Given the kind of data-centric world we are heading for, organizations need to take extra care while integrating and migrating data on the cloud.

Cloud-based data migration is simply the process of moving enterprise information and infrastructure from on-premise to cloud computing infrastructure. This involves transferring data, workloads, applications, etc. One area which is steadily gaining traction is cloud data warehouse. Today,ÌýÌýboast of workloads in the public cloud. Think of a as a database that is primarily used to tackle explosive data growth while storing information from a wide variety of sources, including Internet of Things (IoT), CRM, and so on.

Why is Data Migration Necessary?Ìý

From new system implementations (think: ERP, CRM, etc.) and mergers & acquisitions to system rationalization and the need for reduced costs, there are multiple reasons why organizations may choose to migrate their data to the cloud. Without real-time, accurate data at hand, enterprises are at a decided disadvantage.

A key enabler of data-driven decision-making, cloud-based data migration can offer a host of business and operational benefits such as:

• Increased scalability, integrity reliability, and security of data with stringent compliance of policies
• Reduced IT costs when compared to cost-intensive on-premise costs such as hardware, software, support, downtime, depreciation cost, etc.
• Environment-friendly, ‘greener’ alternative to on-premise systems and increased energy savings due to reduced need for physical materials for setup
• Increased speed-to-market when delivering projects
• Maximized uptime while backing up data, recovery, and system upgradation, ensuring business continuity
• Easy data availability on-demand leading to increased business agility
• Greater opportunity to leverage newer technologies such as machine learning, artificial intelligence, etc. and drive innovation
• Increased productivity due to consolidated data centers and the ability to outsource data management capabilities to a third party, allowing enterprise resources to focus on more high-value tasks
• Accelerated digital transformation and organizational growth as CTOs and leaders can opt to digitize core functionalities easily and seamlessly
• Better product delivery that is aligned with the user’s needs as the team focuses more on the user experience instead of constantly struggling with the data

Top 7 Cloud Data WarehouseÌýBest PracticesÌýfor Migration Success

1. Involve all departments of the organization instead of making it an IT-only job.

Most enterprises view data migration as the sole responsibility of the IT team–a big mistake. To drive migration success, consider the ‘human element.’ In other words, enterprises need to engage in behavioral management and involve all stakeholders across the board at all stages of data migration. This can be done by driving stakeholder participation and ensuring that the key resources are always available during the migration process. Doing so can throw light on hidden issues early on in the project and take the guesswork out of the equation.

The reason:

In reality, data migration failure is not always hardware- or software-dependent; it is behaviorally driven and a collective effort. Enterprises need to relook at their data migration process via a collaborative lens and ensure that every team is on board.

2. Plan extensively to understand the ideal cloud-migration approach.

Instead of viewing the data migration process as an isolated event that involves moving data from point A to point B, view it as a continuum of moving parts working together to enhance organizational performance and competitive edge. Simply put, there needs to be a greater effort in the ‘planning’ stage to ensure that enterprises:

• Benefit from an end-to-end, repeatable process flow
• Understand the best-associated methodology and approach for data migration
• Engage in a cost-benefit analysis to integrate a pay-as-you-go model instead of paying for additional infrastructural costs

The reason:

There are numerous types of cloud migration strategies that enterprises can adopt depending on their size of business and data complexity. Some of the most common strategies include re-hosting, re-platforming, re-architecting, re-purchasing, and retiring. Without proper planning, organizations will not be able to understand their existing IT environment and the kind of cloud vendor to choose to advance their data migration goals. Additionally, it can lead to prolonged downtime due to a chaotic data migration process, eventually costing organizations $5,600 per minute, according to estimates byÌý.

3. Engage in data profiling

Data profiling is essential for two reasons. One, it helps uncover risk early on. Two, it empowers enterprises to understand the data inside out fully. Moreover, using the profiling results gathered, stakeholders can be engaged across diverse aspects such as:

• Driving root cause analysis to identify business process flaws within the data
• Having empirical discussions about the data state
• Driving discussions on prioritizing requirements that are essential versus ‘nice to have’
• Identifying and analyzing inconsistencies in the data between the environment’s documented state and the true state
• Gauging the level of effort required to ensure that the data is fit for the prioritized purposes

The reason:

Accurate estimates about the time and effort required cannot occur without data profiling at the beginning of the migration process. In other words, if you want to successfully migrate data without letting guesswork get in the way, data profiling is necessary.

4. Build a culture of data governance

Data migration is as much about the people and processes engaged in the process as it is about technology. To cater to the ‘human’ factor of the data migration aspect, ask the following questions before deep-diving into the data migration process:

• Which resources need to be involved?
• What do ‘good data’ and ‘data duplicate’ constitute?
• What to do when remediating a data quality exception?
• What does the escalation process look like if a decision cannot be made?
• How to deal with the initial spike of remediation needs versus the steady state?
• How to ensure data quality after going live?

The reason:

To effectively manage data governance, leaders need to ask these questions and devise a strategic plan–one that aims at establishing a culture of data governance from top to bottom within the organization, and caters to the needs of the end-user as well as the internal stakeholders.

5. Pivot as needed and embrace change

As organizations get more visibility into the data, iterations may be required at every step of the data migration flow. This lowers risk and improves the quality of the final output. It also doubles up as an opportunity to add cleansing rules with every iteration. As a thumb rule, organizations should iterate as often as needed. The end goal is to embrace change as readily as possible.

The reason:

Leaders need to focus on strategizing ways to help drive ‘change management.’ In simpler words, this translates to enabling people to readily embrace the changes in adaptability by:

• Training employees on the new system
• Designing, implementing, and monitoring a plan to support–and back up–the change keeping in mind the end-user and the team’s needs

6. Segregate the data from the application

Most organizations make the mistake of not separating the distinct needs of the data and the application. While these two may be connected, they have individualized requirements. Another key area of concern is rushed application and data assessment, where enterprises do not completely assess the workloads. This leads to a skewed understanding of which migration approach to undertake for the specific data and applications.

The reason:

Without segregating the data from the application, there will be blind spots and ‘overlooked data’ within the architecture. Plus, data segregation also helps to understand the remediation aspects to undertake for the project. Finally, it empowers enterprises to understand the specific migration requirements and eliminate the chances of scope creep.

7. Select the right cloud provider with security of data as a key focus point

When it comes to finding the best fit for migration support, tools, safety, or approaches, selecting the right cloud provider is paramount. Companies should investigate whether the cloud providers have the necessary data migration tools to move data by considering factors of vendor lock-in and portability.

Think of it as ‘expectation management’ between the two parties involved by having proper written and authorized documents–or an SLA (Service Level Agreement–as it is commonly known. Considering that most organizations would want to outsource their infrastructure services to the cloud providers so that they can focus on the core business, selecting the right cloud service partner can allow for better strategic business goals alignment as well as profitable outcomes.

The reason:

Most CTOs opt for a migration partner based on low pricing or familiarity instead of factoring in ‘experience.’ This can lead to mistakes and rework, causing your cloud migration costs to go off the rails. In addition, there are increasing concerns with respect to security protocols between the client and cloud providers, leading to the inevitable ‘trust deficit.’ Finally, if the vendor does not plan and execute the security protocols properly, data breaches will become an everyday issue, costing the enterprise millions.

The Bottom Line

Data migration will continue to gain momentum with the global public cloud spending poised to exceed $480 million by 2022, according toÌý. Clearly, not building a roadmap for the data migration journey is already ensuring that the organization is set up for failure. After all, moving data from legacy to cloud is not a simplistic, linear approach.

To help drive better decisions and deliver a safe, cost-effective, and results-oriented data migration process, sticking to the best practices outlined above can make the process simpler, more streamlined, and scalable while being cost-effective.

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Small businesses are now moving from paper-based systems and Excel spreadsheets to modern ERP automation, including digital scheduling and analysis of production tools. It has been observed that the development and emergence of several ERP software programs over the last decade has made business operations easier. As per a report byÌý, the ERP market is expected to grow at a CAGR of 9.8% from 2020 to 2027 to reach $86 Billion by 2027. The bulk of the growth is expected from ERP systems from SMEs.

Inventory Management, Invoice Tracking, Market Research, Accounting, Capacity Planning, Quality Control, etc., are a part of an integrated system that can be shared and managed through a centralized database to streamline business operations. With the help of a Cloud ERP suite, manufacturers can turn their daily data into actionable business intelligence and automate processes on the cloud.

Key benefitsÌýof incorporating ERP solutions for SMEs

The ERP software allows enterprises to condense workflows, streamline processes, and automate, eliminating the need for manual input. ERP system from SMEs is benefiting the SMEs through –

• Cost Reduction:ÌýWhen employees have fewer manual processes to weigh them down, they’ll be free to take on more innovative, profitable projects. Ease of workflow increases, with less turnaround time and high production capability.

ERP is a solution that provides transparency about how problems affect the rest of the process, allowing managers to analyze, gain better insights, and make effective decisions. It provides real-time financial data that offers a competitive advantage, increases efficiency, and helps ensure cost savings.

• Custom Reporting:ÌýOrganizations rely on reporting to track KPIs across every department. If one department or functionality falls behind, it can affect all the other departments. However, you can’t produce useful reports without a proper system. An effective ERP solution generates reports that clearly show where you’re already efficient and where there’s room for improvement.
• Scalable Production: ERP brings a game-changing level of scalability to business processes. The automation of operations takes the burden off employees and guarantees a solid record of workflow. That frees your most valuable resource – your people – to handle the other important operations. A cloud ERP suite can be used to scale the business processes easily by automation of businesses processes on the cloud.

Successful ERP implementation case studies

Ìýis a beverage company in Washington State. Before adopting the ERP system, they wasted a lot of time via manual processes. To assign a customer’s purchase to the corresponding account, employees had to manually populate the table with data from individual tables for further analysis. To perform historical analysis, employees must re-enter the information from the system into the table and compare it to the same data for the year. Extracting data for a month’s corporate transactions can take up to 15 minutes. This led to a loss of productivity.

To improve their productivity level, they installed a new ERP solution, so all company information was now stored in one system, not in myriad spreadsheets. This solution is used for accounts payable and accounts receivable, financial reporting, and production planning and scheduling. This includes processing the entire inventory turnover, from the start of job orders to the planning, consumption, and shipping of these orders.

They can now run ad hoc reports and queries in minutes instead of days. It reduced the risk of out-of-stock, late delivery, and lost invoices and orders, significantly improving customer experience. Sales have also grown by double digits; the new system has certainly given the company a competitive advantage.

, a civil engineering consultancy, was facing difficulty measuring which projects and segments were the most profitable. They decided to implement a new ERP system which increased the cash flow and profitability between jobs by connecting the entire company, including projects, finance, and material management.

They are now able to track sector-level performance and adjust the budget in order to manage project costs and maximize resource utilization. The ERP system measures performance against set goals and provides executives with the reports they need to focus their business development efforts on.

The company reduced its monthly closing from 5 days to 2 days and now pays twice a month in just 2 hours. In addition, easy access to historical data makes bid estimates more accurate, attracting more profitable contracts.

Western Digital is a technology company offering products in the areas of data storage, data systems and data solutions. The merger ofÌýWestern Digital, SanDisk and HGSTÌýin 2019 was a major challenge for the company. Western Digital aimed to centralize its ERP system so that all the three companies could be managed together. The company chose an ERP software which led to the integration of the business units in the areas of Information technology, HRM, Payroll, Customer relationship analysis, etc.

Drawbacks of ERP system

To get the maximum benefit from an ERP system, there are some challenges that enterprises must overcome.

Cost:ÌýThe main deterrent to ERP is the cost of pre-integration and ongoing subscriptions and licenses related to technology. Depending on the size of your company, purchasing, implementing, and maintaining your ERP system can be expensive. On-premises systems also require enterprises to consider infrastructure costs such as IT support and server hardware. As your organization grows and operational needs grow, companies may need to purchase additional supplementary services or software.

Implementation Challenges:ÌýEven with the complete implementation checklist, running an ERP system requires time, money, and resources. Organizations need to allocate internal staff resources to determine which ERP system to purchase. This can be a detailed process involving managers of finance, operations, IT, and in some cases, sales, marketing, and human resources. For companies with diverse spreadsheets and stacks of paper files, there may be a long data migration process that may require the hiring of an integrated specialist.

Conclusion

The need for a cheap, well-designed ERP system for small businesses is recognized in the current competitive organization culture. Small businesses require ERP systems to perform fast-paced and complex manufacturing processes. SMEs must carefully assess their requirements and select theÌýÌýthey want to use to enjoy the maximum benefits such as reduced costs, operational planning, CRM, etc.

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What is Big Data?

Big Data is a term that refers to large volumes of hard-to-manage data that inundate businesses on a day-to-day basis. It also refers to the field that deals with ways to analyze and systematically extract information or just deal with data sets that are too large or complex to be dealt with by traditional data-processing software.

The challenges to big data analysis include data capture, data storage, search, sharing, transfer, visualization, querying, updating, privacy, and data source. The complexity of big data makes it impossible to process using traditional methods. The concept of big data gained momentum in the early 2000s when Dough Laney defined big data as the three V’s- Volume, Velocity, and Variety.

Volume

Organizations collect data from a variety of sources and storing all of those data would have been too costly if not for new and cheaper alternatives like data lakes, Hadoop and the cloud.

Velocity

Internet of things warrants timely processing of data streams traveling at unprecedented speeds. These torrents of data need to be dealt with at near-real-time speeds.

Variety

Data can be of many types – structured numeric data in traditional databases to unstructured text documents, videos, audios, financial transactions etc.

Big Data in the present context

Big Data is synonymous with predictive analytics, user behavior analytics, or other advanced data analytics methods that extract value from big data. We rarely refer to a particular size of data set when big data is mentioned. The relevant characteristic of this new data ecosystem is to find new correlations to spot business trends, prevent diseases, combat crime and more.

The advent of widespread technology, including mobile phones, has tremendously increased the size and number of available data sets. Internet of Things (IoT) devices, aerial sensing, software logs, cameras, microphones, Radio Frequency Identification (RFID) and wireless sensor networks are just a few of the many cheap and numerous information sensing devices that add to the data pile.

ApplicationsÌýof Big Data

The increasing use of data-intensive technologies by developed economies has spurred the demand for information management specialists and data analysts across the globe. Between 1 billion and 2 billion people are accessing the internet and there are 4.6 billion mobile-phone subscriptions worldwide.

• Government Use of Big Data

Big data use in government processes allows efficiencies in cost, productivity and innovation. But it does have its flaws; for instance, theÌýÌýto search for patterns that have the potential to lead to illegal activities. Privacy is an aspect that is still being debated in the context of big data.

• International Development

Big data offers cost-effective opportunities to improve decision-making in critical development areas such as health care, employment, economic productivity, crime, security, natural disasters and resource management. User-generated data is also an avenue for the unheard to be heard. However, technological infrastructure and economic and human resource scarcities remain challenges to developing regions. These scarcities can potentially exacerbate existing big data concerns like privacy, imperfect methodology, and interoperability.

Big data has been significant in the fight against poverty by providing satellite imagery and machine learning for poverty prediction. For instance, the labor market and the digital economy were studied using digital trace data in Latin America.

• Healthcare

Big data analytics plays a significant role in healthcare through:

• Personalized medicine
• Prescriptive analytics
• Clinical risk intervention
• Predictive analytics
• Waste and care variability reduction
• Automated external and internal reporting of patient data
• Standardized medical terms and patient registries

Human inspection is impossible at the volumes of big data scales, and intelligent tools for accuracy, believability control and handling of information are required. These are conducted with the help of specific analytic tools. Data-driven analysis has helped in exploratory biomedical research as it can move forward faster than hypothesis-driven research. Computer-aided diagnostics is another aspect of healthcare that depends heavily on big data.

• Media

By providing actionable insights into millions of individuals through big data, a message or content that aligns with the consumer’s mindset can be conveyed. Consumers are tapped into with the help of targeted content that reaches people at optimal times in optimal locations. The current advertising ecosystem is a good example of this.

• Internet of Things (IoT)

The media industry, companies, and governments use the data extracted from IoT devices to accurately target their audience and increase media efficiency. The sensory data gathered from the devices are used in medical, manufacturing and transportation contexts. Ideally, the data from devices would help track and count everything and tremendously reduce waste, loss and cost.

• Information Technology

Big data has helped business operations as a tool to streamline the collection and distribution of Information Technology and improve employee efficiency. Deep computing and machine intelligence help IT departments predict and prevent issues.

• Machine Learning and Artificial Intelligence

Big data is a crucial component used in the training of complex models and facilitating Artificial Intelligence.

What is Cloud Computing?

It is the on-demand availability of computer system resources like data storage and computing power without direct active management by the user. Large clouds often have their functions distributed over multiple locations, with each location acting as a data center. Reliance on sharing resources to achieve coherence is a characteristic feature of cloud computing.

Cloud storage makes it possible to save files on a remote database instead of a proprietary hard drive or local storage device.

Types of Cloud Computing Services

Cloud computing services provide users with a series of functions including:

• Email
• Storage, backup, and data retrieval
• Creation and testing of apps
• Data analysis
• Video and Audio Streaming
• Software on demand

Benefits of Cloud Computing

1. Increased productivity, reduced costs, speed, efficiency, performance, security, and portability are just a few of the reasons why Cloud Computing has seen an increase in popularity lately. The heavy lifting involved in processing data is done by the remote system instead of the device that a person carries to work.
2. Being a fairly new service, cloud computing is used widely in the business space by big corporations, small businesses, non-profits, government agencies and even individual consumers.
3. Before cloud computing, businesses were required to purchase, construct and maintain costly information management infrastructure. Now companies can swap costly server centers and IT departments with fast internet connections over which employees can interact with the cloud to complete their tasks. This has resulted in tremendous cost and time savings.
4. Reliance on tangible software upgrade methods like discs and flash drives is replaced by faster methods of internet upgrades. It also helps individuals save storage space on their desktops or laptops through customer data cloud services.

Cloud Computing Concerns

1. Security has always been a natural concern when it comes to sensitive medical records and financial information. Encryption protects vital information, but the loss of the encryption key would result in data loss. The issue of security in cloud computing is an ongoing issue, with regulations forcing service providers to shore up their security and compliance measures.
2. Then there is the issue of server damage. For instance, a server company can fall victim to natural disasters, internal bugs, power outages and more. A blackout in one part of the world could affect the users accessing that data from another country.

Big Data on The Cloud

Big data before the cloud strained the financial and intellectual capital of even the largest businesses by the sheer amount of computing resources and software services needed to support the effort. With the advent of cloud computing technology that provides almost limitless computing resources and services, big data initiatives are now possible for any business, big or small.

Enterprise Case Studies for Big Data on the Cloud

• Netflix on AWS

Being one of the largest media and technology enterprises in the world, Netflix stores billions of data sets in its systems concerning audio-visual data, consumer metrics and recommendation engines. AWS gave the company a solution that would allow it to store, manage, and optimize viewers’ data and offered a platform that would enable quicker and more efficient collaboration on projects. It enabled Netflix to discover and respond to issues in real-time, ensuring high availability and a great customer experience.

• 2. mLogica on 51·çÁ÷HANA Cloud

mLogica, a technology and product consulting firm, wanted to move to the cloud to better support its customer’s big data storage and analytics needs. 51·çÁ÷HANA Cloud enabled the move from on-premises infrastructure to a more scalable cloud structure. It helped them to manage growing pools of data from multiple client accounts, improve slow upload speeds for customers, move to the cloud to avoid maintenance of on-premises infrastructure and integrate the company’s existing big data analytics platform into the cloud.

Advantages of Big Data in the cloud

• Scalability

The physical constraints of a typical business data center significantly throttle its ability to conduct business. A public cloud negates the space, power, cooling and budget requirements of a big data infrastructure by managing hundreds of thousands of servers spread across a fleet of global data centers. It also translates to savings in time as the cloud infrastructure is already built and ready to go.

• Flexibility

Businesses can choose to employ the required number of servers according to the budget and task at hand, and then later release them when the task is complete.

• Cost

Hardware, facilities, power and maintenance are just a few of the expenses for building and maintaining a business data center. The prohibitive cost and wastage are removed through a flexible rental model where resources and services are available on-demand and follow a pay-per-use model.

• Accessibility

The significant global footprint of cloud services enables resources and services to be deployed in most global regions. The data and processing activity can take place proximally to the area where the big data task is located instead of moving that data to another region.

• Resilience

Replication of cloud data across its servers is standard practice by service providers to ensure high availability in storage resources and maintain cloud data resilience.

Cons of Big Data in Cloud Computing and Ways to Overcome them

Even though the value of big data in cloud computing is tremendous, it’s not without its pitfalls. Businesses need to consider potential drawbacks before adopting a public cloud or a third-party big data service.

• Network Dependence

The effect of outages is a significant concern in any big data use of the cloud. Cloud use requires complete network connectivity from the LAN, through the internet, to the cloud provider’s network. An outage at any point in this chain could result in increased latency at the best of times and complete cloud inaccessibility at worst. Data replication and usage of reliable networks and cloud services can reduce this risk considerably.

• Storage Cost

Data storage can be a substantial long-term cost for big data projects on the cloud. The loading of data into the cloud is time-consuming and the storage instances incur a regular fee. Data migration, which refers to the transfer of data between servers, may incur additional fees and loss of time. Hence, comprehensive data retention and deletion policies must be employed by businesses to prevent retaining unnecessary data and deal with time-sensitive big data sets.

• Security

The data involved in big data projects can contain proprietary or personally identifiable data that is subject to data protection laws and other industry or government-driven regulations. Security of such data is paramount. Cloud users must take the steps needed to maintain security in cloud storage and computing through adequate authentication and authorization, encryption and copious logging of access and usage of data.

• Lack of Standardization

As there is no single way to implement and operate a big data deployment in the cloud, there is a potential for poor performance and exposure to security risks. Documentation of big data architecture, along with policies and procedures of use, should be prioritized by business users to sidestep potential problems. This documentation can become a foundation for future optimizations and improvements.

Choosing the right customer data cloud provider

Even though the underlying hardware gets the spotlight, it’s the analytical tools that make big data analytics possible. Providers such asÌýÌýcan arrange for support and consulting to help businesses optimize their big data projects so that they won’t need to start their initiatives from scratch.

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