Single most important strategy - Master Data Management

 Author: Sathish CS

||    Slug: Data Governance    |    Intro: MDM is a vital process in Data Governance    ||

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Data management professionals and senior business managers today are increasingly becoming aware of the importance of Data Governance(DG) both because of the external pressures such as compliance and regulations as well shareholder rights and privileges and the internal business pressures- I am a strategic and collaborative management technology consultant based in the UK.

I deal primarily with banks and financial services institutions in areas of Master Data Management with focus on new age frontiers like Blockchain, IOT, Big Data which are disrupting the very fabric of economics. And thus making it  mandatory to understand the mechanics, virtues and ongoing operations of instituting Data Governance within an organisation.

"The objective of data governance is predicated on the desire to assess and manage the risks that lies hidden within the enterprise information portfolio," and maintaining: "One of the critical values obtained via ‘Data Governance’ is to ensure there exists only one version of the “Truth” for customer or product or any critical data domain and this is the  common point of reference across departments, systems, regulators. Thus achieving a 360-degree view of customer data dimension.

Hence, ‘value in the form of data’ flows across the enterprise without any contamination or loss of information. And ‘Master Data Management (MDM)” program as part of data governance portfolio will ensure consistent data is published across lines of business "

‘Data is the New OIL'

DATA GOVERNANCE PRACTICE      

    

Success of MDM goes hand in hand with a a well-defined ‘Data Governance practice  & implementation’. and organisations have in the past decade understood the importance of data as an asset and hence are not categorising data management-related programs as just another cost center initiative. Rather, they are being prudent by giving these initiatives the utmost attention and investment- look at the "".

"Data (customer and master/reference data) quality issues, which largely was one of the key issue which led to the 2008 financial crisis & has literally have forced regulators to issue mandates via accord changes from BASEL committee for the global banking and financial sector-even today organisations are struggling to address the above” .

A simple depiction of data governance leading to MDM as part of a data strategy journey would look like:

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3 PILLARS
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In a layman terms "Data Strategy" means :

• Aligning business strategy and ensuring business users, owners, stakeholders & sponsors are engaged right from the beginning of the journey.
• Define & Align with corporate objectives & ensure allocation of resources, gain CXO executives buy-in & guiding principles for achieving quality & insightful data - "business asset".
• Not all data is equal, so it is very important to assess how, and which data will be used and be part of the data strategy, governance & MDM journey.  

Data Policy & Stewardship encapsulates

• Data policy, definitions, controls, metadata, ownership via a steward are pre-requisite.
• There can be more than one data steward for an attribute or set of attributes- define & assign clear roles & responsibilities.
• Data profiling will be the core deliverable wherein business rules, values, exceptions, audit rules, owners, authority, amendment frequency, regulatory and in-country requirements, etc, are defined and agreed.

Data Quality Maintenance & Framework (DQM)

• Monitoring & post-event stage of the data lifecycle management will include system, business process and quality validations. In-spite of stringent checks, data quality can take a hit due to various other unforeseen reasons or human errors.
• DQM is an evolving layer wherein over a period of 1-2 years on an average the framework of data quality matures, and this is when ROI of the entire data governance will surface.

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CEMENT THAT BONDS

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MDM is the cement that bonds the enterprise wide business intelligence, workflow and analytical systems to that of the core operational side of the business systems and processes.

we are into a decade now and still some of the causes below which led to the infamous 2008 crisis are still open and vulnerable- more importantly organisations are struggling to arrest these and reasons are multi-fold:

•        Failure of management to implement a stringent data strategy or governance and lack of having any feedback loop to validate the data quality issues
•          Regulators should have enforced data governance on enterprise scale as a mandate and we are seeing very complex structured products are launched without the underlying clean data in place
•      Presence of multiple versions of truth of customer data lead to sub-standard reporting of financials and in the past and continue to haunt the CEOs even today in 2019. 

Besides, regulators could not nail these loopholes or pitfalls due to lack of stringent tools and feedback mechanism to alert the institutions in 2008 and down a decade it has only made it more difficult with digital assets/crypto currency plundering the markets. 2008 gave birth to a different animal in the form of 'BitCoin' and an awesome underlying technology 'Block Chain'.

I believe that new age technologies can help alleviate the data quality pains. "However, technology can only do what it has been designed to address. Hence, a watertight data governance framework will only help the organisations achieve near pristine data quality. For example, blockchain technology possibly can help alleviate the doubts of insider fudging or false reporting or for that matter, bypassing regulations. MDM on a blockchain can ensure that the entire spectrum of data quality checks are in place if the data strategy, policy, stewards, quality framework are done with absolute diligence,".

Below are he impact of not implementing data governance in an organisation:

Data lifecycle has key stages where data collation-consumption happens and at every stage the error correction cost multiplies 5-10-fold in terms of cost(numbers are for a Global Organization).

The stage 1 is data correction after initial data input.

•      Data is not consumed by any major systems and the cost of the correction is minimal, but the issue is identifying these errors is near impossible without data governance in place.

The stage 2 is data correction after being consumed by systems.

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•     Data gets into the enterprise’s internal systems used by the business community to support business operations. This set of ingested data with issues gets processed and used in many business supporting systems and often used as part of decision making and business supporting models and calculations and across line of businesses.

The stage 3 is where data correction is done after being consumed enterprise-wide and published externally (regulators etc).

• Data or information has hit the stage of no return meaning the incorrect data has been used across the enterprise and also published to external entities. 
• Leads to serious issues like incorrect accounting statements- data impacting revenue predictions, cost allocation, etc, and lead to impact on capital adequacy that could end in greater capital ingest.

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CXO LEVEL INTERVENTION-Critical

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Final take home is that given the advent of Big Data, FinTech, Block Chain AI etc., has only made DG & MDM mandatory across business domains and these have become the  core building blocks, enabling organisation’s pursuit to innovate, survive, thrive & grow. Ultimately the success of the above is solely dependent on the level of CxO involvement & commitment – either it can lead the organisation to achieve near pristine data or end up with multiple tactical silo implementations leading to army of people maintaining a single change or data issue. 

Humungous Data is in created every second & at the speed  of light across the globe and massive data lakes are generated with structured & unstructured data pouring in- which is "THE" source for data scientists to draw out in-sights.  With a water tight data governance & MDM in place, the insights can be more meaningful and help organisations to grow organically during these uncertain times and with rapid advancement of technologies & delivery channels.


CIO, CDO and more importantly CEOs are to embrace the fundamental change in the financial business model which is converging to embedded solution(s) within a mobile or an immersive implants within human bodies performing financial transactions. So a branch less bank running on de-centralised Block Chain infrastructure offering all the traditional products which are currently offered via brick & mortar model plus additionally exotic products via digital assets are real & no more a hype.

Datadios!!!