Search
START TYPING AND PRESS ENTER TO SEARCH

data analytics

Data Ingestion, Data Integration, Data Quality,Driven Organization
  • No Comments

The Importance of Data Ingestion, Data Integration, and Data Quality in becoming a Data-Driven Organization.
The Importance of Data Ingestion, Data Integration, and Data Quality in becoming a Data-Driven Organization. 650 486 Exist Software Labs

Data ingestion, integration, and quality are crucial steps in becoming a data-driven organization:

Ingesting, storing, organizing, and maintaining the data generated and gathered by an organization is known as data management. A key component of implementing IT systems that power business applications use to deliver analytical data to support operational decision-making and strategic planning by corporate executives, business managers, and other end users is effective data management.

Data management is a collection of many tasks that aims to guarantee correct, accessible, and available data in business systems. The majority of the work is done by the IT and data management teams, but business users also contribute.

Want to learn more about Data Solutions and Services? Click here.

These are the key steps in transforming a company into a data-driven organization.

What is Data Ingestion, Data Integration, and Data Quality?

  1. Data Ingestion: It is the process of acquiring data from various sources and bringing it into a centralized data repository for analysis and reporting. Without effective data ingestion, data silos can form, making it difficult to access and integrate data across the organization.

 

It involves acquiring data from different sources, such as databases, cloud storage, or even manual input, and ensuring that the data is transformed and formatted in a way that can be easily integrated and analyzed.

 

  1. Data Integration: This process merges data from different sources into a unified view, making it easier to analyze and make informed decisions. Lack of data integration can lead to inconsistencies, duplications, and errors in data analysis.

 

This step requires removing duplicates, resolving conflicts, and transforming data into a consistent format so that the data can be used effectively for analysis and decision-making.

  1. Data Quality (Cleansing): Cleaning data ensures that it is accurate, consistent, and free of errors. Poor data quality can negatively impact decision-making and hinder the effectiveness of data analysis.

 

The data quality process involves validating the data, correcting errors, and removing inconsistencies, to ensure that the data is trustworthy and fit for its intended use. These three steps are crucial for organizations to effectively leverage their data to make informed decisions, drive business growth, and achieve their goals.

By focusing on data ingestion, integration, and quality, organizations can ensure that they have a solid foundation for their data analysis and decision-making processes. It enables organizations to gain valuable insights, make informed decisions, and ultimately drive business growth and success.

Next is How to Operationalize the Data in a data-driven organization:

 

  • Establish a clear data strategy: The first step is to create a clear data strategy that aligns with the overall business strategy. This strategy should define the business problems that data can help solve, the data sources to be used, the tools and technology required, and the KPIs that will be used to measure success.

 

  • Identify data requirements: Determine what data is required to support the business strategy and goals. This involves identifying the types of data needed, the sources of data, and the frequency of data collection and updates.

 

  • Collect and process data: Collect the relevant data and process it in a way that makes it usable for analysis. This may involve data cleaning, normalization, and transformation.

 

  • Analyze data: Use analytics tools and techniques to analyze the data and derive insights that can inform business decisions. This may involve descriptive analytics, predictive analytics, and prescriptive analytics.

 

  • Communicate insights: Communicate the insights to stakeholders in a way that is clear and actionable. This may involve creating dashboards, reports, or visualizations that highlight the key findings and recommendations.

 

  • Integrate insights into operations: Use the insights to inform business operations and decision-making processes. This may involve integrating insights into existing workflows, processes, and systems.

 

  • Monitor and evaluate: Monitor the impact of the data-driven initiatives and evaluate the success against the KPIs identified in the data strategy. Make adjustments as needed to improve performance.

Overall, operationalizing data in a data-driven organization requires a culture that values data-driven decision-making, a commitment to continuous improvement, and the right technology and tools to support data collection, analysis, and communication.

 

Microservices
  • No Comments

Microservices in Digital Banking 2023 – What are the Impacts?
Microservices in Digital Banking 2023 – What are the Impacts? 650 486 Exist Software Labs

Microservices are becoming increasingly popular in digital banking due to their ability to facilitate agility, scalability, and flexibility in software development.

Technology innovations shaped the world that we live in. It is now a necessity. This is also applicable in the Banking and Financial industry. With the new game-changers in this vertical, the competition became stiffer. Now that the competition rises, dynamic innovations and adapting digitization are now becoming the standard in the banking and financial industry. 

Time to Market is very crucial as well as ensuring the stability and security of the application. To keep up with the demands of the market and at the same time balance the importance of these three factors, applications should now be built using Microservices.


MICROSERVICES

Microservices are a modern approach to software whereby application code is delivered in small, manageable pieces, independent of others (Spring). Having said that, using microservices architecture, could help developers implement each component individually. They can execute innovations into one certain application without the involvement of other applications. It is not a large-based code that when something wrong happens, will affect the whole project.


WHY
MICROSERVICES?

WHY MICROSERVICES?

Since Microservices operate as a variety of applications that have a specific functionality under one system, microservices are easier to maintain, scale, a faster turnaround time in deployment and have a better resilience to technical faults.

WHAT ARE THE IMPACTS OF MICROSERVICES?

  • SCALABILITY 

Microservices Architecture develops single software systems which are running independently enabling the banking businesses to evolve as they scale without difficulty. When there are new offerings and features in their mobile banking application, they can still operate even if some parts are under development. By doing so, users can still use the mobile app and do other transactions.

  • AGILE SOFTWARE DEVELOPMENT

Projects are developed incrementally. New features can be broken down as small projects which can be done in parallel by different teams. Using DevOps, microservices are being optimized and can be deployed smoothly. This approach allows banks to introduce new innovations to the public faster. Moreover, banks can now update their mobile banking applications while operating transactions simultaneously. 

  • FLEXIBILITY OF USING DIFFERENT TECHNOLOGY STACKS

When it comes to integrating different technologies to use for multiple features in a digital bank, software developers can easily push frameworks, databases & etc,. In addition, new technologies can be employed in the application when there are major changes. Banks with specific requirements can be tailor-fit based on their needs.

At Exist, we want you to go where the possibilities are boundless. Since being superior in digital experiences are essential in staying relevant, we are empowering banks and financial institutions in digitalization to rapidly grow and make their business future-ready.

Exist Software Labs, Inc. is committed to extending our assistance to several banks with their Digital Onboarding requirements. We have been in the business for 20 years, and the mission is to enable corporations to go on digital and have a competitive advantage in this fast-changing world.

Be where your customers are!

Transform your bank into an omni-channel platform.

Contact Us
Befriending Your Data in 2021, Java, Java Philippines
  • No Comments

Befriending Your eye-opening Data in 2021
Befriending Your eye-opening Data in 2021 768 487 Exist Software Labs

It’s the new year and everybody is still living in the wake of the COVID-19 pandemic. We all need a friend in times of trouble and this is no different in the case of business organizations.

This year, 2021, the friend that your company needs more than ever, especially in these trying times, is data.

Given the disruption that this virus caused in the preceding year, enterprises need to start (if they haven’t already) befriending their own internal data, and perhaps external data as well if they are to at least stay viable and at most grow.

The following are some insights from respected data management leaders on how to make friends with your data this year:

  • Eran Vanounou, CEO at Varada

    “Data warehouses are not going to disappear. Data warehouses will continue to be an important legacy technology that organizations will use for mission-critical business applications well into the future.

    With the transition to the cloud, data warehouses got a fresh new look and offer some modern attractive capabilities including self-service and serverless.

    With the rise of the cloud, data lakes are the new kid on the block. Data lakes are becoming a commodity, a legacy technology in their own right. Their rapid emergence from the innovation stage means two things going forward.

    First, organizations will demand simpler, easier to manage, and more cost-effective means of extracting usable business intelligence from their data lakes, using as many data sources as possible.

    Second, those same organizations will want the above benefit to be delivered via tools that do not lock them into proprietary data management platforms.

    In short, 2021 will begin to see the rapid introduction and evolution of tools that allow users to keep their data lakes in one place and under their control while driving performance up and cost down.”

  • Paige Roberts, Open-Source Relations Manager at Vertica

    “Distributed analytical databases and affordable scalable storage are merging into a single new thing called either a unified analytics warehouse or a data lake house depending on who you’re talking to.

    Data lake vendors are scrambling to add ACID capabilities, improve SQL performance, add governance, resource management, security, lineage, and all the things that data warehouse vendors have been perfecting for the last three or four decades.

    During the ten years, while data lake software has been coalescing, analytical databases have seen their benefits and added them to their existing stacks: unlimited scale, support for widely varied data types, fast ingestion of streaming data, schema-on-read, and machine learning capabilities.

    Just like a lot of things used to claim to be cloudy before they really were, some vendors will claim to be a unified analytics warehouse when they’ve just jammed the two architectures together into a complicated mess, but everyone is racing to make it happen for real.

    I think the data warehouse vendors have an unbeatable head start because building a solid, dependable analytical database like Vertica can take ten years or more alone.

    The data lake vendors have only been around about ten years, and are scrambling to play catch-up.”

  • Raj Verma, CEO at SingleStore

    “One single SQL query for all data workloads

    The way forward is based not only on automation but also on how quickly and widely you can make your analytics accessible and shareable.

    Analytics gives you a clear direction of what your next steps should be to keep customers and employees happy, and even save lives. Managing your data is no longer a luxury, but a necessity–and determines how successful you or your company will be.

    If you can remove the complexity or cost of managing data, you’ll be very effective.

    Ultimately, the winner of the space will take the complexity and cost out of data management, and workloads will be unified so you can write one single SQL query to manage and access all workloads across multiple data residencies.”

  • Prat Moghe, CEO at Cazena

    “Expect more enterprises to declare the battle between data lakes and data warehouses over in 2021 – and focus on driving outcomes and modernizing.

    Data warehouses can continue to support reporting and business intelligence, while modern cloud data lakes support all analytics, AI and ML enablement far more flexibly, scalably, and inexpensively than ever – so enterprises can go transform quickly.

    Cloud migrations and related cloud data lake implementations will get demonstrably faster and easier as DIY approaches are replaced by turnkey SaaS platforms.

    Such solutions will slash production cloud data lake deployment times from months to minutes while controlling costs and providing the continuous operations, security and compliance, AI and ML enablement, and self-service access required for modern analytics initiatives.

    That means that migrations that used to take 9-12+ months are complete in a fraction of the time.”

  • Adi Paz, CEO at GigaSpaces Technologies

    “Co-locating analytics and operational data results in faster data processing to accelerate actionable insights and response times for time-sensitive applications such as dynamic pricing, hyper-personalized recommendations, real-time fraud and risk analysis, business process optimization, predictive maintenance, and more.

    To successfully deploy analytics and ML in production, a more efficient Data Architecture will be deployed, combining OLTP (CRM, ERP, billing, etc.) with OLAP (data lake, data warehouse, BI, etc.) systems with the ability to build the feature vector more quickly, and with more data for accurate, timely results.”

To summarize the various points made by these industry pundits:

1

SQL-driven data warehouses are here to stay and will continue to be the data analytics platform of choice for enterprises in the current year.

2

Data management platforms that integrate well with existing data lakes will dominate as opposed to platforms that focus on one or the other.

3

Data management platforms that have built-in AI/ML functionalities will dominate as well, as this eliminates the cost and complexity of separate AI/ML analytics platforms.

4

Data management platforms that are cloud-ready will also have an edge over those that are not.

Is there a data management platform that possesses all these qualities and has a proven track record in Fortune 500 companies?

Yes, there is. It’s called Greenplum. Read about it here.

Enter your
text here