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How to Choose the Right Managed Cloud Services Provider for Your Business?

Businesses are increasingly relying on cloud services to support their business infrastructure (databases, performance, storage, networking), software, or services to support performance, flexibility, innovation, scalability, and provide cost savings at the same time. In order to support system reliability and remote work and also meet the demand in the market for personalized customer experience, around 90% of organizations have noted a massive surge in cloud usage amid the accelerated digital transformation due to Covid-19.

The whole process of selecting a managed cloud services provider for your organization can be very complicated because of the absence of a common framework for assessing those CSPs with the fact that no two CSPs are the same. To help you get through with it, we’ll be discussing several factors that are needed to be taken into consideration to identify a CSP that can best match your technical, business, and operational needs.

Factors to Take into Consideration while choosing a CSP

1. Cloud Security: You should know exactly what your security goals are, what security measures each provider offers, and what strategies they utilize to secure your apps and data. Also, you must ensure that you understand the exact areas in which each party is responsible. Consider what security measures are included free of charge with each vendor you’re considering, what additional premium services are available from the providers, and where you might need to augment with third-party technologies. Since security is a key priority in the cloud (and everywhere else these days), it’s vital to ask specific and extensive questions about your specific applications, industry, legal needs, and any other issues you may have.

2. Cloud Compliance: Next, make sure you select a cloud architecture platform that can assist you in meeting industry and organizational compliance criteria. Whether you will comply with GDPR, SOC 2, PCI DSS, HIPAA, or any other standard, be sure you know what it will take to attain compliance once your applications and data are hosted on a public cloud. Make sure you know what you’re responsible for and what areas of compliance the supplier will assist you with.

3. Architecture: Consider how the architecture will be integrated into your workflows today and in the future when selecting a cloud provider. You should also think about cloud storage designs while making your decision. The three major providers have identical designs and offer many types of storage to meet different needs when it comes to storage, but they all have different forms of archive storage. Each service provides options for regularly storing and accessing data vs. infrequently retrieving data (hot vs. cool storage). Cool storage is typically less expensive, but it comes with many limitations.

4. Manageability: As an organization, you should spend some time understanding what various cloud platforms will require of you in terms of management. Each service integrates with a variety of other services and supports multiple orchestration tools. If your company relies on certain services, make sure the cloud provider you choose has an easy way to integrate them (or that your organization is comfortable porting over to a similar service that is supported). Before you make a final decision, you should figure out how much time and effort it will take for your team to handle various components of the cloud infrastructure.

5. Support: Another aspect that must be properly considered, is supported. Will you be able to receive help quickly and easily if you need it? In some circumstances, chat service or call service will be your only source of assistance. You may or may not find this acceptable. In other circumstances, you may have access to a specific resource, but time and access would most likely be limited. Before you choose a cloud service, ask questions about the level and type of support you will receive as an organization in times of crisis.

6. Costs: While cost should never be the sole or most essential consideration, there’s no denying that it will influence your choice of the cloud service provider(s). It’s worthwhile to consider both the sticker price and the accompanying charges (including personnel you may need to hire to manage your instances).

7. Service Levels: When businesses have stringent requirements for availability, reaction time, capacity, and support (which, let’s face it, practically all do these days), this consideration is important. When picking a provider, Cloud Service Level Agreements (Cloud SLAs) are an important factor to consider. It’s critical for a cloud service user and a cloud service provider to create a clear contractual relationship (read: legally enforceable). Legal requirements for the security of data hosted in the cloud service should also be considered, especially in light of GDPR rules. You must have faith in your cloud provider to do the right thing, as well as a legal agreement that will protect you if something goes wrong.

While the seven factors listed above will assist you in developing a sound analytical framework to use when deciding which managed cloud services provider to entrust your data and apps to, you can add granularity by doing a thorough examination of your organization’s requirements to uncover extra aspects that will help in your decision-making.

About Artha Solutions

Artha Solutions is a premier business and technology consulting firm providing insights and expertise in both business strategy and technical implementations. Artha brings forward thinking and innovation to a new level with years of technical and industry expertise and complete transparency. Artha has a proven track record working with SMB (small to medium businesses) to Fortune 500 enterprises turning their business and technology challenges into business value.

3 Ways To Reduce Data Governance Failures

No organization is immune to data governance failures. They can be costly and embarrassing for businesses when things go wrong. Data governance is a complex process, and there are many moving data governance solutions for enterprises. For example, data governance solutions include data quality, data security, and data compliance. Even if a single part of the data governance process fails, it can have a ripple effect on the entire organization.

Fortunately, there are ways to reduce the risk of data governance failures. There are many measures that can help reduce the risk of data governance failures, such as:

Data Discovery and Profiling
Data Cleansing and Standardization
Data Security and Compliance

Here are three ways to reduce Data Governance Services failures

1. Deliver trusted data to the people who need it

Data governance is about more than just managing data. It’s also about making sure the right people have access to the right data. For example, Data Discovery and Profiling can help you identify which data is most important to your organization. Once you’ve identified the key data, Data Security and Compliance can help you control who has access to it.

Delivering trusted data to the people who need it, can help reduce the risk of data breaches and other security threats.

Poor and uncontrolled data access is one of the main causes of data breaches. For example, in 2017, the Equifax data breach occurred when hackers gained access to the personal information of 145 million people. One of the main reasons the hack was successful was that Equifax had poor data security controls.

Data Discovery and Profiling can help you avoid a similar fate by delivering trusted data to the people who need it.

2. Ensure data quality across your organization

Data quality is another important part of data governance. Inaccurate or incomplete data can lead to problems down the line. Suppose you’re a retailer and you have a customer’s address in your database. If the address is inaccurate, the customer may not receive their purchase. Inaccurate data can also lead to compliance issues. For example, if you’re required to report data to a government agency and the data is incorrect, you could face fines or other penalties.

There are several measures that can help organizations ensure that the quality of their data is accurate and complete:

Data cleansing: This feature can help you clean up inaccuracies in your data.
Data standardization: This feature can help you ensure that all of your data is consistent.

As a result, these measures can help improve the accuracy of your data and avoid compliance issues. Please note that data quality is an ongoing process. You should continuously monitor your data for inaccuracies and take steps to correct them.

3. Automate data governance processes

Data governance is a complex process, and there are many moving parts. As a result, it can be challenging to keep track of everything. This is where automation comes in. Automating data governance processes can help you:

Save time: Automating data-intensive tasks can free up your time so you can focus on other things. For example, if you’re manually cleansing data, it can take a lot of time. But if you use automating data governance processes, you can automate the process and save yourself some time.

Improve efficiency: Automating repetitive tasks can help improve your organization’s overall efficiency. If you’re manually standardizing data, it can be easy to make mistakes.

Reduce errors: Finally, automating data governance processes can help reduce the risk of human error. Errors can be costly and time-consuming to fix. By automating data governance processes, you can help reduce the risk of errors.

Conclusion

Data governance is a complex process, but it’s important for any organization that wants to avoid data breaches and other security threats. The three data governance solutions mentioned can help reduce the risks of data governance failures in several ways. If you’re looking for a way to improve your organization’s data governance, these measures are a good place to start.

About Artha Solutions:

Future of Data Governance Services: Top Trends For 2022 and Beyond

There was a time in the early 2000s when data governance was not really a thing. Surely, there were pioneers back then who laid down the groundwork for data governance, but it wasn’t still taken seriously. Cut to the present time and Data Governance Services are in high demand.

As the rules and trends of data governance keep evolving every year, let’s look at the following trends that are going to stand out in 2022 and beyond.

1. Operational Data Modelling

One of the most meaningful operational actions to be derived from data governance this year comes from data modelling. Interchanging data between different systems as part of one collective data fabric remains more indispensable now than ever as more companies keep adopting this approach for data management.

Expressive data models that have clear taxonomies and semantics can use machine intelligence to figure out how different schemas of various data systems are blended for frictionless integration. So, you get similar details in various systems and the governance has the maps regarding how it is expressed in these systems. Governance solutions can be involved in real-time in this case.

This particular approach spares time and cost by bypassing the need to write special programs to make the most of what happens in the data governance arena.

2. Metadata Insights

In 2022 and beyond, inferences regarding metadata in the data models will streamline the taxonomies for entertainment and media content engines, for instance, across local and global sources to gain real-time results. There will be quicker automation of metadata inputs thanks to cognitive computing methods. Otherwise, all metadata descriptions are going to be manual.

So, in other words, detailed visibility of metadata might presage events or offer a complete roadmap of previous events to make sure data quality and lineage remains intact. Thus, one can expect the following positive changes related to metadata this year:

Traceability of metadata: The traceability of metadata is crucial for trusting and understanding the details presented in analytics.

Root Cause Analysis: All aberrations and outliers in procedures related to analytics can be easily illustrated through metadata analysis. When someone notes an error or something appears as an anomaly on the dashboard, there will be a graph to show what went amiss.

Impact Analysis: Metadata will be scrutinized for each phase of SQL to extract information through rows, columns, and tables of data. The graph that will accompany the process will outline the exact changes.

3. Activation of Data Stewardship

The fact that data stewards are empowered is a direct repercussion of changing data governance from passive employment to an active one. Modern innovations regarding controlled data access (focusing on data stewards) are essential for speeding up the time necessary to utilize data. At the same time, it is necessary for conforming to the governance standards regarding which users can see which data.

Such shared data governance approaches issue the automated approval and centralized governance regulations in infrastructural setups. For instance, the owner of sales data can decide what part of the data he wants to allow John Doe to access.

Automating this distribution of the centralized governance regulations into decentralized sources can remove the IT bottleneck for data access. It will facilitate low-latent data sharing. Thus, data stewards from Talend Data Management Services – the people who understand the data best – remain at the forefront of delegating and deciding data access.

4. Data Quality

Data quality, along with the attendant features of data reliability and data validation, happens to be the substratum on which all forms of data governance, specifically in an operational setting, depends.

You will not be able to augment or automate processes when your data is not high quality or healthy to start with. Thus, the trend would be to embed a data governance staple like superior metadata management into the operational systems to generate metadata specifics in real-time. It will need proper data validation measures to make sure that it is sensible and adherent to the best practices.

The organization will use different means of ensuring data security and quality at a level that is reliable for operations and traditional decision-making. As the nuances of data governance are changed, the organizations will derive greater profits from their IT initiatives.

About Artha Solutions

7 Best Practices That Help To Avoid Common Data Management Mistakes

Considering big data applications are growing at such a rapid rate, more and more firms are opting for digital transformation to stay relevant and up to date with the latest trends.

Organizations have acknowledged the value of big data and are treating it as an asset (perhaps the most valuable of all as it has the power to determine growth trajectory and provide an upper hand over competitors), but they are yet to get any valuable insights from it.

True, data leveraging isn’t for everyone, and translating it into information that is consistent, correct, and thorough, is frequently found to be lacking. Many companies throughout the world have increased their enterprise data management efforts in recent years, hiring experts in data management services, but the rate of success of these projects has been discouraging.

Those of you who work with big data will agree that businesses are having a difficult time controlling and making sense of the huge amounts of data that must be controlled and made sense of in order to sustain competitiveness, meet customer needs, and, obviously, comply with the law. The battle to design programs that permit organizational sustainability and determine corporate integrity is real. With effective data management services, this can be easily achieved.

In this article, we have mentioned seven best practices for your business to consider for effective and efficient data management.

Build strong file naming and categorizing conventions

If you want to use data, you must first be able to locate it. If you can’t manage it, you can’t measure it. Create a user- and future-friendly reporting or file system, with descriptive, standardized file names that are easy to identify and file formats that enable users to search for and discover data sets while keeping long-term access in mind.

A typical format for listing dates is YYYY-MM-DD or YYYYMMDD.

It’s ideal to use a Unix timestamp or a defined 24-hour notation, such as HH:MM:SS when listing times. Users can keep note of where the information they need comes from and find it by time zone, whether your organization is national or global.

Consider Metadata for data sets carefully

Metadata is essentially descriptive information about the data you’re working with. It should include details about the data’s content, structure, and permissions so that it can be found and used in the future. You can’t rely on being able to use your data years down the road if you don’t have this precise information that is searchable and discoverable.

Items in the catalog include data author, what data is contained in this set, descriptions of fields, when and where was the data created, why was it created, and how was it created.

This information will then assist you in creating and analyzing a data lineage as the data flows from its source to its destination. It also comes in handy when mapping relevant data and recording data relationships. The first step in developing a solid data governance process is to collect metadata that informs a secure data lineage.

Data Storage

Storage strategies are a necessary part of your workflow if you ever want to be able to access the data you’re creating. For all data backups and preservation methods, develop a strategy that works for your company. Consider your requirements carefully because a solution that works for a large corporation may not be suitable for the demands of a small initiative.

Consider the following storage options:

Desktops/laptops
Networked drives
External hard drives
Optical storage
Cloud storage
Flash drives

The 3-2-1 methodology

The 3-2-1 approach is a basic and widely used storage strategy. The following strategic
recommendations are suggested by this methodology:

3: Back up your data three times
2: Using two different storage techniques
1: Storing one of them offshore

Without being unduly redundant or complicated, this strategy provides smart access and ensures that a copy is always available in case one type of place is lost or destroyed.

Documentation

We can’t disregard documentation when it comes to data management best practices. It’s generally, a good idea to create numerous levels of documentation that explain why the data exists and how it can be used.

Documentation levels:
Project-level
File-level
Software used
Context

Commitment to Data Culture

Ensuring that your department or company’s leadership prioritizes data experimentation and analytics is part of a commitment to data culture. This is important when leadership and strategy are required, and if budget and time are needed to ensure that adequate training is conducted and received. Furthermore, having executive sponsorship and lateral buy-in will enable better data collaboration throughout your organization’s teams.

Data Quality Trust in Security and Privacy

Building a data-quality culture necessitates a commitment to creating a secure environment with high privacy standards. When you’re trying to provide secure data for internal communications and planning, or when you’re trying to develop a trusting relationship with a customer by ensuring that their data and information are kept private, security is important.

Your management procedures must demonstrate that you have secure networks and that your staff is aware of the importance of data privacy. Data security has been acknowledged as one of the most important decision-making elements in today’s digital market when firms and consumers make purchasing decisions. One breach of data privacy is too many. So, plan accordingly!

Invest in Quality Data Management Software

It is recommended, if not needed, that you invest in quality data-management software when evaluating these best practices together. Organizing all of the data you’re collecting into a usable business tool can make it easier to find the information you need.

Then you can construct the appropriate data sets and data-extract scheduling to meet your business requirements. Data management software will help you design your best governance plan by working with both internal and external data assets. Tableau has a Data Management Add-On that can assist you in implementing these best practices in your analytics environment.

Using trustworthy software to help you build, catalog, and control your data can help you gain confidence in the quality of your data and lead to self-service analytics adoption. Take your data management to the next level with these tools and best practices, and build your analytics culture around managed, trustworthy, and safe data.

Trying to solve all of your data challenges in the early stages of data management is a recipe for disaster. To minimize difficulties and accomplish your organizational demands on schedule, we recommend going slow and taking baby steps.

Data Governance Vs Data Management The Difference Explained

People often wonder if there is any difference between Data Governance and Data Management. Well, the answer is yes. However, they are related.

Data governance is the definition of organizational structures, data owners, policies, regulations, processes, business terminology, and measurements for the end-to-end lifespan of data (collection, use, storage, protection, deletion, and archiving).

The technical implementation of data governance is data management.

Data Governance Solutions are little more than documentation if they aren’t put into action. Enterprise data management allows processes and policies to be executed and enforced.

Simply put, data governance solutions help develop policies and procedures surrounding data, whereas data management solutions implement those policies and procedures to assemble and use the data for decision-making. To better grasp how these notions work together in practice, it’s helpful to first understand what each of them is.

What is Data Governance?

Let’s look at some aspects of data governance, shall we?

People

People are essential to data governance because they are the ones who generate and manage the data, as well as the ones who gain from well-governed data. Subject matter experts in the business, for example, can identify the organization’s standardized business terms as well as the levels and types of quality standards required for various business processes.

Data stewards are in charge of resolving concerns with data quality. IT professionals take care of the architecture and management of databases, applications, and business processes. Data privacy and security are the responsibility of legal and security personnel. And cross-functional leaders, who make up the governance board or council are in charge of settling conflicts among various functions inside an organization.

Rules and Policies

If policies specify what should be done, rules specify how it should be done. Policies and regulations are used across processes and procedures by organizations; popular categories include consent, quality, retention, and security. You might, for example, have a policy that stipulates that consent for processing must be sought before personal data can be used. When personal data is acquired, one rule might outline the consent alternatives that users must choose (such as billing, marketing, and third-party sharing). Another rule can state that prior to providing a promotional offer to a customer, marketing consent must be confirmed.

Metrics

What is measured will be managed. The number of duplicate records in an application, the correctness and completeness of data, and how many personal data pieces are encrypted or masked are all common technical metrics. While these metrics aid in data technical management, leading businesses are also attempting to define how these technical metrics affect business outcome measurements.

For instance, Days Sales Outstanding (DSO) is a typical business indicator used by financial analysts and lenders to assess a company’s financial health. When client address data is inadequate or faulty, the billing cycle time and, as a result, the DSO will increase. Analysts and lenders may view a higher DSO than the industry average as an indication of risk, downgrading the company’s outlook or raising the cost of financing.

What is Data Management?

Let us now dig into some tools and techniques for data management.

Cleansing and Standardization

Data quality policies can be implemented and enforced with the help of cleansing and standardization. Profiling allows you to compare the data’s validity, correctness, and completeness to the data quality parameters you set. You may then rectify issues like invalid values, misspelled words, and missing values. To enforce data quality at the point of entry, you can also incorporate cleansing rules into data entry processes.

Profiling also aids in the identification of similarities, differences, and links between data sources so that duplicate records may be removed and consistency can be enforced across all sources.

External data, such as DUNS numbers, demographics, and geographic data, can be used to enrich internal data. Many firms also establish a centralized hub to assist in maintaining master data semantic consistency across data sources.

Masking and Encryption

Masking and encryption assist you in enforcing and implementing privacy and protection policies. Tools and techniques for data discovery and classification assist you in identifying sensitive and personal data and categorizing it as requiring protection based on internal requirements and external regulations such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and the General Data Protection Law of Brazil (LGPD). These tags can then be utilized to implement suitable security measures. Some users may be authorized to access raw data, while others may require the data to be dynamically masked upon query, depending on classification and access regulations.

Internally and externally, data flow modeling can help you understand how data is acquired, processed, stored, and distributed. Based on classification and privacy policies, you can then decide on relevant protection mechanisms. Data masking, for example, maybe sufficient for access within your firewall, but data must be encrypted before being shared with other parties outside your organization.

Archiving and Deletion

The use of archiving and deletion aids in the implementation and enforcement of retention policies. When data is no longer required for day-to-day operations but is still required to meet regulatory requirements such as tax reporting or long-term storage, it is archived. Data archiving tools also keep track of how long data should be kept, index it for quicker retrieval for uses such as legal discovery, and enacts necessary access and data masking/encryption controls. Data is permanently destroyed after the predefined retention period has expired.

While this may appear simple, balancing the retention needs of industry rules (such as BCBS 239 and CCAR) with the erasing requirements of governmental and regional regulations is a difficult process (like GDPR and CCPA).

While data governance and data management are two separate entities, their goals are the same: to build a solid, trustworthy data foundation that allows your company’s finest employees to do their best job.

Unmask the 3 Levels of Holistic Data Governance Strategy

Gathering quality data is the first step towards business success. However, the growth of the same business relies on the usage of given data. The trick to any successful business nowadays is defined not by the data collected, but by the best use of data.

As important as data is to a successful business, it is not any good on its own. It is only as good as its usage, and that is governed by Data Governance.

It is not easy to decide the best use of collected data, keeping in touch with every single department of the organization, taking the needs into account, and ensuring that they are all met is confusing, and challenging.

However, organizations that cannot spare their resources on setting great data governance strategies, seek help from the experts who are behind the most successful businesses. Here are the three things that you will find common inside every single strategy.

A data governance strategy is like the foundation of the process that allows a company to base its operations on. Understanding the strategy truly allows the organization and the individuals to carry the business towards a successful outcome.

Data governance strategies are unique to every business model. Like every new idea for a business is unique, the strategy to make it work is unique too. However, these 3 strategies are the common denominator everywhere.

Framework

Taking into account the different departments of an organization and their needs, building a framework that accommodates the growth of every individual department, and building a framework that syncs up each department with the other while making use of the data that is collected is the goal.

Bring in the framework that supports a greater ROI. Changes will be common, the change in the collected data will change too. The framework should allow changes in the collection of data and the steps that will be taken.

Understanding the efforts that will be put in extracting the best out collected data and how individual teams are to meet their set goals is what building a framework is all about.

After the framework, comes the planning.

Planning

Setting expectations and requirements is tough, sure but drawing a route map of execution is rough too. Knowing what we want from a company from the beginning and understanding where to take it in the next time frame is the agenda of data governance strategy.

Drawing up a route map is however a step towards achieving the said ROI. A process on how each individual in the organization and each team will lead the company towards the desired goal of success.

Fixing how the individual teams will work and the operations being carried out every day, and on a bigger annual basis is what efficient planning looks like. Keeping in mind that there will be some unexpected circumstances and preparing the company as a whole for them includes the best use of resources.

This also means drawing up an execution strategy that supports the data growth and methods to imbibe the best data usage policies keeping in mind to adhere to the requirements of the organization.

Adherence

Building a strategy that is easy to execute is one of the most important aspects that help in adhering to it. Knowing that a strategy will be possible and is in fact a scalable target will help in adhering to it.

Keeping in mind that data governance strategies are the center of a company’s operations, it is important to notice that it is also just a plan that is a well thought out idea for a company’s growth.

These are the three levels of data governance strategies that decide the growth of a company. Now, there are many different approaches to understanding each of these levels and attempting to personalize the strategies at each level to suit the business model, but the intention of each level is to meet the company growth targets in the swiftest, most economical and efficient way possible.

What’s The Foundation of Hybrid Cloud Self-Service Automation?

In the last one decade, cloud application delivery has become extremely important but undeniably complex, sometimes getting out of direct control. This has become a roadblock towards achieving total self-service automation within budget control principles.

According to a report by the IDX, 69% of enterprises believe that they are overspending on the cloud and the lack of automation is the number one reason they cite. It all boils down to data governance because that’s what essentially, well, governs who can access what, where, and for how long.

This makes data governance not just essential but crucial for self-service automation. Naturally, the question arises as to what is the foundation upon which it rests.

Hybrid Cloud Self-service Automation

Since the cloud is not a singular destination, it must be adaptable to change and not averse to evolution. Self-service automation provides the necessary agility which enables the end users to provision their applications into the right cloud based on their needs, whether they want a public cloud or a private cloud – a truly hybrid synergy becomes the need of the hour.

However, governance becomes even more important with such cross-cloud environments where the control needs to be more poignant and strong.

Data Governance: The Foundation of Hybrid Cloud Self-service Automation

As mentioned above, data governance is key to better comprehend the value that a cloud provides which makes it the most important, foundational need of the cloud environment, especially a hybrid cloud or multi-cloud.

Having said that, developing and implementing a common governance model that’s adaptable to the various requirements and complexities in such environments is a challenge in itself. Therefore, there has to be a shared control plane that enables centralized governance across clouds and other associated technologies.

Most companies fall short with data governance when they treat it as just another tool in their cloud arsenal – it’s so much more than that. It includes all the required integrations into the existing technologies that organizations have deployed over the years along with any operational links that enable collaboration among them, across the lifecycle of an application.

With a foundational data governance framework in place, businesses can assign and manage the applicable multitenancy, role-based access controls, and policies.

Principles of Good Governance

Data governance isn’t confined to data. In fact, it blankets the people, the processes, and the technology that surrounds the data. As such, there’s a need for auditable compliance for these three areas that are well-defined and agreed-upon. When done correctly, this could help organizations make data work for them.

Moreover, organizations need to think macro, not micro. They must consider the entire data governance lifecycle instead of monitoring in siloes. Although this could prove to be overwhelming, especially for the small and medium enterprises, it’s also extremely important and worthy of detailed attention.

Some of the key areas organizations must focus on includes:

Data Discovery & Assessment
Classification of Sensitive Data
Data Catalog Maintenance
Data Sensitivity Level Assessment
Documentation of Data Quality
Defining & Assignment of Access Rights
Regular Audits for Evaluation of Security Health
Enabling Encryption & Other Additional Data Protection Methods

With these guiding principles, organizations are able to create a highly effective data governance strategy that enables them to achieve control over their data assets and maintain total visibility. This translates into a culture that’s data-driven, helping organizations make better decisions, improve risk management, and most importantly, maintain regulatory compliance as per industry standards.

Choosing The Best Methodology for a Successful Data Migration

Modern-day businesses need modern-day data operation solutions. A company that excels at its core competence and yet fails to manage its data well, will underperform in the market because data is the basic infrastructural unit of every business now.

Data migration is one such process that companies need to strategize for effective data operations within the company.

Data migration refers to transferring data from one system to the other. This might sound as simple as watching the old Windows illustrate on-loop file transfer animation.

But this is a complex and crucial process.

Companies undergo data migration continuously and for various reasons. Sometimes, it is a change of data warehouse, merging new data from different sources, system updates, and hardware updates.

An un-strategized data migration process can come with consequences like data loss, inaccurate or repetitive data migration, and many other complications that can take a toll on the company’s data operations.

So, here is the best methodology for successful data migration:

Assess the source and target systems

If there is one rookie mistake that most companies make before setting up their data migration process, it is to not assess the quality and compatibility of source and target systems.

Too many times, companies lose important data in the migration process before the migrated data is not supported by the target system.

Before putting the process into action, it helps to evaluate the data and detect any inaccurate, incomplete, or problematic data.

So as step one, assess the source and target system’s compatibility and quality of data to migrate.

Once the major barrier is out of the way and you have sorted your systems and data, it is time to ponder over the methodology or approach that works best for you.

1. The ‘go once and go big’ data migration method

If one can afford to do this, this is highly recommended because it’s not only cheaper but is much less complicated. This is a method where you completely turn off the system operations and make it unacceptable to any user and just migrate the data all at once, and then proceed with the new system.

The only problem however is during this process the systems are practically going through downtime and might take away from the productivity or pause vital operations in the company.

So usually, companies carry out this type of migration during public holidays to avoid losses from downtime.

2. The ‘phased out’ data migration approach

This is a method where data migration is broken down into parts to run for several days or weeks based on the weight of the data.

This method is recommended for companies that cannot afford to shut their operation down for a while or for data migration processes that are estimated to take a longer time to migrate.

This process will need a lot more strategizing than the previous approach, given that the migration takes place alongside the regular operations.

The size of the data needs to be accurately estimated along with the time in transfer to get the time taken to migrate it. The target system, since it is functioning and carries its data, needs enough space to accommodate the data in migration.

Once you have narrowed down the methodology, it is important to remember a couple of things to ensure smooth data migration.

Always start the process with professional assistance, especially if the data is sensitive and critical or bigger. An unassisted data process is prone to going astray with data loss and malfunction.

Always go through a dedicated data cleaning before migration, because it does not do to transfer inaccurate, unnecessary space-eating, and inferior quality data to the new system to inherit all the same problems to the system.

Digital Transformation Services: Company Transition Strategy and Framework

For a long time, Digital Transformation existed as a futuristic organizational fantasy but quickly transformed into a reality as the pandemic took over the world.

Digital transformation is a lucid term as it can mean different things for different organizations. That’s why you will always find it coupled with the word ‘strategy’. Loosely put, digital transformation refers to a company’s metamorphosis into a more automated operational infrastructure.

This can mean a company adopting e-commerce, employing effective software, enhancing the IT infrastructure, migrating bookkeeping services to an automated format via SaaS tools, etc..

Companies providing digital transformation services enable this transition in the most cost-effective and resource-efficient ways, making business more efficient and foolproof for any organization.

However, digital transformation is mostly an umbrella term. What it means for an accounting firm will be completely different in comparison to an enterprise. Even within similar organizations, digital transformation and its impact can differ largely based on the size of the organization.

As seamless as it sounds, digitalization especially when done on a large scale, can be a two-edged sword. While it can exponentially save a company’s expenses by automating and accelerating the processes, it can also become a financial blunder when digitalization is done without a strategy.

The fundamental benefit of digitalization is better profits with lesser efforts. Companies sometimes do get overboard with the idea of absolute automation or rapid digitalization and then struggle for their investment to be returned.

Digital transformation can only be implemented strategically and never instinctively. Building a generic one-for-all strategy for digital transformation for organizations will always fall short of expectations and results.

However, there are a few things that can roughly become a good framework for a successful transformation.

Let the transformation be based on data

Collecting data about the company goals, revenue, expectations, digitalization expense, expected returns, and much more is extremely important. A company’s journey through digital transformation has to be based on facts, not assumptions. Data relating to the pain points, the number of hours taken to resolve an issue or carry out a task, etc can really make digitalization more effective.

Hire an expert to execute it

An entrepreneur, or even a business owner, may have ideas of the level and pace of digitalization that they desire for the business. However, they will need an expert to extract accurate and predictive data to arrive at an effective strategy. Digitalization will require transforming and automating many departments based on their priority and expense.

One process at a time

Digital transformation is more like digital evolution. An organization will not become digitally efficient overnight. What do they need to automate on priority? What areas take the most effort and highest investment? For a company that stores sensitive data, protecting itself from a data breach might be of the highest priority.

So they could assign an AI-based tool that can detect IT vulnerabilities and instantaneously provide patching. Similarly, for some companies, it could be their bookkeeping processes, billing processes, or even the hiring processes.

Understanding the key pain areas of an organization will give you a roadmap to its digital transformation.

Having a collaborative approach

The most beautiful thing about digital transformation has the potential to become the scariest factor too. When a company goes through a digital experience, it has to be open to involving more people in the fabric of business, who will collectively make it happen, someone might provide you with a SaaS that can monitor your staff performance and productivity, or an AI-backed tool for you billing process, etc.

With a digital transformation, the three things that organizations aim to achieve are to save time, save money and enhance efficiency. Digitalization is a bridge between company revenue and customer experience.

It is not only the private and financially blooming organizations that are opting for a digital transformation. Even government processes (public sector) including registering and taking appointments for vaccinations, applying for a driver’s license, exam assessments, and much more have already transformed to the digital space.

Typical Data Migration Errors You Must Know

Data migration is the process of transferring data from one software or hardware to another software or hardware. Although the term only means as much, it is typically used in reference to more prominent companies with huge amounts of data. These companies may be moving their data from one software to another to revamp their technical infrastructure and gain more security for their data.

In recent times, data has become the fuel of every organization. Losing some amount of data might mean that the organization loses its time, energy, clients, or even money. That’s why data migration is an extremely sensitive process. When done carelessly or without adequate technical support and knowledge, a company can suffer a lot.

Here is a list of some of the most common data migration errors:

Error caused by inadequate knowledge

While migrating higher quantities of data, it is vital that all essential information about the nature of data be available and considered. It is a standard error to assume that your data in the existing form would be compatible with the new system. However, minor spelling errors, missing information, incorrect information, and duplicate data could lead to critical failures in the process.

The detailed data analysis

Often during data migration, it is difficult to have a complete picture of the nooks and corners of the system that has valuable data. This leads to a taxing miscalculation of the available data, leading to incomplete and outdated data being migrated.

Often these errors are only brought into notice when the migration is halfway done, or the system is completely set, and it is often too late by then to correct the data. Data migration should always follow a thorough data analysis and a holistic idea of migrating the available data.

Human and coordination error

Data migration is an arduous process that involves multiple people, multiple systems, multiple sources, and multiple phases. Human beings are destined to make judgment and combination errors, which leads to a loss of data or a chaotic and scattered migration process. This is why organizations must make sure that the process of data migration happens in a transparent and integrated way, with every stage being recorded to avoid any miscommunication or misinterpretation.

Not backing up the backup

This is the most nerve-wracking part of data migration. How many backups do you need for your backup? When do you know that all your data is 100% secure? Data migration often costs data itself, and all systems are subject to their share of risk. When data is being migrated, it is always recommended to be strategically backed up in different places.

If the data is securely backed up, the process can afford some errors as the data can be recovered even if lost or migrated.

Hardware issues

On top of the software compatibility issues, sometimes the destination hardware cannot essentially hold securely the amount of data migrated either due to smaller memory margins or substandard quality of the hardware or simply due to the lack of compatibility, hardware issues can lead to a severe loss of data. This is why checking through the hardware quality and running its compatibility to the data being migrated is vital for the successful conduct of the process.

Lack of strategy

Data migration is all about its management. People often presume a degree of simplicity to the process. It is easy to assume that data migration is all about sound technology and backing up the data. But without a proper migration strategy, the entire process can go astray. Without properly segmenting and labeling the data, even the data that’s successfully migrated might be hard to locate. Without knowing exactly what segment of data to migrate and in what order, the process can be chaotic and lead to loss of data.

The list of errors can go on. But what’s more important is to understand that certain processes, however simple they sound on paper, need professional assistance. It is always better to get a job done by someone that knows how to do it than settle for a work half done and with half data lost.

Data migration, in short, occurs due to –

Failure of copy processes
Server crashes
Crash or unavailability of storage device
Array failure (data center issue)
Complete system failure (significant data loss)
Data corruption during the process
Data was terrible all along

To be fair, data migration, especially in prominent organizations with higher volumes of data accumulated over many years that need to be migrated, some degree of error is inevitable. There will be some data corruption and loss. And if not that, there will be at least device and system incompatibilities. If the software and hardware work, human judgment is always subject to make mistakes. If not this, even the lack of a proper system leads to data migration errors.

What this means is that data migration is more about prioritizing and placing data than just migrating it across devices. However plain and technical of a job it sounds, data migration is vastly dependent on human judgment and prone to human error. The success of a data migration project will depend on the coordination of the team, the stability of the system at hand, the strategy applied, and the quality of the data.