gluedata01

[gluedata01]

8 Factors Contributing to Data Migration Success in 2022!

Data migration is a process of transforming Data between storage systems, computers, and data formats. Data migration is a core task for any data professional, and it is a difficult and risky process. Data loss could be bad for your business. This process takes a lot of time, planning, better strategy, and implementation. So finding a company with Data migration services is a must! GlueData is one of the best companies you can hire for Data migration. The company has a team of data analysis experts, and data archiving specialists. They will assist you with sap data migration, data migration services, sap data solutions, data quality management, master data management services, Data Quality Management, and data integration services and solutions. Isn't it great to get various services and data management tools under one roof?

According to our data migration experts, there could be 8 factors that will lead to data migration success in 2022! 1. Engagement: At every stage of the migration, ensure that the "Business" is engaged. The more business is engaged, the more chances of data migration will be successful. 2. Rules: Ensure that you understand and document business rules, which may be used to determine the complexity, scope, effort, and associated risk. 3. Data Standards: Define data standards clearly, so that, they can be used to determine overall data quality and to determine complexity and scope. 4. Data Quality: The migration becomes more complex and risky if the data is bad. Prioritize data quality, enrichment, and cleansing as the first steps in the data migration process. 5. Timeliness: Data must be available to prevent delays in testing and validation, as well as potential disruptions to the system. 6. Complexity & Scope: Ensure that you are aware of the difficulty and scope of your migration project. Complexity and scope creep are factors that impact resource allocation and increase risk. 7. Risk & Management: Compile your document thoroughly. Project success is directly affected by effective risk management, which enables proactive and nimble response. 8. Test & Validation: Ensure data quality, mitigate risk, and clarify overall complexity through effective and routine testing and validation. These are the major factors that will help you in the Data Migration process. For better understanding and services you can reach out to our data analysis expert. The company is offering its services in South Africa, Europe, the UK, and the USA. Get in touch with them now!

[gluedata01]

Guide for Beginners To Understand Master Data Management (MDM)

Master Data Management is an information management system, that offers opportunities for data quality management experts and master data governance experts. Master Data Management is a need for creating strategies for data quality management and toolset.

Let's understand what is MDM? Experts define MDM in various ways and it is up to you which expert's definition you understand better. Here are some prominent definitions by experts: 1. “MDM is the practice of defining and maintaining consistent definitions of business entities, then sharing them via integration techniques across multiple IT systems within an enterprise and sometimes beyond to partnering companies or customers” — Philip Russom PhD. Industry Analyst, TDWI 2. “MDM is a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets. Master data is the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise including customers, prospects, citizens, suppliers, sites, hierarchies and chart of accounts.” — Gartner Why Master Data Management is becoming sought after? The following points will help you to understand the reason behind MDM's popularity. 1. The Impact of MDM issues on the Businesses - Master data is among the most important data that an organization holds, so it is imperative that problems of the past be fixed; minor errors in master data can lead to viral problems when propagated across a federated network. In the last decade, enterprise MDM has gained significant recognition for its ability to differentiate businesses. 2. Escalating Complexity and Globalization - A Master Data Management system goes right to the point of why Information Development is necessary. Organizations are becoming more and more federated, with more information and integration on a global scale than ever before. A successful approach relies on reducing complexity. From a data management perspective, globalization created a variety of additional challenges. It also includes issues relating to multilingual and multi-character sets, as well as a requirement for 24x7 data availability from global operations. 3. All Sides See a Major Opportunity - System integrators and product vendors can take advantage of MDM as it is a big, complex problem. Data hubs, which are part of MDM, have been developed as a new MDM technology. Each information management vendor has a strategy for solving the problem, and application-centric vendors (which started the MDM trend) see this as a new area of opportunity for integration and applications. Organizations with MDM issues are also taking a similar approach: confronting a variety of information management challenges provides them with a clear framework for framing the issue. What Challenges does Master Data Management Present? The following key points can help you to understand the challenges presented by MDM. 1. It is often difficult for people to decide where to start, prioritize, and focus 2. A lack of information governance (stewardship, ownership, policies) leads to high levels of complexity across an organization 3. The problem of domain values is especially challenging when they are stored across a number of different systems, especially product information 4. In many large organizations, customer data is stored in multiple systems across the enterprise, resulting in a high degree of overlap in the master data. 5. Many organizations have complex data quality issues related to master data, particularly for customers and address data from legacy systems. About GlueData: GlueData is an independently owned and managed company. The company is a global data and analytics consultancy that assists SAP clients in mastering their data. The company provides the best data management tool, data management tools, data migration services, and master data management services with help of their data migration consultants and experts. Our SimpleData Management methodology is aimed at reducing the complexity of data governance by focusing on what is most important from a data domain or objective point of view.

[gluedata01]

The Role of Data Quality Management in Master Data Governance

Large organisations have understood the value of data and data quality for the last few years. In recent years, leaders in the private and public sectors have realised that accumulated data can be a strategic asset when data volumes and speeds are increasing. 

When people discuss corporate data, we have heard words like “Master data governance” and “Data Quality Management”. These two words are closely related to the need for Data Management. Let’s understand what is the meaning of both? And how are they connected?

What is Data Quality Management?

Data Quality management is a principle that needs an amalgamation of the right people, processes, technologies, and the common goals of enhancing data quality. Improving Data quality is not the only purpose of DQM but achieving good business outcomes depends upon high-quality data. Data quality management ensures that confidential information of businesses is accurate, complete, and consistent across multiple domains within the enterprise. 

What is Master Data Governance?

Master data governance provides a framework through which businesses manage their data within the organisations, certify its security, and make sure data can be available to the people who need it.  In other words, we can say that Master data governance means controlling security and regulatory risks and maximising the value of the organisation's data.

How Master Data Governance and Data Quality Management are Related?

The goal of Data Quality Management and Master data governance is to ensure that the data within the organisation is trusted, secure, and accessible when needed. Businesses can make sure that the value of their data assets is maximised by creating a strategy around these two essential pillars. Data Quality Management and Master data governance could be seen as two sides of the same coin. 

About GlueData: GlueData is an independently owned and managed company. The company is a global data and analytics consultancy that assists SAP clients in mastering their data. The company provides the best data management tool, data management tools, data migration services, and master data management services with help of their data migration consultants and experts. Our SimpleData Management methodology is aimed at reducing the complexity of data governance by focusing on what is most important from a data domain or objective point of view.

[gluedata01]

The Dirty Data Dilemma (and how to avoid it) by GlueData

GlueData has a team of SAP data specialists that helps global SAP clientele master their data with the help of our data management tools, services and solutions. We pride ourselves because we provide outstanding results to our clients across the globe. This week we are covering the topic "The Dirty Data Dilemma and how to avoid it". Read out our entire blog.

Data hygiene isn’t easy, but it is an essential part of your business process, and if neglected it can cause unthinkable damage. On average, dirty data is the data that is inaccurate, incomplete or inconsistent and costs a business 15% to 25% of revenue, and the US economy over $3 trillion a year. Quite simply, with bad data, you’re leaving money on the table.

Data informs nearly every facet of industry decision making, deeply affecting our lives - from why groceries aren’t selling and how people move through airports to the unique order of our Netflix feeds, and the music Spotify serves. If you consider that companies around the world believe 26% of their data is inaccurate or corrupt, or that only 16% of business executives are confident in the accuracy that underlies their business decisions, the dilemma of dirty data (or rather the missed opportunities) becomes crystal clear.

In addition to the revenue loss, dirty data impacts businesses in more dangerously subtle and stealthy ways. When you can’t rely on your own data, something quickly needs to be done to increase your data accuracy and reliability.  

What makes data dirty?

Human error accounts for more than 60% of all dirty data – which should come as no surprise. The human brain is simply inept at mastering fault-free manual inputs. The other 40% is a combination of inaccurate records and poor data strategy, which often circles back to human error anyway.

Dirty data is also commonly the result of departmental miscommunication – different teams feeding the system with related data from separate siloes, without cooperation or internal data logic. It’s the old adage of the left-hand vs the right-hand. Internal bureaucracy can mean dirty data goes unchecked or unnoticed for years. In fact, it’s reported that over 57% of companies only discover dirty data when it’s reported by the customer or prospects.

How can you get it clean?

Data cleansing or data cleaning is the (often painstaking) process of detecting and correcting corrupt or inaccurate records. This involves identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, editing, or deleting the dirty entries until you’re left with crisp, clean, useful data.

The challenge is, that there are many tools that can identify the source of your dirty data, but few can actually fix the problem. As SAP data experts and official Build Partners, we realised that our clients needed a solution within the SAP framework that identified their data challenges and solved them simply, affordably and permanently. So, we built one.

Welcome a revolutionary in-SAP tool that cleans your dirty data

SimpleData Management (SDM) is a master data management tool embedded within SAP. It’s the first product of its kind to rapidly identify and solve your data challenges, proactively setting up structures to prevent them from reoccurring.

One of the most comprehensive SAP data solutions, SDM offers in-built data governance and management that’s simple, affordable and sustainable. It makes dirty data clean and drives a culture of high-quality data in your organisation.

Understanding dirty data isn’t just helpful, it’s essential. The good news is anyone can master data management with the right data management tools. Explore our best in class tool.

[gluedata01]

GlueData awarded SAP Gold Partner Status

As of 1 July 2020, GlueData Services has been awarded SAP Gold Service Partner Status. This is an extension to the previously held Silver Service Partner Status and is in addition to the Recognized Expertise Partner status for EIM (Enterprise Information Management) that was awarded to GlueData in 2017.

“We have come a long way and achieved much success, thanks to the commitment of our amazing employees, and the strong partnerships with our customers and consulting partners” states Brett Schreuder, Managing Director of GlueData Services.

“Nearly a decade ago, myself and Malan Barnard recognized that data is the glue between system configuration and the processes that make a company profitable, and this was the concept that led to the founding of GlueData. We had a clear vision for the company and our role in the SAP industry and are grateful that South African Customers trusted a fledgling business. From our very first customer, we have aimed to meet and exceed expectations.” says Brett.

Malan and Brett founded GlueData in 2011, and shortly after, hired Ben Strydom as their first employee. Ben is now a key member of the Executive Team, along with Jonathan Leenstra, Paul McCormick, Kathy Carrick, Jaco Boshoff and most recently Etienne Hefer.

“GlueData is a dynamic team, with a common passion for SAP data. We number nearly 70 consultants, working in South Africa and around the globe, and are positioned as market leaders in SAP Data Solutions, Services and Products.” continues Brett

“Our solutions and expertise have matured to cover the SAP Data lifecycle including Data Quality, Data Management, and Data Migrations & Integrations, with a focus on S/4 HANA and Data Governance. In recent years, we have also started delivering technically complex BI solutions and SAP Data Archiving services. We also have South Africa’s largest team of skilled and experienced SAP MDG (Master Data Governance) consultants and we are seeing growth in the success of this SAP application across many industries” states Malan Barnard.

“We believe our success is due to the fact that we have not diversified outside of the SAP Data arena and remain focussed as a niche solution provider, partnering closely with SAP.  This is why it is so rewarding to achieve this highest level of SAP partnership” continues Malan.

“In addition to our proven solutions, we are also launching Version 2 of our cornerstone product SimpleData Management (SDM) in the next months and we look forward to sharing our success with the industry as we begin implementing the new version to assist customers to improve their data quality throughout the data lifecycle,” states Malan

“Whilst we have invested heavily into building sustainable, efficient and cost-effective solutions and products, the primary factor that has led to our success is the values and culture of the company. The culture is one of knowledge sharing, caring & assisting one another, being passionate about the subject area, and finally, as a result, adding value to our customers and their objectives” concludes Malan.

To find out more, contact GlueData.

GlueData Boilerplate

GlueData was founded on the principle of achieving quality data, knowing that data drives the process and process drives profitability.

GlueData is a leading SAP Data Solutions company and has proven rich methodologies, skilled consultants and relevant implementation experience to rapidly deliver sustainable value to SAP customers. The company started in 2010 and has offices in Cape Town, Johannesburg, Durban and Belgium that provide SAP Data Solutions and Products to customers of all industries around the globe.

[gluedata01]

Data Preparation before Data Migration - GlueData

Data Preparation can be seen as the practical pre-processing of certain data for a specific purpose such as a new implementation, business transformation, system conversion (upgrade) or migration.

A Data Migration Strategy is vital in establishing the scope, principles, responsibilities and approach for the migration. Typically, this strategy would speak to the traditional “E-T-L” process whereby data is Extracted, Transformed (validated, cleansed/prepared, transformed) and then Loaded to the target system in one continuous process. However, the variability and validity of the data being migrated can cause serious issues and is usually one of the biggest threats to the success of a Migration project.

“Many projects only identify the effort and complexity involved in preparing the data mid-project when it may already be too late.” States Ben Strydom, Account Executive at GlueData Services.

Data-readiness is always a key factor affecting project risk (timelines and delivery). Data ownership (integral to driving data cleansing) may be poorly defined or non-existent meaning the identification of requirements and implementation of relevant measures to fix adds complexity and additional burden to business and project resources.

Furthermore, the full effort and complexity of data cleanup for migration are always relatively unknown until the final design is bedded down.

“Ultimately, data of poor quality can technically be loaded to a system, but the accuracy of said data can be critical to a business’s processes. This results in unnecessary and increased risk which would add to business overhead (the cost to rectify) and could even halt key operations indefinitely

Generally, the appetite for such a Data Preparation initiative is low due to the perception that only short-term benefit is achieved but imagine going live with a new system having loaded bad quality data, which causes critical process fall over at the same as you are trying to improve data quality!” continues Ben

GlueData recommends:

Shifting the bulk of the activities related to the preparation of data from the Transformation phase of the migration project into a separate stream or project, which starts before the Migration project.

Incorporating Data Preparation elements into the organisations Data Quality and Data Governance initiatives for long-term gain.

Data Preparation should start as soon as possible after the decision has been made to move to S/4HANA to have enough time for the design, implementation and execution of the majority of the Data Preparation activities before Data Migration project dependencies occur.

The key principles recommended for Data Preparation are as follows:

Data Cleansing is a highly iterative process. The earlier one starts the more iterations can be completed. This should result in much improved Data Quality at the time the Data Migration Realisation phase starts.

The full scope and effort for Data Preparation are usually unknown at the outset. By starting with SAP Standard and Best Practice requirements (which are technically known) the majority of the scoped objects can already be addressed immediately.

Technical Readiness speaks to system configuration, code and other mechanical components and technical readiness assessments are typically concerned with ensuring a technically sound conversion.

Data Preparation emphasises Data Readiness and should be executed in conjunction with Technical Readiness Assessments to obtain deeper insight into conversion requirements which includes the dependencies between data and the system configuration.

There are various means of specifically limiting the scope, during Data Preparation. The first step is to define what constitutes Business- & migration-relevant data (“Data Relevancy”).

By focusing only on relevant data and excluding irrelevant data the volumes and effort are continuously managed.

Some Organisations may already have mature Data Quality and Data Governance solutions. In such cases, the Data Preparation stream should incorporate these solutions to speed up the processes of defining data ownership, cleansing approach, and the like.

Data Owners are responsible for the data in a specific system, process, sub-set of data, Data Object, Business Area or module, etc.

Their responsibilities include controlling the Data Quality of Legacy data and will naturally extend to the same areas of control on the new S/4HANA system. Therefore, they must also own all the Data Preparation and Data Transformation steps applied to the data in between these systems.

There is no point in developing a Cleansing approach for Data Preparation without being able to assign the task of making the changes.

During Data Preparation, the necessary cleansing of data must be executed within the Source system to ensure a single source of truth for the relevant data during Data Quality, Preparation, Migration and general Governance. Having one standardised source for relevant data will ensure lower complexity in terms of consolidation, design and even reconciliation.

Due to the fact Data Preparation deals with pre-processing of data before the Data Migration project is ready to execute this processing may result in overlap of timelines between Data Preparation and Data Migration.

By running Data Preparation in parallel this allows for the gradual transfer of relevant activities and solution elements onto the migration project at a pace the project can absorb.

Alternatively, the Data Preparation can be run as an independent stream (perhaps within the migration project once it starts). In this case, more emphasis should be placed on properly planning the integration between Data Preparation and Data Migration.

The DPL will expand to integrate the Data Migration Landscape when the Data Migration project kicks off. This reduces the need for additional applications, systems, licenses and so forth. It also allows for visibility and seamless technical integration between the two landscapes.

By using this recommended approach to Data Preparation the organisation can materially reduce the risk and effort during the Data Migration project. But there are numerous other possible benefits to be realized:

During the Data Preparation process, the organization can leverage the learnings and insights for both Project and Business-as-usual benefits.

Planning stages for the Data Migration project are improved through insights as to how data is used within existing Business Processes (as well as how these will change moving to S/4HANA). Recognising the Target system requirements, for example, allows Business to gain understanding about what life will be like post-migration and to implement the right processes and solutions in preparation thereof.

Architecture and solution design will be clearer as a result of these insights which, by extension, may reduce fit-gap requirements later on in the project.

Data Preparation, in general, improves the likely success of the migration project in multiple ways.

Reduce complexity and ‘noise’ across project phases

Early adoption by business

Reduced risk to project timelines and delivery by moving Data Preparation activities (effort) out of the migration project

Use Data Relevance to further manage scope and volume

Clarify dependencies between Data Quality & Data Migration

Improved integration planning and visibility of data impacts & dependencies

Greater agility to respond to scope changes (e.g. new data sources & business requirements)

Training (the ‘human factor’) cannot be overlooked and should also be addressed early on to support adoption & ownership of the new systems and processes. Ideally, the relevant resources are included as stakeholders and recipients of training during the project.

Rules, Data Quality requirements and Data Ownership enriches the various (ongoing) Data Governance solutions and principles.

Reduced overhead in rectifying issues

Maturity in the understanding of data requirements for critical processes

Clarity of design for future data control & management solutions

Reduced data cleansing requirements (continuous improvement)

Enriched rule sets for Data Quality/Data Governance

GlueData Boilerplate

GlueData is an SAP Service Partner and an SAP Special Expertise Partner in Enterprise Information Management (EIM). The company is founded on the principle of achieving quality data, knowing that data drives process and process drives profitability.

GlueData has proven rich methodologies, skilled consultants and the relevant implementation experience to rapidly deliver sustainable value for our customers. The company started in 2010 and has offices in Cape Town, Johannesburg and Durban. GlueData provides SAP Data Solutions and Products to customers of all industries within South Africa and across the globe.

[gluedata01]

Data Cleansing made Simple using SimpleData Management

Companies will never truly eliminate data quality inconsistencies. But the goal is to get the volume to a manageable and sustainable level, acceptable to the Data Governance Team.

“The largest challenge is bringing governance and control to master data. From daily management to Stewardship, to reporting, Executives are confronted with twin challenges. To gain control and keep control.” States Brett Schreuder, Managing Director at GlueData.

A typical approach is to periodically prepare business cases and then deliver on long data cleansing projects where data is extracted in order to apply rules, only to enter the data back into SAP.

Many companies train data specialists or implement a Centre of Excellence and a set of policies, business rules and controls to ensure high-quality data.  Where they struggle, is the ability to find a fit for purpose master data management application that is capable of ensuring high quality data without overcomplicating the Data Steward’s daily functions.

Let’s simplify this, with SimpleData Management.

SimpleData Management, or SDM, is a multi-domain, embedded SAP application that requires no interfaces or specialist IT skills.

To control a new Domain or Data Object, SDM is simply turned which results in role-based changes to the SAP screens based on business rules.

It’s features include Screen Control, Data Stewardship, Point of Entry validation and derivations, Data Quality reporting and the Data Sprint Solution.

SDM integrates SAP Business Rules Framework Plus (BRF+) into its core components and functionality so that, when deployed, business relevant rules, defined per object, validate data at point of entry, when new data is entered or existing data changed. Fields can also be derived and screens tailored by role and user. More importantly, existing data is also validated against the same Business rules.

SDM has reports that provide visual representation where the data fails the rules. This failed data is linked to Data Stewards who are responsible for the maintenance of the data.

Stewardship in SDM is a role-based method that matches fields in the SAP Data Object to Stewardship roles in SDM. (This is not related to SAP authorisations or roles, but it is an SAP SDM configuration which SAP authorisations will supersede if there is any conflict.)

The Data Sprint Solution provides the ability to measure and manage data cleansing by assigning users to cleanse defined sprints of field data.  It provides a mechanism for the Data Steward to plan for a Cleansing Sprint in the following ways:

Which SAP Data Object do I want to base the Cleansing Sprint on (Material, Vendor etc)?

Which Stewardship Role is applied to pull up those related field exceptions?

How many exceptions do I want to include in the Cleansing Sprint?

How long do I want the Cleansing sprint to run?

When do I want to start the Cleansing Sprint?

Which Data Stewards will perform in the Cleansing Sprint?

Display some key metrics.

When the Sprint is published, exceptions will be assigned to each of the Data Stewards who will then use the Sprint ID generated to resolve the exceptions assigned to them.

The Sprint Manage transaction, provides valuable information about the assigned exceptions:

Status indicating that the message has been resolved when green and outstanding when red.

Status indicating if Auto Propose is possible.

Icon indicating if field can be updated in with MASS transaction. This is configurable in IMG, allowing customers to choose which fields SDM can mass update, reducing the risk of mass updating in error.

Auto propose value, derived from the Sprint Manage message (this can be overwritten if required).

Data Stewards will have 4 options to resolve the exceptions assigned to him or her:

Right clicking on the message and selecting the update field option.

Selecting multiple messages and updating using the Process Multi Auto option.

Creating an excel file, which can be edited if required, to then be used in the MASS transaction, but accessible via the SDM Sprint Manage Screen, which provides all the feedback and statuses of updates for the Data Steward.

Right click on the message and enter the change transaction to manually fix the message in the object.

“SDM Sprint Solution embeds typical data cleansing projects into the Data Steward’s daily routine, eliminating the need for expensive projects that remove key people from business.  We at GlueData believe this approach to data quality and data management puts the power into the Data Steward’s hands and the transparency and measurement into the hands of data management team. The number of errors is immediately recognisable and the approach and speed to cleanse the errors can be planned and managed as part of the daily workload.” concludes Brett.

GlueData Profile

GlueData is passionate about data. We are founded on the principle of achieving quality data, knowing that data drives process and process drives profitability. We also provide data migration services, data integration services, master data management services, and make sure to offer you the best data migration tools.

We are a dynamic company, with a proven track record in delivering sustainable value to our customers operations and bottom line through data.  We utilise SAP Applications together with unique data management tools, services and methodologies developed by our team of highly skilled and experienced consultants.

When changing your SAP landscape, we enable the migration and integration of data. While running your business, we support your data management and governance.  From rapid data assessments to long-term projects, we help resolve data challenges, achieve data accuracy, and enable informed decisions, so that you can achieve success through data.

GlueData started in 2010 and has offices in around South Africa and in Europe.  We provide solutions to customers of all industries across the globe.

[gluedata01]

GlueData Takes on BI: A Step Towards Master Data Management Solutions

With eight years of expertise in SAP Master Data Management, Data Integration and Data Migration, GlueData has been a prominent figure in the South African market. “Data is our passion, and we know that data architecture is key to everything from the data source, through data lineage to reporting. The effectiveness of a companies’ data is driven through the underlying data architecture and not the frontend reporting” States Brett Schreuder, Managing Director at GlueData.

With its new Business Intelligence solutions, GlueData offers a full range of services and solutions. “BI is possibly one of the most contentious, and “unloved” topics for many of our clients. We find that discontent is often founded in customers’ high aspirations of the value of data (as reported in multiple BI whitepapers) and their subsequent experiences of non-existent or underwhelming analytics or the high cost of BI, to mention but a few reasons.” Continues Brett. GlueData knows Business Intelligence and Quality Data go hand in hand. Despite the symbiotic nature of these two areas, they require at a minimum: 1. BI and data must be supported by a well-defined, shared architecture. 2. Understand industry verticals or specific business processes to assist with data conversation; convert data into meaningful information (KPI's); and leverage data to provide sound analytics - be it descriptive or prescriptive. 3. An understanding of data management and BI principles. Our belief is that the tools and technology should influence the solution, not drive it. GlueData is an SAP partner and primarily SAP-centric. In order to support the need for optimum data management, we see a clear need for the end-state BI landscape to integrate other suitable technologies. In designing and delivering business intelligence solutions, all factors such as agility, cost drivers, and appropriate local skills are considered. “Through our newly established BI competency, GlueData will now provide a more complete service which is centred around our core skills in Data, Master Data, Data Quality, Modeling of Data, Data Integration, Data Migration and the Management of Information culminating in Data Analysis and Data Insights.  We look forward to discussing the complete data landscape with our existing and future customers.” Concludes Brett. To know more about our services such as sap data solutions, s 4hana data migration, etc. Kindly visit our website!

[gluedata01]

Introducing SimpleData Management and Data Quality Management from GlueData

In todays’ business, data drives processes and processes drive profitability.

The smooth running of any process is impacted by the system data that the process utilises. Research from Experian shows that inaccurate data has a direct impact on the bottom line of 88% of companies, with the average company losing 12% of its revenue as a result (https://econsultancy.com/the-cost-of-bad-data-stats/).

Poor quality data adds costs and slows down processes. For example, in the Order to Cash process, the incorrect unit of measure will create an incorrect sales order and an out of date customer address will result in delays in delivery. In the Procure to Pay process, too much is spent on purchasing and incorrect products are returned.

Increasingly important, with the introduction of GDPR and POPI, incorrect or out of date data will lead to lack of compliance and will become subject to fines.

Ultimately, poor data results in poor deliverables, poor information and poor decision making. Time, resources and opportunities are continually lost. “The best way to ensure good quality data is to validate data when it is entered in the system whilst also retroactively fixing data already in the system. To do this, you need a clear set of rules as well as timely and accurate reporting.” States Brett Schreuder: Managing Director of GlueData.

GlueData’s SimpleData Management (SDM) does exactly this for data in your SAP system:

• New data is verified against rules and entry is simplified by highlighting, hiding and greying out fields. • Existing data is highlighted through exception reports and cleansed systematically in sprints. • Workflow is used to approve new data captured. • Future plans will ensure duplicate master data is prevented and many other additional features.

“SDM will ultimately become a single solution for integrated data management and data governance. Data management with Point-of-Entry control, workflow, data exception reporting and sprint cleansing. Data governance will have tangible and measurable capabilities where the business has an accurate role and KPI reporting that ties data governance into the organisation and does not operate as a top-down and bottom-up approach to data, but rather an integrated and single view of data management.” Concludes Brett.

GlueData is a company that is serving customers since 2010 globally and providing the best data integration solutions, data migration services, data migration tools, data migration consultants with the help of data analysis experts and data archiving specialists. Visit the website for more information!

[gluedata01]

Data Migration into SAP S/4 HANA: Defining the Strategy

Moving to SAP S/4 HANA is no easy feat - whether customers have been running an older version of SAP or a non-SAP legacy system and regardless of which implementation or upgrade method is chosen. This is because the way customers do business has changed and therefore people, processes, and technology are forced to change as a result.

Business has become digital and not keeping up means that customers can be swallowed whole by hungry competition. IT budget is therefore focused on enabling all the good, exciting things that the Executive Committee variously are after - Automation, Artificial Intelligence, Machine learning, Digital Supply Chains and true eCommerce This is all good and well, but focusing on the prize without focusing on the foundation and lifeblood of the system, i.e. its data, can cost a customer dearly.“I have been called into too many meetings recently where data has been the main issue preventing a successful go-live for a customer that has embarked on a new or Greenfields implementation of the latest ERP from SAP, SAP S/4 HANA“ states Malan Barnard – Director at GlueData Services“The meetings all have a common theme with complaints such as: ● We have not been able to do a successful mock data load yet and we are already in the UAT (User Acceptance testing) phase of the project.

● We thought S/4 HANA came with a toolset to load data and that would be sufficient.

● We never expected data to be our challenge and we started looking at the data too late.

● We did not expect such a big effort for data clean-up as we were doing a system conversion.

● We made too many assumptions and never executed our test cycles with migrated data. Now that we have, a whole can of worms has been opened causing massive project delays.The list goes on” continues Malan.

The planning stage of the SAP ERP Implementation is the best time to make the data topic a relevant and focused stream - the earlier the better. A sound strategy is required that, at the very least, outlines the following:

● The SAP Data Migration approach for ERP and other systems (e.g., BI). For example, for a Greenfields implementation, defining the archiving and data resolution approach.

● The reconciliation approach (recon techniques can for example range from field by field comparisons to spot checks), figuring out what makes business sense for each "data object" (e.g. Customer Master) is key.

● The project phases which outlining the number of mock cycles lined up to the project plan together with entry and exit criteria.

● The systems landscape covers tooling and technical approach (i.e. which systems to load which mock into).

● The Data Migration Scope: an inventory of all data to be migrated; the source of the data, target, data owners and what data needs to be migrated, archived, or stored in Near line Storage.

● Roles and Responsibilities - defined especially if the implementation team is separate from the data team.

It also does not help to implement SAP S/4 HANA with good quality data without a strategy for keeping the quality standards after go-live. A master data management strategy should therefore also be implemented, and as with the normal business processes, master data processes should also be defined. Data Governance 

Contact GlueData to find out more about offerings.

capabilities should be established so that there is a framework of policies, rules, processes and roles defined to enable the business to operate successfully post-go-live.

Look out for future articles where Malan will dive into some of the technical elements and options available when performing the data migration.

GlueData has proven rich methodologies, skilled consultants and the relevant implementation experience to rapidly deliver sustainable value for our customers. The company started in 2010 and has offices in Cape Town, Johannesburg and Durban. GlueData provides SAP Data Management Solutions and Products to customers of all industries within South Africa and across the globe.

GlueData Boilerplate

GlueData is an SAP Gold Service Partner and an SAP Special Expertise Partner in Enterprise Information Management (EIM). The company is founded on the principle of achieving quality data, knowing that data drives process and process drives profitability.

[gluedata01]

S4HANA Data Migration | Data Migration Tools - GlueData

No matter the implementation method, SAP's 4hana data migration and data migration services are no easy feat - regardless of whether customers run an older SAP version or a non-SAP legacy system. This is because the way customers do business has changed and therefore people, processes and technology are forced to change as a result.

Without keeping up with the digital transformation, businesses can lose customers to hungry competitors. As a result, the IT Budget aims to enable all the exciting, good things that the Executive Committee is generally seeking - Automation, AI, Machine Learning, Supply Chains and real eCommerce. Although this is good and well, focusing only on the prize without observing the foundation and lifeblood of the system, i.e. its data, can prove costly for customers.

According to Director at GlueData Master Data Solutions and an SAP data expert Malan Barnard states "I have been called into too many meetings recently where data has been the main issue preventing a successful go-live for a customer that has embarked on a new or Greenfields implementation of the latest ERP from SAP, SAP S/4 HANA"

  A relevant and focused data topic should be a part of the planning of the SAP ERP implementation - the earlier the better.

A strategy for keeping the quality standards after go-live is also necessary when implementing SAP S/4 HANA with good quality data. Therefore, a master data management strategy should also be created, and the master data process should be defined as with the normal business processes. An effective Data Governance capability should be established so that policies, rules, processes, and roles are in place to enable a successful post-go-live operation.

Keep an eye out for Malan's next article, which will cover some of the technical elements and options available during the data migration.

Visit our website for more information!