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To become an Intelligent Enterprise, organizations need to put resources into three key areas: an intelligent suite, smart technologies, and a digital platform. Data is the heart of any organization. Although, IT teams have to work very hard with regards to data management and delivery operations. Over the years, there is a need for a smart platform handling IT delivery operation. DeepCoding’s Delivery Intelligence Platform (DIP) saw that need which gathers raw data in any format across all existing IT delivery management tools to create a consolidated 360 view of the entire organization’s IT delivery operation.Standing Out in Immerse Market Seasoned Leaders
Both DeepCoding founders are B2B software and IT delivery veterans: Sebastien Adjiman, Co-founder and CEO is a seasoned business leader with over two decades of experience founding businesses and assuming executive roles at Enterprise Software companies. Prior to founding DeepCoding, Sebastien served in a variety of top executive roles, including VP business development at BiScience, a Business Intelligence solution provider and VP and Head of EMEA at Clarizen, a leading PPM solution provider. Arnon Yaffe, Co-founder and CPO, has more than 20 years of experience in delivering very large-scale IT projects, especially when he was CEO of the largest local Oracle ERP implementation partner. Both are passionate about helping their customers, CIOs of very large IT organizations; cope with the increasing pressure brought by the digital transformation revolution, expecting IT to deliver more, faster and with the same amount of resources!Cutting-edge Intelligence tools Predict and Prevent Failures Awareness and Anticipation
Business Intelligence has helped leaders be aware of what is happening in their organisations, and it has allowed them to know when failures occur. This has streamlined today’s innovation, but the company thinks that by the time leaders are notified that a failure is happening, it’s too late. That’s why DeepCoding is overtaking and building on BI with Operational Intelligence (OI), the solution that truly drives today’s innovation and addresses its problems. OI allows leaders to know about risks before they happen and finds its root causes so that managers are not reacting to problems as they arise. Instead, with this new proactive control tower, leaders can anticipate them and focus on driving smooth innovation. DeepCoding is the first company to apply Operational Intelligence to IT Delivery Operations.Industry’s First IT Delivery Intelligence Platform
DeepCoding has created the industry’s first IT Delivery Intelligence Platform. Its unique technology is already helping large organizations get control, visibility, transparency, predictability and productivity out of their IT operations. The company operates with a transparent company culture, which creates an informed, motivated team that drives innovation.Secure Friction Against Challenges
Since DeepCoding is an AI first startup, the raw material of its product is data. The major challenge for the company was to bring the platform to the highest level of security, in order to comply with the infosec and privacy requirements of the largest group and companies in the world. This took a lot of efforts and time, but the company was able to leverage Israel’s expertise in cybersecurity and data protection to build a secure platform that answers the highest standards so its customers felt comfortable.Notable Achievements
The company raised US$1.6 million from leading VCs StageOne and AxessVentures. It has been selected to be part of the prestigious Walmart/Coke bridge program, where US fortune 500 select specific startups out of hundreds to be part of a dedicated commercialization program. DeepCoding is right now working with leading banks, software companies, retail groups as well as very large hi-tech companies.Prospective Revolution
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Business Intelligence (BI) software has had a bit of a struggle as of late. Although the concept has been around for decades, it’s been overshadowed by the shiny new thing, Big Data, which has its roots in BI but adds on much more.
Part of the problem centers around the fact many people don’t even know the difference between BI software and Big Data tools, and if you don’t know what they are, you don’t know what they do. IT consultant Eric D. Brown summed it up nicelyby stating “Business Intelligence helps find answers to questions you know. Big Data helps you find the questions you don’t know you want to ask.”
By that logic, BI software will always have a place and won’t be supplanted by Big Data because they are both working toward two very different ends. One thing Big Data does have over BI is the tools. BI tools have not been terribly actionable and often couldn’t identify what was meaningful and what should be ignored. They just presented a report on everything, which can be no better than the raw data in some instances.
Gartner notes that the analytics market, the overarching umbrella that cover both BI and Big Data, is splitting into two groups: the traditional business intelligence market and the new data discovery market, which is primarily Big Data.
In its latest Business Intelligence Magic Quadrant report, Gartner noted the differences in the two markets. BI is driven by IT and queries existing repositories, using monitoring and reporting tools and primarily digested by consultants. Big Data is used by business groups on data that’s coming in, and is primarily examined by line of business executives with visualization tools.Where BI Software Has Been, Where it’s Headed
So BI software is in no risk of being killed off by Big Data, but at the same time, it cannot stay as it is. BI must evolve, and in 2014, it went through its share of changes. So here’s where BI has been and where it is going.
1) A more user-friendly UI: BI dashboards have traditionally been designed for IT pros, but the concept of easier to read, more user-friendly UIs is bleeding over from Big Data. Companies like Tableau, Qlikview, TIBCO and Microsoft (through Excel, no less) are putting a friendlier face on BI than has traditionally been.
In its Magic Quadrant report, Gartner states that by 2024, the shifting tide of BI platform requirements will move from reporting-centric to analysis-centric apps, which means the majority of BI vendors will make governed data discovery capabilities “an expansion of, and the prime competitive capability for, their BI platform offerings.”
2) More analysis: Gartner says that by 2024, the shifting tide of BI platform requirements, moving from reporting-centric to analysis-centric, will mean the majority of BI vendors will make governed data discovery capabilities an expansion of, and the prime competitive capability for, their BI platform offerings.
In the wake of big data and its promise of real-time analytics and response, BI can’t settle for generating a report and waiting for someone to read it and act upon the data. What there are for real-time dashboards only provide a small glimpse into business activity.
To improve situation awareness for decision makers, dashboards are being upgraded to include data from multiple sources and the data brought together in a broader view of events. New data sources, beyond the usual data warehouse, are now being considered, like news feeds, industry data feeds, weather feeds, and social media.
3) Going mobile: As part of this, BI software is moving into the mobile space as smartphones and tablets become standard issue for the workforce. These devices certainly can’t do BI computation with their limited processors but they can certainly display it. This will allow for decisions to be made in the field and for remote workers to collaborate.
Already Oracle, SAP and Tableau have mobile offerings and there are undoubtedly more in the pipeline. By 2024, Gartner predicts more than 50 percent of mobile BI users will rely exclusively on mobile devices for insight delivery.
The impact of Business intelligence on online slots and the entire iGaming industry is going deeper.
The online gaming industry’s popularity is only increasing as new casino sites pop up every day. Players all around the world use their devices to access favorite games and discover new ones. So, it’s difficult for a gaming provider to secure its market share, which is why business intelligence may come in handy.
Business intelligence is a combination of analytics, data collection, visualization, and many other tools. However, its impact on online slots and the entire iGaming industry goes deeper. For that reason, let’s go further into details and see its effects.Compliance With Relevant Regulations
Many people still have misconceptions about online gaming regulations, so it might surprise some that they’re not an issue in most countries. Naturally, an online casino needs to have a regulatory license to be able to operate legally. That’s why online gambling sites stay dedicated to securing business transparency and fair playing conditions for players.
Keeping up to date with regulations and laws is even more important because gaming licenses are temporary, and online casinos need to renew them from time to time. And since it’s not easy to produce accurate and comprehensive reports for authorities, business intelligence comes in handy. BI ensures that casinos promptly produce and deliver regulatory reports to be able to keep offering
to their dedicated user base.Slot Games Online Delivery
It might come as a surprise that something as simple as slots is so challenging to develop. Keep in mind that playing style and developing software are two different matters. Therefore, many seasoned professional developers need to put in a lot of time and hard work to create a persistent slot growth.
So, if you want to
play Gates of Olympus slot machine online at Mega Casino
, you should take into account the effort put into placing the game in front of you. This is especially true if the games come with additional bonuses, incentives, or progressive jackpots — in fact, you have business intelligence to thank for many of these things.
Acknowledge the developers’ struggles by starting your online slot journey prepared — with a good technique and bankroll that will help you stay on the right path.Detailed Player Location Analysis
Naturally, online casinos aim to deliver their content to multiple locations and countries worldwide. For a variety of reasons, casinos benefit from knowing their players’ location. With a little help from business intelligence, operators can determine where slot games are most popular, whether it’s Finland, Canada, or some other country.
Having such information might help in deciding whether localized expansion is a good thing.New Trend Predictions
Another thing that business intelligence does perfectly is predict new trends. BI provides accurate reports that help identify current and future trends with players. This helps developers determine future interests, and it keeps them one step ahead of the competition.
For example, such a report can discover that a certain game has had 10x more players in one month compared to the previous one. Such information can help developers make assumptions about future trends, decide on the right time to promote the game, etc.
Some companies even go as far as to create free games to predict future trends, but you can read more about why free slots are taking over the world in
Naturally, the iGaming industry wouldn’t have grown as fast as it has without technological innovations like mobile-oriented play. The mobile gaming market was worth a staggering $79.5 billion in 2023. Reports indicate that it will keep growing and reach numbers three times as high in only seven years.
Hardly a day goes by that I don’t see another case study, white paper, research report, or press release promoting embedded Business Intelligence or analytics. However, far too many of them either ignore or minimize the two big traps of embedded BI.
Before diving into the traps, let’s define terms. Many software vendors and IT departments have added analytics or BI functionality into existing enterprise applications. The earliest examples were when finance departments asked IT to add financial modeling tools to their existing general ledger applications within their Enterprise Resource Planning (ERP) suites. Initially these tools were from third parties (Hyperion’s Essbase is a great example) that were bolted onto an organization’s Oracle or SAP financial module.
Fast forward a decade and the ERP vendors acquired those BI players and started embedding those analytics functions into a host of other apps, too, including CRM, SCM and HR. And as the vendors began to saturate their installed base, and their customers began drowning in Big Data, a big new opportunity appeared: to extend access to embedded BI way beyond a few financial analysts.
Indeed, this new goal promoted embedded BI and analytics into a host of business processes used by just about every individual in the organization. It was a nifty way for BI vendors to get beyond the 20 percent penetration rate of their current installed base.
This gave rise, however, to the two biggest traps in embedded analytics:
Trap number one: believing that there’s a substantial return on investment. The return on embedding BI remains unquantified, to a large extent.
A recent report by the Aberdeen Group, “Embedded BI: Boosting Analytical Adoption and Engagement,” lists the top four benefits of embedded BI and the percentage of the 174 respondents who achieved these benefits:
(Source: Aberdeen Group, February 2012)
The report’s author, research director Michael Lock, explains the benefits of embedded BI this way:
“Using the embedded BI approach can spread the analytical mindset to more people, and more areas of the company. Companies that develop an analytical culture can realize process improvements and add value to existing software investments. When the workforce at a company has a data driven mindset, and places great value on analytical decision making, the company is in a position to improve vital business processes and increase the yield of their investments in enterprise software.”
When asked about the quantification of the RoI of embedded BI, Lock acknowledged that “proving a tangible RoI is always a challenge.” He noted that improvements in operating margins or revenue growth are typically seen in best of class companies that use embedded BI and make it pervasive, but that it is very difficult to establish the cause and effect relationship.
I doubt if the above table and description has enough dollars and cents quantification to persuade a CFO to approve a multimillion dollar purchase order for the software, servers and other costs involved.
Trap number two: the costs. The incremental investment in expanding embedded BI access from a few highly trained analysts to a broad range of employees who depend on embedded BI to do their jobs is more than just the additional software licensing fees.
This article was published as a part of the Data Science BlogathonIntroduction
Imagine walking into a bookstore to buy a book on world economics and not being able to figure out the section of the store that has this book, assuming the bookstore has simply stacked all types of books together. You then realize how important it is to divide the bookstore into different sections based on the type of book.
Topic Modelling is similar to dividing a bookstore based on the content of the books as it refers to the process of discovering themes in a text corpus and annotating the documents based on the identified topics.
When you need to segment, understand, and summarize a large collection of documents, topic modelling can be useful.Topic Modelling using LDA:
Latent Dirichlet Allocation (LDA) is one of the ways to implement Topic Modelling. It is a generative probabilistic model in which each document is assumed to be consisting of a different proportion of topics.How does the LDA algorithm work?
The following steps are carried out in LDA to assign topics to each of the documents:
1) For each document, randomly initialize each word to a topic amongst the K topics where K is the number of pre-defined topics.
2) For each document d:
For each word w in the document, compute:
The last step is repeated multiple times till we reach a steady state where the topic assignments do not change further. The proportion of topics for each document is then determined from these topic assignments.Illustrative Example of LDA:
Let us say that we have the following 4 documents as the corpus and we wish to carry out topic modelling on these documents.
Document 1: We watch a lot of videos on YouTube.
Document 2: YouTube videos are very informative.
Document 3: Reading a technical blog makes me understand things easily.
Document 4: I prefer blogs to YouTube videos.
LDA modelling helps us in discovering topics in the above corpus and assigning topic mixtures for each of the documents. As an example, the model might output something as given below:
Topic 1: 40% videos, 60% YouTube
Topic 2: 95% blogs, 5% YouTube
Document 1 and 2 would then belong 100% to Topic 1. Document 3 would belong 100% to Topic 2. Document 4 would belong 80% to Topic 2 and 20% to Topic 1.
This assignment of topics to documents is carried out by LDA modelling using the steps that we discussed in the previous section. Let us now apply LDA to some text data and analyze the actual outputs in Python.Topic Modelling using LDA in Python: Reading the Data:
We are interested in carrying out topic modelling for the ‘Text’ column in this dataset.Importing the necessary libraries:
We will need the NLTK library to be imported as we will use lemmatization for pre-processing. Additionally, we would also remove the stop-words before carrying out the LDA. To carry out topic modelling, we need to convert our text column into a vectorized form and therefore we import the TfidfVectorizer.import nltk from nltk.corpus import stopwords #stopwords from chúng tôi import WordNetLemmatizer from sklearn.feature_extraction.text import TfidfVectorizer stop_words=set(nltk.corpus.stopwords.words('english')) Pre-processing the text:
We will apply lemmatization to the words so that the root words of all derived words are used. Furthermore, the stop-words are removed and words with lengths greater than 3 are used.def clean_text(headline): le=WordNetLemmatizer() word_tokens=word_tokenize(headline) cleaned_text=" ".join(tokens) return cleaned_text rev['cleaned_text']=rev['Text'].apply(clean_text) TFIDF vectorization on the text column:
Carrying out a TFIDF vectorization on the text column gives us a document term matrix on which we can carry out the topic modelling. TFIDF refers to Term Frequency Inverse Document Frequency – as this vectorization compares the number of times a word appears in a document with the number of documents that contain the word.vect =TfidfVectorizer(stop_words=stop_words,max_features=1000) vect_text=vect.fit_transform(rev['cleaned_text']) LDA on the vectorized text:
The parameters that we have given to the LDA model, as shown below, include the number of topics, the learning method (which is the way the algorithm updates the assignments of the topics to the documents), the maximum number of iterations to be carried out and the random state. The parameters that we have given to the LDA model, as shown below, include the number of topics, the learning method (which is the way the algorithm updates the assignments of the topics to the documents), the maximum number of iterations to be carried out and the random state.from sklearn.decomposition import LatentDirichletAllocation lda_model=LatentDirichletAllocation(n_components=10, learning_method='online',random_state=42,max_iter=1) lda_top=lda_model.fit_transform(vect_text) Checking the results:
We can check the proportion of topics that have been assigned to the first document using the lines of code given below.print("Document 0: ") for i,topic in enumerate(lda_top): print("Topic ",i,": ",topic*100,"%") Analyzing the Topics:
Let us check what are the top words that comprise the topics. This would give us a view of what defines each of these topics.vocab = vect.get_feature_names() vocab_comp = zip(vocab, comp) sorted_words = sorted(vocab_comp, key= lambda x:x, reverse=True)[:10] print("Topic "+str(i)+": ") for t in sorted_words: print(t,end=" ") print("n")
In addition to LDA, other algorithms can be leveraged to carry out topic modelling. Latent Semantic Indexing (LSI), Non-negative matrix factorization are some of the other algorithms one could try to carry out topic modelling. All these algorithms, like LDA, involve feature extraction from document term matrices and generating a group of terms that are differentiating from each other, which eventually lead to the creation of topics. These topics can help in assessing the main themes of a corpus and hence organizing large collections of textual data.About Author
an array of industries.
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Cyberwarfare isn’t a threat of the future; it is a visible and present menace. Although the cyberwarfare theme may sound like some CGI-integrated modern game or a sci-fi film, the reality is that our linked world is filled with multiple security gaps, which is very unfortunate.
Mobile applications and e-commerce have greatly facilitated consumer convenience due to the digital revolution. Furthermore, expanding the cloud and transitioning to remote work settings benefit productivity and performance. Nevertheless, the contemporary internet gives criminals and political activists a chance to further their goals, whether monetary gain, political influence, or societal unrest.
There have been several reports of operational tech attacks recently, including hardware and software assets such as monitors, control equipment, or assets & processes. Some sources revealed that these attacks were primarily developed from immediate process disruption ranging from shutting down a plant to compromising the integrity of industrial environments with the intent of causing a menace to it.
Cyberwarfare threats can take many forms, some of which are as follows −Website Defacement
It is a low-level cybercrime that usually targets small websites with poor management and security. Although the perpetrators are frequently young amateur hackers with no malice in their hearts, the propaganda around such incidents is a worrying trend for international relations.
Juvenile pro-Iranian hackers accepted responsibility for the 2023 website defacement, posting their social media usernames with protest notes. For several years, organizations in China and Taiwan carried out reciprocal defacing assaults, throwing gasoline to the flames of an already contentious relationship.Attacks via Distributed Denial of Service (DDoS)
DDoS attacks use various devices to concurrently overwhelm the security of an IT network with a flood of data from several sources. Hackers employ this tactic to disrupt the system and deflect security personnel from a more demonic incursion, such as the introduction of Ransomware.
This attack is becoming more common in business settings, particularly in the financial sector. And in the middle of 2023, DDoS attacks targeted 200 institutions in Belgium, including the websites of the government and parliament.
Cyber warfare can be launched by one person, a group of individuals, a corporation, or even a nation-state. Security professionals closely monitor the development of DDoS attacks, examining their sources and how they impact individual companies and entire countries.Attacks using Ransomware
Ransomware is a sort of malware-malicious software-that prevents victims from accessing computer files, data, or applications unless they pay the attacker. Cybercriminals typically provide an ultimatum: pay the ransom to obtain a decryption code to open their IT systems or lose everything forever.
This rising problem has gone beyond personal attacks, forcing firms to pay millions to extortion groups. Ransomware assaults were more widespread than ever in 2023, affecting everything from pipelines to hospitals. While the bad actors’ goals for these assaults are primarily financial, the same strategies may be utilized as a part of a varied array of attacks as part of a comprehensive cyberwarfare campaign.Definitive roles and responsibilities of individuals
Assign an Operational Technologist security manager for every site, who will assign and document security duties and responsibilities for all employees, senior managers, and third parties.
Make sure that adequate training and awareness are provided.
Every Operational Technologist employee must possess the skills required for their job. Employees in every site must be instructed about security risks, the most common attack vectors, and what to do in the security incident event.Incorporate and evaluate incident response procedures
Ascertain that each facility adopts and upholds an operational technologists-specific security incident management procedure. This procedure consists of four stages: incident planning, attack detection, cyberattack analysis, containment and eradication strategies, and post-incident activities.Disaster Recovery, Backups, and Restoration
Ascertain that your disaster recovery, backup, and restoration procedures are adequate. Avoid keeping backup data in the exact location as the backed-up system to lessen the effects of natural disasters like wildfire. Additionally, backup copies must be protected from unlawful use or disclosure. The backup must be able to be restored on a new server or virtual machine to handle high-severity circumstances.Set up Correct Network Isolation
Networks used for operational technology must be physically, logically, and externally segregated from all other networks. All network communication among an OT and any other network component must pass via a secure gateway solution, such as a demilitarized zone (DMZ). To authenticate at the gateway, interaction with OT must employ multi-factor authentication.Implement Real-Time Detection and Collecting Logs
Appropriate rules or processes for an automatic logging and evaluating prospective and actual security incidents must be in place. These should contain specific retention durations for security logs and safeguards against manipulation or unauthorized alteration.Implement a Safe Configuration Strategy
Endpoints, servers, network devices, and field devices all require secure settings to be defined, standardized, and deployed. Endpoint security software, like anti-malware, must be enabled and activated on any OT components that support it.The Formal Patching Procedure
Create a procedure for qualifying patches from equipment makers before deployment. We will only apply the patches once they have been certified and only on the suitable systems and at a specific frequency.Why Is 2023 a Game-Changer in Cybersecurity?
The most significant distributed Amazon stopped the denial of service (DDoS) assault in February 2023. However, as we approach 2023, we must consider more than just e-commerce security. Political discontent between numerous superpowers has already had some media sources expressing forecasts of a “Cyber Cold War.”
To create a global Counter-Ransomware Initiative, the United States sponsored a meeting in October 2023 with participation from 30 nations. The online discussion of the National Security Council was the first important step in forging a unified defensive front and engaging law enforcement on serious cybersecurity risks, such as the unauthorized use of bitcoin.Conclusion
Imagine learning that your nation has been the subject of a significant, well-coordinated cyberattack when you turn on the television in the morning. Banks, energy and utility firms, transportation hubs, and hospitals have all been affected by interruptions brought on by hackers who have penetrated the highest levels of the government and critical infrastructure.
While it may appear unlikely, this situation is feasible today. As technology progresses and political turbulence shatters international connections, particularly between powerful countries, businesses must do more to secure their systems.
Cyberspace warfare is unpredictable and hard to observe. Nevertheless, security teams learn something from every incident. Before an actual disaster occurred, many warned OpenSea and Poly Network about their vulnerabilities. Governmental organizations might not be so lucky.
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