BigData, Author at Outsource Bigdata Blog

Web Data scraping, AI & Automation: Made for each other

Automation and Artificial Intelligence (AI) are the hot topics in tech world today.  Research reports state that AI is going to take over the world and that will change the world faster than ever before.

If we look at the e-commerce world, it is heading towards building artificially intelligent digital commerce platform.  The new e-commerce platform could intelligently recommend most preferred products to the customers. And, if we look at the key source of developing artificial intelligence is data – properly trained data.

There are various sources of data. One key source of data is internet and there are various method to crawl data from the internet. Automated extraction of data from various websites can be termed as web data scraping. Now, let us look at a few aspects of automation in web data scraping or web data crawling.

Why is automation needed for web data scraping?

When automation is synced with any term — the thought is fast, accurate and flexible. According to World Wide Web survey, there are nearly 5 billion websites in 2018. Is it possible to access data manually from all those sites? It is unrealistic. This is when the importance of automated tools to get the data comes into the picture. There are numerous automation tools from which one can scrap data. R and Python are the two major open source software tools used for automated web data scraping.

Here the bot should be able to navigate through different pages and should collect the data. The thing is, different website uses different navigation systems which results in complexity, so the developer, writing web data crawling bots should have sound technical knowledge. There should be a minimal human interference once the machine is programmed.

We can find dozens of coding languages in use today. But, we need to identify the best coding language which gives us the maximum automation efficiency. Python is top-ranked open source programming language used by Reddit, Instagram, Venmo according to Coding Dojo press. And has become extremely popular among data scientists

Web Data scraping, AI & Automation: Made for each other

Web Data Scraping: Steps to Follow

  • Analyse the source data and build custom script
  • Crawl Data using right scripting language
  • Store crawled data in desired format
  • Use right tool to perform cleansing, de-duplication, formatting, & analysis
  • Store output results from analysis
  • Present visualization of results using suitable BI tool
  • Periodic web data crawling – as per the business needs

Automated web data scraper resolves the problems of big data and human errors, by reducing the time and effort it takes to solve manually.

The major advantages of automated web scrapers are, they save costs of manpower and man-hours required for the same job if it’s done manually. For some straight forward web data scraping, software’s could do it much faster compared to humans, which is pretty obvious. Once web data scraper is deployed with the proper mechanism, it efficiently extracts from every single source. This is highly probable human error.

To summarize, application of AI and automation in web data scraping is immense. The consistency of web data scrapers is unmatchable with the humans; they will continue to extract data until it gets over or it is instructed to stop. Web data scrapers do not require lot of maintenance over long period of time, which adds to its value. There are boundless potential for improvements in web data scraping automation leveraging AI and hence, the scraping tools become truly intelligent.

Top 10 IT predictions in 2018 and beyond

Top 10 IT predictions in 2018 and beyond

Organizations are vying for eliminating or augmenting IT jobs. Focus is towards long-term and maximum impact. In information technology world, digital organizations and smart machines started eliminating process steps or processes by itself particularly those requiring lots of human involvement, analysis and judgment. The elimination of processes, rework and errors — as the bots execute the transactions leveraging Artificial Intelligence and Robotic Automation.

IT development always leads to the innovation of numerous technologies, which can be a path of business success. Improvement in the hardware in combination with smarter applications gives you many tools to solve complex problems. And this innovation leads to improvement in data storage, smarter apps, quick processing and wide distribution of information. What’s more?  Advancement expands esteem, upgrades quality, and lifts profitability.

  1. By 2021, early adopter brands that redesign their websites to support visual- and voice-search will increase digital commerce revenue by 30%
  2. By 2020, five of the top seven digital giants will willfully “self-disrupt” to create their next leadership opportunity
  3. By the end of 2020, the banking industry will derive $1B in business value from the use of blockchain-based cryptocurrencies
  4. By 2022, most people in mature economies will consume more false information than true information
  5. By 2020, AI-driven creation of “counterfeit reality,” or fake content, will outpace AI’s ability to detect it, fomenting digital distrust
  6. By 2021, more than 50% of enterprises will be spending more per annum on bots and chat bot creations than traditional mobile app developments
  7. By 2021, 40% of IT staff will be versatilists, holding multiple roles, most of which will be business, rather than technology-related
  8. In 2020, AI will become a positive net job motivator, creating 2.3M jobs while eliminating only 1.8M jobs
  9. By 2020, IoT technology will be in 95% of electronics for new product designs
  10. Through 2022, half of all security budgets for IoT will go to fault remediation, recalls and safety failures rather than protection

    IT Process Automation – A new normal in IT Industry:

    Process Automation can be described as a strategy, which explains the digital transformation software and the use of advanced technology methods. This can eventually help in automation of a set of IT activities that are usually repetitive. As the idea of work has changed, so too has the technique of robotization. One such process automation trends is Robotic Process Automation (RPA).  According to a report in FORRESTER, the RPA market, while only $250 million in 2016, and will grow to $2.9 billion in 2021.

    IT Process Automation help providing an exact vision of IT workflow. Also, it helps to establish KPIs and metrics, which then forms the basis for process improvement. This can also eliminate the redundant steps, speeds up your IT processes and improves operational efficiency. Rather than assigning work on the basis of who happens to be available, automating work activities makes you able to match work with the staff, who have appropriate skills.

    IT Process Automation is also one of the quality benefits, by standardizing processes, which drastically eliminates the human errors. Workflow system acts as overwatch thus reduces the management oversight of daily IT operational issues.

    All the above IT predictions are hard core facts? Not sure. Though not 100 %, these IT predictions are relatively certainties, or at least firmly defined trends across the software industry.

BI Analytics Reporting and Dashboarding

Next Gen BI Are you ready for Self-Serving BI analytics, Reporting & Dashboarding?

There is a saying “Everything’s better when you can do it yourself”. Don’t you notice this? We are in the era, where self-agendas, self-workouts are gaining more prominences rather than dependency. Business always needs constant updating policy to top the race, because the traditional business-centric are unable to match the speed at which changes are happening.

When the data is the core of operations, decision making and optimization efforts, the new self- service BI platforms are essential to businesses.

Gartner predicted that by 2020 self-service BI platforms will make up 80% of all enterprise. For years together we had dependent on the busy IT teams and scattered spread sheets for all our reporting needs. It is the right time to lookout for self-service Business intelligence analytics, which can furnish a single view of relevant insights from varied data resources.

There are ‘N’ numbers of reasons why an organizations need to adopt business intelligence and analytics by themselves.  Self-service BI shatters the misperceptions that, BI is costly, difficult to use and deploy, and slow to deliver real business value. Self-service BI helps all sizes of organizations.

Employers can conduct their own analysis:

There is no need to contact IT to run special reports or to gather data manually from various sources into spreadsheets. Empowering the workers with the data and tools to perform their own analysis, of course the outcome will be great.

Gives a break to your IT & finance team:

Self-service BI integrates the data from different systems and delivers complete automated reporting and dashboards to user. This also gives you a 360-degree view of business operations and setting your IT & finance team free.

Start analyzing the number behind the numbers:

To know the root cause of the problems or to grab the isolated opportunities self-service BI is must, which allows users to get into the right numbers.

Save your money:

Self-service BI tools and automated reporting and dashboards not always require either a data warehouse or database. This alone eliminates a considerable expense. And also by accurate identification and leaving the unprofitable products lines can save a lot of money.

Shifting the corporate culture from reactive to proactive:

As soon as the new data arrives, the BI provides access to the most up-to-date data, helping you gain the new insights of data. With fewer resources and less risk one can more effectively manage promotional and incentive programs.

BI Analytics and dashboarding

Automated reporting and dashboards are easy to understand because it’s a one page summary of the analysis of the collected information and can be prepared in a detailed manner as per the needs of the end user. It is always customizable in terms of users and expectations. Every decision can be customized to present the most important and useful information.

Real automated reporting and dashboards help to see the data and also the results instantly. It’s nothing getting the actionable data related to the product without waiting. Self- service makes the data available across every dashboards and custom reports almost immediately.

Every organization needs relevant data from various tools to make their business top the race. When you do things, by your own then the insight you get on your business is somewhat different then what you experience from others. That’s how you’re benefitted by self-service analytics, automated reporting, and dashboarding. And now, it’s the time to get started with self-serving BI analytics, reporting and dashboarding.

Reports already started seeing the impact of self-serving BI analytics, automated reporting, and dashboarding trends in 2017, and we could expect them to fast-track in the year to come.

Industry 4.0 Data management

Is your Data Team ready for Industry 4.0 related Data Management?

Industry 4.0 is an era where sensor technology and inter-connectivity of digitally connected devices, predominantly Internet of Things – is driving the industry forward. The world has witnessed the tremendous change in the stages of industrial development.  There are numerous efforts by our ancestors for the present status of our industries. The start of revolution in industries is sequentially lined from industry 1.0 to industry 4.0

Every evolution leads to the development of other and now we are in the period of industry 4.0. During the revolution from industry 1.0 to industry 3.0 there was not much to concentrate on data management. But today it is the key to success in industry 4.0.

Industry 4.0 is something we can relate to smart hub. People sometime put an equation stating that artificial intelligence plus big data is industry 4.0. The newly rising digital industry makes it possible to collect and analyze the data across machines, making faster, more flexible, and more convenient processes to produce a higher quality products at reduced price.

The fourth generation is to be ruled by artificial intelligence and IOT. Here in fourth industrial revolution all types of machines and devices interact, communicate and learn so much from each other. Artificial intelligence will reduce the burden of decision making and helps us in getting better strategy. IBM states that the artificial intelligence and Internet Of things have something common in them and the common factor is nothing but Data. When we speak about data we mean actionable data, which has a lot of information, knowledge, insights and other kinds of data driven intelligence and analytics.

Usage and interpretation of data will surely bring the difference in the market. And the technologies from the R&D labs will reach your homes, offices and day-to-day lives. Big data is the third pillar which will also drive this revolution besides AI and IOT.

A Proper way of collecting and managing data will leads to an innovation economy. In 4.0 industry revolution, one of the main aims is to have a closer interaction with the end customers. This can be done by the collection of each and every data points of end users. Data becomes a new revenue stream. It is clear that to reach the peak of efficiency in industry 4.0, it’s compulsory to go behind the data. Big data analysis is somewhat of a bet on the future.

Industry 4.0 Data management

This is how the data plays a major role in the industry 4.0 revolution. Everything you want to gain from industry 4.0 is by proper data management. Now it is the time to evaluate, that the team of ours is really ready for effective data management? If not, you will be out of the race. Make sure to get in, and top the race by managing the data with high concern.

 

 

 

 

Web Data Scraping Service

7 Tips to choose the right Web Data Scraping Service Provider?

Outsource partners are experts in leveraging their collective experience to help overcome difficult and complex web data scraping requirements. As outsourcing service providers frequently work with many companies on a variety of projects with various levels of complexity, these data partners can quickly build critical skills and expertise to any web scraping requirement.

Outsource partner teams usually leverage their collective knowledge and experience to solve problems and innovate new ways of web data scraping or web crawling. In fact, smart web scraping outsourcing companies hold daily stand-up meetings, where web scraping expert resources share their daily progress and experiences on what they are working on – including their development successes, key challenges they come across and creative solutions blended with best practices they follow to their fellow colleagues. Web scraping experts lead their projects and share insights and disseminate information that can eventually help increase the efficiency of each web scraping developer.

Data once extracted or scraped should be managed and is taken for analyzing the business insights or any other data consumption. Web data extraction is a major process and hence, it is important to choose the best outsourcing service provider – if you do not have the internal capability to scrap data — so that quality is not compromised and delivery will be as per the expected timelines with minimum cost.

Outsource web scraping services are increasingly being used in many industries and across the organizations including marketing, operations, recruitment, delivery, etc. So, it’s important to choose the right web crawling service provider to get the best quality output at short time span with the least cost.

Now, let’s check-out with some points which can help us to choose the right web scraping service provider for the business.

1.   Scalability:

The web crawling service provider that you pick should be versatile and future-evidence. This implies, as your data necessities continue getting greater, the crawling service shouldn’t slack and back you off. Your web crawling specialist should have extraordinary assets and framework to take into account your future information needs be it huge or little.

2.   Transparency of pricing structure:

Search for a web creeping specialist organization with transparent and straightforward evaluating. Evaluating models that are exceptionally mind-boggling are regularly irritating and may even imply that they have shady concealed expenses. It is smarter to stay away from such organizations and go for one that keeps their pricing direct. A decent valuing structure is one that can be comprehended initially. Preferably, the evaluating plan should enable you to anticipate your future expenses easily.

3.   How do they deal with changes in the website?

Sites that you should be crept may frequently experience changes and challenges. The progressions may be restorative or now and again auxiliary and the crawling service that you pick might be one that watches out for such changes. Changes to the site would require the crawler to be altered and tailored in like manner. In the event that a web crawling service isn’t checking these progressions appropriately, you should need to stay away from them.

4.   Can they bypass Anti-scraping mechanisms?

Numerous sites have instruments actualized on them to avoid extracting information. A decent web crawler should have innovation that can deal with such circumstances.

5.   Data delivery formats

The primary inquiry would be what designs/document composes do you need the information to be conveyed in? Whichever data format you are looking for, ensure that web data scraping service provider can deliver it. For example, on the off chance that you need it in JSON design, pick a web spidering service supplier that conveys the information in JSON. It’s smarter to go for the one that can convey information in various configurations with the goal that you can simply depend on them, even if your requirement changes.

6.   Customer support:

Client support is critical while managing petabytes of information. You will dependably require answers to your inquiries instantly for any of your web data scraping questions. With extraordinary client support set up, you don’t need to stress if something turns out badly every so often. Client support is really one of your best needs while chasing for the best web scraping service.

7.   Quality of data:

The information scraped from the web is at first unstructured and not in usable shape unless it is cleansed up by the web scraping specialist with the help of data quality management team. How great and organized it turns out, at last, will thoroughly rely upon the nature of the organization you pick. So you should pick one that deals with data cleaning and arranging the garbage information into readable and helpful information for you – ready to consume form. The nature of the last data is very important because the analysis will be impacted by it. This how you can search for the best outsource web scraping services.

To conclude, when you are evaluating your shortlist of web data scraping outsourcing partners, ensure that you evaluate their success stories – in-detail, with a representative sample data output. Good to meet with the service provider – web scraping experts – in person, if possible.  Understand their delivery organization, best practices they follow, web scraping tools and scripts they use, the kind of web scraping experts they have on roll, etc.  Also, ask for the business continuity management process? Let us not miss any key evaluation criteria.

How Small and Medium size companies can benefit Automation

How Small and Medium size companies can benefit Automation

Automating everyday business process and IT processes have become the topic in almost every business boardroom in 2018. Companies started realizing that manual processes and repetitive IT activities can be automated.

Today, in an age of outsourcing and automation, increasing productivity and accomplishing customer needs are the focus for business. In this journey of outsourcing and automation, data management has turned into the biggest challenge for many organizations in these days. As it always needs enough budget, staff or specialized expertise to establish, maintain and of course for consistent handling.

Based on the latest IDC research report, Asia Pacific region – Automation and Robotics Spending to reach $93 Billion in 2019.

Worldwide

Automation & Robotics Spending to reach $93 Billion in 2019.

Why is automation such a critical requirement in today’s data management environment?

Being small or midsized company, the first few years or months will always their surviving period. During these days – in most of the cases the scenario is likely to be a small fish trying hard to stamp its existence in the ocean, where already the giant ones are ruling. The thought of automation may be still far for them, but the need to explore automation is a must today for the existence of a company in the industry. Let’s see how the automation drives a positive impact on your business.

Generally, one can guess the primary benefits of automation such as cost reduction, productivity, availability, reliability and superior performance.

Cost reduction: Automation will be an intelligent option to reduce the cost. A recent New York Times article states that manufacturing output has achieved a record high in the most recent quarter. Workers now are producing 47 percent more than 20 years ago. This is just an example and there are many more. Through the development of automation, robotics, and advanced manufacturing, the sector has bounced back along with the overall economy.

Productivity:  As companies continue to automate their business and IT processes, robotics offers a new opportunity to gain productivity and throughput improvements beyond the existing automation efforts.

 The benefits of automated systems

Availability: Yes, you can find mangoes in winter. As the effect of automation each and every service, you can afford at any time when the end users demands which is complete complements to the availability of IT professionals.

Reliability: Here comes the real gem of your service, automation allows you to maintain the consistency and superior quality of your daily processes. Resulting to more happy end users and also you can stay without confusion and chaos. The baseline model uses previously collected data from human-in-the-loop experiments where the automated teammate performs with 100% reliability.

Superior Performance: If the business witness all these four things then obviously the performance will be great. This strengthens scalability and can monitor the workflow to have a better chance of succeeding.

For automation, there are ‘N’ numbers of tools. Some of the open tools such as Automation anywhere, Blue Prism, Selenium, R, Python, tableau, and apps like parse hub, smack can be used. If one can have these many advantages, then it’s purely a walk on the success path. Along with finding solid base the small and mid-sized companies should explore the automation as quickly as possible.

In short, no matter which industry you belong to — day-to-day workflows need to revisit and explore opportunities for automation. Real-time data process, data collaboration, and real-time analytics are already in place.  Automation accelerates organizations of all sizes; and financial and productivity benefits are materialized. As an outcome, budgets of IT and outsourcing departments started shrinking. So it’s the right time to evaluate yourself that where you stand in the race?

IOT CUSTOMER EXPERIENCE

How connected devices and IOT can drive increased customer experience

Personalization and contextualization, the one-to-one in-person experience is the outcome of the digitalization. Today, personalization leads to increased customer experience and increased revenue. As the customer continues to get smarter, digitalization and IOT enables them to provide increased customer experience at every touch points.

According to IDC report, at the end of 2020, there will be about 30 billion connected devices across the world which will bring increased customer experience. Now researchers are carried out, to connect every possible device to the internet and covert them smarter and smarter.

Global IoT market can expand greatly to 3 trillion dollars connecting 50 billion devices by 2020. As of now, you can see many devices which are connected to the internet such as a tablet, mobile, Car, TV, Gate, House, etc. Forbes reports that 4,800 devices are being connected to the network, but ten years from now it will be 152,000 per minute. We can notice within the last decade, Smartphones have emerged as a must-have device for consumers globally. Today people are eager in getting services by connected devices. Imagine the scenario at the end 2020.

For instance, how about this experience? That your fridge automatically orders the groceries from FreshDirect or Amazon Fresh. Shortly you may go to a kart and fetch for a washing machine which displays a discreet notification on your iTV. Maybe a full milk smart tank can signal a smart truck to come pick up farm-fresh milk. That is the magic of connected devices and IOT.

IOT CUSTOMER EXPERIENCE

The connected devices are always more efficient in processing. It is when the power of individual technologies and devices are made to work together. Which intern saves time and reduces costs. This is because of access to real-time data from sensors.

Eliminating the waste and maximizing the resources are some of the main goals of every business. To achieve this, one should have access to right data.

The connected products achieve the heights of autonomy ever before and become smart by the combined capabilities of monitoring, control, and optimization.

Internet of things is redefining connectivity in two ways which benefits the customers.

  • Users connecting to smart devices
  • Smart devices connecting with ordinary objects.

IOT helps in multichannel involvement and interaction in a contextualized contact with consumers. This helps in better customer insights and experiences. Ability to track and organize deliveries along with fleet management is the key to customer satisfaction.

Virtual connection enables ones to monitor their things, by having a smartphone in their hands. Efficient usage of electricity and energy makes the customer adopt for IOT.

Convenience is the new customer service, customer experience, and contextualization strategy. IOT makes everything contextualized, smart, and maybe smart homes, smart cities and much smarter world around.

 

 

 

 

Machine learning use cases data management

Machine Learning Use cases in Data Management

Today, there are an incredible number of challenges around the world across the industry domain that can solve by providing the right training data – sample — to the right machine learning algorithms. Great thanks to the latest developments in Machine Learning algorithms.

If we look at the basics of machine learning, the perspective of handling data is a way different to computers when compared to humans. The process is fast, accurate and flexible with computers. Data management is not a separate industry sector; it’s an integral part of each and every organization. And need to be handled with high priority.

Machine learning continuously evolving over a period of time which enables to handle the data to get the best use of it across the industries. Sometimes data management becomes more important than algorithms to drive the solutions. It is said in Forbes publication, enterprise investments in machine learning will nearly double over the next three years, reaching 64% adoption by 2020.

We can picture the use of ML in various fields of data management, a resource that enhance the business benefits in several industries.

Sorting through dark data:

To sort and handle different types of emails, documents and images stored on different servers, machine learning, and its combined algorithm power will be helpful.

Deciding which data to cutoff:

AI, machine learning, and analytics can systematically identify the seldom data and indicates that data is obsolete. Which can take the maximum time of employers.

Aggregation of data:

Sometimes there is a need for aggregation of data for queries, and it needs integration to access the data from different sources. But using machine learning, it makes the process so efficient by automatic mapping between the sources and data repository application.

Organized data storage system for best access:

There are different kinds of data such as most used, seldom and never used data. IT departments to use “smart” storage engines which uses machine learning algorithms to classify different types of data. This eliminates the concept of manual address storage optimization.

Managing healthcare data:

The decline of archaic analytics to extract insights from images, EHR system reports and voice recordings, made a way for ML to extract meaning from all these diverse data sources with its powerful algorithms. Clinical data analysis and imaging technologies are new bloom in health care.

Machine learning in finance data management:

The two main purposes for the adoption of ML in finance and banking sector are to extract customer intelligence and lifetime value of a customer from data and for fraud detection. Machine learning algorithms can grab the customer’s financial history and analyze the market aspects. Today, the entire financial industry is working on multiple machine learning projects to understand Customer buying behavior and spend pattern to drive their business growth.

ML for managing marketing customer data:

The ever-growing unstructured data on social media, prospective companies can mix “listening technologies”. The human customer service can soon replace by machine learning algorithms along with the help of NLPs.

ML for manufacturing sector:

Machine learning algorithms and platforms are helping manufacturing companies to find new and innovative business opportunities, refine product quality, design, and optimize manufacturing operations. Asset Management, Logistics and Supply Chain Management, and Inventory Management are some of the hottest areas of machine learning today.

These are just a few samples of machine learning applications in data management, still there are many such as data security, personal security, online search, smart cars, and many more. Keeping the value of faster time, storage cost, and potential person power for data management ML plays a major role. Due to its unique capability, ML is the only answer for smart digital development. And it continues to play a crucial role in the future of enterprise data management. The only catch is machine learning is it works only when your training data is representative of the population.

 

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