Business Intelligence, Big Data Management, Big Data
Article | May 15, 2023
Every business tries to improve their return on investment (ROI) every year by deploying different marketing strategies and technologies. Businesses are constantly adding new technologies to their content stack in order to enhance their efficiency and boost their revenue and growth. Data is inevitable in today's digital era, and Dan Zarrella correctly describes its role in marketing. He stated, "Marketing without data is like driving with your eyes closed."
Indeed, the development of better marketing analytics tools and methodologies in recent years has provided business leaders with tremendous added decision-making power. Marketing analytics enables businesses to harness data points about their prospects and their journey through the selling process to enhance the effectiveness of their go-to-market efforts while optimizing ROI. The benefits can be experienced across teams and business segments. According to Hubspot, over 75% of marketers are reporting on how their campaigns are directly influencing revenue because of marketing analytics tools. So, let’s dive deeper and understand why marketing analytics matters.
Why Does Marketing Analytics Matter?
Marketing campaigns are just tossed into the world with little or no information about how your target audience responds to your marketing strategies. This happens in cases where business analytics tools are not used. Without employing marketing analytics, it can be said that a business is operating in the dark. Here are the reasons why marketing analytics matters.
Quantifiable Actions
Marketing analytics tools provide you with reliable matrices and insights into the varied marketing strategies that are implemented. Whenever numbers are presented, concrete data for the marketing effort is provided. For example, if you launched a content marketing campaign and have reliable data, it's easy to see that overall sales improved as a result of that marketing push.
Campaign Analyses
Only marketing analytics can provide a complete overview of how a marketing campaign or strategy actually performed. The data can be dug deeper to track individual messaging across a broad spectrum of outlets, making sure no approach is wasted.
Plan for the Future
Once you have an understanding of which marketing strategies are meeting expectations, you will be able to plan strategically for future marketing initiatives. Not only is this helpful for organizing marketing efforts, but it also makes it easier to allocate funds across boards.
Maximize ROI with Marketing Analytics
When marketers use marketing analytics tools, they can find patterns and signs that can be used to improve the performance of their company.
This data can assist account managers to acquire new prospects, reallocate marketing expenditures to the most effective channels, and forecast future possibilities. Integration of marketing analytics software into the sales process can save time, boost revenue, and maximize ROI.
Lead Prospecting
Marketing analytics can enhance customer acquisition in multiple ways.
Many marketers merely acquire data about website visitors and ad viewers via ad networks. They just receive basic demographic data, not tips about how to convert leads to sales.
Marketing analytics tracks every prospect in your sales funnel or website in real-time. With a detailed picture of your potential customers, you can recognize qualified leads and target them with marketing. Using data insights, you can boost sales, get rid of bottlenecks, increase conversions, and find opportunities that were hidden in plain sight.
Campaign Performance Monitoring
Online advertising and marketing have the distinct advantage of allowing campaign managers to keep checks on ad performance in real-time. Businesses can use marketing ROI metrics like clicks, impressions, and conversions to figure out which ads work best.
Real-time campaign monitoring is a valuable tool for today's marketers. Placements that are underperforming are paused or modified, while those with a great ROI could get extra ad revenue. These insights usually result in more efficient ad spending. Information from different media channels and data from online applications can be put together to learn about the prospect-to-customer journey.
Demand Forecasting
With suitable data at the right time, marketers gain more power. Tracking historical data is essential to identifying patterns and predicting demand. Seasonal patterns, for example, can have a significant impact on how well a campaign performs.
Detailed research can indicate these factors and assist you in re-allocate or altering your marketing investment. Understanding the product or campaign performance helps to identify which items will be in high demand in the future quarter through the use of marketing analytics.
Boost Sales
Consumers are more knowledgeable than ever before. Reviews, social networks, blogs, etc., now influence most purchasing decisions. Marketing analytics provides valuable information.
Focus on how marketing impacts sales to evaluate ROI. When to contact a potential customer, which product would have the most impact, and who is best suited to close the deal. Find sales-boosting marketing strategies. Marketing analytics can enhance revenue by:
Understanding the decision-making process of a consumer.
Tracking website user behavior and sales trends.
Discussing your ROI strategy with the entire company, rather than just the sales or marketing teams.
Summing Up
For marketers, the use of marketing analytics technology is undoubtedly going to grow over time. You can boost your marketing ROI by using the best marketing analytics tools. Marketing ROI is mostly determined by how successful you are at developing and executing your company's marketing strategy.
If you use the right marketing analytics, you can cut your marketing costs, make more people want your brand, and increase sales.
FAQ
What are the main components of marketing analytics?
An effective marketing analytics strategy must have the following three capabilities:
Scalability: Your approach must be able to grow and adapt to the changing requirements of the future.
Sustainability: Having the appropriate team is essential to long-term sustainability.
Affordability: Analytical is a sound investment, but the budget must be in sync with projected growth.
What technology do most marketing analysts use?
Marketing analysts can require various technologies related to:
Statistical analysis software (e.g., R, SAS, SPSS, or STATA)
SQL databases and database querying languages.
What is digital marketing analytics?
Customer behavior is translated into actionable company data through digital marketing analytics. Businesses can use digital analytics tools to learn more about what customers are doing online, why they're doing it, and how this behavior can be used in digital marketing campaigns.
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Business Intelligence, Big Data Management, Big Data
Article | July 10, 2023
With precise sentiment and text analysis, businesses amplify their initiatives to drive high ROI. Learn how leading text analytics companies help businesses ensure customer-centric strategies.
Contents
1. Text Analytics as a Core Component of Strategic Decision-making
2. Understanding the Approaches and Techniques of Text Analytics
3. Key Advantages of Text Analytics for B2B Businesses
4. Top Text Analytics Software Providers Enabling Business Success
4.1 Displayr
4.2 Chattermill
4.3 Forsta
4.4 DataWalk
4.5 Canvs AI
4.6 Kapiche
4.7 Acodis
4.8 Lumoa
4.9 Wonderflow
4.10 Thematic
5. Wrap Up
1. Text Analytics as a Core Component of Strategic Decision-making
Recent strides in machine learning,natural language processing (NLP), and big data technologies have tremendously strengthened the applications and capabilities of text analytics, turning it into a powerful decision-making tool for businesses. Text analytics software utilizes machine learning to extract crucial information from vast amounts of unstructured text data, enabling companies to leverage actionable insights, fine-tune business strategies, and boost profitability.
Correspondingly, with text analytics, businesses can retrieve critical details like keywords or company information from free-form texts like emails. It can further be used to classify unstructured texts, such as customer feedback or reviews, based on themes, sentiments, and patterns. For instance, by analyzing customer sentiment on social media, businesses can easily optimize their services and fine-tune their strategic initiatives for higher ROIs. Text analytics, therefore, facilitates informed decision-making by offering crucial insights to companies, empowering them to identify upcoming trends, areas of improvement, market dynamics, buyer preferences, and so on.
2. Understanding the Approaches and Techniques of Text Analytics
The core strategies in text analytics aim to highlight deeper information, like patterns and trends, made visible through data visualization techniques. The quantitative insights gained by companies significantly help them make sound decisions and fine-tune their operations.
Here’s a list of the prominent text analytics methods that enable prompt decision-making:
Topic Modeling Technique
The method involves recognizing key themes or subjects in vast text volumes or documents to retrieve relevant keywords. Such identification helps companies classify texts according to prevalent themes and additionally enables exploratory analysis.
Sentiment Evaluation
Reflecting the emotional tone of various non-formatted texts, including customer interactions, social media posts, and product reviews, this text analytics method focuses on sorting emotions under negative or positive categories. It further emphasizes a detailed categorization for identification, such as disappointment, anger, or confusion.
Document Grouping
Document grouping or clustering is another valuable text analytics technique that groups congruent documents together. This method helps companies classify large datasets and extract associated information. It is particularly beneficial for improving search results, as it augments relevance for users by grouping similar documents.
Text Summarization
The advantageous text summarization approach aims to simplify large texts, transforming them into shorter summaries while retaining key points or themes. Accordingly, this technique helps people and machines understand large chunks of text data with greater ease and agility.
Entity Chunking Approach
Also called Named Entity Recognition (NER), this natural language processing approach automatically derives structured entities from free-form texts. In other words, it classifies vital information within an unstructured text into pre-set categories such as events, organizations, places, and people.
TF-IDF Technique
With the Term Frequency—Inverse Document Frequency (TF-IDF) text analytics technique, companies can establish the importance of a term in the context of a document and an entire corpus. While term frequency showcases the number of times a term appears in a single document, inverse document frequency assesses the whole document collection to highlight terms with higher relevance.
3. Key Advantages of Text Analytics for B2B Businesses
From ensuring the most effective marketing strategies to higher lead conversions, employing text analytics tools ushers in many benefits for B2B businesses. Some of its key advantages include:
Targeted Improvements and Better User Experience
By understanding open-ended comments across varied platforms, like social media, surveys, and customer service interactions, text analytics software offers vital insights into customer preferences and enables companies to optimize their strategies. It further empowers businesses to refine their existing products, make new offerings, ensure targeted enhancements, and elevate user delight.
Reduced Time and Efforts
Text analytics automates the retrieval of meaningful data from vast amounts of unstructured texts, significantly lessening the time and resources needed for data processing and empowering businesses to focus on innovation and critical endeavors.
Customer Acquisition
With text analytics platforms, businesses can explore raw data from points of origin like social media and emails to effectively recognize potential leads. Companies can, therefore, focus on rewarding opportunities by adequately assessing leads’ requirements and interests.
Risk Handling
B2B businesses can further leverage advanced text analytics tools to recognize and diminish risks. These tools promptly analyze and address user complaints, read market fluctuations, and cater to supplier sentiments.
Expense Management
Companies notably employ text analytics to assess textual data related to expenses, resource allotment, and procurement. Such evaluation gives them crucial insights into existing shortcomings and cost-saving opportunities, empowering decision-makers to implement cost-cutting strategies and amplify expense management.
Market Intelligence
With text analytics, businesses can understand different patterns, trends, and dynamics of the market, gaining excellent market intelligence from varied sources like news articles, social media and industry reports. Businesses can evaluate their competitors’ behavior and stay ahead of the curve by employing leading-edge technologies and optimizing their processes.
Strategic Decision-Making and Increased ROIs
Last but not least, text analytics helps businesses assimilate the latest trends and developments, rendering actionable insights to optimize their pricing and promotion strategies. Additionally, companies harness text and data analysis to boost marketing, benefitting from effective personalization and augmenting customer satisfaction to drive higher ROIs.
4. Top Text Analytics Software Providers Enabling Business Success
Leading text analytics software providers efficiently empower companies to design strategic initiatives. With their advanced features and technologies, they induce enviable business success.
The following list showcases some of the top text analytics companies that offer powerful platforms for reliable text data analysis:
4.1 Displayr
Displayr is a transformative data analytics and reporting software provider that helps market researchers and businesses gain meaningful insights. The company’s forward-thinking platform makes intricate tasks like text analytics easy, enabling stakeholders to harness the power of information. It combines visualization, reporting, and data science to assist users in making informed decisions.
With an impressive array of features, such as ML and text coding tools, responsive dashboards, and auto-updating abilities, the company strengthens varied research requirements, increasing data preciseness and reliability. It further boasts tools for user opinion analysis, brand analytics, text analytics, pricing research, and survey analysis, ensuring dependable conclusions across diverse data collections and corpora.
4.2 Chattermill
A leader in customer experience (CX) intelligence, Chattermill provides actionable insights to customer support and product teams, enabling them to augment customer experiences, meet their expectations, and ensure retention. It employs leading-edge deep learning technology to automate information retrieval from extensive non-formatted customer data, such as user reviews, customer service interactions, surveys, and social media.
With Chattermill, businesses can effortlessly track retention rates and leverage the sentiment analysis tool to assess opinion trends at scale. The company’s platform further helps businesses understand positivity drivers, strengthening product and pricing strategies along with brand standing. Its distinct features include its proprietary model, Lyra AI, which eliminates the gap between tactical customer feedback analysis and strategic business objectives along with an Experience-Led Growth roadmap that checks CX maturity and suggests meaningful applications. The company also offers training and promotes community engagement initiatives, enhancing members’ expertise.
4.3 Forsta
At the forefront of experience and research technology, Forsta promotes a human experience (HX) approach and drives informed decision-making. The company’s platform effortlessly combines market research, customer experience, and employee experience, rendering an all-inclusive understanding of audience interactions and enabling companies to fine-tune their strategies.
Notably, Forsta boasts an incredible suite of features, which includes personalization solutions, expert consultation, and advanced analytics. The company strives to augment retail businesses’ revenue by encouraging repeat purchases and maximizing conversions. With state-of-the-art tools, it further allows companies to conduct in-depth interviews and community studies, empowering them to grasp customer sentiment, transform qualitative data into actionable insights, and earn increased revenues with sound decisions.
4.4 DataWalk
Employing innovative software technology, DataWalk helps users eliminate data silos and convert raw data into intelligible components, such as transactions, individuals, or events. The company caters to government agencies and commercial enterprises, facilitating data visualization, analysis, and sharing endeavors. Significantly, its leading-edge tools, like text analytics, enable users to make rational decisions by deriving actionable insights from consolidated data.
Through its holistic platform, the company empowers users to analyze extensive data across different applications. Businesses can employ team-based graph analytics to identify hidden patterns and connections within varied data sources, gaining insights for strategy refinement. DataWalk further effectively ensures fraud mitigation and enhances efficiency in anti-money laundering. Its many use cases entail customer intelligence, social network analysis, analytics modernization, root cause analysis, etc. Combining machine learning with end-to-end processes, the company strongly supports entities to boost their operational efficiency and earnings.
4.5 Canvs AI
Canvs AI is a premier insights platform provider specializing in analyzing open-ended texts, like audience feedback, ad tests, and customer surveys, and turning them into actionable business intelligence. The company leverages state-of-the-art artificial intelligence (AI) and NLP technologies, like text analytics, to expedite insights retrieval for leading global brands and agencies. Known for its unmatched efficiency and preciseness in gauging user sentiments, Canvs AI focuses on emotion measurement to promote sound decision-making and stronger customer relationships.
Significantly, the company’s platform has an easy-to-use insights dashboard for quick data filtering and in-depth text analysis, strengthened by Boolean search capabilities. With its striking features, it helps organizations identify chief themes and sentiments within social comments, interpret colloquialisms, and leverage reliable insights. Its high-tech customization options further allow businesses to tailor emotions, codes, and topics, enabling them to customize what they want to focus on. Also facilitating multi-source data integration, Canvs AI ensures workflow integration, optimized action planning, and augmented decision-making.
4.6 Kapiche
Top global companies rely on Kapiche, a pioneering feedback analytics platform provider, to analyze vast volumes of user feedback from varied sources, like support interactions and CRM systems. With its powerful text analytics solutions, the company facilitates the streamlining of data integrations, offers analysts quick and accurate insights for influential decision-making, and elevates the efficiency of CX metrics analysis.
From centralizing data from multiple sources to allowing accurate evaluation of CX impacts, Kapiche helps businesses make impactful decisions. It further speeds insights extraction compared to the established practices, eliminating the need for manual tagging and intricate setups. It also promotes collaboration through tailorable dashboards and automated reporting and renders live status monitoring into user opinion dynamics, boosting operational efficiency and customer experiences.
4.7 Acodis
A renowned name in the intelligent document processing (IDP) field, Acodis is transforming data management by enabling businesses to convert non-formatted documents into structured data. The company employs AI-powered data extraction to manipulate documents at scale speedily. Catering to top manufacturing, chemical, and pharmaceutical companies, Acodis augments operational efficiency by eliminating data entry tasks and offering personalized workflows.
The company’s leading-edge platform combines with existing systems, such as CRM, ERP, and RPA, via API for tailored process automation and analytics. It employs predictive analytics technology, elevates resource efficiency, and eliminates GxP compliance concerns through document digitization. Powered by AI, it further secures data precision and security with traceability elements and tailorable parameters. Notably, the company promises excellent data extraction from both formatted and unstructured data in addition to any format of batch records, augmenting businesses’ analysis endeavors and empowering them to implement advanced text analytics for in-depth insights.
4.8 Lumoa
As a business empowerer, Lumoa allows extraordinary CX management through its revolutionary Generative AI platform. It combines feedback data from telephonic conversations, surveys, customer reviews, etc., into an integrated platform, effectively allowing businesses to understand customer sentiments at various touchpoints. By providing real-time actionable insights tailored to business metrics, Lumoa enables companies to undertake customer-oriented approaches, ensuring growth and high customer retention.
Among the key features of the company’s platform remain its incredible analysis capabilities, multi-language support, collaborative tools for feedback management, phrase detection for enhancement scope, and GPT integration. Its executive dashboard lets users get detailed insights into users’ journeys, while automated event creation and feedback analysis help businesses monitor live KPIs. Furthermore, through GDPR compliance and ISO certifications, the company demonstrates a stellar commitment to data security, strengthening customer satisfaction and brand performance.
4.9 Wonderflow
Wonderflow is a leading AI-powered platform provider committed to evaluating the voice of customers (VoC) across diverse touchpoints, such as reviews and customer service records. The company empowers businesses to make data-backed strategies and amplify operational efficiency by converting vast customer feedback data into implementable insights with advanced NLP and text analytics technologies. The company’s mission and endeavors reverberate the ‘think global, act local’ maxim, helping businesses drive enhanced audience experiences.
Correspondingly, the company offers comprehensive VoC analytics solutions, including sentiment analysis, predictive analytics, and competitor assessment capabilities. Wonderflow’s innovative platform facilitates text analysis automation to quickly recognize trends, topics, and sentiments, along with their impact on critical metrics. Furthermore, it boasts a simple setup, which makes adopting VoC practices and interpreting feedback data easier. Businesses can effortlessly visualize data, replicate practical approaches, and tailor customer engagement tactics with the forward-thinking platform.
4.10 Thematic
A next-generation AI-powered CX solutions provider, Thematic specializes in text analytics and supports businesses’ data analysis efforts. It converts unstructured text feedback from multiple channels into usable insights, like product refinement opportunities, payment pain points, or order cancellation causes. Trusted by premier organizations, it empowers companies to amplify their products, services, and customer interaction through in-depth thematic analysis of feedback data and subsequent strategic decision-making.
By integrating customers’ feedback from various channels, like chats and surveys, the company renders an advantageous unified approach to data analytics and allows quick access to excellent insights. Thematic’s innovative platform implements robust visualizations for user-friendly data review and boasts features like real-time analytics and intuitive theme editing. Its Thematic Answers feature combines generative AI with a trust layer, ensuring the feasibility and reliability of its insights. Also, its Conversation AI feature enables effortless assessment and monitoring of solution details, ensuring high user delight and profitability.
5. Wrap Up
Text analytics technology has emerged as a vital aspect of modern business intelligence, which harnesses machine learning and natural language processing to convert non-formatted data into pragmatic suggestions. Today, distinguished text analytics software providers empower companies to identify user sentiments and drive higher customer fulfillment and business productivity with advanced analysis technologies.
Yet, as analytics technology advances, the future of text analytics remains full of possibilities for further refinement. For instance, with the progression of machine learning, the capability of data analytics software to provide more sophisticated assessments and predictive insights would increase manifold. One would especially witness an increased emphasis on multilingual text analytics for global e-commerce, IoT and blockchain integration, ethical considerations, and data security. These developments reflect that text analytics would not only offer accurate observations but also promote meaningful ventures and growth.
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Big Data Management, Data Science, Big Data
Article | April 28, 2023
Headquartered in London, England, BP (NYSE: BP) is a multinational oil and gas company. Operating since 1909, the organization offers its customers with fuel for transportation, energy for heat and light, lubricants to keep engines moving, and the petrochemicals products.
Business intelligence has always been a key enabler for improving decision making processes in large enterprises from early days of spreadsheet software to building enterprise data warehouses for housing large sets of enterprise data and to more recent developments of mining those datasets to unearth hidden relationships. One underlying theme throughout this evolution has been the delegation of crucial task of finding out the remarkable relationships between various objects of interest to human beings.
What BI technology has been doing, in other words, is to make it possible (and often easy too) to find the needle in the proverbial haystack if you somehow know in which sectors of the barn it is likely to be. It is a validatory as opposed to a predictory technology.
When the amount of data is huge in terms of variety, amount, and dimensionality (a.k.a. Big Data) and/or the relationship between datasets are beyond first-order linear relationships amicable to human intuition, the above strategy of relying solely on humans to make essential thinking about the datasets and utilizing machines only for crucial but dumb data infrastructure tasks becomes totally inadequate. The remedy to the problem follows directly from our characterization of it: finding ways to utilize the machines beyond menial tasks and offloading some or most of cognitive work from humans to the machines.
Does this mean all the technology and associated practices developed over the decades in BI space are not useful anymore in Big Data age? Not at all. On the contrary, they are more useful than ever: whereas in the past humans were in the driving seat and controlling the demand for the use of the datasets acquired and curated diligently, we have now machines taking up that important role and hence unleashing manifold different ways of using the data and finding out obscure, non-intuitive relationships that allude humans. Moreover, machines can bring unprecedented speed and processing scalability to the game that would be either prohibitively expensive or outright impossible to do with human workforce.
Companies have to realize both the enormous potential of using new automated, predictive analytics technologies such as machine learning and how to successfully incorporate and utilize those advanced technologies into the data analysis and processing fabric of their existing infrastructure. It is this marrying of relatively old, stable technologies of data mining, data warehousing, enterprise data models, etc. with the new automated predictive technologies that has the huge potential to unleash the benefits so often being hyped by the vested interests of new tools and applications as the answer to all data analytical problems.
To see this in the context of predictive analytics, let's consider the machine learning(ML) technology. The easiest way to understand machine learning would be to look at the simplest ML algorithm: linear regression. ML technology will build on basic interpolation idea of the regression and extend it using sophisticated mathematical techniques that would not necessarily be obvious to the causal users. For example, some ML algorithms would extend linear regression approach to model non-linear (i.e. higher order) relationships between dependent and independent variables in the dataset via clever mathematical transformations (a.k.a kernel methods) that will express those non-linear relationship in a linear form and hence suitable to be run through a linear algorithm.
Be it a simple linear algorithm or its more sophisticated kernel methods variation, ML algorithms will not have any context on the data they process. This is both a strength and weakness at the same time. Strength because the same algorithms could process a variety of different kinds of data, allowing us to leverage all the work gone through the development of those algorithms in different business contexts, weakness because since the algorithms lack any contextual understanding of the data, perennial computer science truth of garbage in, garbage out manifests itself unceremoniously here : ML models have to be fed "right" kind of data to draw out correct insights that explain the inner relationships in the data being processed.
ML technology provides an impressive set of sophisticated data analysis and modelling algorithms that could find out very intricate relationships among the datasets they process. It provides not only very sophisticated, advanced data analysis and modeling methods but also the ability to use these methods in an automated, hence massively distributed and scalable ways. Its Achilles' heel however is its heavy dependence on the data it is being fed with. Best analytic methods would be useless, as far as drawing out useful insights from them are concerned, if they are applied on the wrong kind of data. More seriously, the use of advanced analytical technology could give a false sense of confidence to their users over the analysis results those methods produce, making the whole undertaking not just useless but actually dangerous.
We can address the fundamental weakness of ML technology by deploying its advanced, raw algorithmic processing capabilities in conjunction with the existing data analytics technology whereby contextual data relationships and key domain knowledge coming from existing BI estate (data mining efforts, data warehouses, enterprise data models, business rules, etc.) are used to feed ML analytics pipeline. This approach will combine superior algorithmic processing capabilities of the new ML technology with the enterprise knowledge accumulated through BI efforts and will allow companies build on their existing data analytics investments while transitioning to use incoming advanced technologies. This, I believe, is effectively a win-win situation and will be key to the success of any company involved in data analytics efforts.
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Data Science
Article | January 12, 2022
The industry of Data Science has been popular since a decade or so. The aim and workflow of the field has undergone a lot of changes since then. From basic reporting and analytics to predictive and cognitive analytics, data science revolutionized the concept of “Computers that can think”. As of today, Data Science and its subfields are one of the greatest in demand and has a great competition in the industry. Apart from improving businesses, Analytics has proved its capability in various sectors and applications. This has changed the overall structure of the field of Data Science and the opportunities available. The large amount of data and its wide scope comes with plenty of various opportunities and developments.
AI, AI everywhere
AI started as a boom few years back wherein it saw great potential, but now it’s everywhere. From Research Labs to Education, Healthcare and even in personal devices, AI has taken up various forms, solving many problems and improving various products and services. Even now, experts states that the full potential of AI is not completely utilised and is expected to be used in a few years’ time. The wide use of Artificial Intelligence has motivated many start-ups to focus on the use of AI to build solutions and products. Industries at all scales have taken a move to include AI in their services / products to increase efficiency through intelligent behaviour. The “Mimicabilty” of human brain functionality comes with great potential – Namely Automation and Optimization of various tasks.
Data Centric is the trend
With a large amount of data generated daily on servers, there comes a need of shifting from model-centric to data-centric. Let us have a glimpse into what each approach means. In model-centric approach, data is kept constant while the model is tweaked to adjust to the data and get a good result. What’s the drawback of this? Not performing good on real world data. But, then why the model gave good results? That is because, model completely got adjusted to the data you gave instead of generalizing to the real-world problem. This issue came in the recent past, thus the trend of Data-centric came in. Experts like Andrew Ng often hosts talks and campaigns to shift the focus on ML Practitioners and Industries to Data rather than model. According to him, “data is like food for the model”.
Data Engineers on Rise
As a trailing topic to the previous one, Data Engineers will have a good rise in the coming future. As seen before, there is a copious amount of existing data and is getting generated at an un-imaginable rate. This might sound good since, “more is good” for analytics, but comes with the disadvantage of difficulty in ensuring data quality. As more data comes in, maintaining quality can be challenging. One reality here is that not all times a cloud pipeline can be used to ensure data quality and automated cleaning. There are times where raw data is logged as it is. This calls for more data engineers to perform cleaning and tuning of data to ensure it meets quality standards. With the oncoming of Data-Centric AI approach, this will have a great hype in the coming recent time. And speaking from the Industry Point of View, Data is one of the core part of Data Science and has no replacement. No good data, no good results and the crash of Data Science.
“Artificial” becoming more on the point for AI
AI started as a research topic long back. It all started like a big-bang – Basic linear regression to Neural Networks.
The latest AI algorithms can now see like a human (Computer Vision), speak like a human (Natural Language Processing) and assist in decision making (Inferencing using Models). The newer models are much more advanced that AI seems to be – Natural Language Processing algorithms can now carry out tasks on speaking, predict a sentence and even tell what sentiment it has.
Computer Vision Algorithms are now assisting doctors to detect defects and diseases through X-rays and MRI images. This all points to the immense capability in the field of AI. In future it is predicted that setting up an AI system will be equivalent to setting up the mind of a human being – A situation called singularity.
More opportunities coming up
As seen previously, there are a lot of opportunities coming up in Data Science field. With its vast number of applications in every sector, there are a lot of openings coming up. Start-ups and developed industries are now shifting to AI solutions because of its dynamic nature and intelligent decision-making capability. The number of jobs in Data Science is expected to rise in the coming years. Especially Post COVID, organizations experienced a surge in various technologies, out of which Data Science is one of the major fields. Data Science and Artificial Intelligence is one of the most demanded fields for research to happen. Aspiring researchers have a good demand in the AI and ML field. Tech giants have marked their presence in improving AI algorithms, making the systems more efficient and “Intelligent”. Artificial General Intelligence is one of the applications of AI which is focused on varied problem solving rather than a restricted problem domain and is expected to bring a significant change in the AI focused problem solving.
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