The complexity of managing projects within the Triple constraints has been increasing day by day. Various factors contributing include shortening product development cycles, changing customer expectations, exponentially increasing usage of the internet as well as more millennials in the project teams.
One of the ways to manage this complexity and the need of changing world, is using digitization. The digitization of Project Development phases will provide all synchronized database available to each stakeholders appropriately and same can be used for Managerial decision making. Building Analytics on this database, Risks affecting Project Performance Parameters – Time, Cost, and Quality can be effectively predicted and controlled. In addition, status will be available for each project to individual project teams whereas Portfolio Dashboard will provide bigger picture for managerial decisions on Strategies & Organizational Priorities. Because of its real-time nature, it can be available across the world at the same time providing a common platform to network and common language to interact.
Analytics can be defined as the systematic quantitative analysis of data to obtain meaningful information for better decision making. It involves the collective use of various analytical methodologies such as statistical and operational research methodologies, Lean Six Sigma, and software programming. Though Analysis and Analytics terms sounds similar but they do have some differences.
Analysis: Analysis can be defined as the process of dissecting past gathered data into pieces so that the current (prevailing) situation can be understood. The analysis presents a historical view of the project performance.
Tools BI Tools
The systems of Data Analysis can be separated into two types of Analytics Applications. They are:
(a) Quantitative Data Analysis
This process involves analyzing the numerical data along with the quantifiable variables which can then be compared or measured statistically.
(b) Qualitative Data Analysis
This process involves less analysis and more of understanding the non – numerical data such as audio, video, images, and other points of view.
Analytics: Analytics can be defined as a method to use the results of the analysis to better predict customer or stakeholder behaviors. Analytics look forward to project the future or predict an outcome based on past performance. Tools Predictive Analytics. Data analytics is the science of analyzing raw data in order to make conclusions about that information. And the abundance of data and its growing complexity mandates harnessing it for fast and sound decisions. In fact, IDC estimates that by 2025 we’ll have created more than 175 zettabytes globally. Data leveraged by citizen analysts alone is a $49 billion opportunity.
Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data. A company can also use data analytics to make better business decisions and help analyze customer trends and satisfaction, which can lead to new—and better—products and services.
Project analytics can help project managers handle complex projects and keep them on schedule and on budget. Using analytics, project managers have the ability to go beyond simply capturing data and completing tasks as they are completed. Now, they can find out a multitude of information, including exactly how projects are performing, and whether or not they are in line with the overall objectives.
Analytics provides project managers the ability to make strategic decisions and improve the project success rate.
Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets. Big Data Analytics deals with the use of a collection of statistical techniques, tools, and procedures of analytics to Big Data.
It is the analytics that helps in extracting valuable patterns and meaningful insights from big data to support data-led decision making. It is because of the emergence of new data sources such as social media, and IoT data that big data and analytics have become popular.
Big Data Sources
There are primarily three sources of Big Data. These are enlisted below:
- Social Data: Data generated because of social media use. This data helps in understanding the sentiments and behavior of customers and can be useful in marketing analytics.
- Machine Data: This data is captured from industrial equipment and applications using IoT sensors. It helps in understanding people’s behavior and provides insights on processes.
- Transactional Data: It is generated as a result of both offline and online activities of users regarding payment orders, invoices, receipts, etc. Most of this kind of data needs pre-processing and cleaning before it can be used for analytics.
The process involved in data analysis involves six different steps:
There are six main steps involved in data analysis: Ask, prepare, process, analyze, share and act.
- During the Ask phase, ask key questions to help frame your analysis, starting with: What is the problem? When defining the problem, look at the current state of the business and identify how it is different from the ideal state. Usually, there is an obstacle in the way or something wrong that needs to be fixed. Another part of the Ask stage is identifying your stakeholders and understanding their expectations. There can be lots of stakeholders on a project, and each of them can make decisions, influence actions, and weigh in on strategies. Each stakeholder will also have specific goals they want to meet.
- After you have a clear direction, it is time to move to the Prepare stage. This is where you collect and store the data you will use for the upcoming analysis process. The first step is to determine the data requirements or how the data is grouped. Data may be separated by age, demographic, income, or gender. Data values may be numerical or be divided by category. Data can be collected from a variety of sources such as computers, online sources, cameras, environmental sources, or through personnel.
- This stage is when it is time to Process your data. In this step, you will “clean” your data, which means you will enter your data into a spreadsheet, or another tool of your choice, and eliminate any inconsistencies and inaccuracies that can get in the way of results. While collecting data, be sure to get rid of any duplicate responses or biased data. This helps you know that any decisions made from the analysis are based on facts and that they are fair and unbiased. For example, if you noticed duplicate responses from a single gym member when sorting through the surveys, yo u would need to get rid of the copies to be sure your data set is accurate.
- Now it is time to Analyze. In this stage, you take a close look at your data to draw conclusions, make predictions, and decide on next steps. Here, you will transform and organize the data in a way that highlights the full scope of the results so you can figure out what it all means. You can create visualizations using charts and graphs to determine if there are any trends or patterns within the data or any need for additional research.
- Once you have asked questions to figure out the problem—then prepared, processed, and analyzed the data—it is time to Share your findings. In this stage, you use data visualization to organize your data in a format that is clear and digestible for your audience. When sharing, you can offer the insights you gained during your analysis to help stakeholders make effective, data-driven decisions for solving the problem.
- And finally, you are ready to Act! In the final stage of your data analysis, the business takes all of the insights you have provided and puts them into action to solve the original business problem.
The next essential part of data analytics is advanced analytics. This part of data science takes advantage of advanced tools to extract data, make predictions and discover trends. These tools include classical statistics as well as machine learning. Machine learning technologies such as neural networks, natural language processing, sentiment analysis and more enable advanced analytics. This information provides new insight from data. Advanced analytics addresses “what if?” questions.
Types of Data Analytics
Data analytics is broken down into four basic types.
Descriptive analytics describes what has happened over a given period of time or historical trends in data. Have the number of views gone up? Are sales stronger this month than last? This often involves measuring traditional indicators such as return on investment (ROI). The indicators used will be different for each industry. Descriptive analytics does not make predictions or directly inform decisions. It focuses on summarizing data in a meaningful and descriptive way.
Diagnostic analytics focuses more on why something happened. These techniques supplement more basic descriptive analytics. They take the findings from descriptive analytics and dig deeper to find the cause. The performance indicators are further investigated to discover why they got better or worse.
This generally occurs in three steps:
- Identify anomalies in the data. These may be unexpected changes in a metric or a particular market.
- Data that is related to these anomalies is collected.
- Statistical techniques are used to find relationships and trends that explain these anomalies.
This involves more diverse data inputs and a bit of hypothesizing. Did the weather affect beer sales? Did that latest marketing campaign impact sales?
Predictive analytics helps answer questions about what will happen in the future. What happened to sales the last time we had a hot summer? How many weather models predict a hot summer this year? These techniques use historical data to identify trends and determine if they are likely to recur. Predictive analytical tools provide valuable insight into what may happen in the future and its techniques include a variety of statistical and machine learning techniques, such as: neural networks, decision trees, and regression.
Prescriptive analytics helps answer questions about what should be done or suggest a course of action. By using insights from predictive analytics, data-driven decisions can be made. If the likelihood of a hot summer is measured as an average of these five weather models is above 58%, we should add an evening shift to the brewery and rent an additional tank to increase output.
This allows businesses to make informed decisions in the face of uncertainty. Prescriptive analytics techniques rely on machine learning strategies that can find patterns in large datasets. By analyzing past decisions and events, the likelihood of different outcomes can be estimated.
Big Data Technologies and trends
Big Data Technologies can be defined as software tools for analyzing, processing, and extracting data from an extremely complex and large data set with which traditional management tools can never deal.
Hadoop Framework was developed to store and process data with a simple programming model in a distributed data processing environment. The data present on different high-speed and low-expense machines can be stored and analyzed. Enterprises have widely adopted Hadoop as Big Data Technologies for their data warehouse needs in the past year. The trend seems to continue and grow in the coming year as well. Companies that have not explored Hadoop so far will most likely see its advantages and applications.
NoSQL includes a wide variety of different Big Data Technologies in the database, which are developed to design modern applications. It shows a non-SQL or non-relational database providing a method for data acquisition and recovery. They are used in Web and Big Data Analytics in real-time. It stores unstructured data and offers faster performance and flexibility while addressing various data types—for example, MongoDB, Redis and Cassandra. It provides design integrity, easier horizontal scaling and control over opportunities in a range of devices. It uses data structures that are different from those concerning databases by default, which speeds up NoSQL calculations. Facebook, Google, Twitter, and similar companies store user data terabytes daily.
Big data is overwhelming for conventional computing. It turns out that traditional machine learning techniques of data analysis flatten out in performance with the increase in variety and volume of data. Analytics faces challenges with respect to format variations, highly distributed input sources, imbalanced input data, and fast-moving streaming data, and Deep learning algorithms quite efficiently deal with such challenges.
Deep learning has found its effective use in semantic indexing, conducting discriminative tasks, semantic image, and video tagging, social targeting, and also in hierarchical multi-level learning approaches in the areas of object recognition, data tagging, information retrieval, and natural language processing.
R is one of the open-source Big Data Technologies and programming languages. The free software is widely used for statistical computing, visualization, unified development environments such as Eclipse and Visual Studio assistance communication. According to experts, it has been the world’s leading language. The system is also widely used by data miners and statisticians to develop statistical software and mainly data analysis.
Storing different data sets in different systems and combining them for analytics with traditional data management approaches prove expensive and are nearly infeasible. Therefore, organizations are making Data lakes, which store data in their raw, native format for actionable analytics.
Data Lakes means a consolidated repository for storage of all data formats at all levels in terms of structural and unstructured data. Data can be saved during Data accumulation as is without being transformed into structured data. It enables performing numerous types of Data analysis from dashboards and Data visualization to Big Data transformation in real-time for better business interference.
Businesses that use Data Lakes stay ahead in the game from their competitors and carry out new analytics, such as Machine Learning, through new log file sources, data from social media and click-streaming. This Big Data technology helps enterprises respond to better business growth opportunities by understanding and engaging clients, sustaining productivity, active device maintenance, and familiar decision-making to better business growth opportunities.
Cloud solutions will power Big Data Technologies
With the Internet of Things (IoT) taking the front seat, data generation is on its rise. Applications involving IoT will require a perfect scalable solution for managing huge volumes of Data. What other than cloud services can do this better. Advantages of Hadoop on Cloud have already been realized by many organizations and technologies pertaining to the coupling of Big Data technologies like Hadoop, Spark, IoT and cloud. These are expected to be well on rising in the coming years.
Kubernetes is one of the open-source tools for Big Data developed by Google for vendor-agnostic cluster and container management. It offers a platform for the automation, deployment, escalation and execution of container systems through host clusters.
Docker is one of the tools for Big Data that makes the development, deployment and running of container applications simpler. Containers help developers stack an application with all of the components they need, such as libraries and other dependencies.
Blockchain is the Big Data technology that carries a unique data safe feature in the digital Bitcoin currency so that it is not deleted or modified after the fact is written. It’s a highly secured environment and an outstanding option for numerous Big Data applications in various industries like baking, finance, insurance, medical and retail, to name a few.
Artificial Intelligence (AI)
Artificial Intelligence is a broad bandwidth of computer technology that deals with the development of intelligent machines capable of carrying out different tasks typically requiring human intelligence. AI is revolutionizing the existing Big Data Technologies.
Data and analytics combined with artificial intelligence (AI) technologies will be paramount in the effort to predict, prepare and respond in a proactive and accelerated manner to a global crisis and its aftermath. AI techniques such as reinforcement learning and distributed learning are creating more adaptable and flexible systems to handle complex business situations; for example, agent-based systems can model and stimulate complex systems – particularly now when pre-COVID models based on historical data may no longer be valid.
Significant investments made in new chip architectures such as neuromorphic hardware that can be deployed on edge devices are accelerating AI and ML computations and workloads and reducing reliance on centralized systems that require high bandwidths. Eventually, this could lead to more scalable AI solutions that have higher business impact. Responsible AI that enables model transparency is essential to protect against poor decisions. It results in better human-machine collaboration and trust for greater adoption and alignment of decisions throughout the organization.
By 2023, more than 33% of large organizations will have analysts practicing decision intelligence, including decision modeling. Decision intelligence brings together a number of disciplines, including decision management and decision support. It encompasses applications in the field of complex adaptive systems that bring together multiple traditional and advanced disciplines. It provides a framework to help data and analytics leaders design, compose, model, align, execute, monitor and tune decision models and processes in the context of business outcomes and behavior. Explore using decision management and modeling technology when decisions need multiple logical and mathematical techniques, must be automated or semi-automated, or must be documented and audited.
Gartner coined the term “X analytics” to be an umbrella term, where X is the data variable for a range of different structured and unstructured content such as text analytics, video analytics, audio analytics, etc. Data and analytics leaders use X analytics to solve society’s toughest challenges, including climate change, disease prevention and wildlife protection.
During the pandemic, AI has been critical in combing through thousands of research papers, news sources, social media posts and clinical trials data to help medical and public health experts predict disease spread, capacity-plan, find new treatments and identify vulnerable populations. X analytics combined with AI and other techniques such as graph analytics (another top trend) will play a key role in identifying, predicting and planning for natural disasters and other business crises and opportunities in the future.
Data and analytics leaders should explore X analytics capabilities available from their existing vendors, such as cloud vendors for image, video and voice analytics, but recognize that innovation will likely come from small disruptive startups and cloud providers.
Augmented data management
Augmented data management uses ML and AI techniques to optimize and improve operations. It also converts metadata from being used in auditing, lineage and reporting to powering dynamic systems. Augmented data management products can examine large samples of operational data, including actual queries, performance data and schemas. Using the existing usage and workload data, an augmented engine can tune operations and optimize configuration, security and performance. Data and analytics leaders should look for augmented data management enabling active metadata to simplify and consolidate their architectures, and also increase automation in their redundant data management tasks.
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