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,…
By now every organisation doing business with or in the EU will be aware of the 25th 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.
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.