This is so far the best article that I have been reading about the Big Data. It is what I have been advocating to people.
1. They talk about “bigness” and “data,” rather than “new questions”
… It seems most of the tech industry is completely drunk on “Big Data.”
… most companies are spending vast amounts of money on more hardware and software yet they are getting little, if any, positive business value.
… “Big Data” is a terrible name for the revolution going on all around us. It’s not about Bigness, and it’s not about the Data. Rather, it’s about “new questions,” being facilitated by ubiquitous access to massive amounts of data.
… If all you’re doing is asking the same old questions of bigger amounts of the same old data, you’re not doing “Big Data,” you’re doing “Big Business Intelligence,” which is itself becoming an oxymoron.
Web Scraping tools are specifically developed for extracting information from websites. They are also known as web harvesting tools or web data extraction tools. These tools are useful for anyone trying to collect some form of datafrom the Internet. Web Scraping is the new data entry technique that don’t require repetitive typing or copy-pasting.
These software look for new data manually or automatically, fetching the new or updated data and storing them for your easy access. For example, one may collect info about products and their prices from Amazon using a scraping tool. In this post, we’re listing the use cases of web scraping tools and the top 10 web scraping tools to collect information, with zero coding.
Use Cases of Web Scraping Tools:
- Collect Data for Market Research
- Extract Contact Info
- Look for Jobs or Candidates
- Track Prices from Multiple Markets
- OutWit Hub
Read more at 10 Web Scraping Tools
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The ability to use data to achieve enterprise goals requires advanced skills that many organizations don’t yet have. But they are looking to add them – and fast. The question is, what type of big data expert is needed? Does an organization need a data scientist or does it need a business analyst? Maybe it even needs both. These two titles are often used interchangeably, and confusion abounds.
Business analysts typically have educational backgrounds in business and humanities. They find and extract valuable information from a variety of sources to evaluate past, present, and future business performance – and then determine which analytical models and approaches will help explain solutions to the end users who need them.
With educational backgrounds in computer science, mathematics, and technology, data scientists are digital builders. They use statistical programming to actually construct the framework for gathering and using the data by creating and implementing algorithms to do it. Such algorithms help businesses with decision making, data management, and the creation of data visualizations to help explain the data that they gather.
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Google the term “advanced analytics” and you get back nearly 23 million results in less than a second.
Clearly, the use of advanced analytics is one of the hottest topics in the business press these days and is certainly top of mind among supply chain managers.
Yet, not everyone is in agreement as to just what the term means or how to deploy advanced analytics to maximum advantage.
At HP, the Strategic Planning and Modeling team has been utilizing advanced operational analytics for some 30 years to solve business problems requiring innovative approaches.
Over that time, the team has developed significant supply chain innovations such as postponement and award winning approaches to product design and product portfolio management.
Based on conversations we have with colleagues, business partners and customers at HP, three questions come up regularly – all of which this article will seek to address.
- What is the difference between advanced and commodity analytics?
- How do I drive innovation with advanced analytics?
- How do I set up an advanced analytics team and get started using it in my supply chain?
Advanced analytics vs. commodity analytics
So, what exactly is the difference between advanced analytics and commodity analytics? According to Bill Franks, author of “Taming The Big Data Tidal Wave,” the aim of commodity analytics is “to improve over where you’d end up without any model at all, a commodity modeling process stops when something good enough is found.”
Another definition of commodity analytics is “that which can be done with commonly available tools without any specialized knowledge of data analytics.”
The vast majority of what is being done in Excel spreadsheets throughout the analytics realm is commodity analytics.
Read more at Supply Chain & Big Data ÷ Analytics = Innovation
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Big Data is the term for massive data sets that can be mined with analytics software to produce information about your potential customers’ habits, preferences, likes and dislikes, needs and wants.
This knowledge allows you to predict the types of marketing, advertising and customer service to extend to them to produce the most sales, satisfaction and loyalty.
Skilled use of Big Data produces a larger clientele, and that is a good thing. However, having more customers means you must also have an effective means of keeping track of them, managing your contacts and appointments with them and providing them with care and service that has a personal feel to it rather than making them feel like a “number.”
That’s where CRM software becomes an essential tool for profiting from growth in your base of customers and potential customers. Good CRM software does exactly what the name implies – offers outstanding Customer Relationship Management with the goal of fattening your bottom line.
With that brief primer behind us, let’s look at five ways that the integration of Big Data and CRM is shaping today’s marketing campaigns.
Read more at How Big Data and CRM are Shaping Modern Marketing
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Big data analytics can help increase visibility and provide deeper insights into the supply chain. Leveraging big data, supply chain organizations can improve the way they respond to volatile demand or supply chain risk–and reduce concerns related to the issues.
Sixty-four percent of supply chain executives consider big data analytics a disruptive and important technology, setting the foundation for long-term change management in their organizations (Source: SCM World). Ninety-seven percent of supply chain executives report having an understanding of how big data analytics can benefit their supply chain. But, only 17 percent report having already implemented analytics in one or more supply chain functions (Source: Accenture).
Even if your organization is among the 83 percent who have yet to leverage big data analytics for supply chain management, you’re probably at least aware that mastering big data analytics will be a key enabler for supply chain and procurement executives in the years to come.
Big data enables you to quickly model massive volumes of structured and unstructured data from multiple sources. For supply chain management, this can help increase visibility and provide deeper insights into the entire supply chain. Leveraging big data, your supply chain organizations can improve your response to volatile demand or supply chain risk, for example, and reduce the concerns related to the issue at hand. It will also be crucial for you to evolve your role from transactional facilitator to trusted business advisor.
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Staying ahead in the game is paramount for any business organization to survive in this competitive world. The future poses challenges that need tackling in the present. Every decision made today has a significant impact on the future of that organization. The rate at which a company responds to challenges in the present and the future is what determines their rate of success. Data Science and Big Data analytics can help organizations in decision making and drive the company to a realistic future.
The Deciding Factor
It is paramount for businesses to understand the big data concept and how it impacts the organization activities. Discussed below are ways in which Big Data facilitates faster and better decision making;
The time cycle for decision making is decreasing rapidly. Companies have to make decisions more quickly in this period than in the past. Accelerating decision-making time is crucial for the success of any organization. The use of Big Data doesn’t change the urgency of decision making. Big Data analytics mitigates.
Customer reaction to a product is an important factor to consider when making a decision. Using data resources to understand the preferences of customers is one way of pointing out gaps existing in the market. However, the problem is how do you integrate and act in real time? The key is to know how to combine Big Data with your traditional Business Intelligence to create a more convenient data ecosystem that allows for the generation of new insights while executing your present plans.
Accelerating your time-to-answer is crucial for customer satisfaction. For example, if your answer time is usually in minutes, Big Data can reduce it to seconds. If it takes weeks for a client to have their problem tackled, then reducing it to days is more convenient for your customers. Customer retention is critical to the success of your organization.
Read more at How Does Big Data Analytics Help in Decision Making
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Flowcasting has often been referred to as ‘the Holy Grail’ of demand driven supply chain planning (and rightly so).
Driving the entire supply chain across multiple enterprises from sales at the store shelf right back to the factory.
So is Flowcasting a retail solution or a manufacturing solution? Many analysts, consultants and solution providers have been positioning Flowcasting as a solution for manufacturers.
While it’s true that some manufacturers have achieved success in using data from retailers to help improve and stabilize their production schedule, the simple fact is that manufacturers can’t achieve huge benefits from Flowcasting until they are planning a critical mass of retail stores and DCs where their products are sold and distributed.
For a large consumer packaged goods manufacturer, this means collecting data and planning demand and supply across tens of thousands of stores across multiple retail organizations, all of which have their own ways of managing their internal processes.
Read more at Is Flowcasting the Supply Chain Only for the Few?
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Big-data analysis consists of searching for buried patterns that have some kind of predictive power.
But choosing which “features” of the data to analyze usually requires some human intuition.
In a database containing, say, the beginning and end dates of various sales promotions and weekly profits, the crucial data may not be the dates themselves but the spans between them, or not the total profits but the averages across those spans.
MIT researchers aim to take the human element out of big-data analysis, with a new system that not only searches for patterns but designs the feature set, too.
To test the first prototype of their system, they enrolled it in three data science competitions, in which it competed against human teams to find predictive patterns in unfamiliar data sets.
Of the 906 teams participating in the three competitions, the researchers’ “Data Science Machine” finished ahead of 615.
In two of the three competitions, the predictions made by the Data Science Machine were 94 percent and 96 percent as accurate as the winning submissions.
In the third, the figure was a more modest 87 percent. But where the teams of humans typically labored over their prediction algorithms for months, the Data Science Machine took somewhere between two and 12 hours to produce each of its entries.
“We view the Data Science Machine as a natural complement to human intelligence,” says James Max Kanter, whose MIT master’s thesis in computer science is the basis of the Data Science Machine.
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Big Data has been one of the most significant and influential aspects of the Information Age as it relates to the enterprise world. Essentially, Big Data is the massive collection, indexing, mining, and implementation of information that emanates from just about any activity that can be monitored and managed electronically. Some of the uses of Big Data include: marketing intelligence, sales automation, strategizing, productivity improvement, and efficient management.
Enhancement of the workforce is one of the exciting and meaningful benefits of Big Data for the business sphere. Recently, human resource managers and analysts have been researching the implementation of Big Data as it relates to employees, and the following trends have emerged:
For many decades, companies and organizations have tried various methods to gain knowledge about what their employees are really like. The productivity that workers can contribute to their employers is based on personal needs as they are balanced against the performance of their duties. With Big Data solutions, both personal needs and performance can be diluted into metrics for efficient analysis.
Modern workplace analytics originates from tracking employee records as well as metrics on their performance, interactions and collaboration. The idea is to focus on the right metrics to create a climate of positive engagement.
Read more at How to Use Big Data to Enhance Employee Performance
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