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A Complete Guide to Creating a Successful Business Intelligence (BI) Strategy

Business Intelligence

In today's environment, running a business can be quite challenging. These ever-changing and dynamic obstacles can make you feel overwhelmed. Maintenance of operations is a time-consuming process that leaves little time for working on the insights needed to gain a competitive advantage. However, organizations of all sizes, particularly SMEs, require accurate and actionable data perspectives. The role of a business intelligence (BI) strategy is to make this data available, which necessitates a deliberate plan.


The central goal of a business intelligence strategy is to use software and services to transform important data into actionable knowledge. This is very important as business intelligence revenue in software was projected to reach $23,258.94 million in 2021. BI tools give users access to analytical data, which includes reports, dashboards, maps, charts, and various other visual representations. Users can get detailed information regarding the state of the company.

“BI is about providing the right data at the right time to the right people so that they can make the right decisions.”

Nic Smith, Microsoft BI Solutions Marketing

Business intelligence strategy includes:
  • Performance management
  • Predictive modeling
  • Analytics
  • Data mining

Why Should Businesses Implement BI?

A business intelligence strategy will allow you to address your data problems, such as clarity, scarcity, insights out of data, and requirements, create a unified system, and sustain it.

You should consider implementing a BI strategy if your business faces the following issues:
  • You generate a lot of data but don't know what to do with it
  • Overstocking or understocking
  • Wasted resources and time
  • Loss of customers
  • Underperforming employees

Data-driven decisions can benefit your business by:
  • Discovering problems and their solutions
  • Analyzing competitors’ data
  • Analyzing customer behavior
  • Planning approach to increase profit
  • Foreseeing trends
  • Optimizing operations
  • Tracking performance


Tips to Create a Successful Business Intelligence Strategy

Business intelligence tools and capabilities are designed to create quick and easy-to-understand portrayals of an organization's current state. Developing a strategy to deal with all of these tools and skills is an essential part of reaping the benefits of business intelligence.

If you want to learn how to build a strong business intelligence strategy, keep reading.

Understand and Assess the Present Status

The first step in implementing a business intelligence strategy is to put together a team that is capable of analyzing and presenting the current state of the company's data. With a dedicated team in place, evaluating an organization's current situation entails thinking about the data collected and the technology used to manage it. Understanding the organization's structures and processes for mining and interpreting data is also critical. At this level, a BI team will seek to assess which data is the most valuable and which is irrelevant to the current operations.

Have a Vision with a Purpose and Direction

A vision is a combination of direction and purpose. Without a vision, there is no strategy. Instead, it presents itself in various critical decisions, such as where we collect our data and who will access the insights.

The following should be explained in the vision statement:
  • Who will be in charge of the business intelligence processes?
  • What is the state of your BI strategy concerning the business and IT strategies?
  • How will it provide help and solutions?
  • What solutions do you want to deploy, and where do you propose them?
  • What kind of infrastructure do you want to provide?


Prioritize Initiative by Developing a BI Road Map

The BI roadmap should provide deliverables at various execution levels and a timetable. On the roadmap, you should have all of the data you wish to organize and arrange, as well as the dates and deliverables for each activity.

Define the Way How the Data Is Going to Be Shared

Another thing to do before establishing a business intelligence strategy is to define the terms and meaning of BI with all of your stakeholders. Because many employees are involved in data processing, make sure that everyone is on the same page and understands the business intelligence development strategy.

At this stage, you should answer all the possible questions from your stakeholders, and the way and process data will be shared with all of them.

Must-have BI Strategy Documentation

A BI strategy document's logic is that it will serve as a point of reference for the entire organization and will be used to communicate the strategy.

The following sections should be in the document:
  • Executive summary
  • BI strategy alignment with corporate strategy
  • Project scope and requirements
  • BI governance team
  • Alternatives
  • Assessment
  • Appendices

Make Regular Reviews to Assess the Progress

A review process is necessary for any effective business intelligence strategy. These review methods should evaluate lessons gained while also documenting and determining the value of the data to the company.

A review process may consider the user's experience and the possibility of changing the business's KPIs year after year. In addition, it helps to understand the progress of the strategy and the benefits it has brought to the company.

Summing Up

Any business's growth requires a BI strategy as it gives you a competitive advantage. You need a solid strategy, planning, and analysis to enjoy the rewards. You can drown yourself in useless analytics if you don't have a structured roadmap in place.

Therefore, staying on track and assessing your methods regularly are critical to reaping the benefits of a BI strategy. The abovementioned steps serve you as stepping stones in developing a successful BI strategy.

Frequently Asked Questions


What is Business intelligence?

Business intelligence is how businesses use methods and technology to analyze both current and historical data. This is done to improve strategic decision-making and gain a competitive advantage.

Which are some of the BI tools?

Data mining, predictive modeling, and contextual dashboards or KPIs are the most popular and widely used BI tools.

Which are some of the major benefits of business intelligence?

The benefits of BI are speedy analysis, intuitive dashboards, data-driven business decisions, improved employee satisfaction, increased organizational efficiency, and many more.

Spotlight

Magnus-Data

Magnus Data is a system integrator and consulting firm bringing expertise and best practices with a focus in Big Data Analytics, Data Warehousing, Predictive Modeling and scale out OLTP database technologies to serve the customers Globally. Magnus Data’s differentiator is the knowledge of the internal workings of Big Data products and experience in implementing such technologies in some of the Fortune 100 companies and mid-market in several verticals. We have been part of developing the Big Data product companies and our best practices are unique from anyone in the industry.

OTHER ARTICLES
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How Companies Are Using Big Data and Analytics

Article | July 4, 2023

“Data are becoming the new raw material of business.” Craig Mundie, Senior Advisor to CEO, Microsoft Currently, the most valuable asset that a company has is data. By analyzing a large quantity of data and drawing valuable insights, companies can use raw materials (data) to work more effectively. In addition, many big data analytics case studies show that data gives businesses a big advantage over their less tech-savvy competitors. Let’s explore more about big data and analytics in this article. Why Do the C-Level Executives Need Big Data? Every C-level executive is on the lookout for new insights that help them keep their company viable. In recent years, the use of data analytics has become crucial for business leaders to make important decisions. According to McKinsey & Company, companies using big data analytics extensively across all business segments see a 126% profit improvement over companies that don’t. With the use of big data analytics, these companies see 6.5 times more customer retention, 7.4 times more outperformance than competitors, and almost 19 times more profitability. Here are some top reasons why the C-suite needs big data. Take Calculated Actions Harvard Business Review estimated that 70% of companies don’t feel that they understand the needs of their customers well enough to recognize what initiatives will drive growth. In such cases, you already know what you need to do, i.e., leverage big data and analytics. Big data analytics for businesses can help in recognizing customer preferences and customer segments on the basis of those preferences. C-suites in any industry can align their structure and product offerings to create value and take calculated actions. Recognize the Data According to Statista, data creation will increase to more than 180 zettabytes by 2025, which is a huge number. So, you can’t keep an approach of ‘gather now and sort it out later.’ With this approach to big data, you will be buried under tons of non-structured data. Start tracking the data early and capture the ones that are customer-generated and provide value to your company. Segment Your Customer’s Experience Analyze your present data and utilize your analytics to evaluate which characteristics a group of customers have in common and which aspects they don’t share. Segment and organize customers according to their preferences to build a clear lifecycle structure for every segment. Biggest Concerns About Big Data Analytics According to Concepta, 80% of C-suites think that data analytics will be a transformative force for businesses, but only 1 in 10 deliberately use it. 48% describe analytics as critical to decision-making, but only 7.4% say they use analytics to guide corporate strategy. So, what are the issues or concerns that tech-savvy C-level executives face when it comes to big data and analytics? Integrating Data with Current Technology "Tech inertia" usually disrupts certain businesses from evolving. Sometimes, the analytics framework businesses have in place is outdated to accommodate new techniques. According to Concepta, more than half the C-suite feel their analytics infrastructure is too rigid, and 75% say that due to inflexibility, they could not fulfill their business needs. Changing or upgrading the current technology would result in a loss of productivity. Companies must get the appropriate tools like Oracle Data Integrator 12c, SAP Data Services, MuleSoft, etc. to handle their data integration challenges. Another option is to seek professional assistance. You may either engage seasoned specialists who are far more knowledgeable about these instruments. Another option is to hire big data consultants. Big Data Silos There is a lot of unstructured data that is collected by different departments within a company, which leads to big data silos. The C-suite plays a critical role in developing a strategy, ensuring all departments communicate and integrate data from various sources to get a holistic picture of their business operations. Integrating your software that collects and stores data correctly is one of the most effective ways to avoid data silos Make a decision to use an all-in-one tool to unify and speed up your data management Spare some time to filter your outdated data Big Data Security Big data security is one of the most difficult tasks. Businesses are often so preoccupied with understanding, storing, and analyzing data that they overlook data security. Unsecured data repositories may become fertile grounds for malicious hackers. A data breach may cost a company up to $3.7 million. Businesses are hiring more cybersecurity experts to protect their data. Other measures taken to secure big data include: encryption of data, data segregation, identity and access management endpoint security implementation, real-time security monitoring, and use of big data security technologies such as the IBM Guardian. Key to Big Success from Big Data To get the most out of your big data and overcome the associated challenges, we have listed some key pointers that make a business successful and show how companies using big data are standing out. Have a Calculated Approach While laying the foundation of big data and business analytics, it is important to have a calculated approach as it reduces the risk in the early stages of setting up big data analytics. So, rather than attempting to implement it all at once, businesses should focus on resources that drive value from big data. Programmatic Integration In an action-driven system, success demands synchronizing big data, relevant analytics, and decision-making platforms at the appropriate time. The most successful companies using big data get insights directly from the data analytics tools used by executives who can act immediately according to the insights from the data. Focus on Building Skills Businesses must expand the big data capabilities of current workers through training and development since data analytics talent still remains one of the major challenges. 54% of the CEOs say that their companies have already set up in-house technical training programs for their employees. State-of-the-Art Technology To create strong big data and analytics capabilities, you need the right tools and technologies. Unfortunately, those who don’t have access to efficient big data analytics tools like Hadoop find themselves falling behind. Conclusion There's no going back when it comes to technology. Business decisions and activities are now made based on the use of data, so businesses that don't learn how to use their data will soon be out of date because data is now at the heart of everything. Businesses can align their data structures according to the requirements of their product offerings to generate value by utilizing big data and analytics. It helps to determine consumer preferences and segment consumers based on insights. FAQ How much data does it take to be called “Big Data”? There is no definitive answer to this question. Based on the current market infrastructure, the minimum threshold is somewhere around 1 to 3 terabytes (TB). However, big data technologies are also suitable for smaller databases. Do I Need to Hire a Data Scientist? The decision to hire a data scientist for your company is often a difficult one, and it depends entirely on your business's position. While there has been a huge demand for data scientists over the last few years, they are not easily available. Many businesses just use the support of a data architect or analyst. How are big data and Hadoop related to each other? Hadoop and big data are almost synonymous. Hadoop is a framework that specializes in big data processing that has grown in popularity with the advent of big data. Professionals may use the framework to analyze large amounts of data and assist companies with better decision-making.

Read More
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Topic modelling. Variation on themes and the Holy Grail

Article | April 13, 2023

Massive amount of data is collected and stored by companies in the search for the “Holy Grail”. One crucial component is the discovery and application of novel approaches to achieve a more complete picture of datasets provided by the local (sometimes global) event-based analytic strategy that currently dominates a specific field. Bringing qualitative data to life is essential since it provides management decisions’ context and nuance. An NLP perspective for uncovering word-based themes across documents will facilitate the exploration and exploitation of qualitative data which are often hard to “identify” in a global setting. NLP can be used to perform different analysis mapping drivers. Broadly speaking, drivers are factors that cause change and affect institutions, policies and management decision making. Being more precise, a “driver” is a force that has a material impact on a specific activity or an entity, which is contextually dependent, and which affects the financial market at a specific time. (Litterio, 2018). Major drivers often lie outside the immediate institutional environment such as elections or regional upheavals, or non-institutional factors such as Covid or climate change. In Total global strategy: Managing for worldwide competitive advantage, Yip (1992) develops a framework based on a set of four industry globalization drivers, which highlights the conditions for a company to become more global but also reflecting differentials in a competitive environment. In The lexicons: NLP in the design of Market Drivers Lexicon in Spanish, I have proposed a categorization into micro, macro drivers and temporality and a distinction among social, political, economic and technological drivers. Considering the “big picture”, “digging” beyond usual sectors and timeframes is key in state-of-the-art findings. Working with qualitative data. There is certainly not a unique “recipe” when applying NLP strategies. Different pipelines could be used to analyse any sort of textual data, from social media and reviews to focus group notes, blog comments and transcripts to name just a few when a MetaQuant team is looking for drivers. Generally, being textual data the source, it is preferable to avoid manual task on the part of the analyst, though sometimes, depending on the domain, content, cultural variables, etc. it might be required. If qualitative data is the core, then the preferred format is .csv. because of its plain nature which typically handle written responses better. Once the data has been collected and exported, the next step is to do some pre-processing. The basics include normalisation, morphosyntactic analysis, sentence structural analysis, tokenization, lexicalization, contextualization. Just simplify the data to make analysis easier. Topic modelling. Topic modelling refers to the task of recognizing words from the main topics that best describe a document or the corpus of data. LAD (Latent Dirichlet Allocation) is one of the most powerful algorithms with excellent implementations in the Python’s Gensim package. The challenge: how to extract good quality of topics that are clear and meaningful. Of course, this depends mostly on the nature of text pre-processing and the strategy of finding the optimal number of topics, the creation of a lexicon(s) and the corpora. We can say that a topic is defined or construed around the most representative keywords. But are keywords enough? Well, there are some other factors to be observed such as: 1. The variety of topics included in the corpora. 2. The choice of topic modelling algorithm. 3. The number of topics fed to the algorithm. 4. The algorithms tuning parameters. As you probably have noticed finding “the needle in the haystack” is not that easy. And only those who can use creatively NLP will have the advantage of positioning for global success.

Read More
Business Intelligence, Big Data Management, Big Data

Data Analytics: Five use cases in telecom industry

Article | May 15, 2023

The telecom industry has witnessed spectacular growth since its establishment in the 1830s. Enabling distant communications, collaborations, and transactions globally, telecommunication plays a significant role in making our lives more convenient and easier. With enhanced flexibility and advanced communication methods, the telecom industry gains more customers and creates new revenue streams. According to Grand View Research, the global telecom market size would expand at a compound annual growth rate (CAGR) of 5.4% between 2021-2028. With the rapidly growing digital connectivity, the communication service providers (CSPs) have to deal with large datasets. Datasets that can allow them better to understand their customers, competitors, industry trends and derive valuable insights for decision making.

Read More
Business Strategy

Metadata Driven Data Fabric

Article | July 22, 2022

The primary purpose of developing an enterprise data fabric is not new. It is the capacity to provide the appropriate data to the right data consumer at the right moment, in the correct form, and irrespective of how or where it is kept. Data fabric is the common "net" that connects and distributes integrated data from many data and application sources to diverse data consumers. What distinguishes the data fabric method from prior, more conventional data integration architectures? The primary distinction of data fabric is its dependence on metadata to achieve this purpose. Developing a metadata-driven architecture capable of providing integrated and enhanced data to data consumers is required when implementing a data fabric. Gartner invented the term "active metadata" to highlight this concept. Data Fabric Relies on Active Metadata Metadata is used to describe many properties of data. The larger the sets of metadata we gather, the better they will help our application scenarios. Historically, metadata categories included: Business metadata - gives meaning to data by mapping it to business terminology. Technical metadata - contains information on the data's format and structure, like physical database structures, data types, and data models. Operational metadata - includes the specifics of data processing and access, like data sharing regulations, performance, maintenance plans, archiving and retention policies. Recently, a new kind of metadata has emerged: social metadata. It usually involves conversations and comments from technical and business users on the data. Business metadata has progressed from just mapping words to now including taxonomies to aid in the interpretation of data context and meaning. What distinguishes active metadata from passive metadata? According to Gartner, passive metadata is any metadata that is gathered. According to some Gartner analysts, active metadata is metadata that is being utilized. We imply the use of metadata by software (like software components inside the data fabric) to enable a wide variety of data integration, analysis, reporting, and other data processing situations. Other analysts take this idea a step further, claiming that the data fabric generates active metadata by evaluating passive information and utilizing the findings to propose or automate operations. Closing Lines In this blog, we emphasized metadata management in data platforms in this blog series. We demonstrated how passive metadata management does not meet the demands of current data platform designs and why it must be supplemented, if not replaced, with independent processes of active data management systems.

Read More
Business Intelligence, Big Data Management, Big Data

Business Intelligence for Actionable Insights: Top BI books to Explore

Article | July 4, 2023

“Data are becoming the new raw material of business.” Craig Mundie, Senior Advisor to CEO, Microsoft Currently, the most valuable asset that a company has is data. By analyzing a large quantity of data and drawing valuable insights, companies can use raw materials (data) to work more effectively. In addition, many big data analytics case studies show that data gives businesses a big advantage over their less tech-savvy competitors. Let’s explore more about big data and analytics in this article. Why Do the C-Level Executives Need Big Data? Every C-level executive is on the lookout for new insights that help them keep their company viable. In recent years, the use of data analytics has become crucial for business leaders to make important decisions. According to McKinsey & Company, companies using big data analytics extensively across all business segments see a 126% profit improvement over companies that don’t. With the use of big data analytics, these companies see 6.5 times more customer retention, 7.4 times more outperformance than competitors, and almost 19 times more profitability. Here are some top reasons why the C-suite needs big data. Take Calculated Actions Harvard Business Review estimated that 70% of companies don’t feel that they understand the needs of their customers well enough to recognize what initiatives will drive growth. In such cases, you already know what you need to do, i.e., leverage big data and analytics. Big data analytics for businesses can help in recognizing customer preferences and customer segments on the basis of those preferences. C-suites in any industry can align their structure and product offerings to create value and take calculated actions. Recognize the Data According to Statista, data creation will increase to more than 180 zettabytes by 2025, which is a huge number. So, you can’t keep an approach of ‘gather now and sort it out later.’ With this approach to big data, you will be buried under tons of non-structured data. Start tracking the data early and capture the ones that are customer-generated and provide value to your company. Segment Your Customer’s Experience Analyze your present data and utilize your analytics to evaluate which characteristics a group of customers have in common and which aspects they don’t share. Segment and organize customers according to their preferences to build a clear lifecycle structure for every segment. Biggest Concerns About Big Data Analytics According to Concepta, 80% of C-suites think that data analytics will be a transformative force for businesses, but only 1 in 10 deliberately use it. 48% describe analytics as critical to decision-making, but only 7.4% say they use analytics to guide corporate strategy. So, what are the issues or concerns that tech-savvy C-level executives face when it comes to big data and analytics? Integrating Data with Current Technology "Tech inertia" usually disrupts certain businesses from evolving. Sometimes, the analytics framework businesses have in place is outdated to accommodate new techniques. According to Concepta, more than half the C-suite feel their analytics infrastructure is too rigid, and 75% say that due to inflexibility, they could not fulfill their business needs. Changing or upgrading the current technology would result in a loss of productivity. Companies must get the appropriate tools like Oracle Data Integrator 12c, SAP Data Services, MuleSoft, etc. to handle their data integration challenges. Another option is to seek professional assistance. You may either engage seasoned specialists who are far more knowledgeable about these instruments. Another option is to hire big data consultants. Big Data Silos There is a lot of unstructured data that is collected by different departments within a company, which leads to big data silos. The C-suite plays a critical role in developing a strategy, ensuring all departments communicate and integrate data from various sources to get a holistic picture of their business operations. Integrating your software that collects and stores data correctly is one of the most effective ways to avoid data silos Make a decision to use an all-in-one tool to unify and speed up your data management Spare some time to filter your outdated data Big Data Security Big data security is one of the most difficult tasks. Businesses are often so preoccupied with understanding, storing, and analyzing data that they overlook data security. Unsecured data repositories may become fertile grounds for malicious hackers. A data breach may cost a company up to $3.7 million. Businesses are hiring more cybersecurity experts to protect their data. Other measures taken to secure big data include: encryption of data, data segregation, identity and access management endpoint security implementation, real-time security monitoring, and use of big data security technologies such as the IBM Guardian. Key to Big Success from Big Data To get the most out of your big data and overcome the associated challenges, we have listed some key pointers that make a business successful and show how companies using big data are standing out. Have a Calculated Approach While laying the foundation of big data and business analytics, it is important to have a calculated approach as it reduces the risk in the early stages of setting up big data analytics. So, rather than attempting to implement it all at once, businesses should focus on resources that drive value from big data. Programmatic Integration In an action-driven system, success demands synchronizing big data, relevant analytics, and decision-making platforms at the appropriate time. The most successful companies using big data get insights directly from the data analytics tools used by executives who can act immediately according to the insights from the data. Focus on Building Skills Businesses must expand the big data capabilities of current workers through training and development since data analytics talent still remains one of the major challenges. 54% of the CEOs say that their companies have already set up in-house technical training programs for their employees. State-of-the-Art Technology To create strong big data and analytics capabilities, you need the right tools and technologies. Unfortunately, those who don’t have access to efficient big data analytics tools like Hadoop find themselves falling behind. Conclusion There's no going back when it comes to technology. Business decisions and activities are now made based on the use of data, so businesses that don't learn how to use their data will soon be out of date because data is now at the heart of everything. Businesses can align their data structures according to the requirements of their product offerings to generate value by utilizing big data and analytics. It helps to determine consumer preferences and segment consumers based on insights. FAQ How much data does it take to be called “Big Data”? There is no definitive answer to this question. Based on the current market infrastructure, the minimum threshold is somewhere around 1 to 3 terabytes (TB). However, big data technologies are also suitable for smaller databases. Do I Need to Hire a Data Scientist? The decision to hire a data scientist for your company is often a difficult one, and it depends entirely on your business's position. While there has been a huge demand for data scientists over the last few years, they are not easily available. Many businesses just use the support of a data architect or analyst. How are big data and Hadoop related to each other? Hadoop and big data are almost synonymous. Hadoop is a framework that specializes in big data processing that has grown in popularity with the advent of big data. Professionals may use the framework to analyze large amounts of data and assist companies with better decision-making.

Read More
Business Intelligence, Big Data Management, Data Science

Navigating Big Data Integration: Challenges and Strategies

Article | April 13, 2023

Massive amount of data is collected and stored by companies in the search for the “Holy Grail”. One crucial component is the discovery and application of novel approaches to achieve a more complete picture of datasets provided by the local (sometimes global) event-based analytic strategy that currently dominates a specific field. Bringing qualitative data to life is essential since it provides management decisions’ context and nuance. An NLP perspective for uncovering word-based themes across documents will facilitate the exploration and exploitation of qualitative data which are often hard to “identify” in a global setting. NLP can be used to perform different analysis mapping drivers. Broadly speaking, drivers are factors that cause change and affect institutions, policies and management decision making. Being more precise, a “driver” is a force that has a material impact on a specific activity or an entity, which is contextually dependent, and which affects the financial market at a specific time. (Litterio, 2018). Major drivers often lie outside the immediate institutional environment such as elections or regional upheavals, or non-institutional factors such as Covid or climate change. In Total global strategy: Managing for worldwide competitive advantage, Yip (1992) develops a framework based on a set of four industry globalization drivers, which highlights the conditions for a company to become more global but also reflecting differentials in a competitive environment. In The lexicons: NLP in the design of Market Drivers Lexicon in Spanish, I have proposed a categorization into micro, macro drivers and temporality and a distinction among social, political, economic and technological drivers. Considering the “big picture”, “digging” beyond usual sectors and timeframes is key in state-of-the-art findings. Working with qualitative data. There is certainly not a unique “recipe” when applying NLP strategies. Different pipelines could be used to analyse any sort of textual data, from social media and reviews to focus group notes, blog comments and transcripts to name just a few when a MetaQuant team is looking for drivers. Generally, being textual data the source, it is preferable to avoid manual task on the part of the analyst, though sometimes, depending on the domain, content, cultural variables, etc. it might be required. If qualitative data is the core, then the preferred format is .csv. because of its plain nature which typically handle written responses better. Once the data has been collected and exported, the next step is to do some pre-processing. The basics include normalisation, morphosyntactic analysis, sentence structural analysis, tokenization, lexicalization, contextualization. Just simplify the data to make analysis easier. Topic modelling. Topic modelling refers to the task of recognizing words from the main topics that best describe a document or the corpus of data. LAD (Latent Dirichlet Allocation) is one of the most powerful algorithms with excellent implementations in the Python’s Gensim package. The challenge: how to extract good quality of topics that are clear and meaningful. Of course, this depends mostly on the nature of text pre-processing and the strategy of finding the optimal number of topics, the creation of a lexicon(s) and the corpora. We can say that a topic is defined or construed around the most representative keywords. But are keywords enough? Well, there are some other factors to be observed such as: 1. The variety of topics included in the corpora. 2. The choice of topic modelling algorithm. 3. The number of topics fed to the algorithm. 4. The algorithms tuning parameters. As you probably have noticed finding “the needle in the haystack” is not that easy. And only those who can use creatively NLP will have the advantage of positioning for global success.

Read More
Business Intelligence, Big Data Management, Big Data

Harnessing the Power of Big Data: Events to attend in 2023

Article | May 15, 2023

The telecom industry has witnessed spectacular growth since its establishment in the 1830s. Enabling distant communications, collaborations, and transactions globally, telecommunication plays a significant role in making our lives more convenient and easier. With enhanced flexibility and advanced communication methods, the telecom industry gains more customers and creates new revenue streams. According to Grand View Research, the global telecom market size would expand at a compound annual growth rate (CAGR) of 5.4% between 2021-2028. With the rapidly growing digital connectivity, the communication service providers (CSPs) have to deal with large datasets. Datasets that can allow them better to understand their customers, competitors, industry trends and derive valuable insights for decision making.

Read More
Business Strategy

Metadata Driven Data Fabric

Article | July 22, 2022

The primary purpose of developing an enterprise data fabric is not new. It is the capacity to provide the appropriate data to the right data consumer at the right moment, in the correct form, and irrespective of how or where it is kept. Data fabric is the common "net" that connects and distributes integrated data from many data and application sources to diverse data consumers. What distinguishes the data fabric method from prior, more conventional data integration architectures? The primary distinction of data fabric is its dependence on metadata to achieve this purpose. Developing a metadata-driven architecture capable of providing integrated and enhanced data to data consumers is required when implementing a data fabric. Gartner invented the term "active metadata" to highlight this concept. Data Fabric Relies on Active Metadata Metadata is used to describe many properties of data. The larger the sets of metadata we gather, the better they will help our application scenarios. Historically, metadata categories included: Business metadata - gives meaning to data by mapping it to business terminology. Technical metadata - contains information on the data's format and structure, like physical database structures, data types, and data models. Operational metadata - includes the specifics of data processing and access, like data sharing regulations, performance, maintenance plans, archiving and retention policies. Recently, a new kind of metadata has emerged: social metadata. It usually involves conversations and comments from technical and business users on the data. Business metadata has progressed from just mapping words to now including taxonomies to aid in the interpretation of data context and meaning. What distinguishes active metadata from passive metadata? According to Gartner, passive metadata is any metadata that is gathered. According to some Gartner analysts, active metadata is metadata that is being utilized. We imply the use of metadata by software (like software components inside the data fabric) to enable a wide variety of data integration, analysis, reporting, and other data processing situations. Other analysts take this idea a step further, claiming that the data fabric generates active metadata by evaluating passive information and utilizing the findings to propose or automate operations. Closing Lines In this blog, we emphasized metadata management in data platforms in this blog series. We demonstrated how passive metadata management does not meet the demands of current data platform designs and why it must be supplemented, if not replaced, with independent processes of active data management systems.

Read More
MORE ARTICLES

Spotlight

Magnus-Data

Magnus Data is a system integrator and consulting firm bringing expertise and best practices with a focus in Big Data Analytics, Data Warehousing, Predictive Modeling and scale out OLTP database technologies to serve the customers Globally. Magnus Data’s differentiator is the knowledge of the internal workings of Big Data products and experience in implementing such technologies in some of the Fortune 100 companies and mid-market in several verticals. We have been part of developing the Big Data product companies and our best practices are unique from anyone in the industry.

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Airbyte Racks Up Awards from InfoWorld, BigDATAwire, Built In; Builds Largest and Fastest-Growing User Community

Airbyte | January 30, 2024

Airbyte, creators of the leading open-source data movement infrastructure, today announced a series of accomplishments and awards reinforcing its standing as the largest and fastest-growing data movement community. With a focus on innovation, community engagement, and performance enhancement, Airbyte continues to revolutionize the way data is handled and processed across industries. “Airbyte proudly stands as the front-runner in the data movement landscape with the largest community of more than 5,000 daily users and over 125,000 deployments, with monthly data synchronizations of over 2 petabytes,” said Michel Tricot, co-founder and CEO, Airbyte. “This unparalleled growth is a testament to Airbyte's widespread adoption by users and the trust placed in its capabilities.” The Airbyte community has more than 800 code contributors and 12,000 stars on GitHub. Recently, the company held its second annual virtual conference called move(data), which attracted over 5,000 attendees. Airbyte was named an InfoWorld Technology of the Year Award finalist: Data Management – Integration (in October) for cutting-edge products that are changing how IT organizations work and how companies do business. And, at the start of this year, was named to the Built In 2024 Best Places To Work Award in San Francisco – Best Startups to Work For, recognizing the company's commitment to fostering a positive work environment, remote and flexible work opportunities, and programs for diversity, equity, and inclusion. Today, the company received the BigDATAwire Readers/Editors Choice Award – Big Data and AI Startup, which recognizes companies and products that have made a difference. Other key milestones in 2023 include the following. Availability of more than 350 data connectors, making Airbyte the platform with the most connectors in the industry. The company aims to increase that to 500 high-quality connectors supported by the end of this year. More than 2,000 custom connectors were created with the Airbyte No-Code Connector Builder, which enables data connectors to be made in minutes. Significant performance improvement with database replication speed increased by 10 times to support larger datasets. Added support for five vector databases, in addition to unstructured data sources, as the first company to build a bridge between data movement platforms and artificial intelligence (AI). Looking ahead, Airbyte will introduce data lakehouse destinations, as well as a new Publish feature to push data to API destinations. About Airbyte Airbyte is the open-source data movement infrastructure leader running in the safety of your cloud and syncing data from applications, APIs, and databases to data warehouses, lakes, and other destinations. Airbyte offers four products: Airbyte Open Source, Airbyte Self-Managed, Airbyte Cloud, and Powered by Airbyte. Airbyte was co-founded by Michel Tricot (former director of engineering and head of integrations at Liveramp and RideOS) and John Lafleur (serial entrepreneur of dev tools and B2B). The company is headquartered in San Francisco with a distributed team around the world. To learn more, visit airbyte.com.

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Big Data Management

The Modern Data Company Recognized in Gartner's Magic Quadrant for Data Integration

The Modern Data Company | January 23, 2024

The Modern Data Company, recognized for its expertise in developing and managing advanced data products, is delighted to announce its distinction as an honorable mention in Gartner's 'Magic Quadrant for Data Integration Tools,' powered by our leading product, DataOS. “This accolade underscores our commitment to productizing data and revolutionizing data management technologies. Our focus extends beyond traditional data management, guiding companies on their journey to effectively utilize data, realize tangible ROI on their data investments, and harness advanced technologies such as AI, ML, and Large Language Models (LLMs). This recognition is a testament to Modern Data’s alignment with the latest industry trends and our dedication to setting new standards in data integration and utilization.” – Srujan Akula, CEO of The Modern Data Company The inclusion in the Gartner report highlights The Modern Data Company's pivotal role in shaping the future of data integration. Our innovative approach, embodied in DataOS, enables businesses to navigate the complexities of data management, transforming data into a strategic asset. By simplifying data access and integration, we empower organizations to unlock the full potential of their data, driving insights and innovation without disruption. "Modern Data's recognition as an Honorable Mention in the Gartner MQ for Data Integration is a testament to the transformative impact their solutions have on businesses like ours. DataOS has been pivotal in allowing us to integrate multiple data sources, enabling our teams to have access to the data needed to make data driven decisions." – Emma Spight, SVP Technology, MIND 24-7 The Modern Data Company simplifies how organizations manage, access, and interact with data using its DataOS (data operating system) that unifies data silos, at scale. It provides ontology support, graph modeling, and a virtual data tier (e.g. a customer 360 model). From a technical point of view, it closes the gap from conceptual to physical data model. Users can define conceptually what they want and its software traverses and integrates data. DataOS provides a structured, repeatable approach to data integration that enhances agility and ensures high-quality outputs. This shift from traditional pipeline management to data products allows for more efficient data operations, as each 'product' is designed with a specific purpose and standardized interfaces, ensuring consistency across different uses and applications. With DataOS, businesses can expect a transformative impact on their data strategies, marked by increased efficiency and a robust framework for handling complex data ecosystems, allowing for more and faster iterations of conceptual models. About The Modern Data Company The Modern Data Company, with its flagship product DataOS, revolutionizes the creation of data products. DataOS® is engineered to build and manage comprehensive data products to foster data mesh adoption, propelling organizations towards a data-driven future. DataOS directly addresses key AI/ML and LLM challenges: ensuring quality data, scaling computational resources, and integrating seamlessly into business processes. In our commitment to provide open systems, we have created an open data developer platform specification that is gaining wide industry support.

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Big Data Management

data.world Integrates with Snowflake Data Quality Metrics to Bolster Data Trust

data.world | January 24, 2024

data.world, the data catalog platform company, today announced an integration with Snowflake, the Data Cloud company, that brings new data quality metrics and measurement capabilities to enterprises. The data.world Snowflake Collector now empowers enterprise data teams to measure data quality across their organization on-demand, unifying data quality and analytics. Customers can now achieve greater trust in their data quality and downstream analytics to support mission-critical applications, confident data-driven decision-making, and AI initiatives. Data quality remains one of the top concerns for chief data officers and a critical barrier to creating a data-driven culture. Traditionally, data quality assurance has relied on manual oversight – a process that’s tedious and fraught with inefficacy. The data.world Data Catalog Platform now delivers Snowflake data quality metrics directly to customers, streamlining quality assurance timelines and accelerating data-first initiatives. Data consumers can access contextual information in the catalog or directly within tools such as Tableau and PowerBI via Hoots – data.world’s embedded trust badges – that broadcast data health status and catalog context, bolstering transparency and trust. Additionally, teams can link certification and DataOps workflows to Snowflake's data quality metrics to automate manual workflows and quality alerts. Backed by a knowledge graph architecture, data.world provides greater insight into data quality scores via intelligence on data provenance, usage, and context – all of which support DataOps and governance workflows. “Data trust is increasingly crucial to every facet of business and data teams are struggling to verify the quality of their data, facing increased scrutiny from developers and decision-makers alike on the downstream impacts of their work, including analytics – and soon enough, AI applications,” said Jeff Hollan, Director, Product Management at Snowflake. “Our collaboration with data.world enables data teams and decision-makers to verify and trust their data’s quality to use in mission-critical applications and analytics across their business.” “High-quality data has always been a priority among enterprise data teams and decision-makers. As enterprise AI ambitions grow, the number one priority is ensuring the data powering generative AI is clean, consistent, and contextual,” said Bryon Jacob, CTO at data.world. “Alongside Snowflake, we’re taking steps to ensure data scientists, analysts, and leaders can confidently feed AI and analytics applications data that delivers high-quality insights, and supports the type of decision-making that drives their business forward.” The integration builds on the robust collaboration between data.world and Snowflake. Most recently, the companies announced an exclusive offering for joint customers, streamlining adoption timelines and offering a new attractive price point. The data.world's knowledge graph-powered data catalog already offers unique benefits for Snowflake customers, including support for Snowpark. This offering is now available to all data.world enterprise customers using the Snowflake Collector, as well as customers taking advantage of the Snowflake-only offering. To learn more about the data quality integration or the data.world data catalog platform, visit data.world. About data.world data.world is the data catalog platform built for your AI future. Its cloud-native SaaS (software-as-a-service) platform combines a consumer-grade user experience with a powerful Knowledge Graph to deliver enhanced data discovery, agile data governance, and actionable insights. data.world is a Certified B Corporation and public benefit corporation and home to the world’s largest collaborative open data community with more than two million members, including ninety percent of the Fortune 500. Our company has 76 patents and has been named one of Austin’s Best Places to Work seven years in a row.

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Big Data

Airbyte Racks Up Awards from InfoWorld, BigDATAwire, Built In; Builds Largest and Fastest-Growing User Community

Airbyte | January 30, 2024

Airbyte, creators of the leading open-source data movement infrastructure, today announced a series of accomplishments and awards reinforcing its standing as the largest and fastest-growing data movement community. With a focus on innovation, community engagement, and performance enhancement, Airbyte continues to revolutionize the way data is handled and processed across industries. “Airbyte proudly stands as the front-runner in the data movement landscape with the largest community of more than 5,000 daily users and over 125,000 deployments, with monthly data synchronizations of over 2 petabytes,” said Michel Tricot, co-founder and CEO, Airbyte. “This unparalleled growth is a testament to Airbyte's widespread adoption by users and the trust placed in its capabilities.” The Airbyte community has more than 800 code contributors and 12,000 stars on GitHub. Recently, the company held its second annual virtual conference called move(data), which attracted over 5,000 attendees. Airbyte was named an InfoWorld Technology of the Year Award finalist: Data Management – Integration (in October) for cutting-edge products that are changing how IT organizations work and how companies do business. And, at the start of this year, was named to the Built In 2024 Best Places To Work Award in San Francisco – Best Startups to Work For, recognizing the company's commitment to fostering a positive work environment, remote and flexible work opportunities, and programs for diversity, equity, and inclusion. Today, the company received the BigDATAwire Readers/Editors Choice Award – Big Data and AI Startup, which recognizes companies and products that have made a difference. Other key milestones in 2023 include the following. Availability of more than 350 data connectors, making Airbyte the platform with the most connectors in the industry. The company aims to increase that to 500 high-quality connectors supported by the end of this year. More than 2,000 custom connectors were created with the Airbyte No-Code Connector Builder, which enables data connectors to be made in minutes. Significant performance improvement with database replication speed increased by 10 times to support larger datasets. Added support for five vector databases, in addition to unstructured data sources, as the first company to build a bridge between data movement platforms and artificial intelligence (AI). Looking ahead, Airbyte will introduce data lakehouse destinations, as well as a new Publish feature to push data to API destinations. About Airbyte Airbyte is the open-source data movement infrastructure leader running in the safety of your cloud and syncing data from applications, APIs, and databases to data warehouses, lakes, and other destinations. Airbyte offers four products: Airbyte Open Source, Airbyte Self-Managed, Airbyte Cloud, and Powered by Airbyte. Airbyte was co-founded by Michel Tricot (former director of engineering and head of integrations at Liveramp and RideOS) and John Lafleur (serial entrepreneur of dev tools and B2B). The company is headquartered in San Francisco with a distributed team around the world. To learn more, visit airbyte.com.

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Big Data Management

The Modern Data Company Recognized in Gartner's Magic Quadrant for Data Integration

The Modern Data Company | January 23, 2024

The Modern Data Company, recognized for its expertise in developing and managing advanced data products, is delighted to announce its distinction as an honorable mention in Gartner's 'Magic Quadrant for Data Integration Tools,' powered by our leading product, DataOS. “This accolade underscores our commitment to productizing data and revolutionizing data management technologies. Our focus extends beyond traditional data management, guiding companies on their journey to effectively utilize data, realize tangible ROI on their data investments, and harness advanced technologies such as AI, ML, and Large Language Models (LLMs). This recognition is a testament to Modern Data’s alignment with the latest industry trends and our dedication to setting new standards in data integration and utilization.” – Srujan Akula, CEO of The Modern Data Company The inclusion in the Gartner report highlights The Modern Data Company's pivotal role in shaping the future of data integration. Our innovative approach, embodied in DataOS, enables businesses to navigate the complexities of data management, transforming data into a strategic asset. By simplifying data access and integration, we empower organizations to unlock the full potential of their data, driving insights and innovation without disruption. "Modern Data's recognition as an Honorable Mention in the Gartner MQ for Data Integration is a testament to the transformative impact their solutions have on businesses like ours. DataOS has been pivotal in allowing us to integrate multiple data sources, enabling our teams to have access to the data needed to make data driven decisions." – Emma Spight, SVP Technology, MIND 24-7 The Modern Data Company simplifies how organizations manage, access, and interact with data using its DataOS (data operating system) that unifies data silos, at scale. It provides ontology support, graph modeling, and a virtual data tier (e.g. a customer 360 model). From a technical point of view, it closes the gap from conceptual to physical data model. Users can define conceptually what they want and its software traverses and integrates data. DataOS provides a structured, repeatable approach to data integration that enhances agility and ensures high-quality outputs. This shift from traditional pipeline management to data products allows for more efficient data operations, as each 'product' is designed with a specific purpose and standardized interfaces, ensuring consistency across different uses and applications. With DataOS, businesses can expect a transformative impact on their data strategies, marked by increased efficiency and a robust framework for handling complex data ecosystems, allowing for more and faster iterations of conceptual models. About The Modern Data Company The Modern Data Company, with its flagship product DataOS, revolutionizes the creation of data products. DataOS® is engineered to build and manage comprehensive data products to foster data mesh adoption, propelling organizations towards a data-driven future. DataOS directly addresses key AI/ML and LLM challenges: ensuring quality data, scaling computational resources, and integrating seamlessly into business processes. In our commitment to provide open systems, we have created an open data developer platform specification that is gaining wide industry support.

Read More

Big Data Management

data.world Integrates with Snowflake Data Quality Metrics to Bolster Data Trust

data.world | January 24, 2024

data.world, the data catalog platform company, today announced an integration with Snowflake, the Data Cloud company, that brings new data quality metrics and measurement capabilities to enterprises. The data.world Snowflake Collector now empowers enterprise data teams to measure data quality across their organization on-demand, unifying data quality and analytics. Customers can now achieve greater trust in their data quality and downstream analytics to support mission-critical applications, confident data-driven decision-making, and AI initiatives. Data quality remains one of the top concerns for chief data officers and a critical barrier to creating a data-driven culture. Traditionally, data quality assurance has relied on manual oversight – a process that’s tedious and fraught with inefficacy. The data.world Data Catalog Platform now delivers Snowflake data quality metrics directly to customers, streamlining quality assurance timelines and accelerating data-first initiatives. Data consumers can access contextual information in the catalog or directly within tools such as Tableau and PowerBI via Hoots – data.world’s embedded trust badges – that broadcast data health status and catalog context, bolstering transparency and trust. Additionally, teams can link certification and DataOps workflows to Snowflake's data quality metrics to automate manual workflows and quality alerts. Backed by a knowledge graph architecture, data.world provides greater insight into data quality scores via intelligence on data provenance, usage, and context – all of which support DataOps and governance workflows. “Data trust is increasingly crucial to every facet of business and data teams are struggling to verify the quality of their data, facing increased scrutiny from developers and decision-makers alike on the downstream impacts of their work, including analytics – and soon enough, AI applications,” said Jeff Hollan, Director, Product Management at Snowflake. “Our collaboration with data.world enables data teams and decision-makers to verify and trust their data’s quality to use in mission-critical applications and analytics across their business.” “High-quality data has always been a priority among enterprise data teams and decision-makers. As enterprise AI ambitions grow, the number one priority is ensuring the data powering generative AI is clean, consistent, and contextual,” said Bryon Jacob, CTO at data.world. “Alongside Snowflake, we’re taking steps to ensure data scientists, analysts, and leaders can confidently feed AI and analytics applications data that delivers high-quality insights, and supports the type of decision-making that drives their business forward.” The integration builds on the robust collaboration between data.world and Snowflake. Most recently, the companies announced an exclusive offering for joint customers, streamlining adoption timelines and offering a new attractive price point. The data.world's knowledge graph-powered data catalog already offers unique benefits for Snowflake customers, including support for Snowpark. This offering is now available to all data.world enterprise customers using the Snowflake Collector, as well as customers taking advantage of the Snowflake-only offering. To learn more about the data quality integration or the data.world data catalog platform, visit data.world. About data.world data.world is the data catalog platform built for your AI future. Its cloud-native SaaS (software-as-a-service) platform combines a consumer-grade user experience with a powerful Knowledge Graph to deliver enhanced data discovery, agile data governance, and actionable insights. data.world is a Certified B Corporation and public benefit corporation and home to the world’s largest collaborative open data community with more than two million members, including ninety percent of the Fortune 500. Our company has 76 patents and has been named one of Austin’s Best Places to Work seven years in a row.

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