Unlock the Potential of Unstructured Data with DataIQ

Data determines the winners and losers in the digital age If we examine the top trends many organizations are focused on today—harnessing big data and analytics, embracing the Internet of Things, investing in artificial intelligence—they all have a common foundation. Data. It’s data that powers digital transformation and the digital economy. The organizations best positioned to win in this data era are those who have superior strategies for collecting and harnessing the untapped potential locked away in this ever-growing ocean of data. Unstructured data driving data sprawl Unstructured data is driving much of this growth. Gartner … READ MORE


COVID-19 Impact: Temporary Surge in Sales of Big Data Analytics in Automotive Product Observed …

Big Data Analytics in Automotive Market 2018: Global Industry Insights by Global Players, Regional Segmentation, Growth, Applications, Major Drivers, Value and Foreseen till 2024

The report provides both quantitative and qualitative information of global Big Data Analytics in Automotive market for period of 2018 to 2025. As per the analysis provided in the report, the global market of Big Data Analytics in Automotive is estimated to growth at a CAGR of _% during the forecast period 2018 to 2025 and is expected to rise to USD _ million/billion by the end of year 2025. In the year 2016, the global Big Data Analytics in Automotive market was valued at USD _ million/billion.

This research report based on ‘ Big Data Analytics in Automotive market’ and available with Market Study Report includes latest and upcoming industry trends in addition to the global spectrum of the ‘ Big Data Analytics in Automotive market’ that includes numerous regions. Likewise, the report also expands on intricate details pertaining to contributions by key players, demand and supply analysis as well as market share growth of the Big Data Analytics in Automotive industry.

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Big Data Analytics in Automotive Market Overview:

The Research projects that the Big Data Analytics in Automotive market size will grow from in 2018 to by 2024, at an estimated CAGR of XX%. The base year considered for the study is 2018, and the market size is projected from 2018 to 2024.

The report on the Big Data Analytics in Automotive market provides a bird’s eye view of the current proceeding within the Big Data Analytics in Automotive market. Further, the report also takes into account the impact of the novel COVID-19 pandemic on the Big Data Analytics in Automotive market and offers a clear assessment of the projected market fluctuations during the forecast period. The different factors that are likely to impact the overall dynamics of the Big Data Analytics in Automotive market over the forecast period (2019-2029) including the current trends, growth opportunities, restraining factors, and more are discussed in detail in the market study.

Leading manufacturers of Big Data Analytics in Automotive Market:

The key players covered in this study

Advanced Micro Devices

Big Cloud Analytics

BMC Software

Cisco Systems


Fractal Analytics

IBM Corporation


Red Hat

SmartDrive Systems

Market segment by Type, the product can be split into






Market segment by Application, split into

Product Development

Manufacturing & Supply Chain

After-Sales, Warranty & Dealer Management

Connected Vehicles & Intelligent Transportation

Marketing, Sales & Other Applications

Market segment by Regions/Countries, this report covers

North America




Southeast Asia


Central & South America

The study objectives of this report are:

To analyze global Big Data Analytics in Automotive status, future forecast, growth opportunity, key market and key players.

To present the Big Data Analytics in Automotive development in North America, Europe, China, Japan, Southeast Asia, India and Central & South America.

To strategically profile the key players and comprehensively analyze their development plan and strategies.

To define, describe and forecast the market by type, market and key regions.

In this study, the years considered to estimate the market size of Big Data Analytics in Automotive are as follows:

History Year: 2015-2019

Base Year: 2019

Estimated Year: 2020

Forecast Year 2020 to 2026

For the data information by region, company, type and application, 2019 is considered as the base year. Whenever data information was unavailable for the base year, the prior year has been considered.

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Some important highlights from the report include:

  • The report offers a precise analysis of the product range of the Big Data Analytics in Automotive market, meticulously segmented into applications
  • Key details concerning production volume and price trends have been provided.
  • The report also covers the market share accumulated by each product in the Big Data Analytics in Automotive market, along with production growth.
  • The report provides a brief summary of the Big Data Analytics in Automotive application spectrum that is mainly segmented into Industrial Applications
  • Extensive details pertaining to the market share garnered by each application, as well as the details of the estimated growth rate and product consumption to be accounted for by each application have been provided.
  • The report also covers the industry concentration rate with reference to raw materials.
  • The relevant price and sales in the Big Data Analytics in Automotive market together with the foreseeable growth trends for the Big Data Analytics in Automotive market is included in the report.
  • The study offers a thorough evaluation of the marketing strategy portfolio, comprising several marketing channels which manufacturers deploy to endorse their products.
  • The report also suggests considerable data with reference to the marketing channel development trends and market position. Concerning market position, the report reflects on aspects such as branding, target clientele and pricing strategies.
  • The numerous distributors who belong to the major suppliers, supply chain and the ever-changing price patterns of raw material have been highlighted in the report.
  • An idea of the manufacturing cost along with a detailed mention of the labor costs is included in the report.

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The Questions Answered by Big Data Analytics in Automotive Market Report:

  • What are the Key Manufacturers, raw material suppliers, equipment suppliers, end users, traders And distributors in Big Data Analytics in Automotive Market ?
  • What are Growth factors influencing Big Data Analytics in Automotive Market Growth?
  • What are production processes, major issues, and solutions to mitigate the development risk?
  • What is the Contribution from Regional Manufacturers?
  • What are the Key Market segment, market potential, influential trends, and the challenges that the market is facing?

And Many More….


4 Reasons Why Businesses Need Data Lakes

It’s common knowledge in the modern business world that big data —that is, the large volumes of data collected for analysis by organizations—is a significant element of any strategy—operations, sales, marketing, finance, HR, and every other department rely on big data solutions to stay ahead of the competition. But how that big data is handled remains another story.

Enter the world of data lakes. Data lakes are repositories that can take in data from multiple sources. Rather than process data for immediate analysis, all received data is stored in its native format. This model allows data lakes to hold massive amounts of data while using minimal resources. Data is only processed upon being called for usage (compared to a data warehouse, which processes all incoming data). This ultimately allows data lakes to be an efficient way for storage, resource management, and data preparation.

But do you actually need a data lake, especially if your big data solution already has a data warehouse? The answer is a resounding yes. In a world where the volume of data transmitted across countless devices continues to increase, a resource-efficient means of accessing data is critical to a successful organization. In fact, here are four specific reasons why the need for a data lake is only going to get more urgent as time goes on.

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90% of data has been generated since 2016

90% of all data ever is a lot—or is it? Consider what has become available to people as Wi-Fi, smartphones, and high-speed data networks have entered everyday life over the past twenty years. In the early 2000s, streaming was limited to audio, while broadband internet was used mostly for web surfing, emailing, and downloads. In that paradigm, device data was at a minimum and the actual data consumed was mostly about interpersonal communication, especially because videos and TV hadn’t hit a level of compression that supported high-quality streaming. Towards the end of the decade, smartphones became common and Netflix had shifted its business priority to streaming.

That means between 2010 and 2020, the internet has seen the growth of smartphones (and their apps), social media, streaming services for both audio and video, streaming video game platforms, software delivered through downloads rather than physical media, and so on, all creating exponential consumption of data. As for the part that is the most relevant to business? Consider how many businesses have associated apps constantly transmitting data to and from devices, whether to control appliances, provide instructions and specifications, or quietly transmit user metrics in the background.

With 5G data networks widely starting to deploy in 2019, bandwidths and speeds are only going to get better. This means as massive—and significant—as big data has already been in the past few years, it’s only going to get bigger as technology allows the world to become even more connected. Is your data repository ready?

95% of businesses handle unstructured data

In a digital world, businesses collect data from all types of sources, and most of that is unstructured. Consider the data collected by a company that sells services and makes appointments via an app. While some of that data comes structured—that is, in predefined formats and fields such as phone numbers, dates, transaction prices, time stamps, etc.—a company like that still has to archive and store a lot of unstructured data. Unstructured data is any type of data that doesn’t contain an inherent structure or predefined model, which makes it difficult to search, sort, and analyze without further preparation.

For the example above, unstructured data comes in a wide range of formats. For a user making an appointment, any text fields filled out to make that appointment count as unstructured data. Within the company itself, emails and documents are another form of unstructured data. The posts from a company’s social media channel are also unstructured data. Any photos or videos used by employees as notes while performing services are unstructured data. Similarly, any instructional videos or podcasts created by the company as marketing assets are also unstructured.

Unstructured data is everywhere, and as more devices connect to deliver a greater range of information, it becomes clear that organizations need a way to get their proverbial arms around all of it.

4.4 GB of data are used by Americans every minute

More than 325 million people live in the US. Nearly 70% of them have smartphones. And even if you don’t count the people currently streaming media, consider what is happening on an average smartphone in a minute. It’s receiving an update on the weather. It’s checking for any new emails in the user’s inbox. It’s pushing data to social media, delivering voicemail over Wi-Fi, delivering strategic marketing notifications from apps, such as when a real estate app pushes a new housing listing. It’s sending text and images via chat apps, and downloading app/OS updates in the background.

Data is everywhere now, which means the minute that just passed while you read the above paragraph, gigabytes of data have been transmitted across the country—4.4 million GB of data every minute, according to Domo’s Data Never Sleeps report. And that’s just the United States; when combined with the rest of the world, the total volume of data grows exponentially. For businesses, collecting this kind of data is vital to all aspects of operations, from marketing to sales to communication. Thus, every organization must put a premium on safe, available, and accessible storage.

50% of businesses say that big data has changed their sales and marketing

Most people think of big data in terms of the technical aspects. Clearly, a company that works through a phone app or provides a form of streaming uses big data and is delivering a service that simply wasn’t feasible twenty years ago. However, big data is much more than delivery of streaming content. It can create significant improvements in sales and marketing—so much so that according to a McKinsey report, 50% of businesses say that big data is driving them to change their approach in these departments.

What’s the reason for this? With big data, organizations have a much more efficient path to understanding customers than in-person focus groups. Data allows for gathering a mass sample of actions from existing and potential customers. Everything from their website browsing prior to conversion to how long they engaged with certain features of a product or service are all available at high volume, which creates a large enough sample size for a reliable customer model. To be in the cutting-edge 50%, an organization needs to have the data infrastructure to receive, store, and retrieve massive amounts of structured and unstructured data for processing.

Basically, you need a data lake

The above statistics all point to one thing—your organization needs a data lake. And if you don’t get ahead of the curve now in terms of managing data, it’s clear that the world will pass you by in all areas: operations, sales, marketing, communications, and other departments. Data is simply a way of life now, enabling precise insight-driven decisions and unparalleled discovery into root causes. When combined with machine learning and artificial intelligence, this data also allows for predictive modeling for future actions.

Learn more about why data lakes are the future of big data and discover Oracle’s big data solutions—and don’t forget to subscribe to the Oracle Big Data blog to get the latest posts sent to your inbox.

(Note: Corrected typo from Domo’s Data Never Sleeps citation.)


How Technology Personifies Your Company

Last week I was interviewed by The Economist on the role of IT in the connection between companies and customer expectations. While I believe it can give businesses the best flexibility to respond to ever-changing customer expectations, I also think a more holistic view on big data is needed to shape their experience and make it more personal. Taking advantage of the data generated in our companies is a priority. High-quality data management enhances our knowledge and adds value to decision-making. It even allows us to anticipate future challenges. However, we’re only just peeling the first … READ MORE


What’s the Connection Between Big Data and AI?

When people talk about big data, are they simply referring to numbers and metrics?


And no.

Technically, big data is simply bits and bytes—literally, a massive amount (petabytes or more) of data. But to dismiss big data as mere ones and zeroes misses the point. Big data may physically be a collection of numbers, but when placed against proper context, those numbers take on a life of their own.

This is particularly true in the realm of artificial intelligence (AI). AI and big data are intrinsically connected; without big data, AI simply couldn’t learn. From the perspective of the team in charge of Oracle’s Cloud Business Group (CBG) Product Marketing, they liken big data to the human experience. On Oracle’s Practical Path To AI podcast episode Connecting the Dots Between Big Data and AI, team members compare the AI learning process to the human experience.

The short version: the human brain ingests countless experiences every moment. Everything that is taken in by senses is technically a piece of information or data—a note of music, a word in a book, a drop of rain, and so on. Infant brains learn from the very beginning they start taking in sensory information, and the more they encounter, the more they are able to assimilate and process, then respond in new and informed ways.

AI works similarly. The more data an AI model encounters, the more intelligent it can become. Over time, as more and more data processes through the AI model, it becomes increasingly significant. In that sense, AI models are trained by big data, just as human brains are trained by the data accumulated through multiple experiences.

And while this may all seem scary at first, there’s a definite public shift toward trusting AI-driven software. This is discussed further by Oracle’s CBG team on the podcast episode, and it all goes back to the idea of human experiences. In the digital realm, people now have the ability to document, review, rank, and track these experiences. This knowledge becomes data points in big data, thus fed into AI models which start validating or invalidating the experiences. With enough of a sample size, a determination can be made based on “a power of collective knowledge” that grows and creates this network.

However, that doesn’t mean that AI is the authority on everything, even with all the data in the world.

To hear more about this topic—and why human judgment is still a very real and very necessary part of, well, everything—listen to the entire podcast episode Connecting the Dots Between Big Data and AI and be sure to visit Oracle’s Big Data site to stay on top of the latest developments in the field of big data.

Guest author Michael Chen is a senior manager, product marketing with Oracle Analytics.


Why Oracle CEO Mark Hurd Thinks You Should Hire Big Data Experts

There are nearly 15,000 petabytes of data on the internet right now (there may be more than 15,000 by the time you read this article), and that data deluge grows by 70 terabytes every second.

To put that in perspective, streaming the latest episode of Game of Thrones probably required about three gigabytes of data. Trying to consume all the data currently stored online would be the equivalent of five billion people downloading the latest Game of Thrones episode at the same time.

But that’s not how online data really works.

One episode of a TV show is a tiny drop in the vast ocean of online data. Much of that data—those 70 new terabytes created every second—is generated by and for businesses as they go about their day-to-day work.

Somehow, all of this data needs to be understood.

During an age of less internet connectivity and fewer people online, it was possible for talented data analysts to make sense of the information flooding into their systems on their own. But today, as Oracle CEO Mark Hurd has said, “Whether you’re looking at information on employees, customers, or whatever it may be, the amount of data that companies now have is beyond the ability for even the most sophisticated data scientists to take advantage of.”

That’s where big data comes into play. Big data talent is more important than ever to modern enterprises. The best big data experts now have access to advanced artificial intelligence and machine learning technologies that can help make sense of the data deluge on a real-time basis. These capabilities allow big data experts to move beyond super scaled number crunching, letting them deploy their intelligence, creativity, and perception to find actionable benefits among the billions of bits and bytes flowing through their employers’ systems.

During his Oracle OpenWorld 2018 keynote, Hurd highlighted the impossibility of manual analysis at scale and went on to point out that this impossibility is, “not true of machine learning. Further, Hurd also noted, “The opportunity to turn all that data into knowledge…[into] information that helps you sell more, [or into] information that helps you save more—AI will affect both.”

This ability to transform torrents of data into actionable business strategies can apply to many of a business’ core operational functions, including human capital management. Hurd made this connection at Oracle OpenWorld 2018 as well, noting, “35 percent of a recruiter’s day is spent sourcing and processing candidates…the ability to know whether a GPA matters, whether a major matters, whether your extracurricular activities in school matter…it’s very difficult to harness all of that data information. Not true when AI is applied.”

Recruiting departments can put big data experts to work for their cause. An analyst working for an enterprise with thousands of employees could save their employer millions of dollars by employing these technologies. By using Oracle HCM Cloud to gather and analyze data throughout the recruiting process, businesses can reduce turnover and improve the quality of hires.

Big data is more than a buzzword. Top talent can achieve measurable results for a wide range of businesses, including manufacturing, healthcare, and retail. You can see many benefits of big data on Oracle’s Big Data Use Cases page. You can also see real results big data experts achieved while working with Oracle Big Data Cloud on the Success Stories database. Big data experts have utilized Oracle Big Data Cloud to (among other things):

Does your organization have big data experts? If it doesn’t, finding them should be a matter of when, and not if. Having talent who can turn your organization’s flood of information into real operational results can make a huge difference in business performance and on the bottom line.