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Data Migration: How to overcome challenges during the migration process?

Every IT organization regularly faces the challenge of having to migrate data. Whether from the old memory to a new one, or straight to the cloud. A data migration is more than copying from A to B, especially due to increasing amounts of data and systems that are becoming more complex. Companies must ensure that data migration to a new storage system works smoothly before getting into the entire migration process.

As data continues to grow in importance, data migrations become top priority projects. A failure of migrations can lead the organization into a disastrous situation. For a successful migration, there are many factors to consider. Das these process are complex and require a high level of expert knowledge is required to ensure they go smoothly. Organizations that identify potential problem areas before the migration and plan accordingly ahead of time save time and money on their migration projects. Here below is a list of few but most common data migration problems.

 

Poor data management: Without a holistic overview of your data, it will be challenging to migrate it in a planned, correct and fast manner.In the absence of regular data management, the data lays in an unstructured manner across different systems – and leads to loss of production because important information is either  hard to access or no longer available. Poorly managed data is also expensive because those responsible are constantly expanding memory in an uncoordinated manner. As the migration of grown databases can consume a lot of time and money, it is highly recommended to clean up the data structure in advance to get rid of legacy issues and to optimize storage costs.

 

Manuel migration: Manually migrating an organization’s huge sets of data takes a lot of time and energy & resources. In particular, when data accumulated over decades have to be processed, manual migration is impossible and error-prone. As a solution, company can anticipate automating repetitive parts of migration with advanced migration tools. Not only process automation saves time and money but also increases the quality and data security.

 

Changing the storage system:  The problems with storage migrations increase when an IT administration wants or has to migrate not just to a new array, but to a completely new system – for example when changing providers or when a contract ends. The new system must then be set up in such a way that it behaves exactly like the old one. Otherwise due to the incomplete integrated into the overall IT infrastructure and cause issues.The team responsible for the migration must configure the target systems thoroughly and carefully before the migration.

 

Recognise and avoid copy errors through the verification process::  Project leader responsible for migrating data wants to be sure that it will arrive correctly at its destination. Incorrect coding of directories that are not displayed correctly can cause incorrect copying of the data in the storage systems involved. The log files of the migration, which many responsible persons use for the verification, cannot sufficiently confirm the correct migration, since they ignore possible weaknesses of the copy tools.

Instead, a modern verification tool can scan data, files and folders including metadata at the source and destination and compare them without further downtime. In this way, errors can be found and addressed. The verification of the data after migration using an independent tool with its own algorithm is essential. It is even mandatory in many sectors. The verification tool also documents the data status at the source and destination at the time of the switch, including the rights, so that the complete migration can be proven afterwards.

 

 

Migration are complex projects and require professional analysis, planning and implementation. The companies that have already set up professional data management have an advantage. If you have not yet implemented data management, or if you lack manpower, tools, know-how and expertise, it can be worth seeking advice from experienced data and migration experts or hiring them for both planning and in implementation. Feel free to talk to our experts about your existing or upcoming projects, we’ll be happy to help!.

Data Management: Cost of poor data quality

Organizations are collecting and generating more information/data than ever before. This information/data is used in almost all activities of companies and constitutes the basis for decisions on multiple levels. But, simply having a lot of data does not make a business data-driven, because issues related to data quality maintenance are infecting numerous businesses. Companies are witnessing that not only the data is growing rapidly in scale & importance but also in complexity. The topic of data quality and what companies should do to ensure a good level of data is one of the biggest priorities within companies that are always being worked on. Since poor data quality affects, among other things, business processes, it can lead to wrong decisions and make it more difficult to comply with laws and guidelines (compliance).

 

Organizations around the world gather so much data that sometimes it’s impossible for them to differentiate the valuable and outdated or inaccurate data. Studies have also shown that the data stays stuck in different systems in inconsistent formats, which makes it unreliable or impossible to share with other team members. According to Gartner’s research, “the average financial cost of poor data quality on organizations is $9.7 million per year.” In other words, the cost of poor data quality is 15% to 25% of revenue.

MASTER DATA MANAGEMENT

Having quality data means getting the right answer to every question. This requires that data is constantly checked for errors, redundancy, and usability. In addition to avoiding errors and gaps, it is also about making data available to every concerning person in a uniform way and making it as easy to use as possible. Master data management (MDM) helps companies to ensure that their data is accurate, trustworthy, consistent, and shareable across the enterprise and value chain by enabling greater data transparency & empowering you to drive better decisions, experiences, and outcomes that benefit your business and your customers.

 

Basically, master data management creates added value on two levels: on the one hand in the administrative areas, for example through more efficient master data maintenance processes or also in IT projects; on the other hand, through increased transparency in the operational areas and thus improved controllability. The benefit in mastering data processes is reflected, among other things, in the reduced effort involved in searching for data, less internal coordination effort, and the fact that there is no duplication of work when changing data or making initial entries. Furthermore, clean master data forms the basis for scalable automation options and reduces the effort for migrations.

 

Mastering your data challenges also delivers a significant competitive advantage. And as the pace of innovation accelerates, the importance of mastering your data will only be beneficial for your business. The benefits of MDM in the administrative and operational areas as well as for compliance ultimately increase the competitiveness of companies. Last but not least, good data quality ensures the satisfaction of customers, suppliers, and employees.

2021: Intelligent Data Management Will Enable the Future of Your Business

2021 Intelligent Data Management Will Enable the Future of Your Business

The EU’s GDPR has a major impact on the data privacy ecosystem. The regulation is an essential step to strengthen individuals’ / Business fundamental rights in the digital era we are living in. After two years of the introduction of the GDPR, the following question still arises: What will 2021 bring in terms of data management and data protection? According to Gartner, by 2023, 65% of the world’s population will have its personal data covered under some kind of modern privacy regulations.

 

It’s predicted that the technology for the preparation, control and administration of data will become much more efficient so that data is available more quickly and reliably. With the focus on foundational components of data integration, data governance, and data preparation the effectiveness of big data projects can be improved. With the right technology, data management can also drive enormous business value and support digital transformation. It’ll certainly help organizations to better manage the availability, usability, integrity, and security of their enterprise data.

 

Data has evolved over the years and will continue to evolve. Today’s organizations are data-centric; they accumulate enormous amounts of information in many different formats. Those who are unprepared to deal with the amount of data will be left behind compared to those ready to welcome all business opportunities that big data has to offer. Here below are 5 main areas that play a huge role in the good preparation of data management.

 

  • Data orchestration

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A frequently used term in the sales and marketing domain for whom data has a high priority as their data is the foundation of just about everything, they do. Simply put, data orchestration is the automation of data-driven processes that includes data preparation, making decisions based on that data, and taking actions based on those decisions. Data and API integration and data movement need to grow together to support all kinds of DataOps (data operations) methods. It’s a process that often spans across many different systems, departments, and types of data. It also requires a combination of different technologies that ensure a central data flow. This is the only way to orchestrate data-related activities – across different locations, on-premise or in the cloud.

 

  • Data discovery

In this process, relevant data insights are uncovering and transferred to the business users who need them. A comprehensive directory for searching, making available, saving, and interpreting data and other objects is becoming more and more important. Advanced Analytics enables the automation of mundane data management tasks and frees up resources to actually generate added value from the data. With the right use of data discovery tools, even the non-IT staff can easily access complex data sets and draw out the information they need. This process of knowledge discovery can be performed by anyone, without the technical know-how that was required in the past.

 

  • Data preparation

“Data preparation is one of the most difficult and time-consuming challenges facing business users of BI and data discovery tools, as well as advanced analytics platforms, Rita Sallam – Research Vice-President at Gartner.” However Artificial intelligence (AI) has solved this problem by creating the basis for advanced data transformation and by enabling automatic cleansing and consolidation of data. This enables users without any prior technical knowledge to use data.

 

  • Model management

Model Management technologies help organizations consistently and safely in developing, validating, delivering, and monitoring models that create a competitive advantage. The focus is to put the central control of all models in a single application instead of the separate management of individual models. In view of the fact that many analytical models never go into production or quickly become obsolete (model decay), it is important that companies can quickly and easily register new models, adapt, track, evaluate, publish, regulate and document them.  Previously, model management referred just to monitoring production models, but it’s beyond that. Models drive new breakthroughs and operational improvements for businesses. According to a McKinsey study, organizations that leveraged models extensively showed a 7.5% profit margin advantage over their peers, whereas those that did not use models had a 2.5% profit margin deficit compared to their peers.

 

  • Data governance

“A data governance plan, supported by effective technology, is a driving force to help document the basis for lawful processing.” Data protection laws require companies to have data governance programs that provide “data privacy by default” and define policies, roles, and responsibilities for the access, management, security, and use of personal data. If they do not proactively advance standards and programs, they not only run the risk of contradicting legal requirements but they could also lose the trust of their partners/customers. With the use of advanced analytics and artificial intelligence in decision-making, they are therefore even more challenged to bring transparency to the algorithms.

 

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