How will ChatGPT affect the practice of business analysis and enterprise architecture?

 

ChatGPT (Chat Generative Pre-trained Transformer), is a language model-based chat bot developed by OpenAI enabling users to refine and steer a conversation towards a desired length, format, style, level of detail, and language.  Many of my colleagues are assessing the impact of Artificial Intelligence products on their practice and the jury is still out there: some of them consider it a threat that will wipe out their business model and others see it as an opportunity to improve productivity and effectiveness of their practice.

I have a somewhat different opinion. Language training models use gigantic amounts of data to train the models but I am afraid if you want to use the Internet data you certainly have a massive amount of data but of dubious and not always verifiable quality.

General Internet data is polluted with commercial content, hoaxes and ambiguous statements that need strong cultural background analysis to make sense of it.

The data that has better quality than general Internet data is almost always protected by a copyright; Therefore use without permission is not always gentlemanlike to say the least.

Another source of training data are the whitepapers and other information packages you get in exchange for your data: e-mail, function, company,… These documents often start with stating a problem in a correct and useful way but then direct you to the solution delivered by their product.

The best practices in business analysis and enterprise architecture are -I am afraid- not on the Internet. They’re like news articles behind a paywall. So if you ask CHAT GPT a question like “Where can I find information to do business analysis for analytics and business intelligence?” You get superficial answers that –at best- provide a starting point to study the topic.

A screenshot of the shallow and casual reply. It goes on with riveting advice like “Stay Informed”, “Training and Certification”, “Networking”, “Documentation”,…

And the question “What are Best Practices in business intelligence” leads to the same level of platitudes and triteness:

• “Align with business goals” who would have thought that?
• “User involvement and collaboration” Really?
• “Data Quality and Governance” Sure, but how? And when and where?

In conclusion: a professional analyst or enterprise architect has nothing to fear from ChatGPT. 

At best, it provides a somewhat more verbose and redacted answer to a question saving you the time to plough through over a billion answers from Google.

Enterprise Architectures for Artificial Intelligence (II)

A generic model for primary processes

Every organisation is unique but most organisations share some basic principles in the way they operate. Business processes have some form (between 5 and 100%) of support by online transaction systems (OLTP). Business drivers like consumer demand, government regulations, special interest groups, technological evolutions, availability of raw materials and labour and many others influence the business processes intended to deliver a product or service that meets market demand within a set of constraints. These constraints can range from enforcing regulatory bodies to voluntary self-regulation and measures inspired by public relations objectives.

This is a high level approach of how AI can support business processes

High level generic architecture

Business drivers are at the basis of business processes to realise certain business goals and delivering products for an internal or external customer.  These processes are supported by applications, the so-called online transaction processing (OLTP) systems.

Business process owners formulate an a priori scoring model that is constantly adapted by both microscopic transaction data as well as historic trend data from the data warehouse (DWH). Both data sources can blend into decision support data, suited for sharply defined data requirements as well as vague assumptions about their value for decision making.  The decisions at hand can be either microscopic or macroscopic. 

Introducing AI in the business processes

As an architect one of the first decisions to make is whether and when AI becomes relevant enough to become part of routine business processes. There are many AI initiatives in organisations but the majority is still in R & D mode or –at best- in project mode.  It takes special skills to determine when the transition to routine process management can provide some form of sustainable added value.

I am not sure if these skills are all determined and present in the body of knowledge of architects but here are some proposals for the ideal set of competences.

• A special form of requirements management which you can only master if the added value as well as the pitfalls of AI in business processes are thoroughly understood,
• As a consequence, the ability to produce use cases for the technology,
• Master the various taxonomies to position AI in a correct way to make sure you obtain maximum value from the technology (more on this in a next post),
• Have clear insights in the lifecycle management of the various analytical solutions in terms of data persistency, tuning of the algorithm and translation into appropriate action(s).

In the next post, I will elaborate a bit more on the various taxonomies to position AI in the organisation. 

How to make progress in a political organisation

Why Business Analysis and politics don’t mix.

After thirty years of practice in all sorts and flavours of organisations there’s one that stands out as a tough conundrum for any business analyst and by extension enterprise architect as well as project managers. It’s the political organisation, so eloquently described by Henry Mintzberg. 
The problem with these organisations for a business analyst, project manager or enterprise architect is identical: setting priorities to determine the first iteration of the development cycle. This lack of priority ranking may lead to scope creep, projects that never deliver the product or a user community that is not on board, etc…

Forces in a political organisation

Wouldn't we all like to work in Tom Davenports Analytical Organisation?

In the paragraph “Decisions, Teams, and Groups at Work, Classification of Decision-Making Environments, I use a simple matrix to describe decision-making contexts for BI projects. But, believe me, you can use it for any project type.

You don’t need much time to determine if you’re in a political organisation. Look for committees that make the ultimate decisions, look for a lack of accountable individuals, slow decision making processes and a track record of projects that failed to deliver the intended product. Of course government bodies are by definition political but you will also find them in the private sector.

How to recognise a political organisation before you’re even at the reception desk?

Maybe this table can help:

Political organisations, by definition, don’t have shared goals. Each alderman, state secretary, each manager, wants to score his goals without letting the team take any credit for it. Because re-election or promotion matter… And political organisations always differ on the cause and effect chains which shows clearly in analytical projects.

Setting priorities in a political organisation

You can imagine that this is the toughest conundrum to solve; if you can’t prioritise “because everything is important” you can’t even start an analysis track. Unless you simply want to sell billable hours… And prepare for a debriefing and passing the buck, dodging any responsibility.
But if you’re a hired gun that may be exactly why you’ve been hired: to take the blame for the organisation’s ineptness to take responsibility and make choices even if they go against some members of the team. (I use “team” for want of a better word in a political organisation)
In this post, I am giving you a few tips and tricks to force the “team” to come up with priorities.

But first some context. The organisation is looking for a new way to analyse structured and unstructured data; Therefore it needs a modern data architecture. Your job as business analyst (and by extension project manager and enterprise architect) is to know what the strategic priorities of the organisation are.  This needs to match with the available data and information needs. You need to check the feasibility and then choose the first iteration to deliver analytical results.  A best practice is to check the organisation’s strategy, its initiatives to improve the organisation’s position in case of a commercial entity or the level of societal utility in case of a governmental or non for profit organisation.
Imagine the first intake with the project sponsor, the product owner and any other stakeholder who has been identified in the project structure.

Here’s the dialogue:

Business Analyst: At the kick off of this analysis track, I’d like to determine with you the first iteration: where we start analysing, designing and building the first deliverables.
The “team”: (silence)
Business Analyst: Do you have a project portfolio and do you use program management to prioritise the management actions? Do you have mission and vision statement for this project?
The “team”: We thought you could formulate the vision and the mission for the project. And no we don’t have a project portfolio. We do have an Excel sheet with a list of all the projects and their status.
Business Analyst: Could we infer from the status what the priorities are?
The “team”: No.
Business Analyst: What if we look at the budget per management project. Maybe the size says something about the priority? Or what if look at rejected project proposals and the reasons? Maybe that says something about the criteria.
The “team”: Not necessarily. First of all, all management project requests are answered positively and funds are allocated to these projects. Some projects may have big budgets but that doesn’t indicate anything about their importance.
Business Analyst: What about the number of full time equivalents allocated to each project?
The “team”: A high number may indicate something about the complexity or the scope but that doesn’t tell you what priority the project has.
Business Analyst: I think this one may help us out: have you indicated the origins of leakages and losses in your business processes and could those numbers give us a hint of what’s important to the management team?
The “team”: Leaks and losses are handled by the management team and as such are equally important.
Business Analyst: Does the amount of data, the connection with business processes and the variety in the data give us a clue where we should start the project?
The “team”: That’s we are hiring you as a Business Analyst.

Now it gets tricky and you make the call, as The Clash sing: “Should I stay or should I go”

Here are few of the killer questions and remarks that will lead you to the exit:

• What projects will get or got the most press coverage?
• What if you had to choose, right now?
• Do you expect me to deliver a successful end result if you don’t know what you want?

 

More on decision making contexts in the book “Business Analysis for Business Intelligence” p. 203 – 213 

Is there way out? Maybe.

The only escape route I can think of is to start with a stakeholder analysis. Try defining the primary stakeholders and map them on a RACI matrix. If that works, you can develop your first iteration with some confidence, knowing that danger is always on the road ahead..

Example of a stakeholder analysis that turns out well: the CEO’s desk is where the buck stops.

If a stakeholder analysis is inconclusive, there must be someone who’s not involved in the official decision making unit (DMU) who is the primary influencer. Now you’ll have to get out of your comfort zone as an analyst and start thinking like an account manager.

I was lucky to have training in the Miller Heiman Strategic Selling method as well as the Holden Power Base Selling method. It sharpened my skills for identifying and influencing these hidden decision makers. So here’s my advice: check out these two books. They will increase your efficiency in political organisations with an order of magnitude.

The new strategic selling is an update of the original, worth reading for any novice in business analysis and project management.
This is Jim Holden’s original book. Of course, as things go in this business, there were many to follow up on his success. Start here anyway.

What if Analytics Drove the Information Architecture Development?

Introduction

Information architecture helps people to understand their work field, their relationships with the real world as well as with the information systems which are supposed to reflect the real world.

Information architecture deals with objects, their relationships, hierarchies, categories and how to store them in and retrieve them from applications, files, websites, social media and other sources I forget to mention…

With the massive expansion of sensor data rebaptised “the Internet of Things”, social media and linked open data, these semi structured and unstructured data are adding complexity to the information architecture.

On the other hand, hypercompetitive environments force agility upon the larger corporations as the next garage start-up may overthrow their business model and their dominance in an incredible short time span. This agility is translated in flexible applications with point and click business process reengineering.

So how does all this affect the information architecture development? That is the approach to submit to your judgement in the next paragraphs.

Analytics, the classical chain of events

In many large organisations, the process can be described in eight separate stages:

A business question is formulated, e.g. who are my most loyal customers from the past that may be vulnerable to competitive offers?

The data analyst starts looking for data that can contribute to an answer by breaking the business question into related questions, e.g. which customers have given proof of price sensitivity? Which customers have shown a downward trend in their net promotor score? Which customers are reducing their purchases of consumables, Etc…

Gathering the data is the next step: in transaction systems, market research data, social media, e-mails,…

Manipulating the data: from simple cleaning and conforming operations to very complex pipeline processing of text and web URLs to make the data useful for analysis

But before that, visualisation may already provide intuitive insights: histograms, heat maps, bubble charts and the likes may show you approaches for further analysis

Analysing the data with the possibilities offered to analyse text, the old dichotomy between quantitative and qualitative research has become obsolete. Modern analytics is about hop skip and jump between the two extremes: quantitative approaches will tell you about the proportion of clients that may look for greener pastures whereas qualitative analytics will probe for reasons and root causes.

Interpreting the data may follow more intuitive paths where extra information is added, opinions are collected using the Delphi technique or other qualitative approaches to add useful meaning and actionable insights to the analysis. E.g. developing a customer scoring model that is broadly used and understood in the organisation.

The hardest part is the last phase: integrate the data and the analytics in the decision making process. To conclude with our example: developing scripts and scenarios for the call centre agents that pop up whenever a client with a potential defection risk calls the company.

Architecture development, the classical chain of events

TOGAF's Architecture Dvelopment Method

Togaf’s architecture development method (ADM) also follows a structured path as the illustration shows.  For a detailed information on the Togaf ADM, we refer to the Open Group website: http://www.opengroup.org/subjectareas/enterprise/togaf

At the heart of Enterprise Architecture development is the management of requirements. These requirements are predominantly based on process support.

User stories like “As a call centre agent, I want to see the entire customer history when call comes in in order to serve the customer better” are process support requirements. The data are defined within the context of the process. In this comprehensive case, some level of enterprise class da ta is attained but what about more microscopic user stories like “As a dunning clerk I want to see the accounts receivable per customer sorted per days overdue”. In this case, no context about why the customer is overdue is in scope. Maybe the delivery was late or incorrect, maybe the customer has a complaint filed with customer service or maybe the invoice was sent late and arrived during the client’s holiday closing…

Yes, we have a shifting paradigm!

I know, in this business the paradigm notion is an overrated concept, abused for pouring old wine in new bags. But in Thomas Kuhn’s strict definition of the term, I think we do stand a chance of dealing a with a paradigm shift in information architecture development.

A must read for anyone in information technology

I see critical anomalies:

inconsistent decision making depending on the flavour of the day and the profile of the decision maker, often based on inconsistent information which is extracted from inconsistent data. With a time to market reducing to smaller and smaller timeframes, the old process based architecture development method may prove to be ineffective to meet the challenges of new entrants and substitute products and services. Although every pundit is touting that information is the new oil, not too many companies are using it as the basis of information architecture development.

The old top down view leads to underperforming data retrieval which is no more sustainable in a digital competitive environment where time to market is often equal to the time it takes to tailor data to your needs, e.g. recommenders in e-business, cross selling in retail, risk assessment in insurance,…

There’s external pressure from the GDPR

By now every organisation doing business with or in the EU will be aware of the 25^th May 2018, date when the general data protection regulation or GDPR, comes into effect which requires:

valid and explicit consent for the use of any data that can identify a person,

data protection by default (anonymization, pseudonymisation and security measures for data,

data breaches communication to the authorities and

records of processing activities.

Data management activities needed for compiance with the new legislation 

This requires organisations to manage their data on individuals far better and more centralised than they did in the past. Data requirements on persons will be at the heart of the information architecture development cycles as dealing with those on a lower level in the architecture framework will be a sure recipe for disaster.

Technology also contributes to this new approach

At least three technology evolutions enable the data centric approach to information architecture development: microservices, master data management tools and hybrid databases.

Microservices enable rapid scaling and reengineering of processes. The use of consistent data throughout the microservices architecture is a prerequisite.

Master data management tools are maturing as each relevant player is expanding from its original competence into the two others. You can observe data governance tools adding data quality and master data management functionality as well as data quality tools developing master data management and governance services and… you know where this is going.

Last but not least, hybrid databases will enable better storage and retrieval options as they support both transactional and analytical operations on structured and unstructured data.

In conclusion: modern information architecture needs flexible and fluid process management support using consistent data to facilitate consistent decision making, both by humans and machines.

In the next post, I will use a case to illustrate this approach. In the meantime, I look forward to your remarks and inputs for a thorough discussion.