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Big data is creating unprecedented opportunities for organizations to achieve faster, better insights that strengthen decision-making. However, the traditional methods and tools for the analysis of data is not sufficient to capture the potential value that big data represents. Capitalizing on the true value of big data requires a completely different approach. Analytics Insight captures an exclusive interaction with Rion Graham, Data Scientist at , who highlights how harnessing effective big data analytics platforms can help organizations successfully drive real business impact. Analytics Insight: Kindly brief us about the company, its specialization and the services that your company offers. Rion: GoodData is an integrated set of data management, analytics, and insight application deployment and management tools and is a leader in the Platform as a Service category. GoodData combines an organization’s internal and/or external data (both structured and unstructured) to deliver business critical insights to users. GoodData goes beyond traditional business intelligence and analytics delivering insights at the moment of action to drive better business outcomes. The company primarily serves insurance, retail, financial services, and ISV customers, but works with various other industries as well. Analytics Insight: Tell us how your company is contributing in the Big Data Analytics industry and how the company is benefiting the clients. Analytics Insight: Kindly share your point of view on the current scenario of the Big Data Analytics and its future. Rion: The analytics product market remains focused on visualizations as a key differentiator. There’s good reason for this, as they help to turn a series of numbers into a story with real-world business context. However, visualizations alone are often insufficient to describe the sheer volume and variety of metrics that are distributed and unstructured big-data is generated. Artificial intelligence (AI) is ideally suited to bridge this gap and is already being used to automate the process of insight discovery. As the role of AI within analytics expands, a new dialect of insights will emerge to complement existing visualizations. Analytics Insight: How are disruptive technologies like big data analytics/AI/Machine Learning impacting today’s innovation? Rion: Machine learning (ML) and AI are impacting innovation in part by extending the domain of analytics beyond traditional tabular business data to include images, video, unstructured text, and more. One interesting example is the use of AI and natural language processing (NLP) to identify and remove malicious bots and other bad actors on social media platforms by identifying patterns of interaction and conversation. Rather than listing static lists of blacklisted words, new AI algorithms can look at new individual users and how their language evolves over time — and how that relates to the platform as a whole. Not only semantic tagging, but also audio/visual tagging. Analytics Insight: How is your company helping customers deliver relevant business outcomes through adoption of the company’s technology innovations? Rion: We work closely with our clients to understand their underlying critical business needs and thereby design with specific end-users in mind, delivering only the data they need, in a manner they expect. By integrating ML-driven insights at the point of work, whether they be classifications, predictions, or suggested actions, we close the insight-to-action loop and drive ROI. For example, we used ML to help one of our clients (a billion-dollar per year company) redesign its quarterly renewals forecasting process — replacing a series of manual, Excel-based business processes and turning them into a streamlined program for greater data accuracy and a broader spectrum of insights. It’s now obvious to users what actions they should take based on the data presented so they are able make a real impact on their business. Analytics Insight: What is the edge your company has over other players in the industry? Rion: GoodData has been built from the ground up to distribute analytics at the point of work. While competitors struggle to connect insights with their audience, especially at scale, GoodData can deploy analytics and ML models to multiple personas and many thousands of users with ease. Additionally, GoodData’s platform provides continuous retraining of ML models, which means they don’t live in a vacuum. Rather, models and their end-users work in coordination to define and improve real-world business practices. Analytics Insight: How does your company’s strategy facilitate the transformation of an enterprise? Rion: Currently, most companies don’t view analytics as “mission critical”. GoodData’s customers rely on our platform largely when the abundance of their data mandates a shift in analytics from being a “cost center” to a mission-critical activity. Our approach of distributing embedded analytics at the point of work highlights actionable insights to the end users, transforming transactional reports into strategic decisions. Analytics Insight: Which industry verticals are you currently focusing on? And what is your go-to-market strategy for the same?

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Top Big Data Tools Of 2023 For Data Analytics And Business Intelligence

To make the colossal data talk intelligent, enterprises need big data frameworks

MongoDB is the next-generation database helping businesses transform their operations by harnessing the power of data open-source document database and leading NoSQL database. MongoDB’s greatest strength is its robustness, capable of far more flexibility than Hadoop, it is written in C++.

Pentaho Apache Cassandra is the leading NoSQL, distributed database management system, well suited for hybrid and multi-cloud environments. Cassandra is a highly scalable, high-performance distributed database designed to handle large amounts of data.

While we all would have heard that data is the new oil, the question that grips enterprises is how to mine this valuable oil for its business gains? Data resides in massive warehouses, pipelines and lakes, and to bridge the gap between enterprises and business markets big data frameworks form an imperative channel helping the businesses to rise to the call and move towards a data-driven future. To address the data needs to the future, Analytics Insight compiles the top big data tools of 2023 for data analytics and business intelligence-Apache Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides a software framework for distributed storage and processing of big data using the MapReduce programming model. Hadoop is a highly scalable storage platform; it can store and distribute big data sets across hundreds of inexpensive servers. Users can increase the size of our cluster by adding new nodes as per requirement without any downtime.MongoDB is the next-generation database helping businesses transform their operations by harnessing the power of data open-source document database and leading NoSQL database. MongoDB’s greatest strength is its robustness, capable of far more flexibility than Hadoop, it is written in C++.Pentaho Big Data analytics is a comprehensive, unified solution that supports an enterprise’s entire big data life-cycle. Pentaho Big Data analytics offers full array of analytics solutions that include data access and integration to data visualization and predictive analytics.Apache Cassandra is the leading NoSQL, distributed database management system, well suited for hybrid and multi-cloud environments. Cassandra is a highly scalable, high-performance distributed database designed to handle large amounts of data.RapidMiner is a software platform for bigdata science teams that unites data prep, machine learning , and predictive model deployment. RapidMiner is free of charge, open source software tool for data and text mining and easily the most powerful and intuitive graphical user interface for the design of analysis processes.

Applications Of Ai And Big Data Analytics In M

Have you heard about the idea of monitoring health with the help of mobile devices?

It is related to the term m-Health that makes use of m-Health apps along with AI and Big Data in healthcare. Owing to the surge in the usage of smartphones and other devices, people have started interacting with doctors and hospitals differently. You will realize there is an app for every task right from managing doctor’s appointments to maintaining records.

At this juncture, where every business is fighting hard to appeal to the interests and goals of the customers AI and big data are redefining the healthcare industry. In this blog, we will take a look at the applications of AI and big data and how it has revolutionized the entire healthcare system.

Let’s begin:

AI in Healthcare

AI in healthcare relates to the usage of machine learning algorithms and software to mimic human cognition that aids in analysis, presentation, and understanding of complex data.

Right from detecting links between genetic codes, putting surgical robots to use, or maximizing hospital efficiency, AI is a powerful tool to streamline the healthcare industry. Let’s see what AI has to offer to healthcare:

1. AI Supports Decision Making

Healthcare developers and professionals must consider a crucial piece of information for app development and diagnosis. They go through various complicated unstructured information in medical records. A single mistake can have huge implications.

AI in healthcare makes it convenient for everyone to narrow down the big chunks of information into relevant pieces of information.

It can store and organize these large chunks of information and provide a knowledge database that can, later on, facilitate inspection and analysis to draw meaningful conclusions. This way, it helps clinical decision support, where doctors can rely on it for detecting risk factors.

One such example of AI is IBM’s Watson that predicts heart failure with the help of AI.

2. Chatbots to Prioritize and Enhance Primary Care

People tend to book appointments even at the slightest of medical issues, which often causes chaos and confusion. Later on, there are usually discovered to be issues that could be taken care of by self-treatment. Here AI can be of great use to enable smooth flow and automation that facilitates primary care. It will help doctors to focus more on critical cases.

The best example is medical chatbots that can save you from medical trips to doctors that could be easily avoided. Chatbots when incorporated with smart algorithms can provide patients with instant answers to patient queries and concerns.

Also read: Top 3 Lessons I Learned from Growing a $100K+ Business

3. Robotic Surgeries

A combination of AI in healthcare and collaborative robots has helped achieve desired speed and depth in making delicate incisions. These surgeries have been given the name of robotic surgeries that eliminates the issue of fatigue and helps in lengthy and critical medical procedures.

With the help of AI, one can develop new surgical methods from past operations that will help gain more preciseness. This accuracy and precision will surely reduce accidental movements during the surgeries.

The best example of robotic surgeries is Vicarious Surgical, which combines virtual reality with AI-enabled robots. The purpose of developing such robots is to help surgeons perform minimally invasive operations.

Another great example of AI in robotic surgery is the Heartlander. It is a miniature mobile robot aimed to facilitate heart therapy. The robot is developed by the robotics department at Carnegie Mellon University.

4. Virtual nursing assistants

Virtual nursing assistants are another example of AI in healthcare that can help in providing excellent healthcare services by way of performing a range of tasks. These tasks include addressing patient queries, directing them to the best and effective care unit, monitoring high-risk patients, assisting with admissions and discharge, and surveying patients in real-time. The best part is that you can avail the services of these virtual nurses 24/7 and get instant solutions to your problems.

When you explore the market, you would realize many AI-powered applications of virtual nursing assistants are in use. They help facilitate regular interactions between patients and care providers that save the patients from unnecessary hospital visits. Care Angel is the world’s first virtual nurse assistant that facilitates wellness checks through voice and AI.

5. Accurate Diagnosis of Diseases

AI in healthcare can surpass human efforts and help in the detection, prediction, and diagnosis of diseases quickly and accurately. Have a look at the specialty-level diagnosis, here AI algorithms have proven to be cost-effective in the detection of diseases like diabetic retinopathy.

PathAI is a machine learning technology that helps pathologists in determining the issues with more accuracy. It aims to reduce errors in cancer diagnosis and develop methods for individual medical treatment.

The Real Price Of The Iphone 5: $207

We all know Apple has a big profit margin on all of their devices and thanks to market research firm IHS iSuppli, we now know how much Apple pays for manufacturing the iPhone 5. The 16GB version of the recently launched mobile phone costs $207, which is $442 less than the retail price without a contract. The 32GB and 64GB cost $217 and $238 respectively. Only the first iPhone was more expensive to build, carrying a bill of materials of $246. That price steadily declined to $188 for the base model iPhone 4S. The components of the iPhone 5 are slightly more expensive than those of its predecessor. The latest iPhone offers a bigger screen with in-cell touch sensing and supports 4G LTE, which accounts for the increased materials costs. The only component to cost less is the NAND flash storage at $10.40 compared to the iPhone 4S at $19.20. According to senior principal analyst Andrew Rassweiler, Apple is the largest buyer of NAND flash storage in the world and the company therefore gets preferential pricing. Advertising costs were not a part of the calculation, as well as licensing fees, royalties and software development costs.

Charting Real Time Data In Excel

I’ve previously written about getting real time data (RTD) into Excel using Python.

In this post I’m going to chart that data as it arrives in Excel. Think real-time stock price updates.

The steps required to get this working are:

Install Python and PyXLL.

Write a Python function to get the real time data and put it into an Excel sheet.

Write VBA event code to process that data into a table.

Write an array formula to fetch the latest 30 data points.

Create a chart to plot the data from 4.

I’ve written the Python function for you so you just need to get Python and PyXLL installed and you can be plotting your own real time data.

Download the Workbook and Python Code

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Function to Fetch Real Time Data

I’m using the same function I wrote to get RTD in this blog post.

I’ve had to make some minor changes like the URL being used to fetch the data.

RTD Data Source

I’m using stock data provided by chúng tôi You can sign up for a free account which allows you to retrieve unlimited test data from their system. They provide a paid service if you want to get real market data.

An example of such a request asking for a quote on Microsoft’s stock price is

The code MSFT specifies that I want information about Microsoft. Change this code to get information on other companies e.g. AAPL for Apple.

{"symbol": "MSFT", "companyName": "Microsoft Corp.", "primaryExchange": "QSDAAN", "calculationPrice": "close", "open": 185.39, "openTime": 1616306324959, "openSource": "cloififa", "close": 187.2, "closeTime": 1625984148002, "closeSource": "fcilifao", "high": 193.8, "highTime": 1628543496620, "highSource": "etcen 1ery p aeiidl5umd", "low": 188.13, "lowTime": 1637173833781, "lowSource": "yred eld1pnua5e m citei", "latestPrice": 184.4, "latestSource": "Close", "latestTime": "May 7, 2023", "latestUpdate": 1594186049584, "latestVolume": 28838019, "iexRealtimePrice": 190.05, "iexRealtimeSize": 100, "iexLastUpdated": 1621380167409, "delayedPrice": 186.2, "delayedPriceTime": 1659707262460, "oddLotDelayedPrice": 183.6, "oddLotDelayedPriceTime": 1657798168478, "extendedPrice": 187.8, "extendedChange": 0.5, "extendedChangePercent": 0.00285, "extendedPriceTime": 1609328086679, "previousClose": 183.04, "previousVolume": 32634011, "change": 1.06, "changePercent": 0.00587, "volume": 29283999, "iexMarketPercent": 0.01933576245291531, "iexVolume": 559683, "avgTotalVolume": 50424989, "iexBidPrice": 0, "iexBidSize": 0, "iexAskPrice": 0, "iexAskSize": 0, "iexOpen": null, "iexOpenTime": null, "iexClose": 188.18, "iexCloseTime": 1610857954764, "marketCap": 1439473962659, "peRatio": 30.49, "week52High": 196.9, "week52Low": 120.18, "ytdChange": 0.14410775142262264, "lastTradeTime": 1641935074822, "isUSMarketOpen": false}

A bunch of data about Microsoft in JSON format.

The Python code will sift through this, pull out the Latest Price and send it back to Excel.

Using the Python Function to Retrieve RTD

The function is used like any other in Excel.

You can pass in the ticker symbol for the company as a string, or reference a cell containing that string.

Every time an update is received, this is written into the calling cell.

Charting the Data

To chart the data you need to keep a record of prices as they come in. I’m going to store prices for each stock in a table on its own sheet.

Using a Worksheet_Calculate() event you can take the new price as it arrives and enter it into a table.

The table looks like this

I’m giving each data point a numeric unique ID which can be used to identify it in subsequent processing. If you wanted you could just as easily use a timestamp as the unique identifier, which is provided in the data returned by IEXCloud.

The first price received has ID 1, the second has ID 2 etc. I want to plot the most recent data so I need to get the prices with the largest ID numbers. I’m going to plot 30 points so I need the 30 largest ID’s.

Using SORT I can sort the data from the MSFT table into descending order based on ID, and then use INDEX to give me the first 30 in this list i.e. the 30 most recent data points.

In this formula ROW is providing the numbers 1:30 for INDEX to grab the 30 points.

Everything is wrapped in IFERROR because when there is no data in the table, the formula will return #REF errors. Because I’m going to use a scatter plot, #REF will be plotted as 0, but using IFERROR I can replace the #REF with #N/A and #N/A is not plotted.

This isn’t a major thing to worry about but does make the chart look a little neater.

Repeat this process on other sheets for other stocks.

With the 30 points sorted out I just need to create my chart, on another sheet, and configure it to plot this data in reverse order.

Plotting in reverse order means the data flows in from the right side of the chart, rather than the left.

Controlling Data Flow

The formula that calls the Python RTD function is actually dependent on this visual indicator. If the system is RUNNING the Python function is called.

If the system is STOPPED, the Python function is not called.

Here’s what it looks like in action. Maximise the video clip to get a better view.

Your browser does not support the video tag.

Summary

This is intended as a demonstration of what can be done using Python to get real time data.

Download the workbook and Python file (below) and you could modify the code to get other financial information, or some other form of data completely.

I haven’t used a timestamp along the x-axis of the chart but you may want to do this. As I said earlier, a timestamp is provided in the data received by the Python function so it’s a case of passing that through to Excel for processing.

Top Challenges Of Big Data & How To Overcome Them

Big data challenges are numerous: Big data projects have become a normal part of doing business — but that doesn’t mean that big data is easy.

According to the NewVantage Partners Big Data Executive Survey 2023, 95 percent of the Fortune 1000 business leaders surveyed said that their firms had undertaken a big data project in the last five years. However, less than half (48.4 percent) said that their big data initiatives had achieved measurable results.

An October 2023 report from Gartner found that organizations were getting stuck at the pilot stage of their big data initiatives. “Only 15 percent of businesses reported deploying their big data project to production, effectively unchanged from last year (14 percent),” the firm said.

Clearly, organizations are facing some major challenges when it comes to implementing their big data strategies. And in fact, the IDG Enterprise 2023 Data & Analytics Research found that 90 percent of those surveyed reported running into challenges related to their big data projects.

So what are those challenges? And more importantly, what can organizations do to overcome them?

If you’re in the market for big data solutions for your company, see our list of top big data companies

Before we delve into the most common big data challenges, we should first define “big data.” There is no set number of gigabytes or terabytes or petabytes that separates “big data” from “average-sized data.” Data stores are constantly growing, so what seems like a lot of data right now may seem like a perfectly normal amount in a year or two. In addition, every organization is different, so the amount of data that seems challenging for a small retail store may not seem like a lot to a large financial services company.

Instead, most experts define big data in terms of the three Vs. You have big data if your data stores have the following characteristics:

Volume: Big data is any set of data that is so large that the organization that owns it faces challenges related to storing or processing it. In reality, trends like ecommerce, mobility, social media and the Internet of Things (IoT) are generating so much information, that nearly every organization probably meets this criterion.

Velocity: If your organizations is generating new data at a rapid pace and needs to respond in real time, you have the velocity associated with big data. Most organizations that are involved in ecommerce, social media or IoT satisfy this criterion for big data.

Variety: If your data resides in many different formats, it has the variety associated with big data. For example, big data stores typically include email messages, word processing documents, images, video and presentations, as well as data that resides in structured relational database management systems (RDBMSes).

Characteristics of Big Data

Volume Big data requires a large amount of storage space, and organizations must constantly scaletheir hardware and software in order to accommodate increases.

Velocity New data is being created quickly, and organizations need to respond in real time.

Variety

Characteristics of Big Data

Volume Big data requires a large amount of storage space, and organizations must constantly scaletheir hardware and software in order to accommodate increases.

Velocity New data is being created quickly, and organizations need to respond in real time.

Variety

These three characteristics cause many of the challenges that organizations encounter in their big data initiatives. Some of the most common of those big data challenges include the following:

The most obvious challenge associated with big data is simply storing and analyzing all that information. In its Digital Universe report, IDC estimates that the amount of information stored in the world’s IT systems is doubling about every two years. By 2023, the total amount will be enough to fill a stack of tablets that reaches from the earth to the moon 6.6 times. And enterprises have responsibility or liability for about 85 percent of that information.

Much of that data is unstructured, meaning that it doesn’t reside in a database. Documents, photos, audio, videos and other unstructured data can be difficult to search and analyze.

It’s no surprise, then, that the IDG report found, “Managing unstructured data is growing as a challenge – rising from 31 percent in 2023 to 45 percent in 2023.”

In order to deal with data growth, organizations are turning to a number of different technologies. When it comes to storage, converged and hyperconverged infrastructure and software-defined storage can make it easier for companies to scale their hardware. And technologies like compression, deduplication and tiering can reduce the amount of space and the costs associated with big data storage.

On the management and analysis side, enterprises are using tools like NoSQL databases, Hadoop, Spark, big data analytics software, business intelligence applications, artificial intelligence and machine learning to help them comb through their big data stores to find the insights their companies need.

Of course, organizations don’t just want to store their big data — they want to use that big data to achieve business goals. According to the NewVantage Partners survey, the most common goals associated with big data projects included the following:

Decreasing expenses through operational cost efficiencies

Establishing a data-driven culture

Creating new avenues for innovation and disruption

Accelerating the speed with which new capabilities and services are deployed

Launching new product and service offerings

All of those goals can help organizations become more competitive — but only if they can extract insights from their big data and then act on those insights quickly. PwC’s Global Data and Analytics Survey 2023 found, “Everyone wants decision-making to be faster, especially in banking, insurance, and healthcare.”

To achieve that speed, some organizations are looking to a new generation of ETL and analytics tools that dramatically reduce the time it takes to generate reports. They are investing in software with real-time analytics capabilities that allows them to respond to developments in the marketplace immediately.

But in order to develop, manage and run those applications that generate insights, organizations need professionals with big data skills. That has driven up demand for big data experts — and big data salaries have increased dramatically as a result.

The 2023 Robert Half Technology Salary Guide reported that big data engineers were earning between $135,000 and $196,000 on average, while data scientist salaries ranged from $116,000 to $163, 500. Even business intelligence analysts were very well paid, making $118,000 to $138,750 per year.

In order to deal with talent shortages, organizations have a couple of options. First, many are increasing their budgets and their recruitment and retention efforts. Second, they are offering more training opportunities to their current staff members in an attempt to develop the talent they need from within. Third, many organizations are looking to technology. They are buying analytics solutions with self-service and/or machine learning capabilities. Designed to be used by professionals without a data science degree, these tools may help organizations achieve their big data goals even if they do not have a lot of big data experts on staff.

In response, many enterprises are turning to new technology solutions. In the IDG report, 89 percent of those surveyed said that their companies planned to invest in new big data tools in the next 12 to 18 months. When asked which kind of tools they were planning to purchase, integration technology was second on the list, behind data analytics software.

Closely related to the idea of data integration is the idea of data validation. Often organizations are getting similar pieces of data from different systems, and the data in those different systems doesn’t always agree. For example, the ecommerce system may show daily sales at a certain level while the enterprise resource planning (ERP) system has a slightly different number. Or a hospital’s electronic health record (EHR) system may have one address for a patient, while a partner pharmacy has a different address on record.

The process of getting those records to agree, as well as making sure the records are accurate, usable and secure, is called data governance. And in the AtScale 2023 Big Data Maturity Survey, the fastest-growing area of concern cited by respondents was data governance.

Solving data governance challenges is very complex and is usually requires a combination of policy changes and technology. Organizations often set up a group of people to oversee data governance and write a set of policies and procedures. They may also invest in data management solutions designed to simplify data governance and help ensure the accuracy of big data stores — and the insights derived from them.

However, most organizations seem to believe that their existing data security methods are sufficient for their big data needs as well. In the IDG survey, less than half of those surveyed (39 percent) said that they were using additional security measure for their big data repositories or analyses. Among those who do use additional measures, the most popular include identity and access control (59 percent), data encryption (52 percent) and data segregation (42 percent).

It is not only the technological aspects of big data that can be challenging — people can be an issue too.

In the NewVantage Partners survey, 85.5 percent of those surveyed said that their firms were committed to creating a data-driven culture, but only 37.1 percent said they had been successful with those efforts. When asked about the impediments to that culture shift, respondents pointed to three big obstacles within their organizations:

Insufficient organizational alignment (4.6 percent)

Lack of middle management adoption and understanding (41.0 percent)

Business resistance or lack of understanding (41.0 percent)

In order for organizations to capitalize on the opportunities offered by big data, they are going to have to do some things differently. And that sort of change can be tremendously difficult for large organizations.

The PwC report recommended, “To improve decision-making capabilities at your company, you should continue to invest in strong leaders who understand data’s possibilities and who will challenge the business.”

One way to establish that sort of leadership is to appoint a chief data officer, a step that NewVantage Partners said 55.9 percent of Fortune 1000 companies have taken. But with or without a chief data officer, enterprises need executives, directors and managers who are going to commit to overcoming their big data challenges, if they want to remain competitive in the increasing data-driven economy.

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