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The Business Value of Master Data

Prepared for:

By Mike Ferguson Intelligent Business Strategies

August 2016

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Copyright © Intelligent Business Strategies Limited, 2016, All Rights Reserved 2

Table of Contents

Introduction - Why Do We Need Master Data? ................................................................. 3 Master Data Anomalies And Their Impact On Business ................................................... 4

Master Data Anomalies In OLTP System Databases ........................................ 4 Master Data Anomalies In Inbound Transaction Data ........................................ 5 The Impact Of Master Data Anomalies On Business Operations ...................... 6

The Impact On Business Processes ....................................................... 6 Business Impact Examples ..................................................................... 7

Master Data Anomalies In Analytical Systems ................................................... 8 Doing Nothing - The Risk To Your Business Going Forward ............................................ 9 Taking Action To Deliver Value ....................................................................................... 11

Key Questions To Help You Formulate A Business Case ............................... 11 Data Strategy ................................................................................................... 12

Key Requirements For Your Data Strategy ........................................... 12 The Central Role Of Master Data .................................................................................... 14

Recommended Actions To Maximise The Value Of Master Data .................... 15 Managing Master Data Using Semarchy To Create Value ............................................. 16

Semarchy Convergence Suite .......................................................................... 16 Master Data Management ..................................................................... 16 Data Integration ..................................................................................... 18 Data Governance .................................................................................. 19

Cloud MDM ...................................................................................................... 19 Incremental And Practical Methodology ........................................................... 19 Integrating Semarchy Into Business Operations And Business Decision Making ......................................................................................................................... 19

Conclusions ..................................................................................................................... 20



INTRODUCTION - WHY DO WE NEED MASTER DATA? Master data is data that describes a company’s core business entities that include Customers, Products, Suppliers, Materials, Employees, Assets, Sites, etc. This type of data is the lifeblood of any business. It is used everywhere across the enterprise, in every department, every core business process and almost every application. It is used in every day operational, tactical and strategic decision-making and in different types of planning. We need it for reporting whether it is for internal use or when producing reports for external government legislative and regulatory bodies. Even customers and suppliers need access to it when using on-line and mobile applications. Given how widely used this kind of data is and the dependency on it across any business, it should not be surprising to know that the business impact of uncontrolled and unmanaged master data can be significant. If master data is unmanaged and out of control it will impact business operations. Incomplete and inaccurate can cause process defects and delays or could even impact sales. For example, missing label attributes, in product master data for a specific country will likely cause a delay in a product being sold in that country. Anomalies may also cause employees, customers, partners and suppliers to struggle in finding information and resolving operational problems. The result is that businesses are slow to respond when they do not have the required data in time or when the data they do have is not fully trusted. This in turn can cause errors that result in customer dissatisfaction. Problems with master data can also affect business decision making leading to incorrect decisions, poor quality decisions or even decisions not being taken. Finally it could even impact on the ability to remain compliant as a result of inaccurate regulatory and legislative reporting. This paper looks at the impact that uncontrolled master data has on a business to explain why managing it is so important. It then looks at what happens if you don’t do anything about data anomalies, which often happens when business professionals don’t understand why data management is relevant to their day job. We then discuss how to go about managing and governing data to deliver business value by first creating a data strategy, defining key requirements and placing master data at the centre of it. Finally we recommend actions to maximise business value from master data and look at how the technology from one vendor, Semarchy, is stepping up to meet those requirements to help companies create business value and how its master data management (MDM) software can integrate into existing operational and analytical environments.

This kind of data is used everywhere across the company

Master data describes core business entities such as Customer, Product, Supplier, Asset etc.

If master data is out of control and unmanaged it will impact business performance

Errors in master data will impact both operational and analytical systems

It will also impact the ability to remain compliant



MASTER DATA ANOMALIES AND THEIR IMPACT ON BUSINESS

In order to appreciate the value of master data to a business, we need to look at the impact that master data anomalies have on every day business. Master data is used in both operational and analytical systems.

MASTER DATA ANOMALIES IN OLTP SYSTEM DATABASES In the case of core operational business processes let’s consider what can happen if master data anomalies exist in on-line transaction processing (OLTP) system databases that run our businesses.

Consider, Enterprise Resource Planning (ERP) systems. ERP systems are at the heart of every business whether they are running on-premises or in the cloud. These systems typically underpin finance, HR and procurement business processes and link to other OLTP systems to allow our core cross-enterprise business processes to operate. They say that “all roads lead to finance”, no matter where you work in a business. All revenues and costs have to be accounted for and an ERP system is typically where that happens.

Now consider what happens if a company has duplicate customers in its ERP system – a common problem in many companies. If this is the case, what then happens if you have to invoice a customer? Which instance of the customer in the ERP system do you attach the invoice to? Is it attached to one of the instances of the customer, all of them or some of them?

What happens when you receive a payment from a customer for the invoice issued? Does your system know which of the duplicate customer records has the original invoice attached to it to match the payment against? If not, then what happens with your received payment? Does it end up in Accounts Receivable for manual resolution? If so, how often does this occur? How many people are employed there doing manual resolution? Oh, and are the general ledger folks giving Accounts Receivable a hard time because they need to book the transaction revenue to close the month end but can’t until all the outstanding problems are resolved? Do you recognise these problems? How many days are you into a new month before you can close the books for the previous month? How much of that is due to data errors?

Also, what happens if you change the details of a customer address in your ERP system? Do you change all duplicate customer records or just one of them? Or, even worse, is it some of them? Also when you change a customer record, does your ERP system send the changed customer data to other systems to try to keep them synchronised? If so does it send all the ‘duplicate’ customer records in its database? What happens if the so-called ‘duplicates’ are not in sync i.e. the change didn’t occur on all records? Does that mean you sent out inconsistent data across the enterprise? How bad is this problem? Have the inconsistencies spread across other application databases?

Duplicate customers in operational ERP systems can affect the most basic but core business tasks e.g. invoicing, payment processing and revenue accounting

Errors in master data can impact month end close

Errors can spread across the enterprise if inconsistent master data is sent to other systems when changes are made

Inconsistencies across systems can therefore get progressively worse over time



MASTER DATA ANOMALIES IN INBOUND TRANSACTION DATA Now lets make the situation worse. We just talked about one system! How many other applications have this problem in your order-to-cash process or your procure-to-pay process or your plan-to-manufacture process? Also many companies operate with both direct sales offices and indirect resellers in different cities and countries around the world. As a result they have to process inbound orders and sales transaction data from different external partners in order to set up accounts, book revenue and manage inventory. This kind of activity occurs every-day and is particularly important when inventory is still owned by the company even after it has been distributed to resellers.

In this case, in-direct orders and/or sales transactions may come into the enterprise via electronic message or file transfer. Within these transactions data describes each order or sale and so it typically contains both master data (e.g. customer, product, site) and transaction data e.g. units sold, unit price etc.

If inbound transaction data contains anomalies such as incorrect or incomplete customer or product master data, then these data errors can cause further problems as transactions are processed and stored in databases across multiple applications participating in the process. This is shown in Figure 1.

Figure 1

The problem with data errors in inbound transaction data, is that they can cause further errors in systems that are ‘down stream’ from the point where the transactions enter the enterprise. It may stall a process (e.g. order-to-cash) until someone identifies what is wrong and takes manual action to resolve the problem. The point here is that each of the errors caused has an unplanned operational cost associated with fixing it. Figure 1 shows three examples of this each with a cost indicated by the ‘£’ currency symbol. Also the cost of fixing an anomaly varies. Some are cheaper to fix than others. To determine the total unplanned cost associated with processing an erroneous transaction we need to add up the costs of fixing each of the anomalies at different points in the process. But that is not the end of it. To calculate the true daily cost to the business you have to multiply the total unplanned operational cost of processing a single transaction by the number of times a transaction of that type occurs in a day. So if there were 10,000 orders a day, then the unplanned operational cost of processing an order would need to be multiplied by 10,000! Also we are only talking about one type of transaction in Figure 1 i.e. orders. What about all the other types of transaction that the company processes? If

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Master data errors can also occur in inbound transaction data e.g. orders

Incorrect customer or product data for example in inbound transactions can spread across multiple OLTP systems as the transactions are processed

Errors can cause other errors ‘down stream’ in a process as the process executes – the domino impact

Every error carries an associated unplanned operational cost to fix it

Adding up the cost of each error in the process caused by poor master data gives the total unplanned operational cost of processing a single type of transaction

Multiplying this cost by the number of transactions of that type that you process gives the total unplanned cost of processing them

But, how many other types of transaction do you process?



data anomalies exist in these transactions as well, then the aforementioned calculation would have to be done for each of these transaction types. The result can easily be shocking. The impact of master data errors can be huge.

THE IMPACT OF MASTER DATA ANOMALIES ON BUSINESS OPERATIONS These are just two examples of what bad master data can do to a business. There is an impact and a significant one at that which affects bottom line profitability and much more. If, as discussed, you have duplicate customers in your ERP system, billing and revenue collection is interrupted, driving up operational costs and slowing down business operation. Month end can get delayed, cash flow doesn’t look as good as it should and so planning and actions like resource allocation decisions can be delayed or restricted leading to slower growth. In short, the business does not perform to its potential.

The Impact On Business Processes The combination of master data anomalies in OLTP application databases and anomalies in inbound transaction data is enterprise wide. Just imagine if that occurred in a Foreign Exchange Trade process in an investment bank and you processed a million trades a day! Would it be acceptable? No. These kinds of anomalies slow process execution, increase costs, and restrict growth. Also back-office headcount may be higher than it needs to be because people are being hired to manually resolve process problems that could be avoided if we managed the data properly. All of this impacts profitability and can affect customer satisfaction if master data defects cause process delays resulting in customers having to wait for products and services that they have ordered.

For companies that are organised around product/service lines, as opposed to customers, the impact can be even greater. Consider an organisation with multiple product or sevice lines. It is often the case that companies organised like this will replicate their order-to-cash process for each product line. Figure 2 shows three product lines and three instances of the same process. If the order-to-cash process on a single product line has the anomalies described in Figure 1 then replicating it will drive up unplanned operational costs and back office headcount as the order rate increases beyond a sinlgle line of business.

Figure 2

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Master data anomalies can interrupt process execution, delay deliveries, delay month end close, create a false picture of cash flow, limit resource allocations all of which causes companies to under perform

The total unplanned operational cost stemming from master data errors can be even higher if processes are duplicated across product lines



That is because the unplanned operational cost seen in Figure 1 applies to each instance of the process as opposed to just one instance. Furthermore, what if these processes (and the systems within them) are replicated for each country that you do business in as shown in Figure 3? Now the impact of master data anomalies becomes very significant with unplanned operational cost potentially spiralling out of control as the business grows. You can imagine what happens if you are organised like this and anomalies are not addressed across all instances of the process. The point here is that once you have identified the cost of the anomalies in this process, don't forget that you have the same problem in other product lines and in other regions. If this realization is not made then the impact on the business as the number of transactions starts to increase can be significant. On paper it looks like great global revenue growth. In practice, it could be a recipe for disaster with unplanned costs taking you into a ‘nose dive’ on profitability. Worse still would be if analytical systems didn’t have common master data for dimensions in which case the C-level executives wouldn’t even see it!

Figure 3

Business Impact Examples Let’s consider some more examples. A food manufacturer receives orders and order changes both electronically via a central system and by telephone direct to local manufacturing plants all from the same retailer customer. They then try to consolidate order data from multiple systems to understand what needs to be manufactured for a single customer. However these source systems have inconsistent customer and product master data, which causes matching errors in order consolidation. Furthermore, lack of capacity in a single plant means that the combined (potentially erroneous) order is then broken up and distributed across multiple plants to make the ordered products. There is then a plan to collate all products (via inter-plant transfer) so that a single delivery can be made to the client distribution centre. However, inconsistencies in client, materials (ingredients) and product master data in different plant planning, manufacturing and distribution systems mean that inter-plant transfer is delayed because of manual checking to make sure what is ordered gets shipped. Because these are perishable products with a finite shelf life, the delay forces the manufacture to ship a partial order to the retailer. The remaining goods then arrive late via inter-plant transport only to be scrapped

BusinessandDataComplexityCanSpiralOutOfControlifProcessesAndSystemsAreDuplicatedAcrossGeographies

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It will rise even further if processes are duplicated across geographies

Food manufacturing: If master data anomalies are not dealt with the impact can cause delays leading to missed sell-by dates, racking up unplanned costs



because the delay means the goods will now not make the sell by date for the retailer. This forces the manufacturer to have to re-manufacture the product at its own cost, causing unplanned operational costs and eroding profit margins. In addition, a further delay is caused on remaining order delivery to the retailer, which in turn impacts on their sales. This keeps happening until a very dissatisfied retailer finally cancels their entire contract with the manufacturer causing the loss of hundreds of jobs and the shut down of a manufacturing plant.

Another example is a healthcare company where customer sales data arriving in electronically from in-direct resellers included customer IDs (master data) from those re-sellers’ own internal systems. Due to the fact that re-sellers all have their own systems, more than one customer ID for the same customer is being submitted in in-bound sales data. Adoption of this in-bound customer ID data ‘as is’ resulted in inconsistent sales data for each customer in operational reporting. This in turn meant that transactions were delayed in the general ledger, month end was delayed and inventory was not being correctly matched up against customers. The problem had to be resolved by hiring more people to manually do the reconciliation. However, it caused major increases in unplanned operational costs and inventory was very complex to track.

MASTER DATA ANOMALIES IN ANALYTICAL SYSTEMS That highlights another point. Master data anomalies are not just confined to OLTP systems. Problems can also occur in analytical systems such as data warehouses and data marts, where master data is known as dimension data.

Many companies build multiple data warehouses (DW) across their enterprise (see Figure 4) such that dimension data exists in each with some degree of overlap. In an ideal world, dimensions for the same data, would be the same across all these systems. But reality tells us that this is not always the case. Customer data in one DW may not be exactly the same as that in another. If dimension data is not kept in sync across analytical systems, it makes it harder to produce management and regulatory reports from data that needs to be integrated across analytical systems. If master data is not synchronised across these systems then the job of integrating data across them becomes challenging and also inconsistencies in reporting could delay decisions and actions as well as compromise compliance. Also planning becomes difficult.

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In the end, the conequences can be very damaging to business

Healthcare: Conflicting master data identifiers can cause real problems in servicing customers, producing reports and managing inventroy

Master data anomalies can also cause problems in analytical systems

Inconsistent dimension data across multiple analytical systems can delay decisions and actions, obstruct the ability to produce insights and result in lost opportunities



DOING NOTHING - THE RISK TO YOUR BUSINESS GOING FORWARD

Given these problems, what happens if you don’t do anything about it? What are the risks going forward?

Perhaps the biggest risk of doing nothing is in operations. If master data is not managed, operational data entry systems will still have a multitude of inconsistencies including:

• Different identifiers and data definitions for the same entity

• Different subsets of master data in each system

• Inconsistent master data in each system

• Varying degrees of duplication of master data in each system

Also, synchronisation issues and data conflicts will remain. Yet, amazingly people sometimes still can’t see the opportunity to solve this especially when it could be done using an agile, iterative approach to implementing MDM.

All of this means operational costs will be much higher than they would be if master data were synchronised across all applications in core processes. Data defects and process errors will continue to require manual intervention to resolve the problems they create. This naturally slows down operations and makes the business less efficient. You could argue that ‘things are OK as they are’ so why bother? I often hear “What has this got to do with my day job” or “If it ain’t broke don’t fix it!” Really? What is clear in these cases is that no one has shown how MDM can help the company go ‘on a diet’ to drop unnecessary complexity and cost of operating because they can’t see why operating at their current level of cost is way short of optimal. But if the impact is as discussed and there is clear opportunity to improve, should you not take it? If you are running at a level of operational cost that can be significantly reduced should you not reduce it?

Perhaps looking to the future might make you think twice about being happy with the status quo. There is no doubt that the objective of every business is future growth. Given what we have discussed about data anomalies, a business would not be able to scale without also driving up unnecessary and unplanned operational costs. The reason for this is because the transaction rates increase as a business grows, meaning that the number of process errors caused by master data anomalies will also increase. This would force the business to have to hire additional back office staff in order to ‘keep pace’ with the anomalies arising in order to manually resolve the growth in problems they cause. The cost of operating is therefore driven up unnecessarily eating into profits. The ideal situation would be to scale without having to add unnecessary headcount. We don’t want Accounts Receivable packed with employees trying to resolve problems caused by duplicate customer records in your ERP system or by poor quality data arriving in inbound transactions.

Also, it is not just costs that are the issue here. The question is can the business keep up the pace of resolving anomalies as the business scales or could this develop into a ‘runaway train’ where anomalies and their domino

The biggest risk of doing nothing about inconsistent and erroneous master data is in operations

Operational costs will be much higher than they would be if master data was synchronised across OLTP applications and processes

If nothing is done about inconsistent and erroneos master data then unplanned operational costs will increase rapidly as transaction volumes grow

This is because more process errors will occur

Back office headcount will also increase to deal with manually fixing errors

Also the business may not be able to keep pace with the error rate if the growth in transactions is rapid



affect are out of control. This kind of operational risk really must be avoided otherwise chaos will reign and growth will stall.

With respect to decision-making, difficulties would remain in producing management reports to see across the value chain in order to match supply to demand and to manage things like inventory. Planning would therefore be difficult and less dynamic if the data needed is split across multiple data stores with dimension data inconsistencies. Inconsistencies in reporting would remain which could lead to delayed decisions and lost opportunities. Also if dimension (master) data cannot be standardised across analytical data stores there would be no opportunity to simplify architecture by reducing the number of data stores and reducing total cost of ownership in existing analytical environments by consolidating data warehouses and data marts using common master data.

No possibility of simplifying analytical architecture

Delayed decisions and lost opportunities

Inability to see across the value chain to match supply with demand is likely



TAKING ACTION TO DELIVER VALUE So what can you do about your master data? Given the aforementioned problems it is clear that if companies are going to get their data under control and deliver value with it, they need a business case and a data strategy.

KEY QUESTIONS TO HELP YOU FORMULATE A BUSINESS CASE The first thing to create is a definitive list of core master data entities used in your business. Once this is done the next question is which of these entities do you tackle first? Answering the following questions can help create solid business cases to prioritise candidate master data projects.

• What are your strategic business objectives and priorities?

• What types of transaction does your business need to process?

• What are your core operational business processes and what types of transaction do they process?

• What applications are used in your operational business process tasks?

• What systems are used to support analysis and decision-making?

• What are your master data issues and which entities are they associated with? E.g. incorrect or missing customer data, late data, duplicate data

• What is the business impact caused by master data anomalies on your business processes?

• Major increases in manual activity to redo tasks • Manufacturing errors • Scrapped product

• Late deliveries • Customer dissatisfaction • Process delays e.g. month end close delayed

• What is the impact caused by master data anomalies on decisions? • Incorrect, delayed decisions, inability to report or decide, lost

opportunity

• Who is affected by the data anomalies (e.g. departments, customers..)?

• What is the estimated unplanned annual cost to the business?

• What is the risk to the business going forward? For example is it that headcount would increase? Would the problems caused get out of control as the business scales? Also where in the business is the risk? Are there multiple risks?

• What is the estimated opportunity cost savings if you could fix it?

• What recommendations need to be made to maximise value?

Need a business case and a data strategy to get data under control

You need to align with strategic business priorities

An understanding of core master data entities, transaction types and business processes is needed

We also need to understand which OLTP applications underpin your business processes

What analytical systems are in use?

What are your master data issues and what is the impact of this on your business?

What is the risk to the business going forward?

How much can you reduce costs by if you eliminate process errors by implementing MDM?



DATA STRATEGY A data strategy defines the framework for managing, governing and producing the information needed to help a business achieve its strategic business goals e.g. a 360o degree view of customer for customer retention, engagement and growth. This may include data for use in operational business processes as well as data for use in decision making and planning. In order to create a data strategy we therefore need to know what trusted data services need to be created and published for others to consume and use and for what business purpose. The framework defined in a data strategy should enable you to create trusted, integrated, commonly understood:

• Master data

• Reference data

• Transaction data

• Data in data warehouses and data marts for query and reporting

• Data in big data platforms available for analysis

• Data available on-demand as a service

• New insights from Big Data

• New master data attributes from big data In the context of this paper we are focussed on master data.

Key Requirements For Your Data Strategy Bearing these questions in mind a data strategy should have a purpose just like a business strategy and needs to include the following:

• A vision / mission statement E.g.

“To create a collaborative, organized, data-driven environment where business and IT work together to produce, trusted, secure, commonly understood data that continuously helps to improve business performance, reduce risk and enable our business to remain compliant with all necessary regulations and legislation”

• A set of objectives to get specific data (e.g. master data) under control, to manage it to help the company achieve its strategic high priority business goals and improve its processes

• Key performance indicators (KPIs) to measure if the data strategy is succeeding (e.g. if master data anomalies are reducing)

• Planned KPI targets to be reached and by when

• Data initiatives (projects) to be undertaken on specific data to help achieve specific business outcomes

o Master data entities to be brought under control o What new (big) data should you bring on board that offers the

greatest competitive advantage? What is your big data strategy?

• Organisational structure, roles and responsibilities to manage, produce and govern data

• A definition of the data to be managed, produced and governed

A data strategy is about producing trusted data to create business value in line with strategic business priorities

A data strategy enables you to deal with master data and a lot more but with the intent to always deliver business value

A data strategy has objectives, goals, projects, accountability, an organisational operating model, a budget together with policies, process, methodologies and technologies to deliver the required business value



• Budget allocated against data management/governance initiatives

• Processes, policies, methodologies and technologies needed to ingest, store, manage and govern data as well as produce new data. This includes supporting tasks like data definition, data modelling, data integration and data synchronisation. Governance includes tasks like data profiling, data validation, data cleansing, data masking (privacy), data security and data lifecycle management.



THE CENTRAL ROLE OF MASTER DATA Central to any data strategy is master data management. Creating clean, integrated commonly understood master data allows companies to eradicate errors in business processes caused by poor quality master data in OLTP applications. This is shown in Figure 5.

Figure 5

It also allows inbound transactions to be validated, and incorrect or missing master data to be corrected/enriched before transactions are processed. This is done by using event-driven data quality services in a data quality firewall to validate and enrich transactions against master data as soon as the inbound transaction arrives (see Figure 6).

Figure 6

Both of these together show how errors, delays and unplanned operational cost caused by poor quality master data can be erradicated from business operations. In additon, introducing master data management to synchronise master data across applications in processes that are duplicated across

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An MDM system is at the heart of any data strategy

An MDM system can synchronise OLTP applications to enable core business processes to run smoothly

An MDM system can be used in a data quality firewall to validate inbound transaction data, fixing anomalies before the transaction is processed

This prevents the ‘domino effect’ of spreading data anomalies across applications causing further process errors



product lines, lays the foundation to remove process duplication, simplify business operations and dramatically reduce the cost of operating. With respect to analytical systems, master data management systems can be used as a data source for dimension data to data warehouses, data marts (see Figure 71) and big data systems so that all analytical systems are consistent. This is particularly important with respect to customer master data and deepening customer insight. Customer MDM is therefore a foundation stone for gaining a 360 degree view of a customer to improve customer engagement and retention as well as facilitate growth. In addition, MDM ensures that analysis and reporting is done on consistent data and that slowly changing dimension data can be correctly captured so that historical analysis can see the impact of change e.g. to customer lifestyle. It also lays the foundation to consolidate data warehouses to simplify the analytical landscape if dimension data is consistent across all data stores.

Figure 7

RECOMMENDED ACTIONS TO MAXIMISE THE VALUE OF MASTER DATA In order to maximise the value of master data it is recommended that organisations at least do the following when implementing MDM:

• Synchronise all OLTP applications involved in core operational business processes with consistent master data

• Synchronise all common dimensions in all analytical data stores with the same master data from a common MDM system

• Implement a data quality firewall via event driven data quality services to validate inbound transaction data against an MDM system

• Implement common processes to maintain master data

• Implement change management to deeply integrate operational OLTP systems with MDM systems so that common master data web services are used by all applications to maintain and access master data

• Consolidate, remove duplication and complexity at every opportunity 1 Note that in Figure 7 ‘D’ indicates Dimension data and ‘F’ indicates Fact data

2 Also known as the Registry or Index approach whereby master data is authored, maintained and stored in disparate source systems but a virtual clean, integrated single view of a golden

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MDM also simplifies ETL processing in data warehouses and data marts since all analytical systems get their dimension data from a common trusted source

Data warehouses with common master data can be consolidated to reduce total cost of ownership, increase agility, simplify architecture and enable management and financial reporting across the value chain

Once an MDM system is built, synchronisation of operational and analytical systems becomes critical to success

A data quality firewall can also be established

Common processes can be established e.g. to create a customer or a supplier

Change management can be implemented to simplify and reduce cost of operating



MANAGING MASTER DATA USING SEMARCHY TO CREATE VALUE

Having understood the business value of master data in terms of how it can contribute to reducing costs and increasing revenue (by improving customer retention and growth), this section looks at how one vendor’s MDM software looks to help companies achieve this. That vendor is Semarchy.

Semarchy was founded in 2011 by the founders of Sunopsis, an ETL product acquired by Oracle in 2006. They currently have approximately 100 customers served by direct operations in North America and Europe with partners worldwide. Their flagship product is the Semarchy Convergence Suite which runs on-premises and/or in the Amazon Web Services (AWS) cloud.

SEMARCHY CONVERGENCE SUITE Semarchy Convergence Suite consists of three major components:

• Master Data Management

• Data Integration

• Data Governance

Master Data Management Semarchy Convergence for MDM is a web based, integrated multi-vector MDM system that comes with a suite of tools to allow you to design, build, maintain and monitor master data entities e.g. Customer, Product, Asset, etc. Master data entities can be built using different implementation approaches including:

• A virtual MDM approach2

• A centralised read-only3 data hub approach with inbound data integration and outbound data synchronisation support

• A centralised read-only data hub approach with inbound data integration and outbound data synchronisation support including synchronising source systems to improve disparate master data quality4

• A centralised read / write5 data hub supporting create, read, update, and delete processing together with outbound synchronisation support

2 Also known as the Registry or Index approach whereby master data is authored, maintained and stored in disparate source systems but a virtual clean, integrated single view of a golden record (with global ID) is available via the MDM system 3 Also known as a Consolidation Hub whereby master data is authored and maintained in source systems but this disparate master data is cleaned, integrated and stored in a centralised database from which downstream systems are synchronised 4 Also known as a Co-Existence Hub 5 Also known as a Transactional Hub whereby master data is authored, maintained and stored centrally in a single database with outbound synchronisation of all changes to all systems

Semarchy are an MDM vendor whose product runs on-premises or in the cloud

Semarchy Convergence Suite enables you to build, manage and govern multiple master data entities

Semarchy Convergence for MDM allows you to build master data entities using a range of implementation approaches



Convergence for MDM includes a suite of web based tools for entity:

• Data modeling

o Includes diagramming, custom data type support, a wizard for defining relationships, model validation to detect errors and missing elements in entity models and model versioning

• Data relationship management (See Figure 8 - a graph / network view)

Figure 8

• Data profiling to assess data quality in source systems

• Data validation (including pre-built and user defined ‘plug-in’ validators to enforce completeness) e.g. validate phone numbers, address completeness…

• Data enrichment (including pre-built and user defined ‘plug-in’ enrichers)

• Data de-duplication

• Data matching (including user defined matching rules)

• Data integration (user defined data consolidation / integration rules) It also automatically generates master data applications that allow business users and data stewards to browse and search data, author new records, edit existing records, verify automatically detected duplicates and manually match or split records. In order to protect and provide personalised views of master data, Semarchy Convergence for MDM supports table views and business objects. The former defines how users and data stewards see master data and provide the ability to create on-line forms for users to create or maintain it. The latter allows master data entities to be combined (e.g. Customer and Products, Supplier and Contracts etc.) to simplify business use. Semarchy Version 4 has renewed focus on the end-user experience on both mobile device and desktop by dynamically adjusting the user interface to the device in use (Figure 9).

SemarchyConvergenceforMDMRela4onshipsView

Source:Semarchy


Semarchy Convergence for MDM provides a web-based tool suite to design, build, maintain, manage, monitor and secure multiple master data entities

It also allows you to manage relationships across entities

Semarchy Convergence for MDM allows you to define the rules needed to build integrated master data

It then generates the code to clean and integrate disparate master data from multiple underlying source systems

MDM applications can be created to allow you to author and maintain master data as well as to enable data stewards to manage the data

Master data can also be protected and access simplified



Figure 9

Workflows (processes) can also be defined to govern and standardise the validation, cleansing, inbound matching / integration and outbound synchronisation of master data as well as the creation of new master data golden records (see Data Integration below). There is also a task In-box for performing master data authoring, maintenance and data stewardship tasks so that when process workflows execute, the systems provide a managed collaborative environment to manage master data.

To ensure business trust in master data, there is support for full customisable views of metadata lineage to track integrated master data all the way back to its data sources. This includes linking to metadata in other data stores. In addition, a new Timeline capability shows a history of a master data record over time. This allows you to see the source records that contributed to the golden master data record. Furthermore, support for access security, data privacy via table views and an audit trail should also bolster trust.

Data Integration Disparate source master data cleansing, integration and matching can be designed and controlled via Semarchy Convergence for Data Integration which includes an built-in, metadata-driven, data integration integrated development environment (IDE) tool called Data Integration Designer. This allows you to specify rules to capture, clean, integrate and map disparate master and reference data from multiple data sources into the Semarchy Convergence for MDM system.

Semarchy Convergence for Data Integration ships with pre-built data source connectors and customisable templates. It supports batch, real-time and change data capture. Data integration jobs are generated from metadata specifications to expedite development and to provide agility if change is needed. Also, Semarchy generates database SQL transformations to exploit the power of the underlying database to transform and integrate the data. Support for an ELT processing approach like this boosts performance. In addition, 3rd party ETL tools can also be accommodated. Once source data is loaded into the system, generated integration jobs are triggered to validate, enrich, match/de-duplicate and consolidate this staged data to create golden master data records that can be accessed via other applications. Once data is fit for business use it can be published from within Semarchy Convergence for MDM after which it is available for access.

DynamicAutoma-cAdjustmentToTheSizeofTheDevice

Source:Semarchy

Copyright © Intelligent Business Strategies 1992-2016!Common processes can be defined to validate, clean, integrate, govern and synchonise master data

Multiple devices can be used to access and maintain master data

Users can access metadata lineage to understand where data came from

A history of master data is maintained over time & versions can be managed

Semarchy also provides a development environment to define the rules needed to capture, clean and integrate master data

Pre-built connectors and templates are provided to expedite development

3rd party ETL tools and links to external metadata are also supported

Semarchy generates the code for in-database ELT processing



Data Governance In support of data governance, Semarchy offers their Semarchy Convergence Pulse module. This is a pre-built data warehouse offering metrics and pre-built configurable dashboards available in Excel to profile master and reference data quality in source systems (including duplicate records), monitor the health of the master data in the data hub and to monitor workflow performance. Alternatively, you can use 3rd party BI tools to access and analyse the metrics available in the Semarchy Convergence Pulse data warehouse. In addition the new Inbox capability supports data steward alerts on data quality, integration job failures. Also collaboration allows virtual communities to be formed to define, maintain and govern master data entities.

CLOUD MDM In addition to on-premises deployment, Semarchy’s Cloud Master Data Management (MDM) and Cloud Reference Data Management (RDM) tools are available on the Amazon Web Services Marketplace (AWS). This includes the Semarchy Convergence Pulse module. Data is secured both at rest in the database and in motion via encrypted connections. Cloud deployment of MDM is particularly useful if a lot of your master data sources are already on the cloud. It also provides a quick, low cost way to get up and running and bridge the gap between cloud and on-premises systems in a hybrid computing environment.

INCREMENTAL AND PRACTICAL METHODOLOGY Accompanying the Convergence Suite is Semarchy’s methodology for building master data entities, associated master data services, applications and processes. This incremental and practical methodology is used in conjunction with the Convergence for MDM tool suite to significantly speed up development and introduces agility into what is often seen as a long-running process. Their methodology is based on initially using the minimum relevant master data to solve a specific business-goal such as creating a composite view of enough customer attributes to make improvements in a specific business process. A second iteration of the methodology will then add enough data to introduce further improvements to that process while also improving a second process. This agile, iterative approach is designed to ensure incremental progress is made towards governing all important attributes in each specific master data entity.

INTEGRATING SEMARCHY INTO BUSINESS OPERATIONS AND BUSINESS DECISION MAKING

In terms of integrating Semarchy Convergence for MDM into existing environments, there are pre-built web services and web service APIs available to integrate with OLTP applications and to support a Data Quality Firewall as in Figure 6. The operational and analytical system synchronisation approaches depicted in Figure 5 and Figure 7 respectively are also supported.

A pre-built metrics data warehouse is available to monitor & govern master data

Pre-built dashboards are available via Excel and 3rd party BI tools can also access

Collaboration brings the right people together to manage specific master data

Master data can be managed on the Amazon AWS cloud as well as on-premises

An incremental methodology can significantly improve agility and expedite development

Semarchy Convergence for MDM also provdes the capability to integrate with existing operational and analytical systems



CONCLUSIONS There is no question that implementing master data management can have a dramatic business impact in terms of business value. A key reason for this is because this type of data is used almost everywhere in a business. It cuts across departments, applications and all lines of business and is used repeatedly across many business processes. It is used in operational transaction processing systems, analytical BI systems, and even content/document/records management systems (e.g. to label and organise contracts by Supplier).

Creating consistent master data and synchronising all operational systems in your core business processes can significantly reduce unplanned operational costs caused by data defects. Further unplanned operational cost reductions can come using an MDM system as a component in the implementation of a data quality firewall to validate and correct inbound transaction data and by consolidating instances of applications and processes once master data has been implemented and synchronised across application databases.

In addition using MDM as a source for analytical systems to provide dimension data to data warehouses and data marts enables companies to get a consistent reporting across multiple data analytical systems. As an example, it enables companies to establish a single chart of accounts to enable better financial reporting. It also makes it possible to consolidate data warehouses on common shared dimension data to see across the value chain, improve the ability to manage the business and enable more dynamic planning. Reducing the number of analytical databases via consolidation on common dimensions, simplifies architectures, reduces the total cost of ownership and makes companies more responsive.

MDM is also central to creating a 360o degree view of a customer, which is vital to improving customer retention, engagement and growth.

Semarchy’s MDM solution is very well suited to helping companies achieve these major business benefits. The component technologies in Semarchy’s end-to-end offering are well integrated and it plugs easily into existing operational and analytical environments enabling integration with both OLTP systems and analytical systems. It also dynamically adapts to web and mobile devices enabling both office based and mobile workers to participate in common processes. It can run on-premises or in the cloud and integrate with both cloud and on-premises systems, providing centralised master data management in a hybrid computing environment. In addition, the ability to integrate virtual communities of users and data stewards in a collaborative environment providing each user with a personalised in-box for MDM tasks and alerts really allows it to pull together the right people to manage specific master data entities and deliver value across the enterprise and beyond. All of this makes it a prime candidate for selection for any company in pursuit of real business value.

Implementing MDM absolutely delivers business value in terms of reducing costs and simplifying business operations to help widen profit margins

It also improves reporting and decision making while helping to consolidate data warehouses to make it easier to manage the business

MDM is central to becoming a customer focussed business and providing a 360o view of the customer

Semarchy’s MDM offering provides an integrated tool suite and pre-built components to design build, manage and govern master data entities

Built-in collaborative capability also helps bring together the right people to deliver business value



About Intelligent Business Strategies Intelligent Business Strategies is a research and consulting company whose goal is to help companies understand and exploit new developments in business intelligence, analytical processing, data management and enterprise business integration. Together, these technologies help an organisation become an intelligent business.

Author Mike Ferguson is Managing Director of Intelligent Business Strategies Limited. As an independent IT industry analyst and consultant he specialises in Big Data, BI/Analytics, Data Management and enterprise business integration. With over 34 years of IT experience, Mike has consulted for dozens of companies on BI/Analytics, big data, data governance, master data management and enterprise architecture. He has spoken at events all over the world and written numerous articles and blogs providing insights on the industry. Formerly he was a principal and co-founder of Codd and Date Europe Limited – the inventors of the Relational Model, a Chief Architect at Teradata on the Teradata DBMS and European Managing Director of Database Associates, an independent IT industry analyst organisation. He teaches popular master classes in Big Data Analytics, New Technologies for Business Intelligence and Data Warehousing, Data Virtualisation Enterprise Data Governance, Master Data Management, and Enterprise Business Integration.

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