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Data Ingestion, Data Integration, Data Quality,Driven Organization
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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.

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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.

 

Big Data, Data Solutions, Healthcare, Retail
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Trends and Industries: How Data Solutions upend existing sectors to new heights in 2023?
Trends and Industries: How Data Solutions upend existing sectors to new heights in 2023? 650 486 Exist Software Labs

The defining era of data is currently upon us. Business model threats and economic shocks are common. Power is changing wherever you look, including in the market, our technological infrastructure, and the interactions between companies and customers. Change and disruption have become the norm. Data Solutions have been useful in innovating the industry.

Data-savvy businesses are well-positioned to triumph in a winner-take-all market. In the past two years, the distance between analytics leaders and laggards has increased. Higher revenues and profitability can be found in companies that have undergone digital transformation, embraced innovation and agility, and developed a data-fluent culture. Those who were late to the game and who still adhere to antiquated tech stacks are struggling, if they are even still in operation.

So, when you create your data and analytics goals for 2023, these are the key trends to help you stay one step ahead of your competitors.

Healthcare

Data Analytics and Data Solutions can be used to improve patient outcomes, streamline clinical trial processes, and reduce healthcare costs. 

Some specific examples of how Analytics is being used in healthcare include:

  1. Improving patient outcomes: Analytics can be used to identify patterns and trends in patient data that can help healthcare providers make more informed decisions about treatment plans. For example, data from electronic health records (EHRs) can be analyzed to identify risk factors for certain conditions, such as heart disease or diabetes, and to determine the most effective treatments for those conditions.
  2. Streamlining clinical trial processes: Data Analytics can be used to improve the efficiency of clinical trials by allowing researchers to identify suitable candidates more quickly and by helping them to track the progress of trials more closely.
  3. Reducing healthcare costs: Analytics can be used to identify inefficiencies in healthcare systems and to help providers implement cost-saving measures. For example, data analysis can be used to identify patterns of overutilization or unnecessary testing, and to develop strategies for reducing these costs.

Financial services

Data Analytics can be used to detect fraud, assess risk, and personalized financial products and services. 

Some specific examples of how Data Analytics is being used in the financial industry include:

  1. Fraud Detection: Data Analytics can be used to identify patterns and anomalies in financial transactions that may indicate fraudulent activity. This can help financial institutions to prevent losses due to fraud and to protect their customers.
  2. Risk Assessment: Analytics can be used to assess the risk associated with various financial products and services. For example, data analysis can be used to assess the creditworthiness of borrowers or to identify potential risks in investment portfolios.
  3. Personalizing financial products and services: Analytics can be used to gain a deeper understanding of individual customers and to personalize financial products and services accordingly. For example, data analysis can be used to identify the financial needs and preferences of individual customers, and to offer customized financial products and services that are tailored to those needs.

Retail

Retail companies can use Data Analytics to optimize pricing, understand customer behavior, and personalize marketing efforts. 

Some specific examples of how Data Analytics is being used in the retail industry include:

  1. Prizing Optimization: Retail companies can use Data Analytics to identify patterns in customer behavior and to optimize their pricing strategies accordingly. For example, data analysis can determine the most effective price points for different products and identify opportunities for dynamic pricing (i.e., adjusting prices in real time based on demand).
  2. Understanding customer behavior: Analytics can be used to gain a deeper understanding of customer behavior and preferences. This can help retailers to make more informed decisions about the products and services they offer, and to identify opportunities for cross-selling and upselling.
  3. Personalizing marketing efforts: Analytics can be used to deliver more personalized and targeted marketing efforts to customers. For example, data analysis can be used to identify customer segments with similar characteristics and to develop customized marketing campaigns for each segment.
  4. Cost Reduction: Being able to have a JIT (Just in Time) procurement and storage of items which in turn increases/optimizes warehouse capacity and reduces spoilage, and improves logistics.

Manufacturing

Data Analytics can be used to optimize supply chain management, improve production efficiency, and reduce costs. 

Some specific examples of how Data Analytics is being used in the manufacturing industry include:

  1. Optimizing supply chain management: Analytics can be used to improve the efficiency of the supply chain by identifying bottlenecks and inefficiencies, and by developing strategies to address these issues.
  2. Reducing fuel consumption: Analytics can be used to identify patterns in fuel consumption and to identify opportunities for fuel savings. For example, data analysis can be used to identify the most fuel-efficient routes or to identify vehicles that are consuming more fuel than expected.
  3. Improving fleet management: Analytics can be used to improve the efficiency of fleet management by identifying patterns in vehicle maintenance and repair data, and by helping fleet managers to develop strategies to optimize vehicle utilization and reduce downtime.
  4. Forecast roadworthiness of vehicles: This can help set trends on when a vehicle would break down or need repairs based on utilization, road conditions, climate, and driving patterns.

Energy

Data Analytics can be used to optimize the production and distribution of energy, as well as to improve the efficiency of energy-consuming devices.

Some specific examples of how Analytics is being used in the energy industry include:

  1. Optimizing the production and distribution of energy: Analytics can be used to optimize the production and distribution of energy by identifying patterns in energy demand and by developing strategies to match supply with demand. For example, data analysis can be used to predict when energy demand is likely to be highest and to adjust energy production accordingly.
  2. Improving the efficiency of energy-consuming devices: Analytics can be used to identify patterns in energy consumption and to identify opportunities for energy savings. For example, data analysis can be used to identify devices that are consuming more energy than expected and to develop strategies to optimize their energy use.
  3. Monitoring and optimizing energy systems: Analytics can be used to monitor and optimize the performance of energy systems, such as power plants and transmission grids. Data analysis can be used to identify potential problems or inefficiencies and to develop strategies to address them.

Agriculture

Analytics can be used to optimize crop yields, improve the efficiency of agricultural processes, and reduce waste.

Some specific examples of how Data Analytics is being used in agriculture include:

  1. Optimizing crop yields: Analytics can be used to identify patterns in crop growth and to develop strategies to optimize crop yields. For example, data analysis can be used to identify the most suitable locations for growing different crops and to develop customized fertilization and irrigation plans.
  2. Improving the efficiency of agricultural processes: Data Analytics can be used to identify patterns in agricultural data and to develop strategies to optimize processes such as planting, fertilizing, and harvesting.
  3. Waste Reduction: Analytics can be used to identify patterns in food waste and to develop strategies to reduce waste. For example, data analysis can be used to identify the most common causes of food waste on farms and to develop strategies to address those issues.

These are just a few examples of the many industries that are likely to adopt Data Analytics technologies as part of their digital transformation efforts in the coming years. 

Other industries that are also likely to adopt Analytics Technologies include Government, Education, and Media, among others. In general, Data Analytics Technologies are being adopted across a wide range of industries because they can help organizations to gain insights from their data, make more informed decisions, and improve their operations. 

As more and more organizations recognize the value of Analytics, it’s likely that we’ll see even greater adoption of these technologies in the coming years.

To learn more about our Data Solutions Services, click here.

Data Science, Science and Technology
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Data Science 101: What are concepts you need to know before entering the Data Science world?
Data Science 101: What are concepts you need to know before entering the Data Science world? 650 486 Exist Software Labs

I was playing around with data and then I found the Science — Yes, my introduction to the world of Data Science has been a part of my research work.

If you’re like me, starting out with Data Science looking for resources that can give you a jump start or at least a better understanding of it or you have just heard/read the term being coined and want to know what it is, of course, you can find a gazillion materials about it, this is, however, how I started and got familiar with the basic concepts.

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What is ‘Data Science’?

Data Science provides meaningful information based on larger amounts of complex data or big data. Data Science, or if you would like to say Data Driven Science, combines different fields of work in statistics and computation to interpret data for decision-making purposes.

Understanding Data Science

How do we collect data? — Data is drawn from different sectors, channels, and various platforms including cell phones, social media, e-commerce sites, various healthcare surveys, internet searches, and many more. The surge in the amount of data available and collected over a period of time has opened the doors to a new field of study based on big data — the huge and massive data sets that contribute towards the creation of better operational tools in all sectors.

The continuous and never-ending access to data has been made possible due to advancements in technology and various collection techniques. Numerous data patterns and behavior can be monitored and it can make predictions based on the information gathered.

In technical terms, the above-stated process is defined as Machine Learning; in layman’s terms, it may be termed Data Astrology — predictions based on data.

Nevertheless, the ever-increasing data is unstructured in nature and is in constant need of parsing in order to make effective decisions. This process is really complex and very time-consuming for organizations — and hence, the emergence of Data Science.

A Brief History / Background of Data Science

The term ‘Data Science’ has been in existence for about three decades now and was originally used as a substitute for ‘Computer Science’ in the 1960s. Approximately 15–20 years later, the term was used to define the survey of data processing methods used in different applications. 2001 was the year when Data Science was introduced to the world as an independent discipline.

Disciplinary Areas of Data Science

Data Science incorporates tools from multiple disciplines in order to gather a data set, process and derive insights from the data set and interpret it appropriately for decision-making purposes. Some of the disciplinary or noteworthy areas that make up the Data Science field include Data Mining, Statistics, Machine Learning, Analytics Programming, and the list goes on. But, we would be doing a brief discussion mainly on the aforesaid topics as the concept of Data Science mainly revolves around these basic concepts, just to keep it simple.

Data Mining applies algorithms to complex data sets to reveal patterns that are then used to extract useful and relevant data from the set.

Statistics or Predictive Analysis uses this extracted data to gauge events that are likely to happen in the future based on what the data shows happened in the past.

Machine Learning can be best described as an Artificial Intelligence tool that processes massive quantities of data that a human is incapable of doing in a lifetime — it perfects the decision model presented under predictive analytics by matching the likelihood of an event happening to what actually happened at a predicted time in the past.

The process of Analytics involves the collection and processing of structured data from the Machine Learning stage using various algorithms. The data analyst interprets, converts, and summarizes the data into a cohesive language that the decision-making team can understand.

Data Scientist

Literally speaking, the job of a Data Scientist is multi-tasking: We collect, analyze and interpret massive amounts of structured and unstructured data, and in a maximum number of cases, to improve an organization’s operations. Data Science professionals develop statistical models that analyze data and detect patterns, trends, and various relationships in data sets.

This vital information can be used to predict consumer behavior or to identify business and operational risks. Hence, the job of a Data Scientist can be described as a story-teller that uses data insights in telling a story to the decision-makers in a way that is understandable. The role of a Data Scientist is becoming increasingly important as businesses rely more heavily on data analytics to drive decision-making and lean on automation and machine learning as core components of their IT strategies.

Present & Future of Data Science

Data Science has become the real thing now and there are potentially hundreds and thousands of people running around with that job title. And, we too have started seeing these Data Scientists making large contributions to their organizations. There are certainly challenges to overcome, but the value of data science from a business point of view is pretty clear at this point.

Now, thinking about the future, certain questions definitely arise — “How will the practice of data science be changing over the next five years? What will be the new research areas of data science?”

“Will the fundamental skills remain the same?”

These are certainly debatable questions, but one thing is for sure — inventions have happened and will continue to happen when there arises any demand for the betterment of the future. And, the world would keep benefiting from data science through its upcoming innovations.

The possibilities of how to utilize Data Science in real-world scenarios are endless! Our Data Solutions team would be happy to help you capitalize on this technology for your enterprise.

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Exist Software Labs Inc and Informatica Pocket Session: Realizing Data Governance Benefits in a Cloud-Hybrid World
Exist Software Labs Inc and Informatica Pocket Session: Realizing Data Governance Benefits in a Cloud-Hybrid World 650 486 Exist Software Labs

Exist Software Labs Inc and Informatica Pocket Session: Realizing Data Governance Benefits in a Cloud-Hybrid World

On September 15, Exist Software Labs, in a joint effort with Informatica, gathered various market leaders from various verticals to conduct another pocket session on Data Governance and its benefits in a Cloud-Hybrid World.

Jon Teo, Data Governance and Privacy Expert at APJ spoke at the event about the benefits of Data Governance. He demonstrates how Data Governance helped various industries such as healthcare, automotive, insurance, manufacturing, power, and others around the world by leveraging its risk and compliance to protect the enterprise, as well as data intelligence that unlocks more value and data opportunity for businesses.

According to him, rapid cloud adaptation and a hybrid ecosystem generate more volume from more sources, making it difficult to discover, manage, and control data, requiring the urgent need for an agile data governance approach.

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Kingsley Dsouza, a Technical Data Governance Privacy Domain Expert, was one of the speakers who also demonstrated Informatica’s Data Governance services. According to him, “Data Governance platform helps users in finding information that will assist them in solving their day-to-day business problems, which most organizations struggle with and take a long time to process.”

It’s no secret that the Asia-Pacific region lags behind the rest of the world in data management, with less than 50% of organizations having standardized data management capabilities. As the amount of data generated in the region continues to grow at an exponential rate, organizations are scrambling to find effective ways to manage and store all of this information, which is where the agile data governance approach comes into play.

Mitigate security risks and ensure compliance with data privacy laws by standardizing your data management! Get in touch with our team to know more.

Download our FREE DATASHEET!

Begin your journey toward data maturity.
and transform into a data-driven organization today!

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Watch the Realizing Data Governance Benefits in a Cloud-Hybrid World Video On Demand now!

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    Exist Software Labs Inc. and Informatica
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    Exist Software Labs Inc. and Informatica held a joint Pocket Session on Intelligent Data Management Cloud at the Shangri-La Fort Hotel in BGC!
    Exist Software Labs Inc. and Informatica held a joint Pocket Session on Intelligent Data Management Cloud at the Shangri-La Fort Hotel in BGC! 650 486 Exist Software Labs

    Exist Software Labs Inc. and Informatica held a joint Pocket Session on Intelligent Data Management Cloud at the Shangri-La Fort Hotel in BGC!

    ‘Data is the new oil. Like oil, data is valuable, but if unrefined it cannot really be used. It has to be managed/processed (integrated, mapped, transformed) to create a valuable entity which provides insights that drives profitable activities.’ – Informatica

    A collaboration with Informatica


    Exist Software Labs inc collaborated with Informatica for an exclusive face-to-face event last July 28, 2022, at the Shangri-La Fort Hotel in BGC. The guests were able to meet with data management expert and Informatica’s Head of Cloud Product Specialist, Daniel Hein, who shared how companies can bridge the gap between technology and business through automation, integration, and data governance, unlocking true business value from data.

     

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    The world is changing, and so are your business’s needs. You must be able to adapt quickly to keep up with the changes. “In the last two years, a lot has changed. We are faced with new ways of doing business; the world is moving to a data-driven digital economy… However, there are CONSTRAINTS that you must overcome.” says Daniel Hein, Head of Cloud Product Specialists, APAC and Japan.

    That is why businesses must change their approach. The new Intelligent Data Management Cloud intends to help clients with that! The first and most comprehensive AI-powered data management solution in the industry. A single cloud platform. Every cloud-native service you’ll ever need for next-generation data management.

    IDMC

    Meet the new Intelligent Data Management Cloud of Informatica!

    IDMC platform cuts through red tape and provides accurate AI models across your organization so you can make timely decisions based on the most up-to-date information.

    It also gives you 360-degree views of your data across all areas of your business—so you can see who has access and what they’re doing with it—and allows easy workflow management.

    It is built on top of an enterprise cloud platform; and is equipped with a powerful security model that helps keep sensitive information secure from hackers.

    If you’re looking for a way to help your company prepare for this transition and stay competitive in an ever-changing marketplace, look no further! We specialize in helping companies not only to keep pace but also to improve their bottom line through digital transformation.

    Download our FREE DATASHEET!

    Begin your journey toward data maturity.
    and transform into a data-driven organization today!

    DOWNLOAD DATASHEET

    Download FREE Datasheet!

      Get Your Free Data Sheet

      exist news data strategy 768x487 1
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      Exist Accents Value of Data Strategy in Transformation Age
      Exist Accents Value of Data Strategy in Transformation Age 768 487 Exist Software Labs

      As part of the objective to provide valuable knowledge on the responsible, effective, and ethical use of tons of enterprise data, Exist, in a joint effort with Microsoft Azure, held the How to Begin Transforming into A Data-Driven Organization webinar last June 10, 2020.

      This 2nd online event in this webinar series aspires to impart insights to help organizations in understanding the necessity of having a clear data solutions strategy in staying competitive during these changing times.

      Exist Data Solutions Architect Warren Cruz shared his knowledge from his years of extensive experience in designing and developing data solutions strategy for organizations. Meanwhile, Mr. Jonas Lim, Exist Vice President for Technical Services, facilitated it.

      “Data, or the ability to harness it, is the key factor that will differentiate between a business organization that is there for the long haul and one that is only on survival mode,” Mr. Cruz said upon highlighting the importance of data for businesses nowadays.

      Among the topics that were elaborated in the event are:

      • What is Data-Driven Transformation and How Can Your Organization Afford to Stay The Same
      • The 4 Key Value-Drivers of Data-Driven Transformation
      • Where Are You Now? Your Organization’s Data Maturity Level
      • The Key Components of a Data-Driven Transformational Journey
      • An Open-Source Data-Driven Transformation Starter Pack

      “With the recently concluded webinar, I hope that I was able to influence the attendees, even if just a bit, towards the direction of putting up a modern data analytics platform of their own,” Mr. Cruz shared.

      “EXIST and I are just here if they need help.” he ended.

      Exist, and Microsoft Azure continue its collaboration in hopes to bring more informative events that will contribute to businesses’ and organizations’ digital development.

      As of now, the two are preparing another interactive and relevant online learning session.

      Stay updated!

      exist blog data webinar 768x487 1
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      Join Microsoft and Exist in an Eye-Opening Data Webinar
      Join Microsoft and Exist in an Eye-Opening Data Webinar 768 487 Exist Software Labs

      The days that “everything digital” is just the utopic, far-fetched future that corporations who aim to be at there prime are dreaming is all in the past now. It is already swinging right before our eyes.

      This ignites the flames to the long-sparking embers of the modern-day businesses’ hottest commodity: data. Every click, every tap, and every action is equivalent to thousands, even millions of it. 

      But setting up your sails for a data-driven technological transformation journey is not that easy. You first have to get things RIGHT.

      RIGHT VISION.

      Effective use of data could bring you promises. It can evolve your organization in ways beyond your expectations. But first, what goals do you want to achieve with your data? Your data will speak to you. But you have to ask the right questions: WHY do you want to use it?

      RIGHT KNOWLEDGE.

      Your collected data is a minefield of valuable bytes convertible to actionable insights. Yet, it requires a different kind of effort. Data needs proper skills for analyzing and formulating the right interpretations. Here, you will question HOW can you efficiently utilize your data at hand?

      RIGHT TOOLS.

      Managing a huge amount of data is tedious work, particularly when you do not take advantage of the modern solutions that you can use to make your process easier and faster. Traditionally, businesses take some time collecting data. Thus, it takes longer for them to transform it into relevant insights. Another question arises, WHAT do you need to use.

      It sounds simple and easy. However, putting it into motion presents another dilemma of not knowing your starting point. 

      HERE IS WHERE YOU BEGIN.

      Exist, in collaboration with Microsoft brings you How to Begin Transforming into a Data-driven Organization – a FREE webinar designed to equip organizations with the necessary knowledge to kick-off their technological innovation powered by data.

      Learn more from Exist’s very own Data Solutions Expert, Mr.Warren Cruz, and Vice President for Tech Services, Mr. Jonas Lim, on June 10, 2020, at 2:00 p.m.

      See how Microsoft and Exist can help in converting your data into actionable, enterprise-transforming insights.

       

      Save your slots now by clicking/tapping this link:

      Register Now

      We are excited to see you!

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      The Metallica of Master Data Management: TIBCO EBX
      The Metallica of Master Data Management: TIBCO EBX 768 487 Exist Software Labs

      In the world of heavy metal, Metallica is considered, arguably, as the G.O.A.T. Some may contest this claim and cite the forefathers, like Black Sabbath or Led Zeppelin, but the prevailing sentiment is that the ‘Tallica boys are at the top of the heap.

      One of the key achievements of this band is that they put out the highest-grossing metal album of all time. Released in 1986, Master of Puppets is Metallica’s best-selling album (surpassing every other metal band in the world in terms of raw sales).

      If Master of Puppets is Metallica’s magnum opus, then Master Data Management’s masterpiece is no other than TIBCO EBX.

      But first…what is Master Data Management?

      Master data management (MDM) is the initiative of an enterprise that is keen on having data work for them to create a single repository of all master data, reference data, and metadata in order to minimize, if not totally eliminate, data errors and redundancy in business processes.

      An MDM solution would typically be an interplay of Data Quality, Data Integration, and Data Governance practices.

      What’s in it for me with Master Data Management?

      The provision of a single point of reference for business-critical information eliminates the costliness of data redundancies that occur when organizations rely on multiple versions of data that reside in departmental silos.

      For example, MDM can ensure that when customer information changes, the Sales & Marketing Department will not reach out to unreachable or different entities, but will consistently have a single, latest, and accurate view of the customer upon which to target their efforts.

      What are the Basic Steps to Master Data Management?

      1. Discover the relevant and pertinent data sources to be mastered in your enterprise.
      2. Acquire the data (Data Integration proper, ETL, streaming, etc.).
      3. Cleanse the data (Data Quality proper).
      4. Enrich the data with data from other data sources that are external to your enterprise but are useful (e.g. social media, websites, etc.).
      5. Match the data with other data and look/flag for duplication.
      6. Merge the data and select the most up-to-date version of the data.
      7. Relate the mastered data with other relatable data in the enterprise.
      8. Secure the mastered data (masking, user roles & privileges, etc.).
      9. Deliver the mastered data to the appropriate and intended consumers and stakeholders.
      10. Govern the mastered data and ensure that master data management becomes a secure, repeatable, sustainable, and value-generating key framework in the enterprise.

      Why rock with TIBCO EBX?

      First, a history lesson. TIBCO EBX was the result of the acquisition of Orchestra Networks, a leader in MDM, by TIBCO Software last 2018. This assimilation proved monumental as evidenced by TIBCO EBX’s rankings in Gartner’s evaluations:

      As you can see, TIBCO EBX is among the Top 2 leaders in the Leader quadrant, alongside the very expensive Informatica.

      In actual MDM use cases, however, TIBCO EBX ranked highest in 5 of 6:

      The latest 2020 Gartner report on the MDM space pretty much tells the same story:

      tibcomdmgartner2020

      Again…why rock with TIBCO EBX?

      ONE PLATFORM FOR ALL YOUR DATA MANAGEMENT NEEDS

      With EBX software, you only need one platform to do the job of multiple products, including MDM, reference data management, product master data management, party master data management, data governance, and hierarchy management.

      SUPPORT FOR ALL TYPES OF BUSINESS FUNCTIONS

      Operational and analytical processes may be different, but they have one thing in common: data powers them all. Instead of managing these assets in multiple, separate applications, the EBX platform provides a single resource to govern and manage them, providing consistency and cohesion to processes across your organization.

      SUPPORT FOR ALL LEVELS OF USERS

      • Business Users: Delivers an intuitive, self-service experience for your business teams. Users view, search, author, edit, and approve changes in a workflow-driven, collaborative interface.
      • Data Stewards: Helps data stewards easily discern the quality of their data and take action using powerful data governance, matching, profiling, cleansing, workflow monitoring, quality analytics, and audit trail capabilities.
      • Developers/Analysts: Supports building and adapting applications quickly, without long and costly development projects. Project teams have full control over data models, workflow models, business rules, UI configuration, and data services.

      FLEXIBILITY AND AGILITY

      Custom applications and purpose-built MDM solutions are hard to change, but EBX software is flexible and agile. It uses a unique what-you-model-is-what-you-get design approach, with fully configurable applications generated on-the-fly. Long, costly development projects are eliminated. And EBX software includes all the enterprise class capabilities you need to create data management applications including user interfaces for authoring and data stewardship, workflow, hierarchy management, and data integration tools.1

      Is that all?

      TIBCO EBX’s best-of-breed capabilities include:

      DATA MODELING

      What you model is what you get. The flexible data model supports any master domain and relationships as well as complex and simple forms of data.

      COLLABORATIVE WORKFLOW

      Collaborate with everyone who touches your data. Manage updates, oversee change requests, and provide approvals through a customizable workflow.

      HIERARCHY MANAGEMENT

      Support any type of hierarchy and create alternate hierarchies without duplication. Now it’s easy to visualize and maintain complex relationships.

      VERSION CONTROL

      Manage and connect every version of data—past, present, and future.

      PLATFORM COMPATIBILITY

      Integrate with multiple platforms on-premises or in the cloud. Works with a wide range of interfaces, application servers, databases, and infrastructures.

      INSIGHT WITH DASHBOARDS AND KPIS

      Track, analyze, and measure data quality and performance through EBX dashboards.2

      How can I buy tickets to the next concert?

      If you want to learn more about MDM and how TIBCO EBX can help your organization eliminate bad data, data silos, and poor data visibility, contact EXIST Software Labs today!

      Keep rockin’!

       

       

       

      Footnotes:
      1 TIBCO EBX Datasheet
      2 ibid.

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