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7 Reasons Why Business Intelligence (BI) is Crucial

7 Reasons Why Business Intelligence (BI) is Crucial

In today’s digital and customer-centric world, businesses are facing stiff competition. Most of these businesses are bombarded with information and are actively exploring ways to derive significant insights and control from the data gathered. For businesses to resolve the issue of data overloading, obtain a competitive edge in the market, and make informed decisions, there is a need to adopt business intelligence. Unfortunately, even with the long list of benefits and the increasing number of users, most companies are very slow in adopting it. Business intelligence empowers you to combine the power of technology and business expertise to make informed decisions and outplay competitors. According to Techjury, more than 46% of businesses are already using a business intelligence tool as a core part of their business strategy.


Swain Scheps rightly highlights the importance of business intelligence in his quote:

“Business intelligence is essentially timely, accurate, high-value, and actionable business insights, and the work processes and technologies used to obtain them.”

Business Intelligence VS Business Analytics

Business intelligence and business analytics are often considered synonyms with the same meaning, definition, and method of working, but that's not the case.

Business intelligence refers to technologies and strategies developed by enterprise industries to analyze existing business data and provide historical, current, and predictive events for business operations. Present-day businesses are widely accepting business intelligence technologies.

Business analytics is the process of technologies and strategies utilized to continue analyzing and extracting insights and performance from historical business data to drive successful future business planning. There is also a long list of the importance of business analytics.

Common Challenges Faced by Today’s C-Suite

The responsibility of the C-Suite and the CEO, in particular, is to accelerate the growth of a company and work towards achieving industrial excellence. They face immense pressure from various stakeholders who sometimes have theoretical expectations regarding the performance of the company and its results. Let’s check out some of the common challenges faced by the C-Suite.

Expectations for Growth Acceleration

Driving growth and achieving a significant increase in the profit margin annually are among the top challenges faced by today’s C-suite. In the event of continued failure in achieving this goal, CEOs can affect their record.

Business intelligence solutions analyze all the company data and assist the C-suite in making informed decisions. They also help in accelerating the growth of the organization by optimizing internal business processes, enhancing operational efficiency, gaining a competitive edge, and others. By extracting important information from unstructured data and turning it into useful information, BI helps to speed up the process.

Stakeholder’s Demands

Stakeholders can sometimes demand theoretical or special reports and data. Failure to fulfill this demand can upset the stakeholders.

While business intelligence tools may not help you meet the special demands of your stakeholders, but it will certainly help you analyse and explain why a particular target could not be achieved. Moreover, it also keeps track of all the activities, your decisions, and how the company has performed, which will reflect your efforts and incremental progress to the stakeholders.

Budgetary Restrictions

According to Betsy Burton, vice president and distinguished analyst with Gartner, the cost of BI tools is high, which limits their implementation in businesses with limited budget access, such as small to mid-sized companies. Despite the demand and need for business intelligence, often a minimum portion of the operating budget is allocated for the improvement and upgradation of data analytics and the business intelligence systems. As a result, progress is not made, benefits of business intelligence are not reaped, and the cycle of challenges continues in the C-suite.

In this case, businesses can either explore adopting business intelligence tools in phases, or they can opt for self-service BI or embedded BI tools, which are more affordable and can be easily integrated with existing systems.

How Can Business Intelligence Make a Difference?

Not only enterprise companies, but even small, mid-sized, and large businesses can benefit from business intelligence. Adopting business intelligence technologies has numerous benefits. Here are the top seven reasons why having business intelligence (BI) is crucial.

Gain Customer Insights

With the help of business intelligence, businesses can analyze their customers’ buying patterns to obtain customer insights and create user profiles as per their behavior. Customer insights will help businesses create better products and enhance the product experience for their customers.

Improved Efficiency Across the Organization

Having an effective business intelligence system significantly improves the efficiency of the overall business processes and has a positive impact on revenue. In addition, access to meaningful insights reduces the waiting time for reports and increases team productivity.


Gain Sales and Market Intelligence

If you are a sales executive or a marketer, you probably keep track of your customers with the help of a CRM solution. A CRM solution aims to collect all the data and make sense of the data about your customers through charts and tables.


Insights into Consumer Behavior

One of the significant benefits of investing in business intelligence is that it increases the ability to analyze and understand customer behavior. It will highlight a customer’s buying behavior and highlight changes in behavioral patterns.


Improved Business Operations Visibility

Understanding the importance of business intelligence helps control business processes. It helps to assess what is going on in a business carefully. Active vigilance over processes and standard procedures can help to fix errors.


Return on Investment (ROI)

Business intelligence helps a company get a better return on its investment (ROI) by improving strategic awareness, speeding up reporting, cutting operating costs, and getting better quality data.


Gives a Competitive Edge

Apart from all the other benefits of business intelligence, having the potential to handle and analyze enormous amounts of data is in itself a competitive advantage. Furthermore, budgeting, planning, and forecasting are effective ways to keep up with the competition, go well beyond ordinary analysis, and are simple to implement with business intelligence tools.


Final Thoughts

Understanding the importance of business intelligence and having a great business intelligence system has become quite essential for businesses these days. Business intelligence is much more than just graphical representation. It is a set of tools that businesses can use to help their employees succeed. BI can change your business by providing the information required to make fast and informed decisions.


FAQ


Will my business data be secure?

Any IT system must have data security and availability as their top priority. A business intelligence solution should provide the high standards of performance, reliability, and security. To keep the data safe, credible business intelligence solutions make use of existing security infrastructures.


My business has already invested in CRM, Accounting, and Marketing Software. So, why should I also invest in Business Intelligence?

While you may utilize a variety of line-of-business systems to administer your company, BI is about integrating data from numerous sources in an organized way to graphically represent information in a meaningful way. A constructive business intelligence solution should be able to connect to daily business software with ease.


Why Is BI Reporting Better Than Conventional MIS Reports?

Management reporting is only a small part of business intelligence. It gives you real-time, quick, and easy access to actionable business information about customers, goods, finance, and the market.

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Business Intelligence, Big Data Management, Big Data

Topic modelling. Variation on themes and the Holy Grail

Article | May 15, 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

Enhance Your Business with Data Modeling Techniques

Article | April 27, 2023

Introduction Data modeling is the study of data objects and their interactions with other things. It's used to research data requirements for a variety of business requirements. The data models are created to store the data in a database. Therefore, instead of focusing on what processes we must conduct, the data modeling methodologies focuses on what data is required and how to organize it. Data modeling techniques facilitate the integration of high-level business processes with data structures, data rules, and the technical execution of physical data. Data modeling best parctices bring your company's operations and data usage together in a way that everyone can comprehend. As 2.5 quintillion bytes of data are created every day, enterprises and business organizations are compelled to use data modeling techniques to handle them efficiently. Data modeling for businesses reduces the budget for programming by up to 75%. It typically consumes less than 10% of a project budget. “The ability to take data – to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it – is going to be a hugely important skill in the next decades.” - Hal Varian, Chief Economist, Google Top Techniques to Enhance Your Data Modeling for Business Data modeling methodology helps create a conceptual model and establish relationships between objects. The three perspectives of a data model are dealt with in the primary data modeling techniques. And they are conceptual, logical, and physical data models. Let us look into some essential data modeling techniques to accelerate your business. Have a Visualization of the Data You're Going to Model It's unconvincing to think that staring at endless rows and columns of alphanumeric entries will lead to enlightenment. On the contrary, most people are significantly more comfortable inspecting and joining data tables using drag-and-drop screen interfaces or looking at graphical data representations that make it quick to spot any irregularities. These types of data visualization techniques assist you in cleaning your data so that it is comprehensive, consistent, and free of errors and redundancies. They also help you identify distinct data record types that correspond to the same real-life entity, allowing you to change them to use standard fields and formats, making it easier to combine data sources. Recognize the Business Requirements and Desired Outcomes The purpose of data modeling best practices is to improve the efficiency of an organization. As a data modeler, you can only collect, organize, and store data for analysis if you understand your company's requirements. Obtain feedback from business stakeholders to create conceptual and logical data models tailored to the company's needs. Collect data requirements from business analysts and other subject matter experts to aid in developing more comprehensive logical and physical models from the higher-level models and business requirements. Data models must change in response to changes in business and technology. As a result, a thorough grasp of the company, its needs, goals, expected outcomes, and the intended application of the data modeling mission's outputs is a critical data modeling technique to follow. According to IBM, “Data models are built around business needs. Rules and requirements are defined upfront through feedback from business stakeholders so they can be incorporated into the design of a new system or adapted in the iteration of an existing one.” Distinguish Between Facts, Dimensions, Filters, and Order when Dealing with Business Enquiries Understanding how these four parts characterize business questions will help you organize data in ways that make providing answers easier. For example, you may make locating the top sales performers per sales period easier and answer other business intelligence queries by structuring your data using different tables for facts and dimensions. Before Continuing, Double-Check Each Stage of your Data Modelling. Before going on to the next stage, each action should be double-checked, beginning with the data modeling priorities derived from the business requirements. For example, a dataset's main key must be chosen so that the primary key's value in each record may be used to identify each in the dataset uniquely. The same data modeling technique can check that joining two datasets is either one-to-one or one-to-many and avoid many-to-many interactions that lead to too complicated or unmanageable data models. Instead of Just Looking for Correlation, Look for Causation Data modeling best practices offers instructions on how to use the modeled data. While allowing end-users to access business intelligence on their own is a significant step forward, it's equally critical that they don't make mistakes. They may notice, for example, that sales of two different products appear to grow and fall in lockstep. Are sales of one product driving sales of the other, or do they rise and fall in lockstep due to another factor like the economy or weather? Confusing causality and correlation could lead businesses to lose resources by focusing on the wrong or non-existent possibilities. Summing Up Data modeling can assist companies in quickly acquiring answers to their business concerns, improving productivity, profitability, efficiency, and customer happiness, among other things. Linking to corporate needs and objectives and employing tools to speed up the procedures in preparing data for replies to all inquiries are critical success elements and part of data modeling techniques. Once these prerequisites are met, you can anticipate your data modeling to provide significant business value to you and your company, whether small, medium, or large. Frequently Asked Questions What are some of the crucial data modeling techniques? There are many crucial data modeling techniques in the business. Some of them are: Hierarchical data model Network data model Relational data model Object-oriented data model Entity-relationship data model Data model with dimensions Data model based on graphs What are data modeling techniques? Data modeling is optimizing data to streamline information flow inside businesses for various business needs. It improves analytics by formatting data and its attributes, creating links between data, and organizing data. Why is data modeling important? Data modeling is essential as a clear representation of data makes it easier to analyze it correctly. Also, it helps stakeholders to make data-driven decisions as data modeling improves data quality.

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Business Intelligence, Big Data Management, Data Science

Evolution of capabilities of Data Platforms & data ecosystem

Article | May 2, 2023

Data Platforms and frameworks have been constantly evolving. At some point of time; we are excited by Hadoop (well for almost 10 years); followed by Snowflake or as I say Snowflake Blizzard (who managed to launch biggest IPO win historically) and the Google (Google solves problems and serves use cases in a way that few companies can match). The end of the data warehouse Once upon a time, life was simple; or at least, the basic approach to Business Intelligence was fairly easy to describe… A process of collecting information from systems, building a repository of consistent data, and bolting on one or more reporting and visualisation tools which presented information to users. Data used to be managed in expensive, slow, inaccessible SQL data warehouses. SQL systems were notorious for their lack of scalability. Their demise is coming from a few technological advances. One of these is the ubiquitous, and growing, Hadoop. On April 1, 2006, Apache Hadoop was unleashed upon Silicon Valley. Inspired by Google, Hadoop’s primary purpose was to improve the flexibility and scalability of data processing by splitting the process into smaller functions that run on commodity hardware. Hadoop’s intent was to replace enterprise data warehouses based on SQL. Unfortunately, a technology used by Google may not be the best solution for everyone else. It’s not that others are incompetent: Google solves problems and serves use cases in a way that few companies can match. Google has been running massive-scale applications such as its eponymous search engine, YouTube and the Ads platform. The technologies and infrastructure that make the geographically distributed offerings perform at scale are what make various components of Google Cloud Platform enterprise ready and well-featured. Google has shown leadership in developing innovations that have been made available to the open-source community and are being used extensively by other public cloud vendors and Gartner clients. Examples of these include the Kubernetes container management framework, TensorFlow machine learning platform and the Apache Beam data processing programming model. GCP also uses open-source offerings in its cloud while treating third-party data and analytics providers as first-class citizens on its cloud and providing unified billing for its customers. The examples of the latter include DataStax, Redis Labs, InfluxData, MongoDB, Elastic, Neo4j and Confluent. Silicon Valley tried to make Hadoop work. The technology was extremely complicated and nearly impossible to use efficiently. Hadoop’s lack of speed was compounded by its focus on unstructured data — you had to be a “flip-flop wearing” data scientist to truly make use of it. Unstructured datasets are very difficult to query and analyze without deep knowledge of computer science. At one point, Gartner estimated that 70% of Hadoop deployments would not achieve the goal of cost savings and revenue growth, mainly due to insufficient skills and technical integration difficulties. And seventy percent seems like an understatement. Data storage through the years: from GFS to Snowflake or Snowflake blizzard Developing in parallel with Hadoop’s journey was that of Marcin Zukowski — co-founder and CEO of Vectorwise. Marcin took the data warehouse in another direction, to the world of advanced vector processing. Despite being almost unheard of among the general public, Snowflake was actually founded back in 2012. Firstly, Snowflake is not a consumer tech firm like Netflix or Uber. It's business-to-business only, which may explain its high valuation – enterprise companies are often seen as a more "stable" investment. In short, Snowflake helps businesses manage data that's stored on the cloud. The firm's motto is "mobilising the world's data", because it allows big companies to make better use of their vast data stores. Marcin and his teammates rethought the data warehouse by leveraging the elasticity of the public cloud in an unexpected way: separating storage and compute. Their message was this: don’t pay for a data warehouse you don’t need. Only pay for the storage you need, and add capacity as you go. This is considered one of Snowflake’s key innovations: separating storage (where the data is held) from computing (the act of querying). By offering this service before Google, Amazon, and Microsoft had equivalent products of their own, Snowflake was able to attract customers, and build market share in the data warehousing space. Naming the company after a discredited database concept was very brave. For those of us not in the details of the Snowflake schema, it is a logical arrangement of tables in a multidimensional database such that the entity-relationship diagram resembles a snowflake shape. … When it is completely normalized along all the dimension tables, the resultant structure resembles a snowflake with the fact table in the middle. Needless to say, the “snowflake” schema is as far from Hadoop’s design philosophy as technically possible. While Silicon Valley was headed toward a dead end, Snowflake captured an entire cloud data market.

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Theory and Strategies

Can you really trust Amazon Product Recommendation?

Article | January 28, 2021

Since the internet became popular, the way we purchase things has evolved from a simple process to a more complicated process. Unlike traditional shopping, it is not possible to experience the products first-hand when purchasing online. Not only this, but there are more options or variants in a single product than ever before, which makes it more challenging to decide. To not make a bad investment, the consumer has to rely heavily on the customer reviews posted by people who are using the product. However, sorting through relevant reviews at multiple eCommerce platforms of different products and then comparing them to choose can work too much. To provide a solution to this problem, Amazon has come up with sentiment analysis using product review data. Amazon performs sentiment analysis on product review data with Artificial Intelligence technology to develop the best suitable products for the customer. This technology enables Amazon to create products that are most likely to be ideal for the customer. A consumer wants to search for only relevant and useful reviews when deciding on a product. A rating system is an excellent way to determine the quality and efficiency of a product. However, it still cannot provide complete information about the product as ratings can be biased. Textual detailed reviews are necessary to improve the consumer experience and in helping them make informed choices. Consumer experience is a vital tool to understand the customer's behavior and increase sales. Amazon has come up with a unique way to make things easier for their customers. They do not promote products that look similar to the other customer's search history. Instead, they recommend products that are similar to the product a user is searching for. This way, they guide the customer using the correlation between the products. To understand this concept better, we must understand how Amazon's recommendation algorithm has upgraded with time. The history of Amazon's recommendation algorithm Before Amazon started a sentiment analysis of customer product reviews using machine learning, they used the same collaborative filtering to make recommendations. Collaborative filtering is the most used way to recommend products online. Earlier, people used user-based collaborative filtering, which was not suitable as there were many uncounted factors. Researchers at Amazon came up with a better way to recommend products that depend on the correlation between products instead of similarities between customers. In user-based collaborative filtering, a customer would be shown recommendations based on people's purchase history with similar search history. In item-to-item collaborative filtering, people are shown recommendations of similar products to their recent purchase history. For example, if a person bought a mobile phone, he will be shown hints of that phone's accessories. Amazon's Personalization team found that using purchase history at a product level can provide better recommendations. This way of filtering also offered a better computational advantage. User-based collaborative filtering requires analyzing several users that have similar shopping history. This process is time-consuming as there are several demographic factors to consider, such as location, gender, age, etc. Also, a customer's shopping history can change in a day. To keep the data relevant, you would have to update the index storing the shopping history daily. However, item-to-item collaborative filtering is easy to maintain as only a tiny subset of the website's customers purchase a specific product. Computing a list of individuals who bought a particular item is much easier than analyzing all the site's customers for similar shopping history. However, there is a proper science between calculating the relatedness of a product. You cannot merely count the number of times a person bought two items together, as that would not make accurate recommendations. Amazon research uses a relatedness metric to come up with recommendations. If a person purchased an item X, then the item Y will only be related to the person if purchasers of item X are more likely to buy item Y. If users who purchased the item X are more likely to purchase the item Y, then only it is considered to be an accurate recommendation. Conclusion In order to provide a good recommendation to a customer, you must show products that have a higher chance of being relevant. There are countless products on Amazon's marketplace, and the customer will not go through several of them to figure out the best one. Eventually, the customer will become frustrated with thousands of options and choose to try a different platform. So Amazon has to develop a unique and efficient way to recommend the products that work better than its competition. User-based collaborative filtering was working fine until the competition increased. As the product listing has increased in the marketplace, you cannot merely rely on previous working algorithms. There are more filters and factors to consider than there were before. Item-to-item collaborative filtering is much more efficient as it automatically filters out products that are likely to be purchased. This limits the factors that require analysis to provide useful recommendations. Amazon has grown into the biggest marketplace in the industry as customers trust and rely on its service. They frequently make changes to fit the recent trends and provide the best customer experience possible.

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Business Intelligence, Big Data Management, Big Data

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

Article | May 15, 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

Big Data in Healthcare: Improving Patient Outcomes

Article | April 27, 2023

Introduction Data modeling is the study of data objects and their interactions with other things. It's used to research data requirements for a variety of business requirements. The data models are created to store the data in a database. Therefore, instead of focusing on what processes we must conduct, the data modeling methodologies focuses on what data is required and how to organize it. Data modeling techniques facilitate the integration of high-level business processes with data structures, data rules, and the technical execution of physical data. Data modeling best parctices bring your company's operations and data usage together in a way that everyone can comprehend. As 2.5 quintillion bytes of data are created every day, enterprises and business organizations are compelled to use data modeling techniques to handle them efficiently. Data modeling for businesses reduces the budget for programming by up to 75%. It typically consumes less than 10% of a project budget. “The ability to take data – to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it – is going to be a hugely important skill in the next decades.” - Hal Varian, Chief Economist, Google Top Techniques to Enhance Your Data Modeling for Business Data modeling methodology helps create a conceptual model and establish relationships between objects. The three perspectives of a data model are dealt with in the primary data modeling techniques. And they are conceptual, logical, and physical data models. Let us look into some essential data modeling techniques to accelerate your business. Have a Visualization of the Data You're Going to Model It's unconvincing to think that staring at endless rows and columns of alphanumeric entries will lead to enlightenment. On the contrary, most people are significantly more comfortable inspecting and joining data tables using drag-and-drop screen interfaces or looking at graphical data representations that make it quick to spot any irregularities. These types of data visualization techniques assist you in cleaning your data so that it is comprehensive, consistent, and free of errors and redundancies. They also help you identify distinct data record types that correspond to the same real-life entity, allowing you to change them to use standard fields and formats, making it easier to combine data sources. Recognize the Business Requirements and Desired Outcomes The purpose of data modeling best practices is to improve the efficiency of an organization. As a data modeler, you can only collect, organize, and store data for analysis if you understand your company's requirements. Obtain feedback from business stakeholders to create conceptual and logical data models tailored to the company's needs. Collect data requirements from business analysts and other subject matter experts to aid in developing more comprehensive logical and physical models from the higher-level models and business requirements. Data models must change in response to changes in business and technology. As a result, a thorough grasp of the company, its needs, goals, expected outcomes, and the intended application of the data modeling mission's outputs is a critical data modeling technique to follow. According to IBM, “Data models are built around business needs. Rules and requirements are defined upfront through feedback from business stakeholders so they can be incorporated into the design of a new system or adapted in the iteration of an existing one.” Distinguish Between Facts, Dimensions, Filters, and Order when Dealing with Business Enquiries Understanding how these four parts characterize business questions will help you organize data in ways that make providing answers easier. For example, you may make locating the top sales performers per sales period easier and answer other business intelligence queries by structuring your data using different tables for facts and dimensions. Before Continuing, Double-Check Each Stage of your Data Modelling. Before going on to the next stage, each action should be double-checked, beginning with the data modeling priorities derived from the business requirements. For example, a dataset's main key must be chosen so that the primary key's value in each record may be used to identify each in the dataset uniquely. The same data modeling technique can check that joining two datasets is either one-to-one or one-to-many and avoid many-to-many interactions that lead to too complicated or unmanageable data models. Instead of Just Looking for Correlation, Look for Causation Data modeling best practices offers instructions on how to use the modeled data. While allowing end-users to access business intelligence on their own is a significant step forward, it's equally critical that they don't make mistakes. They may notice, for example, that sales of two different products appear to grow and fall in lockstep. Are sales of one product driving sales of the other, or do they rise and fall in lockstep due to another factor like the economy or weather? Confusing causality and correlation could lead businesses to lose resources by focusing on the wrong or non-existent possibilities. Summing Up Data modeling can assist companies in quickly acquiring answers to their business concerns, improving productivity, profitability, efficiency, and customer happiness, among other things. Linking to corporate needs and objectives and employing tools to speed up the procedures in preparing data for replies to all inquiries are critical success elements and part of data modeling techniques. Once these prerequisites are met, you can anticipate your data modeling to provide significant business value to you and your company, whether small, medium, or large. Frequently Asked Questions What are some of the crucial data modeling techniques? There are many crucial data modeling techniques in the business. Some of them are: Hierarchical data model Network data model Relational data model Object-oriented data model Entity-relationship data model Data model with dimensions Data model based on graphs What are data modeling techniques? Data modeling is optimizing data to streamline information flow inside businesses for various business needs. It improves analytics by formatting data and its attributes, creating links between data, and organizing data. Why is data modeling important? Data modeling is essential as a clear representation of data makes it easier to analyze it correctly. Also, it helps stakeholders to make data-driven decisions as data modeling improves data quality.

Read More
Business Intelligence, Big Data Management, Data Science

Predictive Maintenance with Industrial Big Data: Reactive to Proactive Strategies

Article | May 2, 2023

Data Platforms and frameworks have been constantly evolving. At some point of time; we are excited by Hadoop (well for almost 10 years); followed by Snowflake or as I say Snowflake Blizzard (who managed to launch biggest IPO win historically) and the Google (Google solves problems and serves use cases in a way that few companies can match). The end of the data warehouse Once upon a time, life was simple; or at least, the basic approach to Business Intelligence was fairly easy to describe… A process of collecting information from systems, building a repository of consistent data, and bolting on one or more reporting and visualisation tools which presented information to users. Data used to be managed in expensive, slow, inaccessible SQL data warehouses. SQL systems were notorious for their lack of scalability. Their demise is coming from a few technological advances. One of these is the ubiquitous, and growing, Hadoop. On April 1, 2006, Apache Hadoop was unleashed upon Silicon Valley. Inspired by Google, Hadoop’s primary purpose was to improve the flexibility and scalability of data processing by splitting the process into smaller functions that run on commodity hardware. Hadoop’s intent was to replace enterprise data warehouses based on SQL. Unfortunately, a technology used by Google may not be the best solution for everyone else. It’s not that others are incompetent: Google solves problems and serves use cases in a way that few companies can match. Google has been running massive-scale applications such as its eponymous search engine, YouTube and the Ads platform. The technologies and infrastructure that make the geographically distributed offerings perform at scale are what make various components of Google Cloud Platform enterprise ready and well-featured. Google has shown leadership in developing innovations that have been made available to the open-source community and are being used extensively by other public cloud vendors and Gartner clients. Examples of these include the Kubernetes container management framework, TensorFlow machine learning platform and the Apache Beam data processing programming model. GCP also uses open-source offerings in its cloud while treating third-party data and analytics providers as first-class citizens on its cloud and providing unified billing for its customers. The examples of the latter include DataStax, Redis Labs, InfluxData, MongoDB, Elastic, Neo4j and Confluent. Silicon Valley tried to make Hadoop work. The technology was extremely complicated and nearly impossible to use efficiently. Hadoop’s lack of speed was compounded by its focus on unstructured data — you had to be a “flip-flop wearing” data scientist to truly make use of it. Unstructured datasets are very difficult to query and analyze without deep knowledge of computer science. At one point, Gartner estimated that 70% of Hadoop deployments would not achieve the goal of cost savings and revenue growth, mainly due to insufficient skills and technical integration difficulties. And seventy percent seems like an understatement. Data storage through the years: from GFS to Snowflake or Snowflake blizzard Developing in parallel with Hadoop’s journey was that of Marcin Zukowski — co-founder and CEO of Vectorwise. Marcin took the data warehouse in another direction, to the world of advanced vector processing. Despite being almost unheard of among the general public, Snowflake was actually founded back in 2012. Firstly, Snowflake is not a consumer tech firm like Netflix or Uber. It's business-to-business only, which may explain its high valuation – enterprise companies are often seen as a more "stable" investment. In short, Snowflake helps businesses manage data that's stored on the cloud. The firm's motto is "mobilising the world's data", because it allows big companies to make better use of their vast data stores. Marcin and his teammates rethought the data warehouse by leveraging the elasticity of the public cloud in an unexpected way: separating storage and compute. Their message was this: don’t pay for a data warehouse you don’t need. Only pay for the storage you need, and add capacity as you go. This is considered one of Snowflake’s key innovations: separating storage (where the data is held) from computing (the act of querying). By offering this service before Google, Amazon, and Microsoft had equivalent products of their own, Snowflake was able to attract customers, and build market share in the data warehousing space. Naming the company after a discredited database concept was very brave. For those of us not in the details of the Snowflake schema, it is a logical arrangement of tables in a multidimensional database such that the entity-relationship diagram resembles a snowflake shape. … When it is completely normalized along all the dimension tables, the resultant structure resembles a snowflake with the fact table in the middle. Needless to say, the “snowflake” schema is as far from Hadoop’s design philosophy as technically possible. While Silicon Valley was headed toward a dead end, Snowflake captured an entire cloud data market.

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Theory and Strategies

Can you really trust Amazon Product Recommendation?

Article | January 28, 2021

Since the internet became popular, the way we purchase things has evolved from a simple process to a more complicated process. Unlike traditional shopping, it is not possible to experience the products first-hand when purchasing online. Not only this, but there are more options or variants in a single product than ever before, which makes it more challenging to decide. To not make a bad investment, the consumer has to rely heavily on the customer reviews posted by people who are using the product. However, sorting through relevant reviews at multiple eCommerce platforms of different products and then comparing them to choose can work too much. To provide a solution to this problem, Amazon has come up with sentiment analysis using product review data. Amazon performs sentiment analysis on product review data with Artificial Intelligence technology to develop the best suitable products for the customer. This technology enables Amazon to create products that are most likely to be ideal for the customer. A consumer wants to search for only relevant and useful reviews when deciding on a product. A rating system is an excellent way to determine the quality and efficiency of a product. However, it still cannot provide complete information about the product as ratings can be biased. Textual detailed reviews are necessary to improve the consumer experience and in helping them make informed choices. Consumer experience is a vital tool to understand the customer's behavior and increase sales. Amazon has come up with a unique way to make things easier for their customers. They do not promote products that look similar to the other customer's search history. Instead, they recommend products that are similar to the product a user is searching for. This way, they guide the customer using the correlation between the products. To understand this concept better, we must understand how Amazon's recommendation algorithm has upgraded with time. The history of Amazon's recommendation algorithm Before Amazon started a sentiment analysis of customer product reviews using machine learning, they used the same collaborative filtering to make recommendations. Collaborative filtering is the most used way to recommend products online. Earlier, people used user-based collaborative filtering, which was not suitable as there were many uncounted factors. Researchers at Amazon came up with a better way to recommend products that depend on the correlation between products instead of similarities between customers. In user-based collaborative filtering, a customer would be shown recommendations based on people's purchase history with similar search history. In item-to-item collaborative filtering, people are shown recommendations of similar products to their recent purchase history. For example, if a person bought a mobile phone, he will be shown hints of that phone's accessories. Amazon's Personalization team found that using purchase history at a product level can provide better recommendations. This way of filtering also offered a better computational advantage. User-based collaborative filtering requires analyzing several users that have similar shopping history. This process is time-consuming as there are several demographic factors to consider, such as location, gender, age, etc. Also, a customer's shopping history can change in a day. To keep the data relevant, you would have to update the index storing the shopping history daily. However, item-to-item collaborative filtering is easy to maintain as only a tiny subset of the website's customers purchase a specific product. Computing a list of individuals who bought a particular item is much easier than analyzing all the site's customers for similar shopping history. However, there is a proper science between calculating the relatedness of a product. You cannot merely count the number of times a person bought two items together, as that would not make accurate recommendations. Amazon research uses a relatedness metric to come up with recommendations. If a person purchased an item X, then the item Y will only be related to the person if purchasers of item X are more likely to buy item Y. If users who purchased the item X are more likely to purchase the item Y, then only it is considered to be an accurate recommendation. Conclusion In order to provide a good recommendation to a customer, you must show products that have a higher chance of being relevant. There are countless products on Amazon's marketplace, and the customer will not go through several of them to figure out the best one. Eventually, the customer will become frustrated with thousands of options and choose to try a different platform. So Amazon has to develop a unique and efficient way to recommend the products that work better than its competition. User-based collaborative filtering was working fine until the competition increased. As the product listing has increased in the marketplace, you cannot merely rely on previous working algorithms. There are more filters and factors to consider than there were before. Item-to-item collaborative filtering is much more efficient as it automatically filters out products that are likely to be purchased. This limits the factors that require analysis to provide useful recommendations. Amazon has grown into the biggest marketplace in the industry as customers trust and rely on its service. They frequently make changes to fit the recent trends and provide the best customer experience possible.

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Sigma and Connect&GO Redefine Data Analytics for Attraction Industry

Sigma Computing | November 07, 2023

Sigma and Connect&GO have recently introduced the new Connect&GO reporting tool, an advanced embedded analytics solution that empowers attractions worldwide to enhance operational efficiency, boost revenue, and evaluate their data in real-time. This no-code platform, a result of Sigma's cloud analytics expertise and Connect&GO's integrated technology, offers an intuitive and customizable dashboard for real-time data insights. It simplifies data analytics, reporting, and sharing, making it suitable for a wide range of attractions industry customers, including marketing, finance, and operations managers, as well as C-suite executives. The new Connect&GO reporting tool equips attractions industry customers with the ability to make informed decisions through customizable dashboards. Operators can effortlessly upload data sets, such as forecasts and projections from various systems, and compare them in real-time with actual data, including budgets. This live data and insights allow them to delve into the granular details of their business, enabling them to address day-to-day challenges, compare data sets, and plan for the future more accurately. These capabilities enable attractions to improve guest satisfaction, foster collaboration, ease the burden on engineering teams, and ultimately generate new revenue streams. For instance, park management can use better data to predict attendance, adjust staffing levels as needed, and ensure appropriate retail, food, and beverage inventory to enhance the guest experience. Sigma has rapidly established itself as a go-to cloud analytics platform, experiencing significant growth over the past years and earning numerous awards, including Snowflake BI Partner of the Year 2023. Sigma's success can be attributed to its mission of removing traditional barriers to data access and empowering business users to extract maximum value from live data without requiring technical expertise. Platform users can directly access and manage data stored in a cloud data warehouse without the involvement of a data team. With a familiar and intuitive interface, they can easily explore data and test different scenarios, gaining new insights and the context needed for decision-making. In contrast to legacy technology platforms that keep data isolated and operations disjointed, Connect&GO's cutting-edge solution, Konnect, is a fully integrated system that enables operators to oversee every aspect of their business seamlessly. This platform uniquely provides operators with real-time data, making it effortless to manage eCommerce, access control, point-of-sale, and cashless payments through proprietary Virtual Wallet technology. With its configurable interface and connected RFID wearables, Konnect enables operators to curate premium guest experiences that drive revenue and enhance engagement. About Sigma Computing Sigma Computing is a prominent cloud analytics solutions provider, offering business users seamless access to their cloud data warehouse for effortless exploration and insight gathering. With its intuitive spreadsheet-like interface, Sigma eliminates the need for coding or specialized training, enabling users to effortlessly navigate vast datasets, augment them with new information, and conduct real-time 'what if' analyses on billions of rows of data. About Connect&GO Connect&GO is a leading integrated technology and RFID solutions provider for the attractions industry. Its flexible operations management platform seamlessly integrates e-commerce, food & beverage, point-of-sale, access control, RFID, and cashless payments using its proprietary Virtual Wallet technology, consolidating all data in one place. The company helps drive revenue and maximize guest engagement with valuable real-time data insights. Connect&GO serves amusement and water parks, family entertainment centers, zoos & aquariums, and other attractions worldwide, integrating user-friendly wearable technology into extraordinary experiences.

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Data Science

Snowflake Accelerates How Users Build Next Generation Apps and Machine Learning Models in the Data Cloud

Business Wire | November 03, 2023

Snowflake (NYSE: SNOW), the Data Cloud company, today announced at its Snowday 2023 event new advancements that make it easier for developers to build machine learning (ML) models and full-stack apps in the Data Cloud. Snowflake is enhancing its Python capabilities through Snowpark to boost productivity, increase collaboration, and ultimately speed up end-to-end AI and ML workflows. In addition, with support for containerized workloads and expanded DevOps capabilities, developers can now accelerate development and run apps — all within Snowflake's secure and fully managed infrastructure. “The rise of generative AI has made organizations’ most valuable asset, their data, even more indispensable. Snowflake is making it easier for developers to put that data to work so they can build powerful end-to-end machine learning models and full-stack apps natively in the Data Cloud,” said Prasanna Krishnan, Senior Director of Product Management, Snowflake. “With Snowflake Marketplace as the first cross-cloud marketplace for data and apps in the industry, customers can quickly and securely productionize what they’ve built to global end users, unlocking increased monetization, discoverability, and usage.” Developers Gain Robust and Familiar Functionality for End-to-End Machine Learning Snowflake is continuing to invest in Snowpark as its secure deployment and processing of non-SQL code, with over 35% of Snowflake customers using Snowpark on a weekly basis (as of September 2023). Developers increasingly look to Snowpark for complex ML model development and deployment, and Snowflake is introducing expanded functionality that makes Snowpark even more accessible and powerful for all Python developers. New advancements include: Snowflake Notebooks (private preview): Snowflake Notebooks are a new development interface that offers an interactive, cell-based programming environment for Python and SQL users to explore, process, and experiment with data in Snowpark. Snowflake’s built-in notebooks allow developers to write and execute code, train and deploy models using Snowpark ML, visualize results with Streamlit chart elements, and much more — all within Snowflake’s unified, secure platform. Snowpark ML Modeling API (general availability soon): Snowflake’s Snowpark ML Modeling API empowers developers and data scientists to scale out feature engineering and simplify model training for faster and more intuitive model development in Snowflake. Users can implement popular AI and ML frameworks natively on data in Snowflake, without having to create stored procedures. Snowpark ML Operations Enhancements: The Snowpark Model Registry (public preview soon) now builds on a native Snowflake model entity and enables the scalable, secure deployment and management of models in Snowflake, including expanded support for deep learning models and open source large language models (LLMs) from Hugging Face. Snowflake is also providing developers with an integrated Snowflake Feature Store (private preview) that creates, stores, manages, and serves ML features for model training and inference. Endeavor, the global sports and entertainment company that includes the WME Agency, IMG & On Location, UFC, and more, relies on Snowflake’s Snowpark for Python capabilities to build and deploy ML models that create highly personalized experiences and apps for fan engagement. Snowpark serves as the driving force behind our end-to-end machine learning development, powering how we centralize and process data across our various entities, and then securely build and train models using that data to create hyper-personalized fan experiences at scale, said Saad Zaheer, VP of Data Science and Engineering, Endeavor. With Snowflake as our central data foundation bringing all of this development directly to our enterprise data, we can unlock even more ways to predict and forecast customer behavior to fuel our targeted sales and marketing engines. Snowflake Advances Developer Capabilities Across the App Lifecycle The Snowflake Native App Framework (general availability soon on AWS, public preview soon on Azure) now provides every organization with the necessary building blocks for app development, including distribution, operation, and monetization within Snowflake’s platform. Leading organizations are monetizing their Snowflake Native Apps through Snowflake Marketplace, with app listings more than doubling since Snowflake Summit 2023. This number is only growing as Snowflake continues to advance its developer capabilities across the app lifecycle so more organizations can unlock business impact. For example, Cybersyn, a data-service provider, is developing Snowflake Native Apps exclusively for Snowflake Marketplace, with more than 40 customers running over 5,000 queries with its Financial & Economic Essentials Native App since June 2022. In addition, LiveRamp, a data collaboration platform, has seen the number of customers deploying its Identity Resolution and Transcoding Snowflake Native App through Snowflake Marketplace increase by more than 80% since June 2022. Lastly, SNP has been able to provide its customers with a 10x cost reduction in Snowflake data processing associated with SAP data ingestion, empowering them to drastically reduce data latency while improving SAP data availability in Snowflake through SNP’s Data Streaming for SAP - Snowflake Native App. With Snowpark Container Services (public preview soon in select AWS regions), developers can run any component of their app — from ML training, to LLMs, to an API, and more — without needing to move data or manage complex container-based infrastructure. Snowflake Automates DevOps for Apps, Data Pipelines, and Other Development Snowflake is giving developers new ways to automate key DevOps and observability capabilities across testing, deploying, monitoring, and operating their apps and data pipelines — so they can take them from idea to production faster. With Snowflake’s new Database Change Management (private preview soon) features, developers can code declaratively and easily templatize their work to manage Snowflake objects across multiple environments. The Database Change Management features serve as a single source of truth for object creation across various environments, using the common “configuration as code” pattern in DevOps to automatically provision and update Snowflake objects. Snowflake also unveiled a new Powered by Snowflake Funding Program, innovations that enable all users to securely tap into the power of generative AI with their enterprise data, enhancements to further eliminate data silos and strengthen Snowflake’s leading compliance and governance capabilities through Snowflake Horizon, and more at Snowday 2023.

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Business Intelligence

Alation Launches Analytics Cloud1 Elevating Data Culture Assessment

Alation | October 12, 2023

Alation, Inc., a prominent data intelligence company, has unveiled its latest offering, Alation Analytics Cloud1. This unified reporting platform empowers organizations to gain insights into their data usage and, in doing so, assess the effectiveness of their data initiatives and the overall maturity of their data culture. The stakes are high in today's data-driven landscape, given the vast opportunities associated with data, analytics, and AI, and organizations can no longer afford to operate with disjointed data efforts that lack a clear connection to value. Alarmingly, a vast majority of Chief Data Officers (CDOs) fail to accurately assess and price the business outcomes generated by their data and analytics efforts, as revealed by Harvard Business Review. Consequently, there is a pressing need for a framework that enables organizations to benchmark and enhance their data management capabilities as they become critical strategic endeavors. However, this void has persisted because organizations have lacked the tools required to quantify the value and impact of their data initiatives on their business operations. Moreover, disparate teams within organizations often employ distinct data analysis methods, emphasizing the necessity for a solution that facilitates the consistent assessment of critical usage statistics, thereby fostering the growth of a robust data culture. The Alation Analytics Cloud offers a framework to articulate the business value of data initiatives. Leveraging Alation's innovative Query Log Ingestion technology, leaders can gain insights into which data sources are most frequently accessed and which teams are executing specific queries. This knowledge enables data leaders to comprehensively map data consumption across the entire organization. These insights serve a dual purpose, facilitating the measurement of the effectiveness of diverse data programs and, subsequently, enabling an assessment of the maturity level of an organization's data culture. These insights can also be harnessed to optimize queries and rationalize data sources, for example, by expediting the migration of frequently used datasets or the retirement of data sources that are no longer in active use, thereby reducing costs. Key benefits of the Alation Analytics Cloud include the ability to: Measure Data Culture Maturity: Organizations can now measure their data culture maturity by examining four vital components: data leadership, data search and discovery, data literacy, and data governance. Score Data Programs: Data leaders are empowered to gauge the progress of their data initiatives using a variety of metrics, such as total assets curated, the number of active users, and many other relevant indicators. Map Data Consumption: Business and data leaders can gain visibility into the efficacy of individual data products by closely tracking actual usage. Reports provide valuable insights into which queries are being executed by specific users on various data stores, including details about the total execution time of database queries, thus pinpointing areas that can be optimized. About Alation Alation is a leading enterprise data intelligence solutions provider, offering capabilities that empower self-service analytics, drive cloud transformation, and enhance data governance. Over 500 leading enterprises, including prominent names like Cisco, Nasdaq, Pfizer, Salesforce, and Virgin Australia, rely on Alation to cultivate a data culture and bolster data-driven decision-making. The company's commitment to excellence is evidenced by its recognition on Inc. Magazine's Best Workplaces list four times, its status as a UK's Best Workplaces in tech and Best Workplaces for Women in 2022, and its continued accolades as a UK's Best Workplaces in 2022 and 2023.

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Big Data Management

Sigma and Connect&GO Redefine Data Analytics for Attraction Industry

Sigma Computing | November 07, 2023

Sigma and Connect&GO have recently introduced the new Connect&GO reporting tool, an advanced embedded analytics solution that empowers attractions worldwide to enhance operational efficiency, boost revenue, and evaluate their data in real-time. This no-code platform, a result of Sigma's cloud analytics expertise and Connect&GO's integrated technology, offers an intuitive and customizable dashboard for real-time data insights. It simplifies data analytics, reporting, and sharing, making it suitable for a wide range of attractions industry customers, including marketing, finance, and operations managers, as well as C-suite executives. The new Connect&GO reporting tool equips attractions industry customers with the ability to make informed decisions through customizable dashboards. Operators can effortlessly upload data sets, such as forecasts and projections from various systems, and compare them in real-time with actual data, including budgets. This live data and insights allow them to delve into the granular details of their business, enabling them to address day-to-day challenges, compare data sets, and plan for the future more accurately. These capabilities enable attractions to improve guest satisfaction, foster collaboration, ease the burden on engineering teams, and ultimately generate new revenue streams. For instance, park management can use better data to predict attendance, adjust staffing levels as needed, and ensure appropriate retail, food, and beverage inventory to enhance the guest experience. Sigma has rapidly established itself as a go-to cloud analytics platform, experiencing significant growth over the past years and earning numerous awards, including Snowflake BI Partner of the Year 2023. Sigma's success can be attributed to its mission of removing traditional barriers to data access and empowering business users to extract maximum value from live data without requiring technical expertise. Platform users can directly access and manage data stored in a cloud data warehouse without the involvement of a data team. With a familiar and intuitive interface, they can easily explore data and test different scenarios, gaining new insights and the context needed for decision-making. In contrast to legacy technology platforms that keep data isolated and operations disjointed, Connect&GO's cutting-edge solution, Konnect, is a fully integrated system that enables operators to oversee every aspect of their business seamlessly. This platform uniquely provides operators with real-time data, making it effortless to manage eCommerce, access control, point-of-sale, and cashless payments through proprietary Virtual Wallet technology. With its configurable interface and connected RFID wearables, Konnect enables operators to curate premium guest experiences that drive revenue and enhance engagement. About Sigma Computing Sigma Computing is a prominent cloud analytics solutions provider, offering business users seamless access to their cloud data warehouse for effortless exploration and insight gathering. With its intuitive spreadsheet-like interface, Sigma eliminates the need for coding or specialized training, enabling users to effortlessly navigate vast datasets, augment them with new information, and conduct real-time 'what if' analyses on billions of rows of data. About Connect&GO Connect&GO is a leading integrated technology and RFID solutions provider for the attractions industry. Its flexible operations management platform seamlessly integrates e-commerce, food & beverage, point-of-sale, access control, RFID, and cashless payments using its proprietary Virtual Wallet technology, consolidating all data in one place. The company helps drive revenue and maximize guest engagement with valuable real-time data insights. Connect&GO serves amusement and water parks, family entertainment centers, zoos & aquariums, and other attractions worldwide, integrating user-friendly wearable technology into extraordinary experiences.

Read More

Data Science

Snowflake Accelerates How Users Build Next Generation Apps and Machine Learning Models in the Data Cloud

Business Wire | November 03, 2023

Snowflake (NYSE: SNOW), the Data Cloud company, today announced at its Snowday 2023 event new advancements that make it easier for developers to build machine learning (ML) models and full-stack apps in the Data Cloud. Snowflake is enhancing its Python capabilities through Snowpark to boost productivity, increase collaboration, and ultimately speed up end-to-end AI and ML workflows. In addition, with support for containerized workloads and expanded DevOps capabilities, developers can now accelerate development and run apps — all within Snowflake's secure and fully managed infrastructure. “The rise of generative AI has made organizations’ most valuable asset, their data, even more indispensable. Snowflake is making it easier for developers to put that data to work so they can build powerful end-to-end machine learning models and full-stack apps natively in the Data Cloud,” said Prasanna Krishnan, Senior Director of Product Management, Snowflake. “With Snowflake Marketplace as the first cross-cloud marketplace for data and apps in the industry, customers can quickly and securely productionize what they’ve built to global end users, unlocking increased monetization, discoverability, and usage.” Developers Gain Robust and Familiar Functionality for End-to-End Machine Learning Snowflake is continuing to invest in Snowpark as its secure deployment and processing of non-SQL code, with over 35% of Snowflake customers using Snowpark on a weekly basis (as of September 2023). Developers increasingly look to Snowpark for complex ML model development and deployment, and Snowflake is introducing expanded functionality that makes Snowpark even more accessible and powerful for all Python developers. New advancements include: Snowflake Notebooks (private preview): Snowflake Notebooks are a new development interface that offers an interactive, cell-based programming environment for Python and SQL users to explore, process, and experiment with data in Snowpark. Snowflake’s built-in notebooks allow developers to write and execute code, train and deploy models using Snowpark ML, visualize results with Streamlit chart elements, and much more — all within Snowflake’s unified, secure platform. Snowpark ML Modeling API (general availability soon): Snowflake’s Snowpark ML Modeling API empowers developers and data scientists to scale out feature engineering and simplify model training for faster and more intuitive model development in Snowflake. Users can implement popular AI and ML frameworks natively on data in Snowflake, without having to create stored procedures. Snowpark ML Operations Enhancements: The Snowpark Model Registry (public preview soon) now builds on a native Snowflake model entity and enables the scalable, secure deployment and management of models in Snowflake, including expanded support for deep learning models and open source large language models (LLMs) from Hugging Face. Snowflake is also providing developers with an integrated Snowflake Feature Store (private preview) that creates, stores, manages, and serves ML features for model training and inference. Endeavor, the global sports and entertainment company that includes the WME Agency, IMG & On Location, UFC, and more, relies on Snowflake’s Snowpark for Python capabilities to build and deploy ML models that create highly personalized experiences and apps for fan engagement. Snowpark serves as the driving force behind our end-to-end machine learning development, powering how we centralize and process data across our various entities, and then securely build and train models using that data to create hyper-personalized fan experiences at scale, said Saad Zaheer, VP of Data Science and Engineering, Endeavor. With Snowflake as our central data foundation bringing all of this development directly to our enterprise data, we can unlock even more ways to predict and forecast customer behavior to fuel our targeted sales and marketing engines. Snowflake Advances Developer Capabilities Across the App Lifecycle The Snowflake Native App Framework (general availability soon on AWS, public preview soon on Azure) now provides every organization with the necessary building blocks for app development, including distribution, operation, and monetization within Snowflake’s platform. Leading organizations are monetizing their Snowflake Native Apps through Snowflake Marketplace, with app listings more than doubling since Snowflake Summit 2023. This number is only growing as Snowflake continues to advance its developer capabilities across the app lifecycle so more organizations can unlock business impact. For example, Cybersyn, a data-service provider, is developing Snowflake Native Apps exclusively for Snowflake Marketplace, with more than 40 customers running over 5,000 queries with its Financial & Economic Essentials Native App since June 2022. In addition, LiveRamp, a data collaboration platform, has seen the number of customers deploying its Identity Resolution and Transcoding Snowflake Native App through Snowflake Marketplace increase by more than 80% since June 2022. Lastly, SNP has been able to provide its customers with a 10x cost reduction in Snowflake data processing associated with SAP data ingestion, empowering them to drastically reduce data latency while improving SAP data availability in Snowflake through SNP’s Data Streaming for SAP - Snowflake Native App. With Snowpark Container Services (public preview soon in select AWS regions), developers can run any component of their app — from ML training, to LLMs, to an API, and more — without needing to move data or manage complex container-based infrastructure. Snowflake Automates DevOps for Apps, Data Pipelines, and Other Development Snowflake is giving developers new ways to automate key DevOps and observability capabilities across testing, deploying, monitoring, and operating their apps and data pipelines — so they can take them from idea to production faster. With Snowflake’s new Database Change Management (private preview soon) features, developers can code declaratively and easily templatize their work to manage Snowflake objects across multiple environments. The Database Change Management features serve as a single source of truth for object creation across various environments, using the common “configuration as code” pattern in DevOps to automatically provision and update Snowflake objects. Snowflake also unveiled a new Powered by Snowflake Funding Program, innovations that enable all users to securely tap into the power of generative AI with their enterprise data, enhancements to further eliminate data silos and strengthen Snowflake’s leading compliance and governance capabilities through Snowflake Horizon, and more at Snowday 2023.

Read More

Business Intelligence

Alation Launches Analytics Cloud1 Elevating Data Culture Assessment

Alation | October 12, 2023

Alation, Inc., a prominent data intelligence company, has unveiled its latest offering, Alation Analytics Cloud1. This unified reporting platform empowers organizations to gain insights into their data usage and, in doing so, assess the effectiveness of their data initiatives and the overall maturity of their data culture. The stakes are high in today's data-driven landscape, given the vast opportunities associated with data, analytics, and AI, and organizations can no longer afford to operate with disjointed data efforts that lack a clear connection to value. Alarmingly, a vast majority of Chief Data Officers (CDOs) fail to accurately assess and price the business outcomes generated by their data and analytics efforts, as revealed by Harvard Business Review. Consequently, there is a pressing need for a framework that enables organizations to benchmark and enhance their data management capabilities as they become critical strategic endeavors. However, this void has persisted because organizations have lacked the tools required to quantify the value and impact of their data initiatives on their business operations. Moreover, disparate teams within organizations often employ distinct data analysis methods, emphasizing the necessity for a solution that facilitates the consistent assessment of critical usage statistics, thereby fostering the growth of a robust data culture. The Alation Analytics Cloud offers a framework to articulate the business value of data initiatives. Leveraging Alation's innovative Query Log Ingestion technology, leaders can gain insights into which data sources are most frequently accessed and which teams are executing specific queries. This knowledge enables data leaders to comprehensively map data consumption across the entire organization. These insights serve a dual purpose, facilitating the measurement of the effectiveness of diverse data programs and, subsequently, enabling an assessment of the maturity level of an organization's data culture. These insights can also be harnessed to optimize queries and rationalize data sources, for example, by expediting the migration of frequently used datasets or the retirement of data sources that are no longer in active use, thereby reducing costs. Key benefits of the Alation Analytics Cloud include the ability to: Measure Data Culture Maturity: Organizations can now measure their data culture maturity by examining four vital components: data leadership, data search and discovery, data literacy, and data governance. Score Data Programs: Data leaders are empowered to gauge the progress of their data initiatives using a variety of metrics, such as total assets curated, the number of active users, and many other relevant indicators. Map Data Consumption: Business and data leaders can gain visibility into the efficacy of individual data products by closely tracking actual usage. Reports provide valuable insights into which queries are being executed by specific users on various data stores, including details about the total execution time of database queries, thus pinpointing areas that can be optimized. About Alation Alation is a leading enterprise data intelligence solutions provider, offering capabilities that empower self-service analytics, drive cloud transformation, and enhance data governance. Over 500 leading enterprises, including prominent names like Cisco, Nasdaq, Pfizer, Salesforce, and Virgin Australia, rely on Alation to cultivate a data culture and bolster data-driven decision-making. The company's commitment to excellence is evidenced by its recognition on Inc. Magazine's Best Workplaces list four times, its status as a UK's Best Workplaces in tech and Best Workplaces for Women in 2022, and its continued accolades as a UK's Best Workplaces in 2022 and 2023.

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