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

Related:

The Evolution of the Data Warehouse in the Big Data Era

About 20 years ago, I started my journey into data warehousing and business analytics. Over all these years, it’s been interesting to see the evolution of big data and data warehousing, driven by the rise of artificial intelligence and widespread adoption of Hadoop. When I started in this work, the main business challenge was how to handle the explosion of data with ever-growing data sets and, most importantly, how to gain business intelligence in as close to real time as possible. The effort to solve these business challenges led the way for a ground-breaking architecture called … READ MORE

Related:

How To Build Your Data Science Competency For A Post-Covid Future

The world collectively has been bracing for a change in the job landscape. Driven largely by the emergence of new technologies like data science and artificial intelligence (AI), these changes have already made some jobs redundant. To add to this uncertainty, the catastrophic economic impact of the Covid-19 pandemic has brought in an urgency to upskill oneself to adapt to changing scenarios.

While the prognosis does not look good, this could also create the demand for jobs in the field of business analytics. This indicates that heavily investing in data science and AI skills today could mean the difference between you being employed or not tomorrow.

By adding more skills to your arsenal today, you can build your core competencies in areas that will be relevant once these turbulent times pass over. This includes sharpening your understanding of business numbers and analysing consumer demands – two domains which businesses will heavily invest in very soon.



But motivation alone will not help. You need to first filter through the clutter of online courses that the internet is saturated with. Secondly, you need to create a study plan that ensures that you successfully complete these courses.

We have a solution.


W3Schools


Developed with the objective of providing you a comprehensive understanding of key concepts that are tailored to align with the jobs of the future, we are launching a series of special short-term courses. These courses will not only help you upskill yourself, it will also ensure that you complete these courses in a matter of a few days.

These short-term courses will have similar content as regular ones, but packed in a more efficient way. Whether you are looking for courses in business analytics, applied AI, or data analytics, these should hold you in good stead for jobs of the future.

Analytics Edge (Data Visualization & Analytics)

About The Course: This all-encompassing data analytics certification course is tailor-made for analytics beginners. It would cover key concepts around data mining, and statistical and predictive modelling skills, and is curated for candidates who have no prior knowledge about data analytics tools. What is more, the inclusion of popular data visualization tool Tableau makes it one of the best courses available on the subject today. Additionally, it also puts an emphasis on widely used analytics tools like R, SQL and Excel, making this course truly unique.

Duration: While the original data analytics course this short-term course is developed from includes 180 hours of content and demands an average of 10-15 hours of weekly online classes and self-study. This course will enable you to acquire the same skills, but within a shorter period of time.

Target Group: While anyone with an interest in analytics can pursue this course, it is especially targeted at candidates with a background in engineering, finance, math, and business management. It will also be a useful skill-building course for candidates who want to target job profiles based around R programming, statistical analysis, Excel-VBA or tableau-based BI analyst profiles.

Data Science Using Python


About the course: Adapted to greatly help candidates when searching for data science roles, this certification covers all that they need to know on the subject using Python as the programming language. While other languages like R are also commonly used today, Python has emerged as one of the more popular options within the data science universe.

This ‘Python for Data Science’ course will make you proficient in defly handling and visualizing data, and also covers statistical modelling and operations with NumPy. It also integrates these with practical examples and case studies, making it a unique online training data science course in Python.

Duration of the course: While the original data science course this short-term course is developed from includes 220 hours of content and demands an average of 15-20 hours of weekly online classes and self-study, this course will enable you to acquire the same skills, but within a shorter period of time.

Target Group: While anyone with an interest in analytics can pursue this course, it is especially targeted at candidates with a background of working with data analysis and visualization techniques. It will also help people who want to undergo Python training with advanced analytics skills to help them jumpstart a career in data science.

Machine Learning & Artificial Intelligence

About this course: This course delves into the applications of AI using ML and is tailor-made for candidates looking to start their journey in the field of data science. It will cover tools and libraries like Python, Numpy, Pandas, Scikit-Learn, NLTK, TextBlob, PyTorch, TensorFlow, and Keras, among others.

Thus, after successful completion of this Applied AI course, you will not only be proficient in the theoretical aspects of AI and ML, but will also develop a nuanced understanding of its industry applications.

Duration of the course: While the original ML and AI course this short-term course is developed from includes 280 hours of content and demands an average of 8-10 hours of weekly self-study, this Applied AI course will enable you to acquire the same skills, but within a shorter period of time.

Target Group: While anyone with an interest in analytics can pursue this course, it is especially targeted at candidates with a background in engineering, finance, math, statistics, and business management. It will also help people who want to acquire AI and machine learning skills to head start their career in the field of data science.

Summary

While the Covid-19 pandemic has witnessed a partial – or even complete – lockdown at several places across the globe, people have been reorienting their lives indoors. But with no end in sight, it necessitates that professionals turn these circumstances into opportunities to upskill.

Given an oncoming recession and economic downturn, it behoves them to adapt to these changes to remain employable in such competitive times. In this setting, Covid-19 could emerge as a tipping point for learning, with virtual learning offering the perfect opportunity to self-learn.

Provide your comments below

comments

Related:

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.

Get Free Sample PDF (including COVID19 Impact Analysis, full TOC, Tables and Figures) of Market Report @ https://www.researchmoz.com/enquiry.php?type=S&repid=2636782&source=atm

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

Deloitte

Fractal Analytics

IBM Corporation

Rackspace

Red Hat

SmartDrive Systems

Market segment by Type, the product can be split into

Hardware

Software

Services

Managed

Professional

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

Europe

China

Japan

Southeast Asia

India

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.

Do You Have Any Query Or Specific Requirement? Ask to Our Industry [email protected]https://www.researchmoz.com/enquiry.php?type=E&repid=2636782&source=atm

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.

You can Buy This Report from Here @ https://www.researchmoz.com/checkout?rep_id=2636782&licType=S&source=atm

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….

Related:

Delivering a Modern Streaming Architecture for 5G

What is the Use for Data Analytics? Analytics offers many benefits to organizations as they embark upon digital transformation, including: Increasing efficiency and driving cost out of operations Maximizing customer satisfaction Developing new products and services Use of streaming data to respond to issues and opportunities in near real-time The number of use cases made possible by data analytics seems limitless and, on top of that, we are only now beginning to glimpse the potential of machine learning and other forms of artificial intelligence to open new frontiers of what organizations can achieve with data. But … READ MORE

Related:

  • No Related Posts

DX Marketing Combines Power of Autonomous Data Warehouse and Oracle Analytics

For most businesses, getting the most out of data means assembling the right tools needed for the development of deep insights. But what happens when your entire business is about doing that for others? Suddenly, your data isn’t just your own internal dataset, but every client’s dataset too. Data management suddenly becomes much more complicated.

The team at DX Marketing (DXM), an award-winning insights company with offices in Greenville, South Carolina, and Savannah, Georgia, found themselves in that exact predicament. As a company focused on providing data-driven digital marketing, every individual client account was essentially a new source of big data.

Data is inherent in everything DXM does. Its products involve collecting data for clients, analyzing that data, and then leveraging it into predictive models, all to deliver insights fulfilling specific end goals. This could be increasing conversion, predicting audience behavior in specific channels, maximizing ROI, entering a new geographic market, or all of the above. With so many data sources, DXM needed a platform to unify it all. Without that, hours of work were wasted performing logistical tasks such as data consolidation and preparation.

To make matters more complicated, several other factors entered the equation. For the most accurate predictive insights for customers, DXM licensed US consumer data from Epsilon. This refreshed the demographic dataset every six weeks. When combined together, it created an intense process for correlation working across datasets. Other logistical factors included being Health Insurance Portability and Accountability Act (HIPAA) compliant regarding security protocols, and providing cloud-based access for a broad team of analysts and data scientists—preferably with an easy-to-learn interface and report generation that could enable greater flexibility in resource usage. In addition, the DXM team wanted to explore the idea of machine learning and artificial intelligence to expedite data preparation and analysis.

In short, it was a lot. And the amount of data coming in wasn’t getting any easier to manage; in fact, as the calendar continued to turn, the data volume followed the worldwide trend of increasing as time moved on. What product could fulfill all of DXM’s needs?

As it turned out, the answer was not a single product but a pair of products working seamlessly together. That’s why DXM went with Oracle’s winning combination of Oracle Autonomous Data Warehouse and Oracle Analytics.

Never miss an update about big data!Subscribe to the Big Data Blog to receive the latest posts straight to your inbox!

A Unified Oracle Platform to Handle Big Data

Let’s examine all these needs one by one:

  • Consolidate Many Data Sources: Oracle’s Autonomous Data Warehouse acts as a smart repository for DXM’s many data sources, including the Epsilon demographic updates that arrived every six weeks.
  • Maintain HIPAA Security Protocols: 1996’s HIPAA law establishes privacy and security rules for electronic health records. Oracle’s platform offers protocol compliance in accordance with AICPA SSAE 18, AT-C sections 205 and 315, among many other global security compliances in defense, finance, and other industries. In addition, because data stays within a single environment rather than being transferred around for processing, risk is inherently minimized.
  • Gain Cloud Access: DXM stressed the need for team members to have access. Fortunately, Oracle Autonomous Data Warehouse and Oracle Analytics Cloud natively provide this, ensuring the access and flexibility required by DXM to keep projects on schedule.
  • Employ Easy-to-Use Interface: Oracle’s platform stresses usability. In particular, Oracle Analytics Cloud makes it easy for business users to create in-depth reports and develop insights without the depth of knowledge of an IT staff member or a data scientist.
  • Harness Machine Learning and Artificial Intelligence: Both Oracle Autonomous Data Warehouse and Oracle Analytics Cloud use embedded machine learning and artificial intelligence in different ways to make the lives of users easier. As a self-running platform, Oracle Autonomous Data Warehouse configures and runs smoother, in addition to expediting the data ingestion and preparation process. For Oracle Analytics Cloud, machine learning and artificial intelligence simplify the data analysis process, speeding up basic tasks while generating new insights.

By combining two powerful Oracle platforms into a single data machine, DXM quickly saw improvements on all levels. Starting with everyday tasks and going all the way to client data, results rolled in for DXM’s two pilot programs. In fact, within six months, customer acquisition cost decreased by 52%, deliverables sped up by 70%, and revenue grew by 25%. How satisfied was DXM with its investment? “It’s been an invaluable tool for us,” says Ray Owens, CEO of DX Marketing. “We’re already, just literally six months in, utilizing some of the key features that we probably wouldn’t have picked up on with traditional database routines.”

To get the full scoop on how DXM made the most out of its Oracle technology, download the complete customer success story or watch the video. And for more about how you can benefit from Oracle Big Data, visit Oracle’s Big Data page—and don’t forget tosubscribe to the Oracle Big Data blog to get the latest posts sent to your inbox.

Related:

DX Marketing Demonstrates Combined Power of Autonomous Data Warehouse and Oracle Analytics

For most businesses, getting the most out of data means assembling the right tools needed for the development of deep insights. But what happens when your entire business is about doing that for others? Suddenly, your data isn’t just your own internal dataset, but every client’s dataset too. Data management suddenly becomes much more complicated.

The team at DX Marketing (DXM), an award-winning insights company with offices in Greenville, South Carolina, and Savannah, Georgia, found themselves in that exact predicament. As a company focused on providing data-driven digital marketing, every individual client account was essentially a new source of big data.

Data is inherent in everything DXM does. Its products involve collecting data for clients, analyzing that data, and then leveraging it into predictive models, all to deliver insights fulfilling specific end goals. This could be increasing conversion, predicting audience behavior in specific channels, maximizing ROI, entering a new geographic market, or all of the above. With so many data sources, DXM needed a platform to unify it all. Without that, hours of work were wasted performing logistical tasks such as data consolidation and preparation.

To make matters more complicated, several other factors entered the equation. For the most accurate predictive insights for customers, DXM licensed US consumer data from Epsilon. This refreshed the demographic dataset every six weeks. When combined together, it created an intense process for correlation working across datasets. Other logistical factors included being Health Insurance Portability and Accountability Act (HIPAA) compliant regarding security protocols, and providing cloud-based access for a broad team of analysts and data scientists—preferably with an easy-to-learn interface and report generation that could enable greater flexibility in resource usage. In addition, the DXM team wanted to explore the idea of machine learning and artificial intelligence to expedite data preparation and analysis.

In short, it was a lot. And the amount of data coming in wasn’t getting any easier to manage; in fact, as the calendar continued to turn, the data volume followed the worldwide trend of increasing as time moved on. What product could fulfill all of DXM’s needs?

As it turned out, the answer was not a single product but a pair of products working seamlessly together. That’s why DXM went with Oracle’s winning combination of Oracle Autonomous Data Warehouse and Oracle Analytics.

Never miss an update about big data!Subscribe to the Big Data Blog to receive the latest posts straight to your inbox!

A Unified Oracle Platform to Handle Big Data

Let’s examine all these needs one by one:

  • Consolidate Many Data Sources: Oracle’s Autonomous Data Warehouse acts as a smart repository for DXM’s many data sources, including the Epsilon demographic updates that arrived every six weeks.
  • Maintain HIPAA Security Protocols: 1996’s HIPAA law establishes privacy and security rules for electronic health records. Oracle’s platform offers protocol compliance in accordance with AICPA SSAE 18, AT-C sections 205 and 315, among many other global security compliances in defense, finance, and other industries. In addition, because data stays within a single environment rather than being transferred around for processing, risk is inherently minimized.
  • Gain Cloud Access: DXM stressed the need for team members to have access. Fortunately, Oracle Autonomous Data Warehouse and Oracle Analytics Cloud natively provide this, ensuring the access and flexibility required by DXM to keep projects on schedule.
  • Employ Easy-to-Use Interface: Oracle’s platform stresses usability. In particular, Oracle Analytics Cloud makes it easy for business users to create in-depth reports and develop insights without the depth of knowledge of an IT staff member or a data scientist.
  • Harness Machine Learning and Artificial Intelligence: Both Oracle Autonomous Data Warehouse and Oracle Analytics Cloud use embedded machine learning and artificial intelligence in different ways to make the lives of users easier. As a self-running platform, Oracle Autonomous Data Warehouse configures and runs smoother, in addition to expediting the data ingestion and preparation process. For Oracle Analytics Cloud, machine learning and artificial intelligence simplify the data analysis process, speeding up basic tasks while generating new insights.

By combining two powerful Oracle platforms into a single data machine, DXM quickly saw improvements on all levels. Starting with everyday tasks and going all the way to client data, results rolled in for DXM’s two pilot programs. In fact, within six months, customer acquisition cost decreased by 52%, deliverables sped up by 70%, and revenue grew by 25%. How satisfied was DXM with its investment? “It’s been an invaluable tool for us,” says Ray Owens, CEO of DX Marketing. “We’re already, just literally six months in, utilizing some of the key features that we probably wouldn’t have picked up on with traditional database routines.”

To get the full scoop on how DXM made the most out of its Oracle technology, download the complete customer success story or watch the video. And for more about how you can benefit from Oracle Big Data, visit Oracle’s Big Data page—and don’t forget tosubscribe to the Oracle Big Data blog to get the latest posts sent to your inbox.

Related:

How Your IT Team Can Take Advantage of Predictive Analytics

Is your company aiming to be a leader in the field of predictive analytics? New research suggests it should. ESG research finds that AI-enabled organizations considered to be the “Most Advanced” IT shops are 2.6x more likely to lead the competition in business intelligence and analytics. Organizations that are categorized as “Most Advanced” use: Modern servers with extensive automation capabilities (i.e., automated updating, monitoring, configuring, provisioning, and issue remediation). Accelerators such as GPUs or FPGAs to support AI workloads. Converged/hyperconverged infrastructure to support AI workloads. ESG refers to the “Most Advanced” AI-enabled organizations as Stage 3, … READ MORE

Related:

What Is Augmented Analytics?

In recent years, the term augmented analytics has joined the conversation around how data scientists extract insights from big data. Rather than representing something completely new, augmented analytics refers to the convergence of business intelligence and emerging computer science fields such as artificial intelligence (AI) and machine learning (ML). Today, augmented analytics is gaining steam, transitioning from the industry’s next big thing to a must-have tool. As the next evolution of the foundation built by BI, analytics, and big data, augmented analytics combines many emerging technologies for a platform that delivers insights at a previously unheard-of speed and level of accuracy.

In the past decade, business intelligence has focused on gathering data from various sources, then processing and outputting it in dashboards and visualizations. This has brought insight to the business world, enabling data-driven decisions. However, this required significant manual preparation from various departments. With advancements in AI and ML, much of this can become automated—and the result is the next big leap forward in business technology.

How Business Intelligence Works

Business intelligence systems emerged in the 2000s, from standalone database tools existing solely on individual desktops to modern systems that connect with multiple data sources and focus on data manipulation. The key here is the ingestion of data; without data, the whole concept of business intelligence is never able to take off. Business intelligence systems evolved as processing power and network connectivity grew, expanding the scope of capabilities to go from an analyst’s local desktop machine to a larger, more interconnected platform.

Currently, business intelligence systems excel at ingesting big data from multiple sources, allowing analysts to take a deeper look at past activity. This results in numerous business insights that explain how and why things have happened, then provides the tools to create visualizations and charts to tell the story surrounding the data. In turn, this enables data-driven decision making.

How Artificial Intelligence and Machine Learning Work

AI and ML are often used interchangeably or even confused with each other, and that’s not totally accurate. AI refers to the larger umbrella of enabling systems to make decisions like humans—that is, making smart decisions based on context and available information rather than simple if/then programming.

ML is a key component in this; ML examines data and looks for patterns which can then provide the context for AI-based decisions. For example, ML might review the processing of data transactions for a credit card company and quickly look for anomalous patterns in user data. AI then flags this and determines if it is a potentially fraudulent transaction worth investigating by customer service.

How Augmented Analytics Brings Business Intelligence, AI, and ML Together

Business intelligence is all about creating data insights. AI and ML are all about learning from large datasets for machine-driven decisions. AA, then, uses the foundation of business intelligence and then adds ML/AI on top of it. A good way to frame this is by considering the current process involved with using business intelligence. As it stands right now, a business intelligence platform ingests data from multiple sources before IT departments prepare the data and data scientists process it for analysis.

An augmented analytics system takes those latter steps (data preparation and initial analysis) and automates them using ML and AI. A simplified explanation is that machine learning handles the data preparation (processing the ingested data, preparing the relevant data, looking for patterns), and AI handles the initial analysis (using models and algorithms built by data scientists). It’s a little more nuanced than that but that’s a good surface-level way to understand how augmented analytics works. Consider the manual labor used in a traditional system:

  • Data preparation by IT staff involves exporting datasets, then combining, structuring, and organizing them for further analysis. If your dataset includes thousands of records, or millions of records, this could require significant hours of preparation per request.
  • Initial analysis by data scientists can be an intensely manual process involving examination of countless records to look for patterns and dig for insights. Many datasets require a first level of analysis, which takes care of broad-stroke conclusions before diving in deeper; this can be recognized automatically using ML and AI, reserving the data scientist’s bandwidth for more intense work.

With ML and AI working in the background 24/7, this process is constantly active. That means that the ML algorithm is constantly refining patterns while looking for new ones. At the same time, the overall AI model is improving through sheer volume of data; the more data consumed, the more accurate the model. This automation streamlines processes, removing manual steps to drill down to relevant data faster. In addition, natural language processing (NLP)—the same technology that powers AI virtual assistants such as Siri and Alexa—means that tasks shift from data preparation to discovery.

Benefits of Augmented Analytics

Augmented analytics provides many of the same benefits as business intelligence, but also delivers a level of efficiency and accuracy only available via computer processing. Thus, the true scope of augmented analytics goes past business intelligence’s native capabilities, including:

Increased accuracy: When data scientists manipulate multiple datasets to prepare for analysis, it is statistically likely that a mistake will occur during that process. The larger the volume of data, the greater the possibility of a mistake, and the longer it takes to run checks for mistakes. When utilizing machine learning for these types of processes, such mistakes are eliminated.

Increased speed: There are two process gaps that can occur when initiating a project with standard business intelligence platforms: the time required to manually prepare data as well as the wait time for associated parties to respond to requests. With AA, request processing begins immediately once the request is submitted, launching the internal AI to cull the appropriate data and begin drilling down to the specific output for the project—all at the speed of a machine, not a human.

Reduced bias: The term “bias” often has a negative connotation, but bias doesn’t have to imply a personal shortcoming. Instead, it can simply refer to habits and routine. As human beings, we revert to patterns in process. Thus, there may be a blind spot for a data scientist because of a personal process that unintentionally overlooks one potential aspect. That type of bias, while not malicious, can lead to overlooked insights. In this case, machines will work more thoroughly and more efficiently without this inherent bias.

Increased resources: There’s a common—and unfounded—assumption that any movement towards automation and AI will reduce the responsibilities of IT staff or data scientists. In fact, the exact opposite is true; augmented analytics can actually increase the value of both because it frees them from manual labor so they can focus on more important tasks. For IT staff, that means supporting ever-growing demands on hardware and connectivity, and for data scientists, it means much more time creating deeper insights. In short, everyone wins with augmented analytics.

Learn More About Augmented Analytics

We’re just on the cusp of the augmented analytics revolution for business intelligence. To get ahead of the curve, make sure you download the new ebook What Is Augmented Analytics? And for more about how you can benefit from Oracle Big Data, visit Oracle’s Big Data pageand don’t forget to subscribe to the Oracle Big Data blog to get the latest posts sent to your inbox.

Related: