT become obsolete? (a more challenging version of "How will SSBI affect IT's role")

Why organisations use SSBI

The problems SSBI can solve are low BI service levels,  elicitating better requirements for the data warehouse as uses will see the gaps in the available data and providing a workaround for slow DWH/BI development tracks. But the majority of answers went in the direction of opening up new opportunities.
The number one reason for SSBI  is time to market: support faster decision making, explore the organisation's creativity better, enhance flexibility, innovation, exploration,.. it's all there. Some teams  came up with deep thoughts about organisational development: "Distributes fact based decision making" was a very sharp one as well as "getting the right information to the right person at the right moment" although both motivations will need to be managed carefully. Because SSBI is not a matter of opening up the data warehouse (or other data lakes) to everybody, the paradox is that the more users are empowered, the more governance and data management are needed.

Conclusion: as always, two approaches to this question emerge: either it solves a problem or it creates an opportunity. My advice is to look for opportunities if you want a concept, a technology  or a new business process to last. Because problem solvers will limit the new technology from the problem perspective which is a form of typecasting the technology whereas opportunity seekers will keep exploring the  new possibilities of a technology.

What are the major problems encountered?

SSBI is not a walk in the park for neither IT nor the business users.
Data quality management, as well as the related management of semantics and governance of master data are the principal bumps on the road. Lack of training is also high on the radar as well as performance and security and integrity. So far, no real surprises. But strangely enough, an issue like "usability" appeared. You'd think that this is the main reason of developing an SSBI platform but apparently it is also the main show stopper.

Conclusion: in this mixed audience of IT and business professionals I have noticed few defensive strategies. Yes, there are problems but they can be solved was the general mood I felt in the room. Maybe this is one of the reasons why Sweden is one of the most innovative societies in the world?

How will SSBI affect IT's role

There was a general consensus between the IT and the business professionals: IT will evolve into a new role when SSBI is introduced. They will develop a new ecosystem, optimise the infrastructure for SSBI and act as an enabler to advance the use of SSBI.
Other interesting suggestions were pointing towards new IT profiles emerging in this ecosystem like data scientists, integrators of quality data, managers of business logic form both internal and external systems. In short, the borders between IT and the business users will become vaguer over time.  But one remark was a bit less hopeful: one group concluded that the CIO is still far away from the business perspective.

Maybe that's because many CIOs come from the technology  curriculum and there are still organisations out there that do not consider ICT as a strategic asset. Every day I praise myself lucky that I worked in a mail order company, early in my career. The strategic role of ICT was never questioned there and it was no surprise that the CIO of my company became the CEO as customer data, service level data, financial data and HRM data were considered as the core assets.

Defining Business Analysis for Big Data

Introduction to an enhanced methodology in business analysis

Automating the Value Chain

In the beginning of the Information Era, there was business analysis for application development. Waterfall methods, Rapid Application Development, Agile methods,.. all were based on delivering a functioning piece of information technology that supports a well defined business process. There are clear signs of an evolution in the application development area.

Core operations like manufacturing and logistics came up with automation of human tasks and the IT department was called the “EDP department”. Some of the readers will need to look up that abbreviation. I can spare them the time: Electronic Data Processing indicated clearly that the main challenge was managing the data from these primary processes.

Information as a business process support becomes an enabler of (new) business processes

This schema gives a few hints on the progress made in automation of business processes: the core operations came first: finance, logistics and manufacturing which evolved into Enterprise Resource Planning (ERP). Later sales, marketing and after sales service evolved into customer relationship management which later on extended into Enterprise Relationship Management (ERM) incorporating employee relationship management and partner relationship management.  Finally ERP and ERM merged into massive systems claiming to be the source of all data. The increase in productivity and processing power of the infrastructure enabled an information layer that binds all these business processes and interacts with the outside world via standardized protocols (EDI, web services based on SOAP or REST protocols).

The common, denominator of these developments is: crisp business analysis to enable accurate system designs was needed to meet the business needs.

The "Information is the New Oil Era"

Already in the mid nineties, Mike Saylor, the visionary founder and CEO   from Microstrategy stated that information is the new oil.  Twenty years later, Peter Sondergaard from Gartner repeated his dictum and added “and analytics is the combustion engine”.  A whole new discipline –already announced since the 1950’s- emerged: Business Intelligence (BI). Connecting all available relevant data sources to come up with meaningful information and insights to improve the corporate performance dramatically.

The metaphor remains powerful in its simplicity: drill for information in the data and fuel your organization’s growth with better decision making.

Yet, the consequences of this new discipline on the business analysis practice remained unnoticed by most business analysts, project managers and project sponsors. The majority was still using the methods from the application development era. And I admit in the late nineties I have also used concepts from waterfall in project management and approached the products from a BI development track as an application where requirements gathering would do the trick. But it soon became clear to me that asking for requirements to a person who has an embryonic idea about what he wants is not the optimum way. The client changes requirements in 90 % of the cases after seeing the results from his initial requirements. That’s when I started collecting data and empirical evidence on which approach to a business analysis method leads to success.  So when I published my book “Business Analysis for Business Intelligence” in October 2012, I was convinced everybody would agree this new approach is what we need to develop successful BI projects. The International Institute of Business Analysis’s (IIBA) Body Of Knowledge has increased its attention to BI but the mainstream community is still unaware of the consequences on their practice. And now, I want to discuss a new layer of paradigms, methods, tricks and tips on top of this one? Why face the risk of leaving even more readers and customers behind?  I guess I need to take Luther’s pose at the Diet of Worms in 1521: “Here I stand, I can do no other.” So call me a heretic, see if I care. 

Luther at the Diet of Worms in 1521

The new, enhanced approach to business analysis for business intelligence in a nutshell deals with bridging three gaps. The first gap is the one between the strategy process and the information needed to develop, monitor and adjust the intended strategic options.

The second gap is about the mismatch between the needed and the available information and the third gap is about the available information and the way data are registered, stored and maintained in the organization.

Now, with the advent of Big Data, new challenges impose themselves on our business analysis practice.

Business Analysis for Big Data: the New Challenges

But before I discuss a few challenges, let’s refer to my definition of Big Data as described in the article “What is really Big About Big Data” In short: volume, variety and velocity are relative to technological developments. In the eighties, 20 Megabytes was Big Data and today 100 terabytes isn’t a shocker. Variety has always been around and velocity is also relative to processing, I/O and storage speeds which have evolved. No, the real discriminating factor is volatility: answering the pressing question what data you need to consider as persistent both on semantic and on a physical storage level. The clue is partly to be found in the practice of data mining itself: a model evolves dynamically over time, due to new data with better added value and / or because of a decay in value of the existing data collection strategy.

Ninety percent of “classic” Business Intelligence is about “What we know we need to know” . With the advent of Big Data the shift towards “What we don’t know we need to know” will increase. I can imagine in the long run the majority of value creation will come from this part.

From “What we know we need to know” to

“What we don’t know we need to know”

is the major challenge in Business Analysis for Big Data

Another challenge is about managing scalability. Your business analysis may come up with a nice case for tapping certain data streams which deliver promising results  within a small scope but if the investment can’t be depreciated on a broader base, you are dead in your tracks. That’s why the innovation adage “Fail early and fail cheap” should lead all your analytical endeavors in the Big Data sphere. Some of you may say “If you expect to fail, why invest in this Big Data Thing?”. The simple answer is “Because you can’t afford not to invest and miss out on opportunities.” Like any groundbreaking technology at the beginning of its life cycle, the gambling factor is large but the winnings are also high. As the technology matures, both the winning chances and the prize money diminish. Failing early and cheap is more difficult than it sounds. This is where a good analytical strategy, defined in a business analysis process can mitigate the risks of failing in an expensive way.

Business Analysis for Big Data is about finding scalable analytical solutions, early and cheap.

So make sure you can work in an agile way as I have described in my article on BA4BI and deliver value in two to three weeks of development. Big Data needs small increments.

Data sources pose the next challenge. Since they are mostly delivered via external providers, you don’t control the format, the terms and conditions of use, ... In short it is hard if not impossible to come with an SLA between you and the data provider. The next challenge related to the data is: getting your priorities right. Is user generated content like reviews on Yelp or posts in Disqus more relevant than blog posts or tweets? What about the other side of the Big Data coin like Open Data sources, process data or IOT data? And to finish it off: nothing is easier than copying, duplicating or reproducing data which can be a source of bias.

Data generates data and may degenerate the analytics

Some activist groups get an unrealistic level of attention and most social media use algorithms to publish selected posts to their audience. This filtering causes spikes in occurrences and this in turn may compromise the analytics. And of course, the opposite is also true: finding the dark number, i.e. things people talk about without being prominent on the Web may need massive amounts of data and longitudinal studies before you notice a pattern in the data. Like a fire brigade, you need to find the peat-moor fire before the firestorm starts.

The architectural challenge is also one to take into account. Because of the massive amount amount of data and their volatility which cannot always be foreseen, the architectural challenges are bigger than in “regular” Business Intelligence.

Data volatility drives architectural decisions

There are quite a few processing decisions to make and their architectural consequences impact greatly the budget and the strategic responsiveness of the organization. In a following article I will go into more detail but for now, this picture of a simplified Big Data processing scheme gives you a clue.

Enabling Business Analysis for Big Data

We are at the beginning of a new analytical technology cycle and therefore, classical innovation management advice is to be heeded.

You need to have a business sponsor with sufficient clout, supporting the evangelization efforts  and experiments with the new technologies and data sources.

Allow for failures but make sure they are not fatal failures: “fail fast and cheap”. Reward the people who stick out their necks and commit themselves to new use cases. Make sure these use cases connect with the business needs, if they don’t, forward them to your local university. They might like to do fundamental research.

If the experiments show some value and can be considered as a proof of concept, your organization can learn and develop further in this direction.

The next phase is about integration:

•  integrate Big Data analytics in the BI portfolio

• integrate Big Data analytics in the BI architecture

• integrate Big Data analytical competences in your BI team

• integrate it with the strategy process

• integrate it in the organizational culture

• deal with ethical and privacy issues 

• link the Big Data analytical practice with existing performance management systems.

And on a personal note, please, please be aware that the business analysis effort for Big Data analytics is not business as usual.

What is the Added Value of Business Analysis for Big Data?

This is a pertinent question formulated by one of the reviewers of this article. “It depends” is the best possible answer.

The Efficiency Mode

It depends on the basic strategic drive of the organization.  If management is in a mode of efficiency drive, they will skip the analysis part and start experimenting as quickly as possible. On the upside:  this can save time and deliver spontaneous insights. But the downside of this non directed trial-and-error approach can provoke undesirable side effects. What if the trials aren’t “deep” and “wide” enough and the experiment is killed too early? With “deep” I mean the sample size and the time frame of the captured data and with “wide” the number of attributes and the number of investigated links with corporate performance measures.

The Strategy Management Mode

If management is actively devising new strategies, looking for opportunities and new ways of doing business rather than only looking for cost cutting then Business Analysis for Big Data can deliver true value.

It will help you to detect leading indicators for potential changes in market trends, consumer behavior, production deficiencies, lags and gaps in communication and advertising positioning, fraud and crime prevention etc…

Today, the Big Data era is like 1492 in Sevilla, when Columbus went to look for an alternative route to India. He got far beyond the known borders of the world, didn’t quite reach India but he certainly changed many paradigms and assumptions about the then known world. And isn’t that the essence of what leaders do?

Preview on "Business Analysis for Big Data, a New Ball Game"

Enabling Business Analysis for Big Data Analytics: the path is the goal