According to an IDC report, the world data is expected to rise from 33 zettabytes in 2018 to 175 zettabytes by 2025. With such a significant upsurge in data volume, experts have realized that sifting through it, restructuring it, and then analyzing it to enhance business outcomes is beyond the scope of a human mind. Therefore, enterprises are now moving towards advanced artificial intelligence algorithms to gain valuable business insights.
In this article, we will talk about how AI helps in gaining better insights into organizational data for better decision making and analytics followed by a peek into the scope of AI in the various Big Data industries.
AI-based analytics fueling business insights
A few decades ago, Big Data caused havoc in the IT industry. Now, even though the “dust has settled”, data size is still expanding. All this insurmountable amount of data is useless if we cannot process it to extract meaningful information.
In such a data-centric era, organizations mastering Artificial Intelligence techniques are sure to become the wave of times. AI results are directly dependent upon the size of the data. This means that Artificial Intelligence algorithms can deliver more accurate results if they are fed large datasets to train on. This, in turn, helps enterprises deliver on customer demands and expectations.
There are numerous benefits of adopting techniques which use both Big Data and AI like:
Advanced algorithms for data analytics
For decades, ‘SQL-like’ queries were used by enterprises to improve their business intelligence outcomes but with the advent of AI algorithms, more and more organizations are looking to explore these new, AI-based approaches for effective BI and analytics.
A quicker decision-making process
Allowing artificial intelligence tools to aid in designing and strategizing business plans instead of the traditional methodologies can enhance BI and analytics substantially. With the rising customer demands, it is only practical to resort to faster decision making processes so that enterprises can provide a pleasing consumer experience.
Enhancing data quality
Data quality problems have often been associated with Big Data processing which hinders its ability to provide reliable insights into businesses. AI-based data cleansing techniques can help detect outlier or missing data values, eliminating data duplication especially in situations where similar information is being provided through a diverse set of technologies. They can also normalize information into a single format which can make subsequent data analysis easier.
Implementing enhanced security with AI
With technology advancing at an ever-increasing pace, there is a considerable rise in the rate of fraudulent behaviors in the tech world. AI provides specialized tools that can help enterprises protect their businesses from such sharp practices.
Chatbots expand online business presence
Chatbots analytics are being increasingly used by businesses to utilize data from a diverse set of sources and then analyzing it using Big Data tools which generates answers to some generalized queries. This is then fed to the Chatbots to facilitate business sales and marketing online.
Big data analytics reduces business expenses
With so much data to train AI algorithms, businesses can track consumer reactions to products and services. This way, it is easier to maintain an inventory of products by predicting the required quantity which avoids unnecessary production and minimizes costs.
Artificial intelligence in Big Data Industries
Artificial Intelligence has established itself in almost all the technological innovations of this era. Let’s look at some of these industries briefly:
AI in healthcare industry
Numerous artificial intelligence algorithms are being implemented in various hospitals across the globe. Google Brain, for example, is trying to predict an estimated time of death for critical patients who are suffering from certain life-threatening diseases. This allows doctors and specialists to alter the existing treatment plans and provide quality life to such patients.
Moreover, in radiological diagnostics, cognitive computing is being used to perform repetitive procedures to form analysis. Similarly, surgical robots can be used to aid in high-precision surgeries in the future. With the way AI is currently evolving, the number of such robots is only expected to rise, resulting in a substantial expansion in the healthcare sector.
AI in the food industry
The rapidly flourishing food industry is one of the popular markets which can use Big Data and AI for improved business outcomes.
Some of the farm owners, for example, from Connecterra, are using AI to predict probable health issues with their cattle so that they can take timely measures to prevent them. Such predictive analysis can help farm owners produce good-quality beef and milk.
Big Data and Artificial Intelligence are also being used by an American startup Brigtseed to recognize some healthy plant compounds which will then be used to create bioactive compounds. This will help in enhancing the nutritional value of food, revolutionizing the health industry completely.
AI in the Construction Industry
The construction industry is one of the oldest professions which is trying to evolve to keep up with the latest technological trends, striving for a safe, healthy environment for its workers. It helps managers in scheduling tasks effectively, avoiding delays, conflicts, and resolving several other related issues. Moreover, construction managers are trying to use AI-based technologies to perform some mundane but critical tasks for their daily operations.
AI is here to revolutionize the IT industry as we know it. According to a report by Gartner, in 2022, one in five workers will be replaced by Artificial Intelligence. Many sectors are trying to adopt AI-based tools to enhance enterprise decision-making and analytics. Moreover, data is not going anywhere in the foreseeable future. Big Data is just getting bigger. It’s high time that businesses strive to put it to good use, extracting meaningful information that can help in booming their business outcomes. Enterprises trying to adopt AI are sure to replace businesses still adopting a more conventional approach.
The introduction of big data processing analytics proved revolutionary in a time when the quantity of data started to grow significantly. One scale to understand the rate of data growth is to determine data generated per second on average per head. While it is true that a proportion does not have access to the internet, most internet users generate more than this average. Thus, the net generation currently stands at 1.7MB per second per person.
Why Choose Big Data Analytics over Traditional Data Mining?
Traditional mining involving data warehouse (DWH) was the approach used for data analysis of all scales before the advent of big data. The introduction of frameworks, technologies, and updates in them are making big data analytics the best approach for data analysis on datasets whose size amounts to terabytes.
Data analysis time reduction
Traditional data analysis using extraction, transformation, and loading (ETL) in data warehouse (DWH) and the subsequent business intelligence take 12 to 18 months before the analysis could allow deducing conclusive outcomes. In sharp contrast, big data analytics roughly take only three months to model the same dataset. Big data also ensures excessively high efficiency which DWH fails to offer when dealing with extraordinarily large datasets.
Data analysis cost reduction
Traditional data analysis costs three times as much as big data analytics when the dataset is relatively large. Besides cost, big data also ensures significant return on investment because big data processing systems used for analytics including Hadoop and Apache Spark are proving to be highly efficient.
Notable Use Cases and Industries for Big Data Applications
Understanding loopholes in business
It is often the case with manufacturers as well as service providers that they are unable to meet targets despite having immaculate products and unparalleled efficiency. Determine why some of the areas in your business model lack expected output while others continue to generate more than anticipated.
Predict with high precision the trends of market, customers, and competitors by assessing their current behavior. It is notable that this prediction is not speculative. Rather, it is powered by real-world records. There are various channels used for data sources depending on the underlying industry. Social media is one of the top choices to evaluate markets when business model is B2C.
Strategic decision making
Crucial corporate decisions should not be based on hit-and-trial methods. Instead, you need to analyze market and streamline future goals accordingly.
Business landscape is changing rapidly in the current corporate sector owing to the growing enterprise mobility technologies and shrinking cycle of innovation. Big data processing analytics provide insightful and data-rich information which boosts decision making approaches.
The amount of new and retained customers in a time period projects the potential of a business. Customers carry various motivational factors to prefer one product over another. Instead of interviewing the potential customers, analyzing their online activities is far more effective.
Before big data was a thing, the enterprises used to perform post-launch marketing. However, this strategy involves significant risks because the product or service might not be as appealing to customers as to you. The leverage of big data analytics in support of decision making process enables companies to perform marketing prior to the launch. Consequently, they can introduce need-based products and services which are highly likely to ensure achieving targeted revenues.
The big data does not only provide market analysis but also enables service providers to perform sentiment analysis. Using this technique, companies can identify context and tone of consumers in mass feedback.
Optical character recognition in combination with big data processing in image processing also assists in sentiment analysis. Apart from social media, the public relation sites are also sources to collect data for such analysis.
Intelligent algorithms are capable of performing this analysis by themselves – a technique usually referred to as supervised machine learning. In other words, companies no longer require multiple human resources to evaluate each feedback.
Big data enables banks, insurance companies, and financial institutions to prevent and detect frauds. The traditional methods to detect financial frauds occurring with credit cards present a dilemma here. A company can either provide unhindered and streamlined experience to its customers or it can ensure security at the cost of miserable experience.
Big data analytics allow ensuring seamless customer experience as well as security at the same time. Using big data analytics, companies have been able to markedly bring down fraudulent transactions and fake claims.
It would be astonishing if you are still unaware of the revolution that big data is causing in the healthcare industry. The technology in combination with artificial intelligence is enabling researchers to introduce smart diagnostic software systems. Big data medical image processing is one of the most mentionable examples.
Besides, it also allows software to prescribe medicine by assessing patients’ history and results of relevant tests. These capabilities are significantly bringing down the cost of operations.
Mob Inspire Methodology for Big Data
Mob Inspire uses a comprehensive methodology for performing big data analytics. The experience of working with various industries enabled our experts to work on a range of tasks. The variety of tasks posed occasional challenges as well when we had to solve a problem which never occurred before.
However, the professionals did not only remain successful but developed enterprise level big data framework too. This framework allows them to revisit documented cases and find out the most appropriate solutions.
Big data often requires retrieval of data from various sources. While the sources vary depending on the project, yet social media and search engine queries are the most widely used sources. Banks use transaction records for fraud detection whereas healthcare companies use data regarding patient’s medical history to train software for intelligent diagnosis and prescription.
The companies providing video on-demand (VOD) services acquire data about users’ online activity. This data enables providers to determine consumer’s choices so that they can suggest them the relevant video content. Companies utilize their own enterprise data to make strategic corporate decisions.
For instance, a construction company aiming to optimize resources would acquire data of a range construction project and process them to find out the areas where cost and time consumption can be minimized.
Thus, data extraction is the first stage in big data process flow. The retrieved data is placed in a repository technically referred to as Data Lake. It is notable here that big data analytics require unstructured data – the kind whose data does not exist in schema or tables. Instead, it is stored in flat hierarchy irrespective of data type and size.
A data lake is a container which keeps raw data. The process of data cleansing provides appropriate filters to ensure that invalid, relatively older, and unreliable data filter filters out before latter stages big data processing.
Data reliability implies the sources from which you acquire datasets. For instance, you may require electronic healthcare records (EHR) to train software for automatic prescription and diagnosis. A collection of fake EHR would spoil the training of AI resulting in exacerbating the automation process.
Data currency indicates how updated is the dataset. Data has to be current because decades-old EHR would not provide appropriate information about prevalence of a disease in a region. For instance, only 1.9% of people in the US had macular degeneration. This percentage is projected to grow beyond 5% by 2050. Using the data from 2010 to perform big data analytics in 2050 would obviously generate erroneous results.
Validity of data explains its relevance in the problem at hand. For instance, a taxi business aiming to determine consumer behavior would assess people who travel by taxi or another ride-hailing service. It would be inefficient to consider people who commute by public transport. Developing and placing validity filters are the most crucial phases at data cleansing phase. Thus, cleansing is one of the main considerations in processing big data.
This phase involves structuring of data into appropriate formats and types. The data acquired and placed from various sources into Data Lake is unstructured. There is no distinction of types and sizes whatsoever. Many analysts consider data cleansing as a part of this phase. However, Mob Inspire treats data cleansing separately due to the amount of tasks involved in it.
The cleaned data is transformed with normalization and aggregation techniques. Transformation makes the data more readable for the big data mining algorithms. For instance, if the data has a broad range, it is plausible to convert the values into manageable equivalents. This transformation process is performed again once the mining is done to turn the data back into its original form.
Training with Machine Learning
This phase is not an essential one but applies to a range of cases making it significant among big data technologies and techniques. Machine learning involves training of software to detect patterns and identify objects. However, ML is must when the project involves one of these challenges. ML can be either supervised or unsupervised.
Supervised Machine Learning:
It refers to the approach where software is initially trained by human AI engineers. They ensure to place certain bounds (bias) so that the outcome does not exceed the logical range. Supervised ML is the best strategy when big data analysts intend to perform classification or regression.
Classification is the identification of objects. Software trained to perform this recognition has to decide, for instance, if an object visible in a frame is an apple or not. The system would generate a probability based on the training provided to it making it a crucial phase in big data processing pipelines.
Regression is performed when you intend to draw pattern in a dataset. For instance, determining the behavior of financial stocks by analyzing trends in the past ten years requires regression analysis.
Unsupervised Machine Learning:
Unsupervised ML implies the approach where there are no bounds and the outcome can be as unusual as it can. This ML provides more flexibility is pattern identification because it does not have limitations on the outcome. Unsupervised ML also considers extremely unusual results which are filtered in supervised ML making big data processing more flexible.
Clustering is one significant use case of unsupervised ML. The technique segments data into groups of similar instances. Thus, members of the same group are more similar to each other than those of the other groups. There are usually wide ranging variables for clustering. Association is the other instance which intends to identify relationships between large-scale databases.
Many projects require reinforcement learning which refers to the technique where a software system improves outcomes through reward-based training.
Segmentation and Visualization
The outcome of ML provides distinctive groups of data regardless of the technique you use. These groups are run through more filters, at times, if needed. The phase of segmentation nurtures data to perform predictive analysis and pattern detection. One notable example of pattern detection is identification of frauds in financial transaction.
The segmented results essentially take the form of relational databases. At this point, data scientists are able to visualize results. Datasets after big data processing can be visualized through interactive charts, graphs, and tables. The result of data visualization is published on executive information systems for leadership to make strategic corporate planning.
Tool, Technologies, and Frameworks
Mob Inspire uses a wide variety of big data processing tools for analytics. Our experts use both Hadoop and Apache Spark frameworks depending on the nature of problem at hand. They have expertise on big data programming and scripting languages including R, Python, Java, and NoSQL. Mob Inspire use SAS and Tableau for visualization.
Big data analytics take your enterprise to unimaginable heights in incredibly short time – provided the analysis is correctly performed. We utilize multiple big data processing platforms depending on the nature of tasks. Contact us to share the specific business problem with our experts who can provide consulting or work on the project for you to fulfill the objectives.
There are obvious reasons that explain the ambition of 83% of the companies to invest in big data in 2019. Research at McKinsey reveals that around 50% of enterprises confirmed that utilizing big data revolutionized their business operations – particularly marketing and sales. Consequently, the global revenue is projected to cross $103 billion in 2027 from current $43 billion. One of the most notable instances which are assisting in achieving this feat is big data in media.
Psychographic Segmentation with Big Data Solutions
Customers are only going to subscribe for a video on-demand service if the provider is able to convince them. The latter needs to find out what drives customers to download the app and which type of content do they want. The answers to these questions vary depending on age groups, socioeconomic situations, trending programs, and user interests.
It is practically inefficient to determine these factors at the level of individual subscribers. Using big data, you can identify the viewers with similar interests and demographics before grouping them together. This grouping, technically referred to as psychographic segmentation, provides the outcome which can then be used to offer video content relevant to each subscriber’s interest.
Apart from consumer analysis and determining consumer trends, big data analytics in media also enables VoD service providers to sketch consumer journey map. The compilation of this map allows companies to find the path followed by consumers before becoming subscriber. Similarly, it also allows determining the causes of drop in subscription percentage.
In all, psychographic segmentation and consumer journey ensure effective market research in VoD industry.
Smart Filters for Big Data in Media and Entertainment
The websites and apps for social media, public relations, and discussion forums often feature instances of hate speeches, abuse, and graphic violence. Facebook alone has 2.38 billion active users as per Statista figures from March 2019. With billions of users participating in online social activities and discussions, the amount of inappropriate content is also growing.
It is impossible to filter each comment, post, picture, and video manually. All notable social networks are and discussion forums have placed multiple checks so that inappropriate content can be removed as soon as possible. The pace of tech development is too fast for legislators to develop corresponding laws in time. Thus, the governments are trying to regulate such platforms with filters using big data without compromising free speech.
Spam and Intrusion Detection for Enhanced Data Security
Cybersecurity challenges are posing more threat than ever as data security is becoming one of the top concerns for service providers, regulators, and consumers alike. IBM research reveals that cost of breach involving over a million records is $42 million. Every breached record costs $148. Since most of the consumers of VoD and social media are non-technical users, they are prone to falling for spam emails and other intrusion scheme.
Big data in combination with machine learning and deep learning algorithms including Naive Bayes and Multilayer Perceptron (MLP), respectively can aid in developing filters which detect spams and issue an alert. Top media outlets have placed comprehensive intrusion detection and prevention algorithms to ensure excessive data security.
Data Science in Media Industry for Predictive Analytics
It is crucial for a service provider in any industry to have knowledge about changing trends. Big data allows companies to make strategic decision making and corporate planning by forecasting future. This big data forecast in media industry depends on relevant data acquired from credible sources.
The predictive analytics based on future projections enable service providers to outperform their competitors by working on content which is projected to be in high demand. It is notable big data in media and entertainment requires authentic data because acquisition of data which is inconsistent, irrelevant, or unreliable results in worsening of analysis. Such erroneous analysis would generate incorrect predictions, and hence result in inefficient corporate planning.
Intelligent Self-trained Virtual Assistant
It would be unwise to keep human assistant who listens and notes down queries and complaints of subscribers. This age of AI provides highly efficient virtual assistants that are more far productive than human for the same job. Using virtual assistants does not only reduce cost but also enable you to offer seamless services.
The most attractive feature of modern virtual assistant is its ability to train itself. In essence, such a software system uses past interactions and improves itself using reinforcement learning technique. You do not need to wait for the system to get matured before using it for interactions with consumers. Instead, Mob Inspire provides such systems which are already trained over variety of datasets.
Virtual assistant is one of the most widely used big data applications. Even the companies with absence of large-scale data are also utilizing robotic assistants. They can provide round the clock access to remote assistance. These systems are not only capable of responding the issue but solving it as well.
Redefining Marketing Strategy
Crafting consumer journey is one of the marketing phases that target customers. However, there are other metrics as well which are essential to determine for marketing. Every significant marketing campaign requires extensive analysis on financial situation. Big data in media industry allows predicting the projected growth volumes in response to various campaigns so that campaigns can be prioritized.
For these reasons, the top providers of video on-demand services are emphasizing on marketing, particularly instant gratification. They are able to assess what consumers would like to watch and present the most relevant results. The remarkable return reflects in 26% growth rate of Netflix as indicated by financial report of Q2 2019.
Mob Inspire is performing research and development in big data for a data now. In this period, we have assisted dozens of clients to refine or redevelop their architecture by using big data solutions. We look forward to developing new partnerships in the industry. Contact us today so that our experts can help you in the most appropriate way.