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4 Types Of Data Analytics To Improve Decision-Making

If you are on CSE stack portal, there’s a good chance that you are already well acquainted with the general terms like ‘Data Analytics’, ‘Big Data’ and ‘Business Intelligence’ lead to different things in different circumstances. But have you thought what would be the right BI platform to hack through a wide number of solutions for business success? In this article, I will knuckle down disambiguating the term ‘Data Analytics’ by splitting it down into 4 different types and aligning them with decision-making objectives. Descriptive Analytics: What happened? The commonest of the common type of Analytics, Descriptive Analytics offers the analyst a comprehensive view of key metrics and measures within an organization. It analyses the data available in real-time as well as historical data to derive meaningful insights regarding the future of a company. The main aim of this basic type of analytics is to discover the reasons behind pretentious success or failure in the past, as a result it is also known as ‘Reporting Bedrock’. A business learns from its past behaviors, and draws inceptions based on those observations about its future outcomes, how they are going to affect. Descriptive Analytics is clouted the best when a business is on its way to understand the overall performance of the organization at an aggregate level and perceive the various aspects. The best example of this would be a profit and loss statement. In the same way, analysts can possess data on a huge population of customers – delving deeper into mastering the demographic information of these customers can be classified as ‘descriptive analytics’. Diagnostic Analytics: What made it happen? The next stop to understand the intricacies of Data Analytics after Descriptive Analytics is Diagnostic Analytics. After assessing descriptive data, brilliant diagnostic analytical tools enable an analyst to go deeper into the problem, with the help of drilldowns and queries to eradicate the root-cause of the trouble. In simple words, in this analytics, historical data are ascertained against other data to reveal the answer of the question ‘why it happened’. With Diagnostic Analytics, the companies are now able to make breakthroughs, to pick out the dependencies and to discern patterns. Organizations prefer this type of analytics as it gives them a deeper perception regarding a specific problem. On the other hand, the organizations should keep all the detailed information by their side, otherwise data collection may turn out to be time-consuming. Effectively designed, well-integrated Business Information (BI) dashboards that assimilate the readings of time-series data, and participating filters and drilldown capabilities are deemed perfect for such analysis. Predictive Analytics: What is going to happen? It is all in the right predictions. Predictive Analytics involve analysis of past data patterns and trends to accurately forecast the future business outcome. It helps in determining realistic goals for the company and its effective execution and moderating expectations, by manipulating the findings of Descriptive and Diagnostic Analytics. Thanks to Predictive Analytics, as it is now easy to identify tendencies, clusters and exceptions, while predicting future trends – all of this makes this analytics an extremely valuable tool of help. By employing numerous machine learning algorithms and statistical approaches, Insight Analytics eventually predicts the likelihood of an event happening in the future, but remember, these assumptions are based on predictions and probabilities, hence not 100% accurate. Big conglomerates like Amazon and Walmart leverage this high-in-value type of analytics to decipher future sales trend, customer behaviors, purchase patterns and lot more. Prescriptive Analytics: What is to be done? This is where Big Data and Artificial Intelligence gets into action. The main objective of Prescriptive Analytics is to prescribe what action is to be taken to address the future problem. It is the next stop after Predictive Analytics to help business understand the underlying reasons of complications and devise the best of course of action. It shares insights on possible results and outcomes that eventually maximize chief business metrics. It works by combining mathematical models, data and numerous business rules. The data can be external as well as internal, while business rules are boundaries, preferences, best practices and other restraints. Machine learning, natural language processing, operations research and statistics area few examples of mathematical models. Though complex in nature, Prescriptive Analytics when used by companies can have a huge impact on the overall operations and future business growth. The best example of this type of analytics is a traffic application that enables you to select the easiest route to home, after paying attention to the distance of the route, the speed of travelling and prevailing traffic constraints in the city you are travelling. The current trends highlight that an increasing number of companies are appreciating Big Data solutions and looking forward to Data Analytics implementation.However, it is just that they should select the right type of analytics solutions to enhance ROI, increase service quality and lessen operational costs. Do you have any other information or thought on this topic? Feel free to share with us by commenting below.
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4 Types Of Data Analytics To Improve Decision-Making 331
4 Types Of Data Analytics To Improve Decision-Making

If you are on CSE stack portal, there’s a good chance that you are already well acquainted with the general terms like ‘Data Analytics’, ‘Big Data’ and ‘Business Intelligence’ lead to different things in different circumstances. But have you thought what would be the right BI platform to hack through a wide number of solutions for business success?

In this article, I will knuckle down disambiguating the term ‘Data Analytics’ by splitting it down into 4 different types and aligning them with decision-making objectives.

Descriptive Analytics: What happened?

The commonest of the common type of Analytics, Descriptive Analytics offers the analyst a comprehensive view of key metrics and measures within an organization. It analyses the data available in real-time as well as historical data to derive meaningful insights regarding the future of a company. The main aim of this basic type of analytics is to discover the reasons behind pretentious success or failure in the past, as a result it is also known as ‘Reporting Bedrock’.

A business learns from its past behaviors, and draws inceptions based on those observations about its future outcomes, how they are going to affect. Descriptive Analytics is clouted the best when a business is on its way to understand the overall performance of the organization at an aggregate level and perceive the various aspects.

The best example of this would be a profit and loss statement. In the same way, analysts can possess data on a huge population of customers – delving deeper into mastering the demographic information of these customers can be classified as ‘descriptive analytics’.

Diagnostic Analytics: What made it happen?

The next stop to understand the intricacies of Data Analytics after Descriptive Analytics is Diagnostic Analytics. After assessing descriptive data, brilliant diagnostic analytical tools enable an analyst to go deeper into the problem, with the help of drilldowns and queries to eradicate the root-cause of the trouble. In simple words, in this analytics, historical data are ascertained against other data to reveal the answer of the question ‘why it happened’.

With Diagnostic Analytics, the companies are now able to make breakthroughs, to pick out the dependencies and to discern patterns. Organizations prefer this type of analytics as it gives them a deeper perception regarding a specific problem. On the other hand, the organizations should keep all the detailed information by their side, otherwise data collection may turn out to be time-consuming.

Effectively designed, well-integrated Business Information (BI) dashboards that assimilate the readings of time-series data, and participating filters and drilldown capabilities are deemed perfect for such analysis.

Predictive Analytics: What is going to happen?

It is all in the right predictions. Predictive Analytics involve analysis of past data patterns and trends to accurately forecast the future business outcome. It helps in determining realistic goals for the company and its effective execution and moderating expectations, by manipulating the findings of Descriptive and Diagnostic Analytics.

Thanks to Predictive Analytics, as it is now easy to identify tendencies, clusters and exceptions, while predicting future trends – all of this makes this analytics an extremely valuable tool of help. By employing numerous machine learning algorithms and statistical approaches, Insight Analytics eventually predicts the likelihood of an event happening in the future, but remember, these assumptions are based on predictions and probabilities, hence not 100% accurate.

Big conglomerates like Amazon and Walmart leverage this high-in-value type of analytics to decipher future sales trend, customer behaviors, purchase patterns and lot more.

Prescriptive Analytics: What is to be done?

This is where Big Data and Artificial Intelligence gets into action. The main objective of Prescriptive Analytics is to prescribe what action is to be taken to address the future problem. It is the next stop after Predictive Analytics to help business understand the underlying reasons of complications and devise the best of course of action.

It shares insights on possible results and outcomes that eventually maximize chief business metrics. It works by combining mathematical models, data and numerous business rules. The data can be external as well as internal, while business rules are boundaries, preferences, best practices and other restraints. Machine learning, natural language processing, operations research and statistics area few examples of mathematical models.

Though complex in nature, Prescriptive Analytics when used by companies can have a huge impact on the overall operations and future business growth. The best example of this type of analytics is a traffic application that enables you to select the easiest route to home, after paying attention to the distance of the route, the speed of travelling and prevailing traffic constraints in the city you are travelling.

The current trends highlight that an increasing number of companies are appreciating Big Data solutions and looking forward to Data Analytics implementation.However, it is just that they should select the right type of analytics solutions to enhance ROI, increase service quality and lessen operational costs. Do you have any other information or thought on this topic? Feel free to share with us by commenting below.

Eshika

Eshika Roy

Blog Author

Eshika Roy is a seasoned copywriter working for DexLab Analyticsby the day, and a hobbyist playing with numbers by the night. She brings to us this new future face of technology and how it would change our world. Beyond this she has an inclination for fiction novels, exploring different cuisines, and confectionery and dessert cooking. LinkedIn

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5 Best Data Processing Frameworks

“Big data” is a phrase that was coined to refer to amounts of datasets that are so large, traditional data processing software simply can’t manage them. For example, big data is used to pick out trends in economics, and those trends and patterns are used to predict what will happen in the future. These vast amounts of data require more robust computer software for processing, best handled by data processing frameworks. These are the top preferred data processing frameworks, suitable for meeting a variety of different needs of businesses. Hadoop This is an open-source batch processing framework that can be used for the distributed storage and processing of big data sets. Hadoop relies on computer clusters and modules that have been designed with the assumption that hardware will inevitably fail, and those failures should be automatically handled by the framework. There are four main modules within Hadoop. Hadoop Common is where the libraries and utilities needed by other Hadoop modules reside. The Hadoop Distributed File System (HDFS) is the distributed file system that stores the data. Hadoop YARN (Yet Another Resource Negotiator) is the resource management platform that manages the computing resources in clusters, and handles the scheduling of users’ applications. The Hadoop MapReduce involves the implementation of the MapReduce programming model for large-scale data processing. Hadoop operates by splitting files into large blocks of data and then distributing those datasets across the nodes in a cluster. It then transfers code into the nodes, for processing data in parallel. The idea of data locality, meaning that tasks are performed on the node that stores the data, allows the datasets to be processed more efficiently and more quickly. Hadoop can be used within a traditional onsite datacenter, as well as through the cloud. Apache Spark Apache Spark is a batch processing framework that has the capability of stream processing, as well, making it a hybrid framework. Spark is most notably easy to use, and it’s easy to write applications in Java, Scala, Python, and R. This open-source cluster-computing framework is ideal for machine-learning, but does require a cluster manager and a distributed storage system. Spark can be run on a single machine, with one executor for every CPU core. It can be used as a standalone framework, and you can also use it in conjunction with Hadoop or Apache Mesos, making it suitable for just about any business. Spark relies on a data structure known as the Resilient Distributed Dataset (RDD). This is a read-only multiset of data items that is distributed over the entire cluster of machines. RDDs operate as the working set for distributed programs, offering a restricted form of distributed shared memory. Spark is capable of accessing data sources like HDFS, Cassandra, HBase, and S3, for distributed storage. It also supports a pseudo-distributed local mode that can be used for development or testing. The foundation of Spark is Spark Core, which relies on the RDD-oriented functional style of programming to dispatch tasks, schedule, and handle basic I/O functionalities. Two restricted forms of shared variables are used: broadcast variables, which reference read-only data that has to be available for all the nodes, and accumulators, which can be used to program reductions. Other elements included in Spark Core are: Spark SQL, which provides domain-specific language used to manipulate DataFrames. Spark Streaming, which uses data in mini-batches for RDD transformations, allowing the same set of application code that is created for batch analytics to also be used for streaming analytics. Spark MLlib, a machine-learning library that makes the large-scale machine learning pipelines simpler. GraphX, which is the distributed graph processing framework at the top of Apache Spark. Apache Storm This is another open-source framework, but one that provides distributed, real-time stream processing. Storm is mostly written in Clojure, and can be used with any programming language. The application is designed as a topology, with the shape of a Directed Acyclic Graph (DAG). Spouts and bolts act as the vertices of the graph. The idea behind Storm is to define small, discrete operations, and then compose those operations into a topology, which acts as a pipeline to transform data. Within Storm, streams are defined as unbounded data that continuously arrives at the system. Sprouts are sources of data streams that are at the edge of the topology, while bolts represent the processing aspect, applying an operation to those data streams. The streams on the edges of the graph direct data from one node to another. These bolts and sprouts define sources of information and allow batch, distributed processing of streaming data, in real-time. Samza Samza is another open-source framework that offers near a real-time, asynchronous framework for distributed stream processing. More specifically, Samza handles immutable streams, meaning transformations create new streams that will be consumed by other components without any effect on the initial stream. This framework works in conjunction with other frameworks, using Apache Kafka for messaging and Hadoop YARN for fault tolerance, security, and management of resources. Samza uses the semantics of Kafka to define how it handles streams. Topic refers to each stream of data that enters a Kafka system. Brokers are the individual nodes that are combined to make a Kafka cluster. A producer is any component that writes to a Kafka topic, and a consumer is any component that reads from a Kafka topic. Partitions are used to divide incoming messages in order to distribute a topic among the different nodes. Flink Flink is a hybrid framework, open-source, and stream processes, but can also manage batch tasks. It uses a high-throughput, low-latency streaming engine that is written in Java and Scala, and the runtime system that is pipelined allows for the execution of both batch and stream processing programs. The runtime also supports the execution of iterative algorithms natively. Flink’s applications are all fault-tolerant and can support exactly-once semantics. Programs can be written in Java, Scala, Python, and SQL, and Flink offers support for event-time processing and state management. The components of the stream processing model in Flink include streams, operators, sources, and sinks. Streams are immutable, unbounded datasets that go through the system. Operators are functions that are used on data streams to create other streams. Sources are the entry points for streams that enter into the system. Sinks are places where streams flow out of the Flink system, either into a database or into a connection to another system. Flink’s batch processing system is really just an extension of the stream processing model. Flink does not provide its own storage system, however, so that means you will have to use it in conjunction with another framework. That should not be a problem, as Flink is able to work with many other frameworks. Data processing frameworks are not intended to be one-size-fits-all solutions for businesses. Hadoop was originally designed for massive scalability, while Spark is better with machine learning and stream processing. A good IT services consultant can evaluate your needs and offer advice. What works for one business may not work for another, and to get the best possible results, you may find that it’s a good idea to use different frameworks for different parts of your data processing.
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5 Best Data Processing Frameworks

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Types Of Big Data

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How Big Data Can Solve Enterprise Problems

Many professionals in the digital world have become familiar with the hype cycle. A new technology enters the tech world amid great expectations. Undoubtedly, dismay sets in and retrenchment stage starts, practice and process catch up to assumptions and the new value is untied. Currently, there is apparently no topic more hyped than big data and there is already no deficit of self-proclaimed pundits. Yet nearly 55% of big data projects fail and there is an increasing divide between enterprises that are benefiting from its use and those who are not. However, qualified data scientists, great integration across departments, and the ability to manage expectations all play a part in making big data work for your organization. It is often said that an organization’s future is dependent on the decisions it takes. Since most of the business decisions are backed by data available at hand. The accurate the information, the better they are for the business. Gone are the days when data was only used as an aid in better decision making. But now, with big data, it has actually become a part of all business decisions. For quite some time now, big data has been changing the way business operations are managed, how they collect data and turn it into useful and accurate information in real-time. Today, let’s take a look at solving real-life enterprise problems with big data. Predictive Analysis Let’s assume that you have a solid knowledge of the emerging trends and technologies in the market or when your infrastructure needs a good maintenance. With huge amounts of data, you can easily predict trends and your future needs for the business. This sort of knowledge gives you an edge over your peers in this competitive world. Enhancing Market Research Regardless of the business vertical, market research is an essential part of business operations. With the ever-changing needs and aspirations of your customers, businesses need to find ways to get into the mind of customers with better and improved products and services. In such scenarios, having large volumes of data in hand will let you carry out detailed market research and thus enhancing your products and services. Streamlining Business Process For any enterprise, streamlining the business process is a crucial link to keeping the business sustainable and lucrative. Some effective modifications here and there can benefit you in the long run by cutting down the operational costs. Big data can be utilized to overhaul your whole business process right from raw material procurement to maintaining the supply chain. Data Access Centralization It is an inevitable fact that the decentralized data has its own advantages and one of the main restrictions arises from the fact that it can build data silos. Large enterprises with global presence frequently encounter such challenges. Centralizing conventional data often posed a challenge and blocked the complete enterprise from working as one team. But big data has entirely solved this problem, offering visibility of the data throughout the organization. How are you navigating the implications of all that data within your enterprise? Have you deployed big data in your enterprise and solved real-life enterprise problems? Then we would love to know your experiences. Do let us by commenting in the section below.
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