Category: Blog

The Role of Data Management in Driving Digital Transformation

Digital transformation goes beyond the mere adoption of new technologies or tools. It entails a fundamental shift in how organizations harness the power of data to drive value, enhance customer experiences, and optimize business processes. Data is the fuel that propels digital transformation forward, posing challenges and opportunities for effective data management. 

Effective data management drives digital transformation by: 

  • treating information as an asset 
  • integrating data from diverse sources  
  • generating actionable insights aligned with strategic goals 

This blog post will explore how effective data management strategies and technologies are crucial for successful digital transformation initiatives. 

Why is Data Management Important for Digital Transformation? 

Data is key to digital transformation as it is a fundamental building block. In the digital realm, every interaction generates valuable data. This data is a foundation for establishing benchmarks and baselines, allowing businesses to track progress on their transformation journey. Historically, data was consumed for traditional reporting and analytics for stakeholders. 

However, in the era of digital transformation, data helps predict and prescribe. Data can help you anticipate customer needs, optimize business outcomes, and automate decision-making. It can further help you innovate new products, services, or business models that create competitive advantage and differentiation. 

To achieve these benefits, you need a robust data management framework that can handle the volume, velocity, variety, integrity, and value of data in the digital world. You must also have a clear data strategy that aligns with your business strategy and defines your data vision, objectives, priorities, roles, responsibilities, policies, standards, and metrics. 

Key Roles Data Management Plays in Digital Transformation 

Improved customer experience: Data management enables you to understand your customers better by collecting and analysing data from multiple touchpoints across their journey. You can use this data to personalize your offerings, tailor your communications, anticipate their needs, and resolve their issues faster. 

Enhanced operational efficiency: Data management lets you streamline your business processes by integrating and automating data flows across your systems and applications. You can use this data to monitor your performance, identify bottlenecks or errors, and optimize your resources. 

Increased innovation and growth: Data management enables you to discover new opportunities and insights by combining and analyzing data from different sources and domains. You can use this data to test new hypotheses, experiment with new solutions, and scale up successful initiatives. 

Five key Data Challenges in Digital Transformation w.r.t Data Management 

Along the transformative journey, businesses often encounter various data challenges that can hinder their progress. Below are some of the key data challenges faced when implementing digital transformation initiatives. 

Data silos and fragmentation: One of the significant hurdles in digital transformation is the existence of data silos across different departments or systems within an organization. This fragmented data landscape makes gaining a holistic business view difficult and hampers data-driven decision-making. 

Data quality and accuracy: Data quality issues can undermine the success of digital transformation initiatives. Accurate, complete, consistent data can yield good insights and reliable outcomes. Implementing data governance practices, including data cleansing, standardization, and validation, becomes crucial to ensure data accuracy and reliability. 

Cost and complexity: They are significant considerations regarding data transformation. The process requires dedicated tools and expertise, which can be financially burdensome and challenging to acquire and sustain. 

Data Integration and Compatibility: Digital transformation often involves integrating new technologies, applications, and data sources into existing systems. However, compatibility issues between legacy systems and modern solutions can pose significant challenges. 

Data culture and data literacy: The transition to data requires the development of a data-centered culture, which sees data as a tool and teaches employees data literacy and data management. 

Effective Data Management Strategies for Digital Transformation 

To fully capitalize on the potential of digital initiatives, businesses must implement effective data management strategies. 

Identify business objectives: Start by understanding what the business units want to achieve with their data and how it will support their digital transformation goals. Identify the key business problems to solve, the key performance indicators (KPIs) to measure, and the expected outcomes and benefits. 

Create robust data processes: Then, think through how the data will be gathered, processed, stored, and shared. Ensure the data is consistent, complete, accurate, and secure across the organization. Define the roles and responsibilities of the data owners, stewards, producers, and consumers. Establish clear policies and standards for data quality, security. 

Establish data governance strategy: Implement a data governance framework that provides oversight and guidance for data management activities. Data governance ensures that the data is trustworthy, compliant, and aligned with the business objectives. It also facilitates collaboration and communication among the data stakeholders and promotes a culture of accountability and transparency. 

Agile data architecture: Digital transformation demands an agile data architecture that can adapt to changing business needs. Organizations should design their data infrastructure to be scalable, flexible, and capable of handling diverse data types allowing for seamless integration of new technologies, such as cloud computing or IoT devices, and ensuring data availability for real-time decision-making. 

Data analytics and insights: Leveraging data analytics and insights is key to deriving value from digital transformation. Organizations can extract meaningful insights from their data by implementing advanced analytics tools and technologies. 

Technology solutions play a vital role in enabling efficient data management for digital transformation. Choosing right technology tools are essential to avoid common data management mistakes. These solutions provide the necessary infrastructure, platforms, and tools to streamline data management processes, ensuring data quality, accessibility, and security. Here are key technologies that are defining for an efficient data management. 

Cloud-based data management platforms: Cloud-based data management platforms allow organizations to store and process large volumes of data without requiring extensive on-premises infrastructure. The flexibility and agility provided by cloud deployment enable businesses to access, analyze, and derive insights from their data more efficiently. 

Data integration and ETL tools: By automating the processes of extracting, transforming, and loading data, these tools enhance data quality, streamline operations, and accelerate data-driven insights with real-time data integration capabilities and advanced features for data transformation. 

Master Data Management (MDM) Solutions: MDM solutions enable organizations to establish a single, trusted source of master data, such as customer or product information. These solutions provide a centralized repository for managing and harmonizing master data across various systems and departments. 

Big data and analytics platforms: They allow organizations to process, analyze, and derive insights from massive volumes of unstructured data. Their scalability and real-time processing capabilities empower organizations to seize the full potential of data. The advanced data visualization tools enable businesses to gain a competitive edge in the digital age.  

Conclusion: 

Data management is critical to successful digital transformation by implementing robust data management strategies and leveraging technologies. Organizations can harness the power of their data to drive innovation, gain a competitive edge, and thrive in the digital age. 

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 Solutions brings forward thinking and innovation to a new level with years of technical and industry expertise and complete transparency. Artha Solutions 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.

Creating A Competitive Edge With Talend Data Management

Talend is an ETL tool that offers solutions for big data, application integration, data integration, data quality, and data preparation. Talend’s big data and data integration tools are widely utilised.

Customers are given access to Data Integration and Data Quality features through the Talend Data Management Platform, which may be used for batch data processing.

What is Data Management?

Creating and maintaining a framework for ingesting, storing, mining, and archiving the data essential to a modern organisation is known as data management. The information lifecycle is connected via a spine called data management.

Together, process management and data management make sure that team choices are supported by the cleanest, most recent data possible. This calls for real-time observation of alterations and trends in the modern environment.

Benefits of Talend Data Management Services

To provide a better customer experience, data management techniques assist firms in identifying and addressing internal pain spots.

Data management, in the first place, gives firms a tool to gauge the volume of data at hand. Any organisation has a plethora of interactions going on in the background, including those between network infrastructure, software applications, APIs, security protocols, and much more. Each of these interactions has the potential to cause a problem (or function as a time bomb) for operations. Data management provides managers with a comprehensive view of business operations, which aids in perspective and planning.

Once data is managed, business intelligence—informational gold—can be extracted from it. This offers numerous benefits to business users throughout the organisation, such as the following:

  • Savvy marketing that targets consumers based on their connections and interests.
  • Comprehensive security that protects important data
  • Adherence to pertinent regulatory standards, which saves both time and money.
  • Automatic and ongoing improvement is powered by machine learning that becomes more environmentally conscious over time.
  • Lowered running costs by only using the storage and computing power necessary for optimum performance.

Additionally, consumers and buyers gain from effective data management. Businesses may provide clients with quicker access to the information they desire by getting to know their preferences and purchasing patterns. Customers and potential customers can benefit from personalised shopping experiences and have confidence that personal and payment information will be utilised and maintained with data privacy in mind, making purchases easy.

Top retailers use data management to develop omnichannel browsing and purchasing experiences that cater to client demand nearly instantly. Data management that is done well powers all of that.

Best Data Management Practices

Creating a framework will ease the way for simpler, more efficient data management solutions, even while specific data needs are particular to every organization’s data strategy and data systems. The three best practises listed below are essential to a winning plan.

Create a plan

Create a data management plan and write it (DMP). This report displays estimated data usage, accessibility standards, archiving techniques, ownership, and other information. A DMP will be updated as circumstances change and serves as a living record and reference.

DMPs also provide investors, auditors, and other interested parties with the organization’s overall data management plan, which is a crucial indicator of how well-prepared a business is for the challenges of the contemporary market.

Store your data

A sound data storage strategy is essential to effective data management, in addition to the specifics stated above. In order to start, you must decide whether your storage requirements are best met by a data warehouse, a data lake, or both, and whether the company’s data should be stored on-premises or in the cloud.

Define a consistent agreement for naming files, folders, directories, users, and more, and make sure it’s followed. This is an essential component of data management since errors and insufficient intelligence will emerge from inconsistent storage of future data, which will be determined by these criteria.

  • Backups and security.
  • The key is documentation.

Any system should be adaptable and have a fair archiving strategy to keep expenses in check. Data storage needs to be able to change as quickly as the technology requires.

Share your data

You should start the process of sharing your data with the right individuals once all the arrangements for storing, safeguarding, and documenting it have been made.

Finding a location and a method for distributing the data is necessary once those and other problems have been resolved. This position, once known as a repository, is rapidly being supplied by big data management-specific software and infrastructure as service models.

Conclusion

Nearly every industry’s firm needs to deal with the effects of big data. Managing all that data becomes increasingly crucial as its use for real-time decision making grows in importance to keep businesses competitive and customers engaged.

A crucial first step toward better data health overall and ensuring that you are getting the most value out of your data is proper data management.

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.

 

Data Science Solutions: Reinvents Business Operations

Data science is a vast subject with numerous possible uses. It reinvents how businesses run and how various departments interact, going beyond simple data analysis and algorithm modelling. Every day, data scientists use a variety of data science solutions to solve challenging problems, such as processing unstructured data, identifying patterns in massive datasets, and developing recommendation engines. They also use artificial intelligence, machine learning, and advanced statistical methods to solve these problems.

Data science has numerous advantages for firms that are felt across a variety of organisational activities. Data Science is being used by businesses to turn data into competitive advantages, hone products and services, and identify customer churn using information gathered by call centres so that marketing may take action to retain customers. Machine learning and product recommendation systems, which consider socioeconomic data points to guide how to market to clients, are used by marketers to target customers.

How is it helping businesses?

Data science aids in the analysis and extraction of patterns from business data, allowing for the organisation of these patterns to support corporate decision-making. Companies can determine which trends are most appropriate for their operations at different times of the year by applying data analysis tools from data science.

Data science practitioners can estimate future client needs for a particular product or service by using tools and approaches based on data trends. Businesses and data science can collaborate closely to better understand consumer preferences for a variety of products and implement more effective marketing strategies.

Data Science currently uses other cutting-edge technologies like machine learning and deep learning to broaden the scope of predictive analytics. This improves decision-making and produces better models for forecasting financial risks, consumer behaviours, or market trends.

Future-proofing judgements, supply chain forecasts, market trends analysis, product pricing planning, automation of various data-driven processes, and other jobs are all aided by data science.

Data Science Solutions Industry Applications

Let’s now examine how Data Science is enabling many business sectors with its interdisciplinary platforms and tools:

Data Science Solutions in Banking: For risk analytics, risk management, KYC, and fraud reduction, the banking sector are heavily reliant on data science solutions powered by big data tools. Advanced Data Science (driven by big data, AI, and ML) is used by large banks, hedge funds, stock exchanges, and other financial institutions for trading analytics, pre-trade decision-supportanalytics, sentiment assessments, predictive analytics, and more.

Data Science Solutions in Marketing: To create recommendation systems and to study consumer behaviour, marketing departments frequently employ data science. When we discuss data science in marketing, “retail marketing” is what we are mostly focused on. Analyzing consumer data is a key step in the retail marketing process since it helps businesses make decisions and generate income. Customer, product, sales, and competition data are frequently used in retail marketing. AI-powered data analytics solutions make significant use of customer transactional data to boost sales and deliver top-notch marketing services. To increase sales efficiency, chatbot analytics and sales representative reaction data are combined.

Data Science Solutions in Finance and Trading: Finance departments employ data science to develop trading algorithms, control risk, and enhance compliance. Data science solutions are used in finance and trade. Data on the financial markets will be largely used by a data scientist in the finance industry. This comprises information on the businesses whose stock is traded on the market, investor trading activity, and stock prices. The financial data is unorganised and disorganised; it is gathered from various sources using various forms. Therefore, the data must first be processed and transformed into a structured format by the data scientist. Building algorithms and other models requires this. For instance, the data scientist might create a trading algorithm that takes advantage of market inefficiencies and profits the business.

Data Science Solutions in Human Resources: Human Resources (HR) departments use data science to manage employee data, acquire top talent, and forecast employee performance. The data scientist in HR will largely make use of employee information gathered from various sources. Depending on the method used for collection, this data may be structured or unstructured. The most typical source is a human resources database like Workday. The data must first be processed and cleaned by the data scientist. Insights from the data require this, hence it is necessary. The data scientist might forecast the employee’s performance using techniques like machine learning. This can be accomplished by using previous employee data and the features it includes to train the algorithm. The data scientist might, for instance, use previous data to create a model that forecasts employee performance.

Conclusion

Data Science is an interdisciplinary science that analyses data and finds patterns using math, engineering, statistics, machine learning, and other fields of study. Any industry or field of study can apply data science applications, however most of them include data analytics for business use cases. Data science frequently aids in your comprehension of potential customers and their purchasing requirements.

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.

Are Your Data Governance Initiatives Failing? You must read this

In today’s dynamic and ever-changing organisational environment, data governance is a pressing need. Businesses today collect enormous amounts of data from several sources while data governance aids in risk management, value maximisation, and cost reduction of the data accumulated.

Data governance, in a nutshell, is the activity of being aware of where your data is, how it is being used, and whether or not it is sufficiently safeguarded. Data integrity, consistency, and proper handling are all guaranteed by effective data governance.

Before the appropriate software can be implemented, the company, its organisational structure, processes, and the roles that need to be specified should be taken into account when it comes to data governance.

Signs of a failed Data Governance Program

Various people within the same organisation have different definitions of the same terminology.

The majority of businesses use a tonne of jargon and language that might signify various things to different people. Everything is highly subjective, and this is typically due to the culture of an organisation. The meaning of different terminology might change depending on how they are used within organisations. And that’s okay, but you should still use caution. Data governance is time-consuming and requires a lot of work, especially in the beginning. It makes sense that people would want to speed up this process.

Inactive stakeholders and a limited budget

The absence of managerial support is another factor in the failure of many data governance initiatives. An effort will nearly never be successful if senior management does not recognise the advantages of data governance and only considers the expenses involved. 

First, there is a chance that the necessary procedures won’t be carried out properly. Additionally, due to costs, important changes might not be made or the programme might need to be terminated early.

Because of the legislation that supports it, such as the GDPR, it is now simpler than ever to find the funding required for a first data governance programme. However, it is essential that management also makes enough long-term resources available to continuously fund all of the roles and responsibilities necessary for effective data governance.

If your stakeholders aren’t willing to back up their claims with deeds, this suggests that the initiative isn’t being taken seriously enough and that its worth isn’t recognised.

Data Governance is only being implemented because of regulations

It is quite tempting for organisations to consider doing the bare minimum to appease the regulator if they are under pressure to implement data governance. This is a serious error because, over time, these organisations wind up working harder than they would have if they had adopted data governance correctly in the first place. Additionally, they pass on all the business advantages that come from enhancing their data management procedures.

The traditional tick-box method of data governance is task-focused and totally disregards the people involved. They provide a list of the tasks that must be completed and issue warnings if the tasks are not finished. As a result, people perform their tasks out of obligation and fail to recognise the true value of their work.

As a result, it will be challenging to implement your data governance system across your organisation, and you will always be pursuing individuals to ensure that they are abiding by the rules. 

Regulators have a history of changing the goal posts, so if you haven’t integrated data governance into your organisation, you’ll probably have to start over every time they alter the rules and update the checklist, which means using the new checklist.

No Data Quality issues being reported

If data users aren’t reporting data quality issues, this means that either they are unaware of your process to investigate and fix issues, they don’t believe you will be able to change anything (possibly due to years of no one being interested in improving data), or they may not realise that the manual workarounds they must perform on a daily, weekly, or monthly basis are due to poor data quality, and that everything could be simplified and improved if the underlying data were of higher quality. 

Whatever the cause, communication is the key. Additionally, any data governance strategy will undoubtedly fail if you don’t engage with your data users.

It is not discussed outside of IT

Getting stakeholders to take charge of data governance projects and take ownership of their data is essential for its success. It is pretty typical for IT to be in charge of the data governance programme when I conduct a health check on data governance for businesses that are having problems.

Always with the finest of intentions, this is done. Even though IT does not actually own the data, they are often the first in an organisation to recognise the need for appropriate data governance since they are aware of the consequences of improper data management.

Due to a misconception that exists between the infrastructure and the data, businesses frequently delegate data governance to IT. It may make sense to give IT control of the data governance endeavour if you work for a company that still thinks IT controls the data.

An IT-led data governance strategy, however, may run into difficulties. An IT-led data governance programme makes it more challenging for the company to take ownership of its data, which is a prerequisite for true data governance to take place.

Building a fail-safe Data Governance Program

 

Assess the success of your governance programme

Data governance is concerned with how decisions are made, not how those decisions turn out. It’s also true that typical corporate performance metrics don’t necessarily apply. The number of people covered by the programme — those assigned specific tasks, trained in processes, or made aware of policies; the number of data sources that have a related governance policy defined and applied to make operational, tactical, or strategic decisions; and observed improvements in the program’s effectiveness — are among the metrics that can help track the success of a governance programme and demonstrate that the organisation is better informed, resilient, and accountable.

Assemble a virtual team of data professionals for compliance

Many of us work in industries with strict regulations, such as the public sector, the medical field, and the financial industry. Though compliance cannot be ensured, it is essential to build trustworthiness and uniformity. A virtual staff with a focus on data policy can keep track of compliance challenges.

Data practitioners, such as database architects, software engineers, and business analysts, who deal directly with the data sources used by the governance programme but do not report to a more official compliance department, should make up the team. The team should regularly review the laws that are relevant to the governance programme, decide where to strengthen or expand the program’s regulations, and keep an eye out for occurrences, problems, and advancement.

Good governance concepts include preparing the way for compliance without obstructing corporate activities. A governance programme that takes compliance seriously lightens the workload and reduces stress for other employees.

Close to the source, protect your data

Today’s security industry is very specialized – the sophistication of threats is increasing. To protect corporate systems from outside attacks, one needs a full-time job. In a large, dynamic firm, it could be challenging to keep up with access rules and permissions.

Effective collaboration between the teams in charge of data governance and security is essential. Data access regulations should be adhered to as closely as feasible to the original data, according to the governance team.

Applying security rules shouldn’t be dependent on client tools like business intelligence or data visualisation systems. By the time a BI user views the data, it may have already passed across open, unprotected channels. Security for BI shouldn’t be regarded as a mission-critical function, despite being a useful feature.

Don’t put your trust in data privacy protections

With good reason, consumers throughout the world are becoming more concerned about data privacy and don’t think businesses have their best interests at heart. A data governance programme helps increase client trust in the company’s business practices.

Be clear about the privacy practices of your business and allow customer control over their information. Nowadays, it’s typical for websites to ask users to specify their cookie policy. Prior to using a customer’s data for things like market research, product creation, and demographic analysis, it’s important to get their permission.

Put policies in place that enforce preferences at all organisational levels. Some regulations may be applied by code or other technological means.

Consider the secondary advantages of sound Data Governance

A well-run system facilitates effective reuse of previously developed analytics and reports and enhances access to data. Policies specify in advance what information is appropriate for a role and may be confidently provided. Ad hoc requests for data access that are disruptive to IT and prone to error, including the compliance risk of over-provisioning permissions just to get the job done, are common in poorly controlled systems.

A choice based on well-governed data is likely to be more collaborative, better understood, and have wider support, even though this isn’t the core purpose of data governance. When teams collaborate to create policies rather than assembling them along departmental lines, confidence in the process creates confidence in the conclusion.

Ensure that you are open, aware, communicative, and trained

A number of these procedures are built upon an organization-wide awareness of the data governance procedure. If data users are unaware of the programme, data governance cannot be successful.

  • Be transparent about the programme, its objectives, and its performance metrics. Share the measurements, describe the processes, and publish the governance approach.
  • All staff onboarding procedures ought to include a section on programme awareness. Work with those teams and HR to get data governance on the same track as compliance training that already exists for topics like harassment issues.
  • Include pertinent portions of the governance plan and how it relates to the tools and platforms under consideration in all technical training connected to data, such as the implementation of BI tools.
  • The significance of data quality, the laws around it, and how to find and reuse authorised data sources must all be covered in training. Particularly data analysts and report writers shouldn’t feel as though policy is being demanded at the expense of adaptability. Instead, they ought to view controlled data as a resource that creates new opportunities for confidently observing the rules.
  • To demonstrate that the data has been correctly governed, a “governance assured” seal of approval can be placed on dashboards, reports, and other artefacts.

Conclusion

Data governance is a challenging process, particularly when you initially begin. On the other hand, a well-governed data infrastructure that follows these best practices will be advantageous to business units, IT, clients, and business partners.

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.

 

Cloud Migration Strategy – 6 Steps to Ensure Success

As organisations progressively shift their apps to the cloud to stimulate growth, success in the contemporary digital environment entails embracing the potential of the cloud.

Despite making such significant investments in the cloud, one in three businesses never reap the rewards. After adopting the cloud, 33% of firms reported little to no improvement in organisational performance. Moving to the cloud is a difficult and expensive process. So, how can a cloud project failure be prevented?

The secret to solving the problem lies in careful planning and selecting the optimum cloud migration approach for your IT assets. In order to develop a path for migration and make the switch to the cloud more easily, this article intends to help you better grasp an appropriate cloud migration plan.

What is a Cloud Migration Strategy?

An organization’s high-level plan for moving its current on-premises and/or co-located application workloads and the data they generate into the cloud is known as a cloud migration strategy. The majority of plans contain a strategy for moving to a public cloud provider, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure, or another. Not all workloads are appropriate for migration, even if the majority of workloads will benefit from cloud migrations.

Prioritizing workloads for migration, selecting the best migration approach for each workload, creating a pilot, testing it, and modifying the strategy in light of the pilot’s findings are all components of a successful enterprise cloud migration strategy. To lead teams through the procedure and enable roll-back if necessary, a cloud migration strategy document should be prepared.

The effort and expense of the migration will be greatly affected by the architecture similarities and suitability of the migration tools between the source and destination platforms.

The ideal outcome is a smooth transition of the applications from the current on-premises infrastructure to the required cloud architecture without interfering with application availability or regular business activities.

Why is it important for Companies to have a Cloud Migration Strategy in place?

Adopting a cloud migration plan aids in locating and carrying out the on-premises to cloud conversion that is the quickest, least disruptive, and least expensive. Additionally, it can be used to decide which workloads of already-existing applications should be replaced or discontinued, which ones should be rewritten, which ones can stay on-premises, which ones should be moved as-is to a cloud platform to run as-is or targeted to be supplemented with native cloud services, and which cloud is the best fit for which application. These methods will be combined in a corporate cloud migration plan to target the complete application portfolio.

Adopting a cloud migration plan aids in locating and carrying out the on-premises to cloud conversion that is the quickest, least disruptive, and least expensive. Additionally, it can be used to decide which workloads of already-existing applications should be replaced or discontinued, which ones should be rewritten, which ones can stay on-premises, which ones should be moved as-is to a cloud platform to run as-is or targeted to be supplemented with native cloud services, and which cloud is the best fit for which application. These methods will be combined in a corporate cloud migration plan to target the complete application portfolio.

Since every organisation is unique, an enterprise cloud migration strategy should be specifically suited to each one’s demands in order to accomplish the required technical and business results. Understanding the business objectives and the application portfolio is necessary to gain insight into the TCO and ROI of a migration project.

Since every organisation is unique, an enterprise cloud migration strategy should be specifically suited to each one’s demands in order to accomplish the required technical and business results. Understanding the business objectives and the application portfolio is necessary to gain insight into the TCO and ROI of a migration project.

What does a Cloud Migration Strategy work?

Organizations should think about the design and requirements of each application before deciding on migration options, as well as the skills, resources, and timeline needed to achieve the desired results. Teams must first examine what is already in place before evaluating the maturity of each workload. This may demand a complete discovery stage for each instance of each application that is currently in use.

Following that, the workload migration process should be planned with milestones, distinct targets, appropriate timeframes for each activity, and an awareness of potential dangers.

Every migration should begin with a test run, possibly by relocating or rehosting. This makes it easier to spot any gaps and make necessary adjustments going forward.

The company should concentrate on three crucial areas of optimization in order to carry out a migration at scale:

  • Application and instance customization for maximum performance
  • Keep your attention on operations and governance.
  • Create the managed services team with the necessary experience to manage the migration and operations.

What must a foolproof Cloud Migration Strategy have?

The key practises to adhere to when creating a cloud migration strategy are listed below.

Set Goals

All parties participating in the cloud migration process must be aware of the plan’s objectives and be on the same page in order for it to succeed. Because of this, creating goals is a crucial phase that must be included in the process. Your cloud migration strategy’s goals should be formally stated and documented. Ideal goals include the baseline for the existing infrastructure and a few key performance indicators (KPIs) to monitor cloud migration activity.

Decide which assets will move when

You cannot develop a cloud migration strategy based solely on an overview of the current infrastructure. The system’s hardware, data, services, and applications all need to be evaluated in detail by the system’s stakeholders. The first step in determining how and when to relocate each component of the system is to create a list of assets and interdependencies. It is quite doubtful that a company could simply forklift its entire infrastructure and move it to the cloud.

Ideally, firms relocate the less-important components first and only move the important ones once the proper support infrastructure is in place. New applications must be developed, migrated, and tested using the selected approach. The strategy should also outline how the older system will be decommissioned when all relevant components have been migrated.

Make use of price estimators

Although cloud expenses initially appear to be modest, given their dynamic nature, they could suddenly increase. Things that appeared to function properly in the on-premise architecture can turn out to be costly errors in the cloud. Enterprises can utilise the cloud cost estimators offered by the majority of IaaS cloud vendors to make sure that these charges don’t snuck up on them.

These calculators can be used by cloud migration teams to estimate the total cost of the intended arrangement. The AWS pricing calculator from Amazon, the Azure pricing calculator from Microsoft, and the Cloud pricing calculator from Google are a few examples of cloud pricing calculators. Additionally, they offer advisers who can offer timely advice for the best cloud configurations. These calculators can also be used to determine how costs will increase in line with the company’s long-term objectives.

Maintain a working disaster recovery plan (DRP)

Unsettling a tried-and-true arrangement is required when migrating to the cloud. Even with the most well-defined plans and execution, backups and fail-safes must be set up to handle unanticipated outages. In this scenario, a disaster recovery plan (DRP) can be useful. Although most firms have DRPs in place, they rarely update and test them. Organizations must make sure their DRP are impenetrable while creating the cloud migration strategy and carry them ahead as necessary.

Educate every staff

Working with cloud technology is very different from working with in-house technology, particularly if a company is switching for the first time from traditional infrastructure to the cloud. In order to operate with a certain cloud provider, employees must receive training. Simply because there are ongoing updates, this training cannot be done once. As a result, the cloud migration strategy must also take into account the time and money spent on this training.

Eliminate vendor lock-in

Given that this will be a long-term partnership, selecting the best cloud vendor is crucial. The decision to select a cloud vendor is based on both the company’s future technology roadmap and the services it already offers. The company will eventually lose its crucial competitive edge if the provider is not on the cutting edge of technology. Some businesses choose to work with many cloud vendors to reduce this risk. Utilizing the top features offered by several vendors is simple with the multi-cloud configuration.

Document Everything

A formal business process, cloud migration requires documentation at each stage. This documentation should cover the goals, materials, migration strategies used, cost analysis, and plans for testing and training. The document will be used by all parties involved and as a reference for compliance audits.

Test and Gauge the success of the Migration

After the actual migration, the process of moving to the cloud continues. It doesn’t end until all of the new cloud setup’s services and applications have been tested and all of the parts from the previous arrangement have been retired. The strategy must include the testing timetable. After testing is complete, short- and long-term success must be evaluated using the KPIs established during the goal-setting phase.

Continue to follow updates

As was previously noted, some businesses only use the cloud to make their infrastructure future-proof. This occurs as a result of the constant release of new capabilities by cloud providers. It is the responsibility of the organisation to update itself and stay current with new features. To guarantee that the organisation fully benefits from the cloud, an update cycle must be incorporated into the cloud migration strategy.

Put automation first

The cloud computing ecosystem is evolving quickly, and changes are ongoing. Many cloud services are effectively uncontrollable “black boxes,” with very few controls that may be overridden by the company. Middleware tools must be used to automate various procedures as necessary. Processes for continuous integration (CI) and continuous delivery (CD) must be established.

Advantages of having a Cloud Migration Strategy

The advantages listed below persuade businesses to move their resources to the public cloud:

Scalability: Cloud computing can scale far more easily to accommodate more users and heavier workloads than on-premises equipment can. To scale up business services in traditional IT settings, businesses had to buy and install physical servers, software licences, storage, and network equipment.

Cost: Managed services from cloud providers can reduce your operational costs and make procedures like upgrading easier. Businesses that move to the cloud might spend a lot less on IT operations. They can invest more money in innovation by creating new items or enhancing ones that already exist.

Performance: Performance and user experience can both be enhanced by moving to the cloud. Cloud-hosted programmes and websites can readily scale to handle more users or higher throughput, and they can operate in close proximity to end users to minimise network latency.

Digital experience: Customers and employees alike can access cloud services and data from any location. This improves the customer experience, supports the digital transformation process, and enables staff workers access to cutting-edge, flexible solutions.

Cloud Migration Strategy best practices

These are the six most popular strategies, collectively referred to as the “six R’s of migration”:

Rehosting (Lift and Shift)

This entails removing your stack from on-premises hosting and moving it to the cloud, as the name suggests. For the quickest return on investment, you move an identical duplicate of your current environment without making major modifications. Rehosting is a good option for businesses with conservative cultures or those without a long-term plan for utilising advanced cloud capabilities.

Replatforming

Replatforming is a version on the lift and shift that entails making a few more modifications to prepare your landscape for the cloud. The fundamental structure of apps remains the same. For conservative firms that wish to boost system performance while establishing trust in the cloud, this is also a smart method.

Repurchasing

Moving your apps to a new, cloud-native product—most frequently, a SaaS platform—means achieving this (for example, moving a CRM to Salesforce). Losing familiarity with older code and educating your team on the new platform present challenges. Even yet, if you’re leaving behind a highly tailored legacy environment, repurchasing might be your most economical choice.

Refactoring

Refactoring, also known as rearchitecting, entails starting over with your applications. This is typically motivated by a business need to utilise cloud capabilities, like cloud auto-scaling or serverless computing, that are not present in your current setup. Refactoring is typically the most expensive choice, but it is also the most forward-compatible.

Retiring

You can discover some applications are no longer useful after evaluating your application portfolio for cloud readiness. Simply turn them off in this situation. The savings that follow could potentially strengthen your business case for relocating applications.

Retaining

Adoption of the cloud is still not practical for some businesses. Are you prohibited from taking data off-site due to compliance issues? Maybe you’re not ready to give a recently updated app top priority. Plan to return to cloud computing later in this situation. Only relocate what is necessary for your business.

Conclusion

Although moving to the cloud can be challenging, it doesn’t have to be if you have the correct information and guidance. Although not absolute, these strategies are a surefire approach to begin planning your migration. The choice of options also depends on the migration model that your firm has adopted, such as Platform as a Service (PaaS), Software as a Service (SaaS), or Infrastructure as a Service (IaaS). There is no one-size-fits-all method, so your migration tactic can combine some of these strategies or use them all.

Take heart! With this innovative technology, you can start too! To determine what would work best for your needs, build a structured framework, and carry out your migration strategy, get assistance from the appropriate cloud services partners.

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.

 

How MDM Lite will help Improve the Standards of Your Master Data Management

Efficiency is the key to functionality in the long run. Companies and businesses go length and breadth to achieve efficiency in all parts of their operations.

From short-run operations to long-term outputs running a business efficiently and effectively is the main task for the top management. It is the management’s responsibility to avail better and brighter forms of functioning for its employees and clients.

Master data management is one such service that aids the management and operations team to give you an efficient single authoritative view of information from the various streams of inefficient data silos.

What is Master Data?

Master data, in simple terms, means critical data pertaining to the business, customers and vendors. A well-known example of master data is clientele information database.

When a business collects customer names, phone numbers and product history, master data management ensures that the data is used to manage, centralise, organize, categorize, localize, synchronize and enrich master data according to the business rules of the sales, marketing and operational strategies of the company.

It gives a trustworthy view of the company’s data and makes it available for all other business functions. This data is necessary for the functioning of analytical and operational needs.

Some of the instances where data management is required are enterprise resource planning (ERP), customer relationship management (CRM), product lifecycle management (PLM), enterprise performance management (EPM), and others.

Here are 7 Reasons Why Proper Master Data Management is Required

  • Reduces Workload – MDM data management centralises the process of data management instead of the responsibility spanning every department to collect and maintain data that is available at a corporate-wide level.
  • Calculated Decision Making – With a holistic and comprehensive view of the company’s complex data, executives at the topmost level can rely on the latest, detailed sets of data to make trustworthy decisions.
  • Improves Data Quality – Unstructured and decentralised data is a deficit for any company. Master Data Management overcomes unstructured decentralised data to reach the functional leads of the company across departments. In simple words, MDM is the single source of truth that is superior in quality and up-to-date.
  • Defines Data Governance and Compliance Procedures – Master Data Management defines a transparent governance process. This implies that each piece of master data is only gathered once. With this, setting up compliance procedures is ensured by a centralised data approach.
  • Reduces Time-to-Market – Instead of manually setting up master data, connecting it to the primary master data system directly provides the required master data. As a result, the time to market for new approaches, solutions, or functionalities is accelerated instantly and organically.
  • Improves Business Process – Master data systems are user-friendly, and employees can access the latest and superior quality master data whenever needed to support their operations. The quick access to the vast knowledge base results in higher efficiency among the management.
  • Eliminates Manual Processes – Handheld operations are a source of inefficiencies, failures and errors. These operations are also poor at communicating across departments with various variables at work all the once. With MDM, an expansive range of integration is possible.

Master Data Management is the key answer to many master data related solutions and at par with its utility, MDM services also require expensive costs to set up which will take a considerable amount of time to break even in a business. This is where MDM Lite can be a breath of fresh air for businesses.

What is MDM Lite?

Sharing the core values of Master Data Management, MDM Lite is a one-stop solution for all kinds of master data. MDM Lite has web-based operations, enables integration and collaboration across the organisation, fulfils monitoring and security and also has a user-friendly interface with access from anywhere to respective operational users.

Benefits of MDM Lite:

  1. Simple and easy to use
  2. Overall Economical
  3. Maximises the value of SAP Licencing cost
  4. Controls and streamlines process
  5. Improves quality and compliance of master data
  6. Reduces manual dependency
  7. Lower processing times
  8. Enables integration and collaborate

Why choose Artha Solutions MDM Lite Data Management Accelerator?

Artha possesses a unique Master Data Management (MDM) proposition in collaboration with Amurta’s Data Insights Platform (DIP) (leading data governance and MDM platform).

Artha’s MDM Lite is a lightweight solution that is easy to build and maintain. Artha delivers this solution using Talend data fabric, joined with Amurta’s Data Insights Platform (DIP), to provide a comprehensive solution.

It demands without the need to spend substantial amounts of money in license costs and years in implementations.

Artha’s MDM Lite goes hand-in-hand with Amurta’s DIP platform, which offers rich data quality and data governance tools that ensure that the input source data is of the highest quality.

To conclude, the MDM Lite Data management accelerator when applied to your organisation, updates legacy systems, corrects conflicting sets of data, reduces human dependency and errors, and provides fuzz-free access among members of the organisation, hence reducing duplication.

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.

 

Make the Most Out of Your Data With a Data Ingestion Framework

Forward-thinking businesses use data-based insights in today’s fast-paced global market to identify and seize major business opportunities, create and market ground-breaking goods and services, and keep a competitive edge. As a result, these businesses are gathering more data overall as well as new sorts of data, like sensor data.

However, businesses need a data ingestion framework that can assist them in getting data to the appropriate systems and applications quickly and efficiently, if they are to swiftly process and deliver relevant, accurate, and up-to-date data for analysis and insight.

You can increase the accessibility of multi-sourced data across your organization, take advantage of new analytics tools like big data analytics platforms, and extract more value and fresh insight from your data assets if you have a flexible, dependable data ingestion framework and a high-performance data replication tool.

What is Data Ingestion Framework?

The process for transferring data from numerous sources to a storage repository or data processing tool is known as a data ingestion framework. Data ingestion can be done in one of two ways: batch or streaming. There are many different models and architectural approaches that can be used to construct a framework. Your data source(s) and how rapidly you require the data for analysis will determine how you ingest data.

1. Batch Data Ingestion

Before the emergence of big data, all data was ingested using a batch data ingestion framework, and this approach is still widely employed today. Batch processing groups data and periodically transfers it in batches into a data platform or application. Even though batch processing typically costs less – since it requires fewer computing resources – it might be slow if you have a lot of data to analyze. It is better to ingest data utilizing a streaming procedure if real-time or almost real-time access to the data is required.

2. Steaming Data Ingestion

As soon as new data is created (or identified by the system), streaming data ingestion immediately transfers it into a data platform. It’s perfect for business intelligence applications that need current information to guarantee the highest accuracy and quickest problem-solving.

In some cases, the distinction between batch processing and streaming is getting hazy. Some software applications that advertise streaming really use batch processing. The procedure is extraordinarily quick since they ingest data at little intervals and work with small data groupings. Sometimes this strategy is referred to as micro-batching.

Data Ingestion Roadmap

Extract and load are normally simple for businesses, but the transformation is often a challenge. As a result, if no data has been ingested for processing, an analytical engine may lie idle. Here are some recommendations for data ingesting best practices to take into account in light of this reality:

Expect Challenges and Make a Plan Accordingly

The unsavory truth about data ingestion is that gathering and cleaning the data is said to consume between 60% and 80% of the time allotted for any analytics project. We picture data scientists running algorithms, analyzing the outcomes, and then modifying their algorithms for the upcoming run – the thrilling aspect of the job.

However, in practice, data scientists actually spend the majority of their time trying to organize the data so they can start their analytical work. This portion of the task expands constantly as big data volume increases.

Many businesses start data analytics initiatives without realizing this, and when the data ingestion process takes longer than expected, they are shocked or unhappy. While the data ingestion attempt fails, other teams have created analytical engines that rely on the existence of clean imported data and are left waiting impassively.

There isn’t a magic solution that will make these problems go away. Prepare for them by anticipating them.

Automate Data Ingestion

Data ingestion could be done manually in the good old days when data was small and only existed in a few dozen tables at most. A programmer was assigned to each local data source to determine how it should be mapped into the global schema after a human developed a global schema. In their preferred scripting languages, individual programmers created mapping and cleaning procedures, then executed them as necessary.

The amount and variety of data available now make manual curation impossible. Wherever possible, you must create technologies that automate the ingestion process.

Use Artificial Intelligence

In order to automatically infer information about data being ingested and largely reduce the need for manual work, a range of technologies has been developed that use machine learning and statistical algorithms.

The following are a few processes that these systems can automate:

  • Inferring the global schema from the local tables mapped to it.
  • Determining which global table a local table should be ingested into.
  • Finding alternative words for data normalization.
  • Using fuzzy matching, finding duplicate records.

Make it Self-Service

Every week, dozens of new data sources will need to be absorbed into a midsize company. Every request must be implemented by a centralized IT group, which eventually results in bottlenecks. Making data intake self-serviceable by giving users (who want to ingest new data sources) access to simple tools for data preparation is the answer.

Govern the Data to Keep it Clean

Once you have taken the work to clean your data, you’d want to keep it clean. This entails establishing data governance with a data steward in charge of each data source’s quality. 

Choosing which data should be ingested into each data source, setting the schema and cleansing procedures, and controlling the handling of soiled data are all included in this duty.

Of course, data governance encompasses more than just data quality, including data security, adherence to legal requirements like GDPR, and master data management. In order to accomplish all of these objectives, the organization’s relationship with data must change culturally. A data steward who can lead the necessary initiatives and take responsibility for the outcomes is also essential.

Advertise Your Cleansed Data

Will other users be able to quickly locate a specific data source once you have cleaned it up? Customers who want point-to-point data integration have no method of discovering data that has already been cleaned for a different customer and might be relevant. Implementing a pub-sub (publish-subscribe) model with a database containing previously cleaned data that can be searched by all of your users is a good idea for your company.

How does your Data Ingestion Framework Relate to your Data Strategy?

A framework in software development serves as a conceptual base for creating applications. In addition to tools, functions, generic structures, and classes that aid in streamlining the application development process, frameworks offer a basis for programming. In this instance, your data ingestion framework makes the process of integrating and gathering data from various data sources and data kinds simpler.

Your data processing needs and the data’s intended use will determine the data ingestion methodology you select. You have the choice of using a data ingestion technology or manually coding a tailored framework to satisfy the unique requirements of your business.

The complexity of the data, whether or not the process can be automated, how quickly it is required for analysis, the associated regulatory and compliance requirements, and the quality parameters are some considerations you must keep in mind. You can proceed to the data ingestion process flow once you’ve chosen your data ingesting approach.

How does your Data Ingestion Framework Relate to your Data Quality?

The stronger your demand for data intake observability, whether here or at any layer or place through which the data will transit, the higher your need for data quality will be. The more insight you need into the caliber of the data being absorbed, in other words.

Errors have a tendency to snowball, so “garbage in” can easily turn into “garbage everywhere.” Small improvements in the quality of this area will add up and save hours or even days of work.

If you can see the data ingestion procedure, you can more accurately:

  • Aggregate the data—gather it all in one place.
  1. Merge—combine like datasets.
  2. Divide—divide, unlike datasets.
  3. Summarize—produce metadata to describe the dataset.
  • Validate the data—verify that the data is high quality (as expected).
  1. (Maybe) Standardize—align schemas.
  2. Cleanse—remove incorrect data.

Data Ingestion Tools

Tools for data ingestion collect and send structured, semi-structured, and unstructured data between sources and destinations. These tools streamline manual, time-consuming intake procedures. A data ingestion pipeline, a sequence of processing stages, is used to move data from one place to another.

Tools for data ingestion have a variety of features and capacities. You must weigh a number of criteria and make an informed decision in order to choose the tool that best suits your requirements:

Format: What kind of data—structured, semi-structured, or unstructured—arrives?

Frequency: Is real-time or batch processing of data to be used?

Size: How much data must an ingestion tool process at once?

Privacy: Is there any private information that needs to be protected or obscured?

Additionally, there are other uses for data ingestion tools. For instance, they are able to daily import millions of records into Salesforce. Alternatively, they can make sure that several programs regularly communicate data. A business intelligence platform can receive marketing data via ingestion tools for additional analysis.

Benefits of Data Ingestion Framework

With the help of a data ingestion framework, companies may manage their data more effectively and acquire a competitive edge. Among these advantages are:

  • Data is easily accessible: Companies can gather data housed across several sites and move it to a uniform environment for quick access and analysis thanks to data ingestion.
  • Less complex data:  A data warehouse can receive multiple forms of data that have been transformed into pre-set formats using advanced data intake pipelines and ETL tools.
  • Teams save both money and time: Engineers may now devote their time to other, more important activities because data ingestion automates some of the operations that they had to perform manually in the past.
  • Better decision making: Real-time data ingestion enables firms to swiftly identify issues and opportunities and make knowledgeable decisions.
  • Teams improve software tools and apps: Data ingestion technology can be used by engineers to make sure that their software tools and apps transport data rapidly and offer users a better experience.

Challenges Encountered in Data Ingestion

Creating and managing data ingestion pipelines may be simpler than in the past, but there are still a number of difficulties to overcome:

  • The data system is increasingly diverse: It is challenging to develop a future-proof data ingestion framework since the data ecosystem is becoming more and more diversified. Teams must deal with a rising variety of data types and sources.
  • Complex legal requirements: Data teams must become knowledgeable about a variety of data privacy and protection rules, including GDPR, HIPAA, and SOC 2, to make sure they are acting legally.
  • The breadth and scope of cybersecurity threats are expanding: In an effort to collect and steal sensitive data, malicious actors frequently undertake cyberattacks, which data teams must defend against.

About Artha Solutions

Data ingestion is a crucial piece of technology that enables businesses to extract and send data automatically. IT and other business teams may focus on extracting value from data and finding novel insights after developing data intake pipelines. Additionally, in today’s fiercely competitive markets, automated data input could become a critical differentiation.

Artha Solutions can give you the tools you need to succeed as your business aspires to expand and achieve a competitive advantage in real-time decision-making. To assist the data ingestion procedure, your company receives continuous data delivery from our end-to-end platform.

Our platform helps you automate and develop data pipelines rapidly while cutting down on the typical ramp-up period needed to integrate new technologies. Make a call to us right away to begin creating intelligent data pipelines for data ingestion.

 

6 Critical Challenges in Implementing Cloud Migration Solutions

Cloud computing has caught momentum with the rise in cloud providers and solutions over the past ten years. Studies show that companies around the world are gradually integrating the cloud into their infrastructure.

However, you should formulate a strategy for cloud migration solutions before your company takes the step towards transformation, including an understanding of how to get through the difficulties created by cloud migration.

Your business should remain vigilant if it wants a successful cloud migration. The transition to the cloud could also be expensive and risky for you, and your data, if you aren’t prepared to handle the obstacles. On the contrary, you will have a considerable advantage if you are aware of these difficulties and how to overcome them.

Resistance to the Adoption of the Cloud Environment

The main obstacle with standard cloud migration difficulties is generally individuals. Cloud migration solutions bring about a lot of change and disruption, sometimes with significantly new systems, processes, and even leadership. Individuals typically oppose change.

You will suffer carrying out a successful cloud journey if you don’t consider the human factors of migration.

Make sure the leadership is knowledgeable of the cloud migration goals and the business requirements. Ask them to inform the organization of the business case. Prioritize integration and usability when selecting cloud solutions.

Cloud Migration Procedure Costs are Unclear

Cloud migration solutions can eventually cut costs by increasing productivity, minimizing expenses, and optimizing procedures. But doing so can seem onerous and expensive.

It can be expensive to migrate to the cloud, especially if your business doesn’t initially calculate the cost. Due to the data migrations cost, compatibility cases, the need for new talents, the unavailability of a data recovery strategy, and an improper migration process, business executives have to cope with migration expenses which continue to be one of the biggest problems.

Cloud platform infrastructure expenses, migration expenses, financial risks associated with slow adoption, and the necessary post-migration preparation should all consider in the estimated cost of cloud migration.

Possessing No Reliable Cloud Migration Strategy

If you don’t plan, you’ll plan to fail. Without a clear strategy in place, organizations shift to the cloud far too frequently. Even though the cloud will simplify operations and procedures, cloud adoption can be difficult.

Planning and employing creative thinking is necessary when migrating crucial corporate infrastructure and resources to the cloud. Instead, some businesses board the train without giving the journey enough thought.

You’ll be able to navigate the change and prevent project delay in the later phases if you have a well-thought-out approach. Include the cost of processing data, moving any required apps, and downtime expenses.

Managing Hybrid Networking Configurations

The most important thing when working with a hybrid networking configuration is to take networking and security into consideration immediately. Protection is mandatory for a hybrid setup, although it might not be necessary once the migration is complete.

Ask the team that responds to cyber incidents about the guidelines or regulations governing a hoisting migration. Keeping the security team together, providing them with their plans and estimates, and giving them enough time and the power to make decisions about the migration is essential.

The network layout is a further issue that has to be made clear. It is essential if you are moving from on-premises and already have a network setup. That on-premise data will presumably be necessary to sync with the current cloud environment.

Compliance and Data Security Risks

Concerns about data security and regulatory issues are still significant obstacles to cloud migration. Despite secure cloud settings, most businesses are hesitant to hand over their private information to security companies.

To create a secure cloud environment, cloud computing providers have business security systems and cloud platforms built with security in mind. The company’s data passes through a recommended routing protocol path during a secure cloud migration, and the data is always safe inside the firewall.

This procedure makes sure the security of confidential information from others during migration. In addition, it ensures compliance and eliminates vulnerabilities like data theft.

Utilizing 12-Factor Application Architecture and Cloud-Native Design

An architectural design pattern called cloud-native helps developers build scalable, dependable, and easy-to-monitor apps for the cloud. Building disposable, lightweight apps that you can effortlessly scale out is the goal of cloud-native development.

The underlying ideas and core of cloud-native applications are the tried-and-true 12-Factor concepts. They demand adjustments to your app’s architecture, your team’s coding process, and other areas. Before implementing cloud-native methods and patterns, one must initially understand the 12-factor principles.

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.

 

Drive Innovation in Business Operations With These 5 Digital Solutions

Digital business solutions are particularly effective in boosting corporate productivity since they eliminate numerous roadblocks in communication. By using digital technologies to automate some operations, businesses may operate and produce more effectively while reducing the chance of human error.

Here are 5 Digital business solutions that can improve the company’s operations.

Project Management

Companies need help keeping track of their projects and goals, especially those that require collaboration between various teams and departments or remote workers. The email could appear adequate at first, but it’s simple for team members to unknowingly miss an update if they aren’t part of the most recent communication.

Working on several projects at once sometimes obstructs the inbox and makes it complex to keep track of anything. ERP project management tools are far more productive at providing a comprehensive and transparent view of the development of each project. With just an internet connection, they could be accessible from anywhere and get real-time updates.

Workplace Communication

Another aspect of Digital business solutions is workplace communication. It gets more challenging to monitor employee conversations as the organization expands. The teams can communicate, share files, and work together effectively if businesses use a platform like Slack. The ability of the team to save, edit, manage, and share documents extensively are made feasible by collaboration suites.

Slack also integrates with other apps to promote a more effective workflow. By setting up a Slack channel, employees can engage in off-topic conversation as they would in a traditional office environment. For distant organizations, this enhances corporate culture and employee engagement.

Slack is accessible on desktop and mobile devices, enabling your team to interact from anywhere.

Personalized Marketing

Email marketing is currently one of the most well-known Digital business solutions and is often used in digital marketing techniques. Building, sending, and evaluating the success of email campaigns is simplistic when using digital tools to drive marketing.

With reference, behavioral, interaction, and demographic data through a Customer 360 platform, you can capture a wider, more in depth perspective of the customer to create categorizations of customers to understand the audience.

To ensure every customer has a unique experience, companies can utilize categorization to deliver personalized email offers to new and returning customers.

Moreover, understanding your customers and audience allows you to engage with them on a deeper level on social media platforms. Every social media platform where the target audience is active in connection to the business website requires the organization to be engaging. With insights into the customer psyche, organizations can easily post content online, interact with users, respond to their inquiries, and take the appropriate steps to show their engagement with the platforms.

Resource Management

A small company may not have a complete human resources team when it initially starts working. In this circumstance, HR management software may be helpful. With it, businesses may do anything from broadcast company news to acquire bank details for direct payment to allocate work shifts to employees. New business owners may overlook several minor aspects that could leave their business vulnerable to legal action and other issues.

Other services that some HR management programs offer include accounting, performance management, recruiting, and applicant monitoring. Several businesses even provide employees the option of utilizing self-service to manage changes and adjustments.

Accounting Programs

Small business owners occasionally disregard accounting since it might be challenging. Not paying enough attention, however, might lead to an IRS audit or failing to include the monthly payroll expense in cash flow analysis.

With digital accounting software solutions, you can do nearly all the tasks, including keeping track of revenue and expenses, paying off debts, generating invoices, creating expense reports, and monitoring debts.

Conclusion

As part of a digital transformation, replacing outmoded tools and infrastructure with modern technology is necessary. By transitioning to cloud-based options, businesses rely less on their own IT equipment and employees and more on supplier data centers and managed services, which decreases the cost of operating your business.

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.

 

6 Master Data Management Strategy Tips Essential for Business Success

Master data management Strategy (MDM) describes the rules for collecting, gathering, combining, de-duplicating, regulating, and managing data collectively throughout a corporation.

Master Data Management Strategy provides a single source of master data that can be used and handled by many diverse organizations by promising control and dependability.

The Importance of the Master Data Management Strategy

Organizations today require significant cost savings, quicker product releases, and more effective regulatory requirements, and all of these aspects depend on having a solid Master Data Management Strategy. Otherwise, inaccurate cross-organizational data might result in faulty decision-making and growth retardation. However, it is not an easy process to create a Master Data Management strategy and implement it across an organization. And ensuring reliable data quality is one of the most serious concerns for businesses.

Here are the six measures the business must develop for Master Data Management Strategy:

1. Determine the Objectives and Success Standards, and Create a Business Case:

Develop precise objectives. It applies to all modern technological enterprises. Success has to have a specific explanation to reduce misconceptions.

Consider this scenario – a company implements a Master Data Management Strategy to precisely evaluate brand profitability but the implementation results in, say, minimizing errors. Although minimized errors serve as a benefit, the fact that cannot be ignored is that the primary goal was not met. It’s crucial for businesses to stay specific to the goals and provide targets that measure the MDM project’s success.

The three main types of Master Data Management Strategy are Operational performance, enhanced business intelligence, and compliance with regulations.

Operational effectiveness consists of:

  • Improved business procedures
  • Business procedures that are more effective which is recovery leading to rapid completion

Developments in analytical and business intelligence usually involve:

  • Transparency across the company, product, customer, and channel
  • More stable and reliable business intelligence
  • Better productivity in analysis

Benefits of regulatory compliance involve:

  • Minimal data connections
  • Better regularity, long-lasting records, and greater accuracy

2. Democratize The Access To Data:

Data democratization makes digital information more accessible to the non-technical, average users of an organization. When this happens, there would be no need for the IT team to involve whenever there’s a need to make changes or updates to the data.

The goal is to allow non-specialists to be able to gather and analyze data without any help – improving the efficiency and productivity of the organization.

Your teams can rely on an acute knowledge of markets, customers, and ecosystems. Therefore, it is important that data circulates widely within your teams. The more it circulates, the more it is enriched, and the more value it generates.

3. Implementing Master Data Business Rules:

The organization must be able to add criteria or actions that change data effectively as an element of the master data management solution.

Business rules must be transferable between different use cases, such as when performing approval processes or importing data. They ought to be centralized, allowing for the creation of a single set of rules that can be used throughout. Without waiting for back-end development, business rules should be put into practice instantly in the user interface.

Business rules have the advantage of allowing one to develop, implement, and monitor enterprise-wide data governance practices. It can help build workflows for decision and approval procedures, define rules that maintain data integrity, and describe the data lifecycle for each pertinent object type using the policies that have been chosen.

The data strategy should significantly rely on business rules to introduce data quality and governance approaches quickly and precisely while lowering risk.

4. Determine The Business Leaders:

A Master Data Management Strategy will improve communication between the business and IT groups. The best approach to combining IT and business is to find and use business executives who are leaders. You cannot consider MDM an “IT problem” if it is to be successful.

On the other hand, a business champion will have to work with a partner that can give IT assistance. This partner should be adequate to assist the project technically while also acknowledging that technology cannot fix all of the issues.

5. Create a Forum for Guidance and Advice:

One should be ready for a constant education campaign, as with most other new programs, to help individuals grasp why accurate data is essential and how to assist in a successful project. Initially, they must identify the associated complexities and the problems to solve via gatherings like brown-bag lunch meetings to discuss the topic, public webinars, or independent training sessions from industry experts or specialists.

6. Learn About the Products and Procedures:

As the Master Data Management Strategy gains traction, careful planning and research will pay off. Therefore, it is essential to employ a wide range of research platforms, including webinars, industry experts, seminars, discussion forums, and connections from other businesses in the same or related industries, to gather the information that will improve your strategy. Although the circumstances won’t probably be the same, such encounters can teach productive approaches.

These 6 Master Data Management strategies are to determine the scope of an MDM project and evaluate the way to start a project that is essential for business success.

About Artha Solutions

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