Business Rules Case Study – Part II

In part one of this case study,  I  discussed questions like:

  1. Why are Business Rules necessary?
  2. What exactly is a Business Rule?
  3. What should happen if the data fails a Business Rule?

I would like to thank the following people for contributing to the discussion to date:

Jim Harris @ocdqblog shared his experience on data migration and data integration projects, and concluded “Sadly, the most common problem was that no business rules were defined at all and the data would be blindly migrated or integrated without even at least some superficial validation checks.” more here.

In Henrik Liliendahl Sørensen’s @hlsdk experience, Business rules divide into External and Internal Business Rules:

  • “External rules that are defined outside your organisation – mostly laws and other regulations you must follow when doing business in a given country (or group of countries like the EU).
  • Internal rules that are defined by and for your business alone – made to make your business competitive.” more here.

Marianne Colwell @emx5 shared recent wins on the project she is currently working on, in which they have captured business rules in a requirements management repository, more here.

Phil Allen would like to know what the most popular choices of software are for handling the recording of Business Rules and what experiences people have had more here.

In part two, I plan to explore:

I will continue to use a case study from an Anti Money Laundering (AML) programme. However, in my experience, all data migration / data population projects face the same challenges.

 

What controls should you have in place to manage Business Rules?

In Sarbannes Oxley (SOX) terms: “If it’s not written down, it doesn’t exist”.  In my experience, you need the following controls to manage business rules:

  1. Business owner (Business responsibility)
    There must be a defined business owner (business area) with responsibility for the data item, and for the business rule(s) relating to it.  The definition must include details of who to contact (the title of a person) with queries regarding the data.
  2. Location of Business rule(s) (Business responsibility)
    The Business owner must identify where the Master business rules are formally documented, and subject to Change Management.  The business owner must also identify where copies of the business rules are held, since they must all be updated when the master copy is updated.
  3. Change Management process for the Master business rules, and copies. (Business responsibility).
    The Business owner must have a documented Change Management process for updates to the Master business rules, and for ensuring that all copies of the business rules are also updated.
  4. Location of source data (Business accountability – Technical responsibility)
    The Business owner must satisfy him/herself that the providers of IT services to the business have a control process in place that identifies where the actual data is held (i.e. the physical location).  If there are a number of physical locations, they should all be recorded, together with details of which is the Master source, which is a replica, and details of the replication process.

Where should you look for Business Rules (if your Enterprise has no Master Business Rules Repository)

Too often, I have worked on data migration/population projects for which there was no master business rules repository.  We had to research the business rules from first principles. If you have to research business rules from first principles, I suggest you consider the following locations.

  • Business Operations Manuals
    Most organisations have some form of operations manuals – in hard or softcopy. Business rules are commonly embedded in this documentation.  Be careful, they are often out of date.
  • Computer System prompt screens / help screens
    The possible/permitted values for a given field are often provided on help screens.
  • Internet sites belonging to the Enterprise
    Internal and external websites are a rich source of business rules.  They can hold product details, fee rates, etc.
    Unfortunately, they are too often out of sync with the Master Business Rules (wherever they are).
  • Data Warehouse(s) within Enterprise
    If you are lucky enough to have a single “Enterprise Data Warehouse”, this is the logical place to find business rules.  In my experience, many enterprises have a number of data bases (often in the Marketing area), at least one of which is referred to as a ‘data warehouse’.
  • Data Protection Area
    In most countries, customers may request details of the data held about them by an Enterprise.  Many Enterprises have a “Data Protection Area” to coordinate gathering the details held about the customer.  Often, the details held contain internal codes, which the Data Protection Area must ‘translate’ into something meaningful for the customer.  In my experience, the “Data Protection Area” translation process is a rich source of Business Rules.
  • Business Rules are often coded into application systems such as:
    • Anti Money Laundering (AML)
    • BASEL II
    • CRM
    • Single view of customer database

The above are all potential sources of Business Rules…however, they share a common characteristic – they are all typically ‘copies’ or replicas of the master business rules.   My experience suggests the following (I look forward to reading your feedback on this ):

  • The ‘Master Copy’ should be the copy used by the production application system (e.g. to apply an interest rate, e.g. to calculate fees due).
    Rationale:
    – The production application system copy dictates the customer experience (e.g. interest rate charged or given).
    – Production ‘Master copies’ are already subject to ‘IT Change Management Processes’ that ensure all changes are authorised by the business, and tested prior to going live.
  • Unfortunately, many production ‘IT Change Management Processes’ do not attempt to identify ‘replica copies’ of the product information, and I believe this is a ‘Gap’ in the process.
  • I recommend that production ‘Change Management Processes’ should be extended as follows:
    • Replica copies of business rules must be identified, together with the business owners of the replica copies.
      (This can be a once-off process).
    • The Business area requesting and authorising a change must contact the business owner of each replica copy, and receive confirmation that the proposed change is understood and accepted.
    • The change to the production ‘Master Copy’ must be synchronised with the change to all ‘replica copies’. e.g. If the interest rate on a product is changed from 3% to 4% – The product information on a website must change at the same time that the rate is changed (probably within 24 hours).
    • Copy ‘owners’ should also perform a periodic control; every 6 or 12 months, to verify that changes made to the ‘production master’ have been reflected in their replica copies.
      (The copy owners require a means of displaying both the master and copy details).

What has all of the above got to do with an AML programme?

My most recent encounter with researching business rules from first principles was on an AML programme.  An AML programme is an “End of food-chain” programme, as are most Data Migration and Data Population programmes like Euro Changeover, Basel II, CRM and Single View of Customer programmes.

End of food-chain programmes share the following characteristics:

  • They depend on pre-existing data
  • They have no control over the quality of existing data they depend on
  • They have no control over the data entry processes by which the data they require is captured.
  • The data they require may have been captured many years previously.

[Update August 2017: Achieving compliance with the EU General Data Protection Regulation (GDPR) faces all of the above challenges of a classic “end of food chain programme”. However, GDPR differs in that it requires organisations to demonstrate that they are in control of their Personal Data Supply Chain. They must be able to show that they:

  • Know the personal data they process and where they store it
  • Know the data entry processes by which they capture personal data
  • Know where the data goes within their organisation; who may and who has seen it
  • Know what personal data they receive from or provide to third parties
  • Know the quality of the personal data they hold and have control processes in place to maintain that quality
  • Understand the legal basis upon which they may process the personal data they hold]

What has your experience been?  Have you identified other places to look for business rules? Please share your experience by posting a comment.   Thank you, Ken.

Business Rules Case Study Part I

I would like to start a discussion about Business Rules.  I hope you will join in.  Over a series of posts I plan to explore questions like:

  1. Why are Business Rules necessary?
  2. What exactly is a Business Rule?
  3. What should happen if the data fails a Business Rule?
  4. What controls should you have in place to manage Business Rules?
  5. Where should you look for Business Rules (if your Enterprise has no Master Business Rules Repository)

I will use a case study from an Anti Money Laundering (AML) programme.

In this AML programme, the client selected a “Best in breed AML vendor solution”.   The vendor specified the data required, and the client was responsible for locating the data to populate the new AML repository, and for the quality of the data entered in the repository.

Why are Business Rules necessary?

A standard AML business requirement is the requirement to monitor “Minor Accounts” (accounts held by customers under 18 years of age) for ‘unusual transaction activity’.  This high level requirement would result in a number of more specific business requirements, such as:

“Generate an AML alert when the total value of Cash lodged in a month, to an account held by a minor, exceeds a predefined amount, say EUR5000”

Having  agreed the above business requirement, the vendor asked the client to provide the Business Rule for identifying a ‘Minor Account’.

So:

1. Why are Business Rules necessary?
Business rules are required to distinguish between product types, customer types, car parts etc. etc.  AML systems require business rules in order to apply different alert rules to different account holder types.

AML business staff are AML experts, not business rules experts.  It was unclear who owned the data and it took a long time for the IT department to research the business rule(s) for the vendor.  Q:  How do business users in your enterprise get details of Business Rules?  Do your business users find it difficult to access the data they require?

Let us suppose the Business Rule supplied to the vendor was:
A minor account may be identified as follows:
1. Account Type: Personal
2. Account SubType:  Minor
3. Customer Age:  Less than 18

The age check was required to manage the risk that an account opened when a customer was a Minor was not converted to a Standard Personal account when the customer reached his/her 18th birthday.

So:

2. What exactly is a Business Rule?

A Business rule provides critical details about data, including the ‘business’ name of the field, the business purpose of the field, the values it may hold, the business meaning of each value, and interdependencies with other data.  Let’s explore this a little further:

  1. Business name of the data field(s):
    In the above example, three data fields are used in the Business Rule:
    ‘Account Type’, ‘Account Subtype,’ and ‘Age’ (probably determined from Date of Birth).’
  2. Business purpose of the data field:
    e.g. ‘Account SubType’ is used to identify different account types, such as ‘Minor’, ‘Mature years’ etc.
  3. Permitted values (also known as enumerations):
    e.g. Permitted values for Account Subtype are 101 to 199.
  4. Business meaning of each permitted value:
    e.g. ‘Account SubType’ value 101 means Minor Account
  5. Interdependencies with other data:
    e.g. ‘Account SubType’ depends on ‘Account Type’
    ‘Account SubType’ value 101 means Minor Account, when Account Type is ‘Personal’
  6. Field precedence:
    This defines the order in which the fields should be interrogated
    e.g.  First check Account Type, then Account Sub Type

The AML vendor configured the AML tool to apply the “MINOR” rule when Account Type was personal, Account SubType =101 (Minor), and Customer Age less than 18.

During testing, few alerts were generated on Minor accounts.  From an AML business perspective, the less alerts generated the better, since the workload for the AML staff is dictated by the number of alerts generated.

The AML business area was pleased with the low number of alerts, and the vendor was pleased that the alert worked ‘as specified’.

However, it was common knowledge that Date of Birth was not populated 100% of the time, so what was happening when there was no Date of Birth present?  There was no culture of  data quality measurement in the Enterprise, and no facilities for data profiling. Custom built SQL queries against the new AML repository identified multiple instances in which the actual data failed to conform to the Business Rules.

So:

3. What should happen if the data fails a Business Rule?
In our AML example, what do you think should happen when:
a) Account Subtype is ‘101’ indicating a MINOR account, but the customer is aged over 18?
b) Account Subtype is ‘101’ indicating a MINOR account, but date of birth is not populated for this customer?

Business Rules define what data fields “should” contain.  On this AML programme, as in all real world data programmes, the actual data content did not always match what was expected.

This only became apparent as a result of custom built data profiling.  Based on the actual content of the data, the AML business area had to ask the vendor to implement Exception Rules to handle the non-conforming data.  In an ideal world, the data would have been corrected.  In the real world of “achieve compliance by a given date, or face a regulatory fine”, workarounds are quite normal.

So – what are Exception Rules?
Exception rules define what must happen when an account contains data that fails to comply with a business rule.

This post is already far longer than I had planned – I hope it hasn’t bored you to tears.
In my next post, I will explore:

Please share your experience by posting a comment – Thank you.

Common Enterprise wide Data Governance Issues #10: No ‘Master’ repository of business rules

This post is one of a series dealing with common Enterprise Wide Data Governance Issues.  Assess the status of this issue in your Enterprise by clicking here: Data Governance Issue Assessment Process

Business rules provide critical details about data fields, including the ‘business’ name of the field, the business purpose of the field, the values it may hold, the business meaning of each value, and interdependencies with other data.

An example of a business rule could be ‘Account Type must be consistent with Account Fee Rate, both must be BUSINESS, or both must be ‘PERSONAL’.  Such a business rule would be critical on an Anti Money Laundering Programme, where you must apply different alert rules to personal and business accounts.

In some organisations, there is no ‘Master’ repository of business rules.  Business rules are not easily researched, not formally documented, and not subject to change control.

Impact: Projects dependent on existing data must research business rules governing that data from first principles, and face the risk of not finding them, or finding inconsistent business rules.  This leads to duplication of effort, project delays, and the risk of making incorrect business decisions based on incorrect business rules (e.g. generating False Anti Money Laundering Alerts on accounts you treat as PERSONAL, when in fact they are BUSINESS.)

Solution:
Agree and implement the following Policies:

  1. Overall ownership for business rules governing data within the Enterprise lies with the CIO.
  2. Ownership for business rules within each Business Unit lies with the CIO and the head of the Business Unit.
  3. Business rules must be formally documented and subject to change control (Enterprise-wide, and Business Unit specific).
  4. The CIO must appoint a person (TitleX) with responsibility for Enterprise wide business rules.
  5. TitleX is responsible for the definition and maintenance of Enterprise-wide business rules, in consultation with business units.
  6. TitleX must provide a single point of contact to handle requests for business rule details.

Your experience:
Have you faced the above issue in your organisation, or while working with clients?  What did you do to resolve it?  Please share your experience by posting a comment – Thank you – Ken.

Common Enterprise Wide Data Governance Issues: #3 No culture of Data as an ‘asset’, or ‘resource’

Some enterprises fail to recognise the true value of their data.  This post is one of a series dealing with common Enterprise Wide Data Governance Issues.  Assess the status of this issue in your Enterprise by clicking here:  Data Governance Issue Assessment Process

Impact:

  • There is little value attributed to capturing and maintaining high quality ‘informational’ or ‘Master data’.
  • The other Enterprise Wide Data Governance Issues in this series are all symptoms of the failure of the enterprise to treat Data as a corporate asset.  An Enterprise that treats Data as a valuable corporate asset understands the value of data, and is likely to have addressed the issues I have identified.

Solution:
Agree and implement the following policies:

  • Data must be treated as a valuable Enterprise asset, that can assist the Enterprise achieve its strategic objectives, and must be invested in proportionally to other Enterprise assets.
  • The CIO is responsible for ensuring that the quality of Master data is measured, target data quality levels are agreed, and measures are implemented to meet the defined targets.

Your experience:
Have you faced the above issue in your organisation, or while working with clients?  What did you do to resolve it?  Please share your experience by posting a comment – Thank you – Ken.

Business is all about data

Technologies may come and go.  At the end of the day, business is all about data.

Take the banking industry:
Hundreds of years ago, banks had Customers, with Accounts, on which Transactions were recorded. Bankers knew their customers personally, and all details were recorded by hand in ledgers, using quills made from feathers. Over time, quills were replaced by fountain pens, and later by biros, to record customer, account and transaction details.

Fast forward to today:
Banks still have Customers, with Accounts, on which Transactions are recorded, only many, many more of them.   Financial Regulators require banks to “know your customer”, but it is physically impossibe for bankers to know their customers personally. Customers can now perform transactions via multiple channels, at the bank branch counter, over the internet, over the phone, using mobile devices.

Customer Relationship Management (CRM):
To provide their customers with the best service, banks have implemented “Customer Relationship Management” or CRM systems. CRM systems analyse data to identify situations when the bank may wish to contact the customer to offer additional services, or otherwise improve the service the bank provides to the customer.

Money Laundering, Fraud, Terrorist Financing:
Banks today face ever increasing risks of Money Laundering, Terrorist Financing and Fraud.  Regulators require banks to implement best practice Anti Money Laundering (AML) and Anti Terrorist Financing solutions.

Best practice solutions:
What is the common thread amongst the best practice AML solutions?  How do Anti Money Laundering solutions enable a bank to “Know your customer”?  How do Anti Money Laundering solutions identify Accounts that require investigation? How do Anti Money Laundering solutions identify “Suspicious Transactions” amongst the millions of transactions the bank processes daily?

The answer:
By analysing the data.  However, the data analysis must be targetted.  The analysis must seek out defined activity patterns, and then alert trained staff to the possibility of wrongdoing.   More sophisticed AML systems can identify transaction activity that is unusual for a given customer type, by performing “Peer Group Analysis”.   For “Peer Group Analysis” to work, a bank must be able to reliably distinguish between different customer types.   Distinguishing between different customer types is often more challenging than one would think…