Quick Insights!

1. 1. By 2025, people will create more than 181 ZB of data.
2. 2. 82% of organizations plan to increase their investments in data modernization in 2023, up from  41% in 2022
3. 3. By 2025, large companies will share data for collaboration, data assets will be treated as products, and interconnected devices will collect real-time information.
4. 4. 61% of companies think the fast-growing data volume limits their ability to fully harness their data.
5. 5. A fifth of digital leaders feel they are effectively using data insights to generate more revenue
6. 6. 22% of chief executive officers believe using data effectively to develop new products and services is an important source of their growth in the next five years.

Webinar Unveiled: The Blueprint for Data Monetization

In collaboration with Srinath Venkatesh Nadkarni, Senior Vice President of the Data and Analytics practice, and Sreenivasan Rajasekaran, Senior Data Architect, presents an exclusive webinar poised to be a game-changer – Accelerate Monetization of your Data Assets with Data Modernization!

Cracking the Code: The Data Monetization Maturity Framework!

This webinar isn’t just another online event; it’s a window into transforming your data into a strategic asset. It delves into the vital role data modernization plays in accelerating monetization efforts. Additionally, it lights the path to a successful modernization journey, sprinkled with best practices to ensure smooth data migration to cloud platforms.

Data monetization maturity is determined by the progressive stages of the model. Organizations embarking on this journey can follow these key phases:

Data Nascent Stage: At the initial phase, companies exhibit low data maturity and externalization. They possess limited familiarity with utilizing data and may lack a clear direction for its application. Despite this, they might opt to externalize the small amount of data they’ve generated, modified, or repackaged. This transition marks the move into the Data Services phase. They might offer basic data services such as APIs or shared databases accessible to external partners.

Data Services Phase: These entities may advance their offerings over time by providing more comprehensive datasets for nominal charges. While not offering high-value data, they present a deluge of data, leaving consumers to discern its value. This phase categorizes them as Bulk Data Providers. The responsibility of extracting value rests primarily with the data consumer. This stage showcases substantial data externalization but lacks maturity.

Information Productization Stage: Recognizing the growth potential, some companies opt for higher maturity by transitioning to the Information Productization stage. This move comes with new market prospects (high externalization, moderate maturity). Companies may streamline their data offerings, replacing the data deluge with higher-value data products tailored to specific market needs. As these businesses understand the market demands their data can fulfill, their maturity deepens. This understanding serves as a foundation for subsequent scaling.

Line of Business Phase: As organizations enhance their data product maturity and develop a profound grasp of their underlying data, they progress into the Line of Business phase within the monetization maturity spectrum. They begin selling larger quantities and a diverse array of data products. This transition marks the point where data licensing transforms into a tangible revenue source. Eventually, the proceeds from data licensing and productization become substantial enough to warrant inclusion in the Profit & Loss (P&L) statements, significantly impacting the bottom line.

This evolution towards a data-driven line of business hinges on complete data externalization and the capacity to cater to external customers. Each stage, from nascent data utilization to establishing a profitable data-focused line of business, is characterized by varying data maturity and externalization levels. This model illustrates how businesses can progress and capitalize on their data assets, generating significant value and contributing meaningfully to their organizational success.

Infusing Value with 2023 Data Insights

Our webinar isn’t just a theoretical exploration; it’s anchored in 2023’s data and analytics trends. Expect recent statistics from top-notch research firms to punctuate the discussion, offering you actionable insights for your strategic decisions. Here are our quick tips!

Gather, Centralize, Analyze: Create a single hub for all your data, making analysis easier. Imagine you’re a retail chain aiming to boost sales. Centralizing data from all your outlets allows you to analyze buying patterns, identify popular products, and enhance inventory management.

Opt for Your Model: Choose between monetizing data internally or externally. Picture a fitness app that tracks user activity. You can use this data to enhance your app’s features (internal monetization) or partner with health insurers (external monetization) to offer personalized plans.

Comply and Secure: Adhere to data regulations like GDPR and ensure data accuracy. Suppose you’re a healthcare provider offering telemedicine. By safeguarding patient data, you gain trust, enabling fruitful collaborations with other healthcare players.

Elevate Your Strategy: What Awaits You

Embrace the future of data monetization by reserving your spot in this exclusive webinar. Armed with the right knowledge, you can propel your organization toward success, leveraging the power of data like never before. The question isn’t whether you should – it’s whether you can afford to miss out. Are you ready to seize the future?

The post Data + Innovate = Value! Mastering Data Monetization for Business Success appeared first on Indium.

Adaptive AI technology can combine the power of machine learning and artificial intelligence to implement highly advanced data modernization strategies for your business. This is why so many companies opt for Adaptive AI systems to streamline their business operations. In fact, Gartner predicts that companies using Adaptive AI will outperform competitors by 25% before 2026.

So, here’s a complete guideline on how Adaptive AI can help businesses make maximum use of data and streamline their business operations.

Understanding Adaptive AI

Adaptive AI is a highly advanced form of artificial intelligence that can learn and improve itself as it encounters changes in the environment. While traditional forms of AI mostly depend on data and algorithms, Adaptive AI can learn from experience. It can automatically adjust its code based on changing situations. This provides Adaptive AI systems with high resilience and adaptability, thereby offering end-users more accurate outcomes.

Adaptive AI has got a big role to play in data modernization. When used efficiently, data can offer a company excellent growth and success in this competitive world, and Adaptive AI can help you make the most out of available data.

Adaptive AI in Data Modernization 

Adaptive AI has a big role to play in data modernization. When used efficiently, it can enhance the operations of the existing systems in your environment with its real-time data analysis capabilities.

With Adaptive AI, you can use your findings to identify various challenges and look for areas for improvement in your business. This is going to allow your business to boost its capabilities.

Here are a few ways in which Adaptive AI can help with data modernization:

Data cleansing: Adaptive AI can be used to remove data inconsistencies from your system so that it offers you more accurate results.

Data integration: Adaptive AI allows for smooth data integration from various sources. This provides you access to huge chunks of data that you can use as per business demands.

Organization of data: Adaptive AI technologies like NLP and entity resolution can organize and use the available datasets for various purposes.

Real-World Applications of Adaptive AI

Here are some real-world applications of Adaptive AI:

Healthcare industry: Adaptive AI has improved the operations of healthcare systems to a considerable extent. It can be used to analyze medical images and help diagnose diseases. Adaptive AI systems can also perform predictive analysis based on the patient’s data and develop personalized treatment plans. It can also monitor patients in real-time. This improves the overall outcome for patients.

Marketing: Adaptive AI has already made an impact in marketing. It can help businesses target their audience in a highly personalized manner. With Adaptive AI systems in place, you will have a better understanding of the likes and desires of your customers, and you can target them based on their preferences. You can also integrate your Adaptive AI algorithms with other marketing tools to increase reachability.

Finance: The finance industry has also largely benefited from Adaptive AI techniques. Adaptive AI algorithms can prevent the occurrence of fraudulent activities across financial sectors. This is done by performing an analysis of financial data obtained from various reliable sources. You can also use Adaptive AI as a tool to predict financial trends in the upcoming years.

Education: Adaptive AI can be used to offer students a highly engaging learning experience based on their learning process. The learning plan can be altered as per the student’s requirements. Adaptive AI can also assess students’ proficiency in different skills. It can motivate the students to remain engaged in their work and not get caught up in the rat race.

Retail: Adaptive AI can improve the customer experience by offering personalized product recommendations and managing inventories. Retail businesses can also use Adaptive AI algorithms to segment customers by analyzing their data and behavior. The prices of products can also be optimized based on in-depth data analysis.

Manufacturing: The entire supply chain process can be optimized using Adaptive AI. Adaptive AI algorithms can analyze market trends and offer manufacturers a better idea of production and distribution. With optimized inventories, companies will no longer worry about overstocking or understocking their products. Delivery time and supplier performance can also be improved by using Adaptive AI in different aspects of the manufacturing industry.

Adaptive AI Techniques Used by Businesses

 Predictive Analysis  

Adaptive AI can be used to perform predictive analysis. This involves predicting future outcomes based on data trends and historical data. Various businesses can benefit from predictive analysis techniques. It can forecast the organization’s sales, revenue generation, and financial state at a certain point in time.

Machine Learning 

Adaptive AI uses machine learning techniques to allow machines to learn from patterns, behavior, and data. Those aspects of Adaptive AI can be used in various ways, like fraud detection and risk management. Machine learning technology is also being implemented to develop chatbots and virtual assistants.

Natural Learning Process (NLP) 

Natural Language Processing (NLP) can be used in Adaptive AI systems to identify patterns in human language and use them for multiple purposes. Natural Language Processing (NLP) is mostly used in designing chatbots and virtual assistants. These chatbots can identify data patterns from customer queries and devise a solution for them. This improves customer satisfaction rates to a huge extent.

Neural Networks and Deep Learning

Deep learning and neural networks are major influences on adaptive AI systems. These technologies can enhance the capability of Adaptive AI to process raw data. Neutral networks can also help Adaptive AI systems acquire the ability to handle diverse input types.

Transfer learning 

Transfer learning involves transferring data from one machine to another. This can significantly reduce a machine’s time to learn a particular data pattern. Transfer learning can also be combined with machine learning to improve the ability of Adaptive AI to adapt to changing environments and learn new things.

The Future of Adaptive AI

Adaptive AI has the potential to transform workplace operations through its data modernization techniques. As time progresses, Adaptive AI systems will be able to improve their ability to predict individual needs, preferences, and behaviors. This would lead to a highly personalized user experience. Future Adaptive AI systems will also have continuous learning capabilities. This will make these systems suitable for all those workplace environments that undergo continuous change.

So, here’s a quick glance at what the future of Adaptive AI looks like:

• Adaptive AI systems will follow a more personalized approach.
• The continuous learning approach will make the systems suitable for changing business environments.
• The Adaptive AI systems will also come with multi-modal capabilities.
• The systems will start collaborating with other AI agents for better results.

Embrace Adaptive AI With Indium Software’s High-End Services 

Indium Software allows you to unleash the power of Adaptive AI in your workplace and set a benchmark for yourself in the industry. As we refine the AI models with quality data collection and appropriate machine learning algorithms, Indium Software helps organizations analyze customer data to understand preferences, behaviors, and needs. The future of Adaptive AI is huge and poised to change how businesses function, so partnering with Indium Software helps overcome the challenges and assists in careful planning and implementation to achieve successful outcomes.

Conclusion 

In this data-driven world, Adaptive AI has a very big role in streamlining workflows and enhancing the productivity of organizations. So, embracing and integrating Adaptive AI technology with data modernization strategies will benefit companies across various industrial verticals. Companies will also gain a competitive edge in this digital era.

The post The Role of Adaptive AI Systems in Modern Data Modernization Strategies appeared first on Indium.

Another reason for modernization is that we continue to develop the same monolithic application, which becomes legacy day by day, and maintaining the app becomes a nightmare, forcing us to redevelop the applications.

Related: Using Mendix to Modernize your Legacy App

Once you’ve decided to redevelop the application and created the logic, the time comes to migrate the data from the legacy system to the new app.

In Third scenario, when changing the domain model in an existing Mendix application or refactoring your app, there will be changes in the domain model or entities and associations, and you will need to migrate from the old form to the new structure.

So, before migrating these data to Mendix, you must consider and examine several factors. Analyse the existing data and determine how it will be transformed for use in the Mendix app.

The legacy system may contain data in the form of:

• RDBMS
• NO SQL Data

The data migration process is divided into three steps. Data extraction, transformation, and loading in Mendix App

Here are a few use cases for data migration from RDBMS to Mendix

• Assume you have an application with data stored in RDBMS such as Oracle or MySQL, and you need to replicate that data into the Mendix app.
• Another use case is when we have master data in the Mendix application that comes from another application and needs to be kept up to always date.

Solution for First Use Case

When we are migrating the Data from NO SQL to Mendix

Use Case – Assume you have one application with data in MongoDB, and you have redeveloped the application in Mendix low-code platform, and you now need to migrate the data into Mendix.

Let us see how we can respond to this

• Which of the following collections will be transformed into which entity?
• How is data related in MongoDB that must be mapped to an association?
• How are you going to read data from an existing database?
• Can you use the connectors, or do you need to customize them?

Once you’ve answered all the questions, you can proceed with the migration in three simple steps.

• Gather the data.
• Convert or transform the data to Mendix entities.
• Load the data and then
  • a. Extract the data.

You might be interested in: What Exactly Is a Low Code Developer, and How Do You Become One?

When we migrate the data from Same Mendix App?

When the domain model in the existing app is changed, you will need to migrate to avoid losing data. Because you can’t remove the old associations or attributes from the entities because they contain data, you can rename them as old and then perform the migration, after which this can be deleted.

• You can achieve the same result by writing Conversion Microflows. You must write the logic to transform the data to the new structure, read the data from the old structure and change the association to the new structure, and map the attribute to new entities. You can include this microflow in the After Start-up microflow or manually trigger it once.
• You can also do the same thing with the Database migration Marketplace module. This module employs the Liquibase database solution, in which the microflow will be executed once during the deployment for one database, and it will retain the state of the migration and will not be executed again.

Indium’s Mendix experts collaborate closely with our customers to analyse existing infrastructure and identify mission-critical apps that need to be updated, replaced, repurposed, or reused. This simplifies migration, allowing businesses to modernise quickly to minimise disruption and maximise resources.

Indium in short

Mendix has been in practice for over 6 years.

Over 50 active customers

Nearly 350 customized applications were created.

Indium is an end-to-end solution provider with one of the most certified low-code communities in the world. We also have a dedicated low-code academy where our team is constantly trained and retrained to meet the needs of our customers.

The post All About Data Migration Considerations appeared first on Indium.

L.L. Bean turned to Google Cloud to fulfill its cloud requirements. By modernizing data on, it experienced faster page loads and it was able to access transaction histories more easily. It also focused on value addition instead of infrastructure management. And, it reduced release cycles and rapidly delivered cross-channel services. These collectively improved its overall delivery of agile, cutting-edge customer experience.

Data Modernization with Google Cloud for Success

Many businesses that rely on siloed data find it challenging to make fully informed business decisions, and in turn accelerate growth. They need a unified view of data to be able to draw actionable, meaningful insights that can help them make fact-based decisions that improve operational efficiency, deliver improved services, and identify growth opportunities. In fact, businesses don’t just need unified data. They need quality data that can be stored, managed, scaled and accessed easily.

Google Cloud Platform empowers businesses with flexible and scalable data storage solutions. Some of its tools and features that enable this include:

BigQuery

This is a cost-effective, serverless, and highly scalable multi-cloud data warehouse that provides businesses with agility.

Vertex AI

This enables businesses to build, deploy, and scale ML models on a unified AI platform using pre-trained and custom tooling.

Why should businesses modernize with Google Cloud?

It provides faster time to value with serverless analytics, it lowers TCO (Total Cost of Ownership) by up to 52%, and it ensures data is secure and compliant.

Read this informative post on Cloud Cost Optimization for Better ROI.

Google Cloud Features

Improved Data Management

BigQuery, the serverless data warehouse from Google Cloud Platform (GCP), makes managing, provisioning, and dimensioning infrastructure easier. This frees up resources to focus on the quality of decision-making, operations, products, and services.

Improved Scalability

Storage and computing are decoupled in BigQuery, which improves availability and scalability, and makes it cost-efficient.

Analytics and BI

GCP also improves website analytics by integrating with other GCP and Google products. This helps businesses get a better understanding of the customer’s behavior and journey. The BI Engine packaged with BigQuery provides users with several data visualization tools, speeds up responses to queries, simplifies architecture, and enables smart tuning.

Data Lakes and Data Marts

GCP’s enables ingestion of batch and stream/real-time data, change data capture, landing zone, and raw data to meet other data needs of businesses.

Data Pipelines

GCP tools such as Dataflow, Dataform, BigQuery Engine, Dataproc, DataFusion, and Dataprep help create and manage even complex data pipelines.

Discover how Indium assisted a manufacturing company with data migration and ERP data pipeline automation using Pyspark.

Data Orchestration

For data orchestration too, GCP’s managed or serverless tools minimize infrastructure, configuration, and operational overheads. Workflows is a popular tool for simple workloads while Cloud Composer can be used for more complex workloads.

Data Governance

Google enables data governance, security, and compliance with tools such as Data Catalog, that facilitates data discoverability, metadata management, and data class-level controls. This helps separate sensitive and other data within containers. Data Loss Prevention and Identity Access Management are some of the other trusted tools.

Data Visualization

Google Cloud Platform provides two fully managed tools for data visualization, Data Studio and Looker. Data Studio is free and transforms data into easy-to-read and share, informative, and customizable dashboards and reports. Looker is flexible and scalable and can handle large data and query volumes.

ML/AI

Google Cloud Platform leverages Google’s expertise in ML/AI and provides Managed APIs, BigQuery ML, and Vertex AI. Managed APIs enable solving common ML problems without having to train a new model or even having technical skills. Using BigQuery, models can be built and deployed based on SQL language. Vertex AI, as already seen, enables the management of the ML product lifecycle.

Indium to Modernize Your Data Platform With GCP

Indium Software is a recognized data and cloud solution provider with cross domain expertise and experience. Our range of services includes data and app modernization, data analytics, and digital transformation across the various cloud platforms such as Amazon Web Server, Azure, Google Cloud. We work closely with our customers to understand their modernization needs and align them with business goals to improve the outcomes for faster growth, better insights, and enhanced operational efficiency.

FAQs

The post Data Modernization with Google Cloud appeared first on Indium.

However, this transition necessitates the creation of a detailed migration plan. What method will a corporation use to transfer data? How will the information be organised? How will a business be able to track data?

Setting goals and expectations for what the firm wants to achieve is the first step in migrating data to the cloud. For instance, how much data does the organisation intend to transfer, and at what point will the amount of data to be moved be reduced? Who will take over the project’s duties and responsibilities? What is the budget for the company, and what cloud platform will be used? The planning phase also includes code development and effective cloud migration strategies. The smoother the relocation process, the more well-planned it is.

Today’s organizations rely heavily on data for decision-making. These new-age data users, who aren’t only business intelligence experts, might make up the majority of an organization’s workforce, which is only growing. Considering this, the automated metadata management platform must be user-friendly in order to modernise data. As a result, the user interface must be so basic that users, regardless of their professional expertise, can readily access data.

Having discussed about the importance of data migration to the cloud for BI, let’s look at some of the data modernization trends.

Data Modernization Trends to look out for

Trend #1 – Uniform Implementation

When migrating to the cloud, the entire organisation must approach data in the same way. Business intelligence teams must teach data concepts, such as how to categorise, insert, and access data, so that the entire organisation can communicate in a common language. The analytics created from the data will be inconsistent if two departments use different categories for the same data. It’s also critical for data teams to teach the rest of the firm how to use and analyse data so that it may be put to the best possible use.

Effective data managers, on the other hand, strive to move the correct data to the right place as they transition their data. Rather than merely scaling up their existing data storage solution, data managers are increasingly attempting to turn the data into more valuable configurations. This shift does more than just storage. Data managers strive to improve analysis, interaction with other systems, and other aspects of data management. Instead of replicating local storage for 20 subsidiary offices, a company might construct global repositories based on data classification, such as worldwide marketing, operations records, logistic records etc

Trend #2 – Multi-functional adoption / Wider adoption

Markets that have previously recognised the value of data migration, such as banking, financial services, and insurance (BFSI), are expected to continue to employ it heavily. Similarly, marketing departments across sectors are expected to maintain their market leadership in data migration adoption and use to strengthen their data analysis capabilities.

However, as the benefits of data migration become more widely recognised, new businesses and professions are seeing the most rapid growth. Human resources is expected to be a fast-growing industry, according to Markets and Markets, as HR professionals seek data migration to integrate multiple data sources for improved data analysis.

Similarly, retail and consumer products are expected to expand the most, as companies strive to better understand customers throughout the product and customer life cycle. As the benefits of data migration and data analysis become more widely acknowledged, we can expect this shifting demand between industries and job functions to continue. While we may not be able to forecast which segments will expand the fastest next year, we can surely expect change to continue.

Trend #3 – Automated storage and storage scaling

Large organisations quickly exhaust the capacity of local hard drive arrays due to the general increase in data volume. Data managers must juggle frequent requests for more capacity while also staying ahead of hardware obsolescence and failure. Migrating data to the cloud will not totally solve these problems, but it will alleviate the urgency and complexity.

The hours of installation, configuration, testing, and troubleshooting necessary to install physical hard drives are readily replaced by mouse clicks on the cloud interface. Scaling storage is simple and perhaps automated, which appeals to data managers and drives the trend of moving more data to the cloud.

The largest corporations continue to exceed their current capacity. Markets & Markets predicts that demand for data migration in total dollars will continue to be dominated by these huge enterprises. However, as the cloud’s flexibility becomes more generally acknowledged in 2022, small and medium-sized businesses are likely to drive the fastest-growing section of the data migration market.

Trend #4 – Capitalizing on Integrational features

Companies that are using cloud for their applications are paying close attention to the integration offerings of cloud vendors. For example, Azure’s integration capabilities are becoming increasingly popular among Azure platform customers, and some organisations have begun to build lightweight integration frameworks on top of AWS’ offerings.

Rapid change will always have an impact on technology; it’s the essence of the business. Still, there’s no disputing that we’re in the midst of a generational transformation, with businesses pivoting faster than ever to stay ahead of the pack. Although digital integration is not new, its impact on a company’s long-term performance and importance has never been greater.

Trend #5 – Data restructuring and refined data transfer

Previous data migrations may have merely shifted data from one repository to another, but with AI, data migration cycles are more optimized. As such, data analysis becomes more efficient and effective. Data modernization, which entails migrating data from legacy databases to modern databases, is driven by this need. Unstructured data is categorised and structured, and old databases are restructured and refined, thanks to data modernization. According to a recent Deloitte poll, 84 percent of respondents have begun data transformation initiatives, with the financial services industry leading the way.

Companies who were among the first to migrate data to the cloud are now actively involved in data migration for data modernization. This contributes to the established industries’ status as market leaders in data migration. Marketing departments, for example, may have been the first to migrate data to the cloud for data analysis, but they are now also leading the way in data transfer from one database to another. Thus they are seeking the benefits of scalability, cost savings, flexibility, performance, and extended capability.

This might interest you: 5 Tips For Successful Data Modernization

Wind-up

We recognise that adopting modern technologies may help firms remain competitive and nimble as they seek out flexible, scalable data infrastructure solutions. Those who have yet to modernise, on the other hand, will face an uphill battle in extracting insights from their data. According to a recent IDG poll, more than 90% of data professionals claimed it is difficult to make data available in a format that can be used for analytics.

Companies should continue to improve their data architecture and keep an eye on industry trends and cutting-edge technology to help them compete with data insights. On-premises and legacy tools, on the other hand, were never designed to handle enormous amounts of data in a flexible and scalable manner. As new trends drive significant changes in data management strategy, businesses must prioritise modernization.

The post Data Modernization Trends: Keep these in mind appeared first on Indium.

Indeed, data is the most valuable, enduring asset of any organization, providing the foundation for digital transformation and strategy.

Effective data management is an essential part of today’s unpredictable business environment. Managing and understanding data better can help companies make informed and profitable business decisions.

The total volume of data that organizations across the world create, capture, and consume is forecast to reach 59 zettabytes in 2021, according to Statista. This data does not only comprise structured data in the form of documents, PDFs, and spreadsheets, it also includes tweets, videos, blog articles and more that make up unstructured data, which is essentially eclipsing the volume of structured data. Therefore, organizations not only face storage challenges but have a significant challenge in processing the wide-ranging data types.

Data Modernization

The process of migrating siloed data to modern cloud-based databases or lakes from legacy databases is known as data modernization. It enables organizations to be agile and eliminate bottlenecks, inefficiencies, and complexities of legacy systems.

A modernized data platform helps in efficient data migration, faster ingestion, self-service discovery, near real-time analytics and more key benefits.

For any modern business focused on building and updating the data architecture to spruce up their data core, data modernization is not only important but essential.

To gain optimal value, accelerate operations and minimize capital expenditure, companies must build and manage a modern, scalable data platform. Equally, it is vital to identify and deploy frameworks of data solutions along with data governance and privacy methodologies.

Data modernization is not without challenges as it requires creating a strategy and robust methods to access, integrate, clean, store, and prepare data.

Tips For Successful Data Modernization

Data modernization is critical for any modern business to stay ahead of the curve. With that said, let us find out how companies can be successful in their data modernization efforts.

Revise Current Data Management Strategy And Architecture

It is important to have an in-depth understanding of the organization’s business goals, data requirements and data analytics objectives when a company starts modernizing.

Thereafter, a data management architecture can be designed to integrate existing data management systems and tools, while innovative methods and models can be leveraged to accomplish the organization’s immediate objectives and adapt to future needs.

A well-designed architecture will enable data modernization to be approached systematically and holistically, thereby eliminating data silos and compatibility issues. It will also deliver consistent value and be flexible to integrate new capabilities and enhancements.

Inventory And Mapping Of Data Assets

If an organization cannot identify where the data assets are and what is protecting them, it will be tough to know if the access provided is suitably limited or widely available to the internet.

It is essential for organizations to first understand what data is being collected, what is being collected and what is being sent out. This helps identify the requirements and how a modern data management technology can help simplify the company’s data and analytics operating model.

The best way to begin a meaningful transformation is to simplify the problem statement. Hybrid cloud is also an integral part of any modern data management strategy.

Data Democratization A Core Objective

Until a few years ago, organizations had one major reason to modernize their data management ecosystems—which was to manage their rapidly growing data volumes.

Today the single, overriding reason is data democratization, which is about getting the right data at the right time to the right people.

It gives organizations wide-ranging abilities such as implementing self-service analytics, deploying large data science and data engineering teams, building data exchanges and zones for collaboration with trading partners and go after more data management activities.

Another key advantage of democratizing data is it helps companies achieve data trust and affords them more freedom to concentrate on transformative business outcomes and business value.

Robust governance is another focus area for organizations, who can thereby reduce data preparation time and give data scientists and other business issues the time to focus on analysis.

Technology Investment

Continuous investment in master governance and data management technologies is the best way to gain maximum control over organizational data.

Assuming ownership of data elements and processes, with leadership support, can often be ignored in data management programs but they are a key enabler in managing complex environments.

It is important for chief information officers (CIOs) to take stock of the legacy technologies present on-premises, the decision support system that is ageing and will be out of contract in a few months and more contribute to data modernization projects being successful.

Data Accountability

Establishing data accountability is a basic yet crucial step in reimagining data governance. Organizations that go beyond process and policy and prioritize insights and quality measures tend to be the most successful when it comes to data modernization.

In today’s rapidly changing world, almost everything is connected and digital. In this scenario, every bit of data about customers, transactions and internal processes are business assets that can be mined to enhance customer experience and improve the product.

Among the key issues facing IT leaders is while digital points continue to increase rapidly, many remain locked to monolithic legacy systems. A holistic look at solution development and delivery that leverage Agile, DevOps, Cloud and more such approaches are essential.

Summary

It is important for organizations to be aware of the evolving data management methods and practices. It could be said that data management is one of the most demanding issues IT leaders are likely to encounter in the year 2021 and beyond. For a company’s data modernization process to be successful, their data management approach should align with their overall business strategy.

The post 5 Tips For Successful Data Modernization appeared first on Indium.

Cloud, IoT, AI/ML and so on are no more mere buzzwords but present in several aspects of your business’ operations. Marketsandmarkets.com estimates the global digital transformation market size to be USD 469.8 billion in 2020 and expects it to grow at a Compound Annual Growth Rate (CAGR) of 16.5% by 2025 to touch USD 1009.8 billion.

However, what these numbers do not reveal is that many of the digitization efforts are small projects and do not span enterprise-wide processes.

According to a Gartner report, digital transformation should have data and analytics at its core, but almost 50% of corporate strategies do not include this. A McKinsey report points out that to leverage cloud, businesses moving from traditional monolithic and configured for fixed/static capacity, called data modernization, cannot expect to enjoy the dynamism of cloud by merely shifting their data to a few data centers.

Another McKinsey report clearly identifies streaming data as the fundamental component that will drive data modernization and streaming integration platforms as being critical to successful digital transformation.

The streaming analytics market is expected to grow at a Compound Annual Growth Rate (CAGR) of 25.2% from USD 12.5 billion to USD 38.6 billion between 2020 and 2025, according to Marketsandmarkets.com. The report also identifies inadequate system integrity as a bottleneck to the growth of this industry.

Data Modernization with Business Agility

While moving data from legacy systems to the modern cloud is an important step in data modernization, equally important is the need to be able to tap data in real time.

Technologies today have leveled the field to some extent by bringing Big Data within reach of organizations of all sizes, across industries and located anywhere in the world.

Speed is the critical differentiator here as any delay in tapping opportunities is equivalent to handing the game to the competitor.

Access to real-time connected data and the ability to create dynamic dashboards to draw insights for making informed decisions has become essential for survival and growth. 

This needs constant data ingestion and the streaming data to be integrated with the enterprise data for further analytics and intelligence.  Stream data integration is key to making data modernization efforts bear fruit.

It can help identify trends and relationships, events and threats, customer behaviors and the opportunities they present, to identify ways to enhance the efficiency and efficacy of operations and minimize risks.

Some of the key areas where stream data integration can be beneficial include:

• Fraud detection
• Supply chain optimization
• Customer service
• Scheduling resource
• Dynamic pricing
• Preventive maintenance
• High-availability of IT systems and services

Striim Stream Data Integration

One of the most popular solutions for stream data integration is Striim, an end-to-end, enterprise-grade platform that enables easy integration of structured, semi-structured and unstructured data with sub-second latency on cloud or on-premises.

A non-intrusive, real-time change data capture solution, it enables in-flight processing and visualization of data with pre-built data pipelines.

Its wizard-based user interface and SQL-like language make it an ideal tool for business analysts and developers.

It facilitates the integration of data across Cloud, Big Data, and IoT devices without being bound to a single topology.

By continuously running queries to filter, transform, aggregate, enrich, and analyze the data-in-motion, it can deliver the output to any target with sub-second latency. It can also do batch processing of data.

With Striim’s Stream Data Integration you can:

• Provide real-time, consistent data of different types including JSON, delimited, XML, binary, free text, and change records to analytical and transactional systems using non-intrusive change data capture (CDC)
• Get an organization-wide view of all your business data from different sources such as databases, log files, sensors, and messaging systems
• Wizards-based development and pre-built integration accelerates the building of streaming data pipelines
• Improve operational decision making with timely insights

It is an enterprise-grade patented tool that ensures security, scalability and reliability of the data, delivering the data as per user requirement in the correct format for high-value operational workloads whenever it is needed.

Indium for Striim Implementation

Indium Software is a two-decade-old IT solutions provider working with cutting-edge technologies to power businesses and set them on a growth trajectory.

It has a dedicated team of Big Data experts with cross-domain experience. Indium enables businesses embarking on the data modernization journey to leverage Striim stream data integration platform and improve the quality and speed of their decision-making by drawing insights from real-time data.

To find out how your data modernization efforts can benefit from streaming data integration, contact us now.

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