Kickstart your career with best deals on top training courses NY10 Click to Copy

Search

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.
Rated 4.0/5 based on 20 customer reviews

4 Types Of Data Analytics To Improve Decision-Making

612
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

Join the Discussion

Your email address will not be published. Required fields are marked *

Suggested Blogs

Top Pros and Cons of Hadoop

Big Data is one of the major areas of focus in today’s digital world. There are tons of data generated and collected from the various processes carried out by the company. This data could contain patterns and methods as to how the company can improve its processes. The data also contains feedback from the customer. Needless to say, this data is vital to the company and should not be discarded. But, the entire set is also not useful, a certain amount of data is futile. This set should be differentiated from the useful part and discarded. To carry out this major process, various platforms are used. The most popular among these platforms is Hadoop. Hadoop can efficiently analyse the data and extract the useful information. It also comes with its own set of advantages and disadvantages such as: Pros 1) Range of data sources The data collected from various sources will be of structured or unstructured form. The sources can be social media, clickstream data or even email conversations. A lot of time would need to be allotted in order to convert all the collected data into a single format. Hadoop saves this time as it can derive valuable data from any form of data. It also has a variety of functions such as data warehousing, fraud detection, market campaign analysis etc. 2) Cost effective In conventional methods, companies had to spend a considerable amount of their benefits into storing large amounts of data. In certain cases they even had to delete large sets of raw data in order to make space for new data. There was a possibility of losing valuable information in such cases. By using Hadoop, this problem was completely solved. It is a cost-effective solution for data storage purposes. This helps in the long run because it stores the entire raw data generated by a company. If the company changes the direction of its processes in the future, it can easily refer to the raw data and take the necessary steps. This would not have been possible in the traditional approach because the raw data would have been deleted due to increase in expenses. 3) Speed Every organization uses a platform to get the work done at a faster rate. Hadoop enables the company to do just that with its data storage needs. It uses a storage system wherein the data is stored on a distributed file system. Since the tools used for the processing of data are located on same servers as the data, the processing operation is also carried out at a faster rate. Therefore, you can processes terabytes of data within minutes using Hadoop. 4) Multiple copies Hadoop automatically duplicates the data that is stored in it and creates multiple copies. This is done to ensure that in case there is a failure, data is not lost. Hadoop understands that the data stored by the company is important and should not be lost unless the company discards it. Cons 1) Lack of preventive measures When handling sensitive data collected by a company, it is mandatory to provide the necessary security measures. In Hadoop, the security measures are disabled by default. The person responsible for data analytics should be aware of this fact and take the required measures to secure the data. 2) Small Data concerns There are a few big data platforms in the market that aren’t fit for small data functions. Hadoop is one such platform wherein only large business that generates big data can utilize its functions. It cannot efficiently perform in small data environments. 3) Risky functioning Java is one of the most widely used programming languages. It has also been connected to various controversies because cyber criminals can easily exploit the frameworks that are built on Java. Hadoop is one such framework that is built entirely on Java. Therefore, the platform is vulnerable and can cause unforeseen damages. Every platform used in the digital world comes with its own set of advantages and disadvantages. These platforms serve a purpose that it vital to the company. Hence, it is necessary to check if the pros outweigh the cons. If they do, then utilize the pros and take preventive measures to guard yourself against the cons. To know more about Hadoop and pursue a career in it, enrol for a big data Hadoop certification. You can also gain better with big data Hadoop training online courses.
Rated 4.0/5 based on 4 customer reviews
1566
Top Pros and Cons of Hadoop

Big Data is one of the major areas of focus in tod... Read More

Types Of Big Data

“Data” is defined as ‘the quantities, characters, or symbols on which operations are performed by a computer, which may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media’, as a quick google search would show. The concept of Big Data is nothing complex; as the name suggests, “Big Data” refers to copious amounts of data which are too large to be processed and analysed by traditional tools, and the data is not stored or managed efficiently. Since the amount of Big Data increases exponentially- more than 500 terabytes of data are uploaded to Face book alone, in a single day- it represents a real problem in terms of analysis. However, there is also huge potential in the analysis of Big Data. The proper management and study of this data can help companies make better decisions based on usage statistics and user interests, thereby helping their growth. Some companies have even come up with new products and services, based on feedback received from Big Data analysis opportunities. Classification is essential for the study of any subject. So Big Data is widely classified into three main types, which are- 1. Structured data Structured Data is used to refer to the data which is already stored in databases, in an ordered manner. It accounts for about 20% of the total existing data, and is used the most in programming and computer-related activities. There are two sources of structured data- machines and humans. All the data received from sensors, web logs and financial systems are classified under machine-generated data. These include medical devices, GPS data, data of usage statistics captured by servers and applications and the huge amount of data that usually move through trading platforms, to name a few. Human-generated structured data mainly includes all the data a human input into a computer, such as his name and other personal details. When a person clicks a link on the internet, or even makes a move in a game, data is created- this can be used by companies to figure out their customer behaviour and make the appropriate decisions and modifications. 2. Unstructured data While structured data resides in the traditional row-column databases, unstructured data is the opposite- they have no clear format in storage. The rest of the data created, about 80% of the total account for unstructured big data. Most of the data a person encounters belongs to this category- and until recently, there was not much to do to it except storing it or analysing it manually. Unstructured data is also classified based on its source, into machine-generated or human-generated. Machine-generated data accounts for all the satellite images, the scientific data from various experiments and radar data captured by various facets of technology. Human-generated unstructured data is found in abundance across the internet, since it includes social media data, mobile data and website content. This means that the pictures we upload to out Facebook or Instagram handles, the videos we watch on YouTube and even the text messages we send all contribute to the gigantic heap that is unstructured data. 3. Semi-structured data. The line between unstructured data and semi-structured data has always been unclear, since most of the semi-structured data appear to be unstructured at a glance. Information that is not in the traditional database format as structured data, but contain some organizational properties which make it easier to process, are included in semi-structured data. For example, NoSQL documents are considered to be semi-structured, since they contain keywords that can be used to process the document easily. Big Data analysis has been found to have a definite business value, as its analysis and processing can help a company achieve cost reductions and dramatic growth. So it is imperative that you do not wait too long to exploit the potential of this excellent business opportunity.
Rated 4.0/5 based on 2 customer reviews
3297
Types Of Big Data

“Data” is defined as ‘the quantities, charac... Read More

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.
Rated 4.0/5 based on 20 customer reviews
How Big Data Can Solve Enterprise Problems

Many professionals in the digital world have becom... Read More

other Blogs