How is Big Data Analytics shaping up Internet of Things?

Big Data Management, Data Science, Big Data

Article | May 16, 2023

We currently live in the age of data. It’s not just any kind of data, but big data. The current data sets have become huge, complicated, and quick, making it difficult for traditional business intelligence (BI) solutions to handle. These dated BI solutions are either unable to get the data, deal with the data, or understand the data. It is vital to handle the data aptly since data is everywhere and is being produced constantly. Your organization needs to discover any hidden insights in your datasets. Going through all the data will be doable with the right tools like machine learning (ML) and augmented analytics. According to Gartner, augmented analytics is the future of data analytics and defines it as: “Augmented analytics uses machine learning/artificial intelligence (ML/AI) techniques to automate data preparation, insight discovery, and sharing. It also automates data science and ML model development, management, and deployment.” Augmented analytics is different from BI tools because ML technologies work behind the scenes continuously to learn and enhance results. Augmented analytics facilitates this process faster to derive insights from large amounts of structured and unstructured data to gain ML-based recommendations. In addition, it helps to find patterns in the data that usually go unnoticed, removes human bias, and allows predictive capabilities to inform an organization of what to do next. Artificial intelligence has brought about an augmented analytics trend, and there has been a significant increase in the demand for augmented analytics. Benefits of Augmented Analytics Organizations now understand the benefits of augmented analytics which has led them to adopt it to deal with the increasing volume of structured and unstructured data. Oracle identified top four reasons organizations are opting for augmented analytics: Data Democratization Augmented data science availability to everyone has become a possibility thanks to augmented analytics. Augmented analytics solutions come prebuilt with models and algorithms, so data scientists are not needed to do this work. In addition, these augmented analytics models have user-friendly interfaces, making it easier for business users and executives to use them. Quicker Decision-making You will receive suggestions and recommendations through augmented analytics about which datasets to incorporate in analyses, alert users with dataset upgrades, and recommend new datasets when the results are not what the users expect. With just one click, augmented analytics provides precise forecasts and predictions on historical data. Programmed Recommendations Natural language processing (NLP) is featured on the augmented analytics platforms enabling non-technical users to question the source data easily. Interpreting the complex data into text with intelligent recommendations is automated by natural language generation (NLG), thus speeding up the analytic insights. Anyone using the tools can find out hidden patterns and predict trends to optimize the time it takes to go from data to insights to decisions using automated recommendations for data improvement and visualization. Non-expert users can use NLP technology to make sense of large amounts of data. Users can ask doubts about data using typical business terms. The software will find and question the correct data, making the results easy to digest using visualization tools or natural language output. Grow into a Data-driven Company It is more significant to understand data and business while organizations are rapidly adjusting to changes. Analytics has become more critical to doing everything from understanding sales trends, to segment customers, based on their online behaviors, and predicting how much inventory to hold to strategizing marketing campaigns. Analytics is what makes data a valuable asset. Essential Capabilities of Augmented Analytics Augmented analytics reduces the repetitive processes data analysts need to do every time they work with new datasets. It helps to decrease the time it takes to clean data through the ETL process. Augmented analytics allows more time to think about the data implications, discover patterns, auto-generated code, create visualizations, and propose recommendations from the insights it derives. Augmented analytics considers intents and behaviors and turns them into contextual insights. It presents new directions to look at data and identify patterns and insights companies would have otherwise missed out on completely- thus altering the way analytics is used. The ability to highlight the most relevant hidden insights is a powerful capability. Augmented analytics, for example, can help users manage the context at the explanatory process stage. It understands the values of data that are associated with or unrelated to that context, which results in powerful and relevant suggestions that are context-aware. Modern self-service BI tools have a friendly user interface that enables business users with low to no technical skills to derive insights from data in real-time. In addition, these tools can easily handle large datasets from various sources in a quickly and competently. The insights from augmented analytics tools can tell you what, why, and how something happened. In addition, it can reveal important insights, recommendations, and relationships between data points in real-time and present it to the user in the form of reports in conversational language. Users can have data queries to get insights through the augmented analytics tools. For example, business users can ask, “How was the company’s performance last year?” or “What was the most profitable quarter of the year?” The systems provide in-depth explanations and recommendations around data insights, clearly understanding the “what” and the “why” of the data. It enhances efficiency, decision-making, and collaboration between users and encourages data literacy and data democracy throughout an organization. Augmented Analytics: What’s Next? Augmented analytics is going to change the way people understand and examine data. It has become a necessity for businesses to survive. It will simplify and speed up the augmented data preparation, cleansing, and standardization of data, thus assist businesses to focus all their efforts on data analysis. BI and analytics will become an immersive environment with integrations allowing users to interact with their data. New insights and data will be easier to access through various devices and interfaces like mobile phones, virtual assistants, or chatbots. In addition, it will help decision-making by notifying the users of alerts that need immediate attention. This will help businesses to stay updated about any changes happening in real-time. Frequently Asked Questions What are the benefits of augmented analytics? Augmented analytics helps companies become more agile, gain access to analytics, helps users make better, faster, and data-driven decisions, and reduces costs. How important is augmented analytics? Augmented analytics build efficiency into the data analysis process, equips businesses and people with tools that can answer data-based questions within seconds, and assist companies in getting ahead of their competitors. What are the examples of augmented analytics? Augmented analytics can help retain existing customers, capitalize on customer needs, drive revenue through optimized pricing, and optimize operations in the healthcare sector for better patient outcomes. 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Business Intelligence, Big Data Management, Big Data

Article | July 18, 2023

Almost every day, we come across some news about data breaches and cyber-attacks, which has forced us to discuss and debate more about the importance of data and how we can protect it. Some of the most significant data breaches of 2021 and 2022 were the Microsoft Software data breach (2021), Facebook data breach (2021), Bureau Veritas cyberattack (2021), Gloucester Council Cyberattack (2022), and many more. Cyber-attacks were rated as the fifth-top risk in 2020 by the World Economic Forum. The COVID-19 pandemic has increased cybercrime by 600% compared to the pre-pandemic period. According to Accenture, 43% of cyber-attacks target small businesses, but only 14% are prepared to defend themselves. As per Cybersecurity Ventures, cybercrime will cost businesses worldwide $10.5 trillion annually by 2025 up from $3 trillion in 2015. Unfortunately, we still don’t have a concrete solution, which can be called a one-size-fits-all approach for preventing security breaches or even handling them when they happen. However, there are numerous methods for minimizing data exposure. Data encryption and regular data back-ups have become two of the most effective and widely used methods for protecting against data exposure. Importance of Encryption The facts and figures mentioned above highlight the growing importance of encryption for data security. No business, regardless of size, is immune to the risk of a data breach. Encryption has become the need of the hour because it is considered the last line of defense. Many applications and websites depend upon user passwords and password verification software to access sensitive information. Apart from knowing how to generate a safe password, users have minimal options to encrypt their password. This is why they use a password manager to keep their passwords secure. A good password manager must use strong encryption to protect what is a gold mine of data. Businesses can choose an encryption type as per their preference and requirements. There are two types of encryption for scrambling or masking data. They are as follows: Symmetric Encryption Symmetric encryption is the simplest way to protect data from hackers. It has just one key, and everyone uses it to encrypt and decrypt data. For example, you encrypt a file and send it to your manager, who uses the same key to unlock or decrypt it. Asymmetric Encryption Asymmetric encryption encrypts using two keys. The public key is used for encryption, whereas the private key is used for decryption. For example, the private key can be used to encrypt a file, but only the manager can use the private key to decrypt it. Reasons why encrypting your data is crucial Encryption has evolved as a critical component in securing data from malicious attacks of any kind. However, some organizations are still hesitant about encryption because they are unaware of the benefits. So let’s look at the top reasons why businesses should encrypt their data. Encryption is the Last Line of Defense When we talk about cyberattacks, companies are often helpless when it comes to preventing them. In this case, encryption acts as a protector making it difficult to encrypt data without the decryption key. This is one of the significant implications of encryption, and hence we call it the last line of defense. Encryption is Cheap to Implement From smartphones to Microsoft Windows, almost every device, software, and operating system today has encryption technology. Also, there are many encryption programs available for free download, programs like LastPass, TunnelBear, HTTPS and others. Encryption protects data on the go One of the biggest data security threats companies face is when data is on the move. It means portable devices, whether mobile phones, USBs, laptops, or tablets containing sensitive data, move outside a company’s security network. A misplaced USB, a laptop left unsupervised, or a mobile phone forgotten in a coffee shop can sometimes be disastrous. Encryption makes sure that if a device is lost or stolen, its data can't be read or misused by anyone who doesn't have a key to decrypt it. Encryption Algorithms to Secure Your Business Network & How Encryption Works Various encryption algorithms help secure your business networks. But before we dive into the details of encryption algorithms, it is important to know the workings of encryption. How Encryption Works Unencrypted information or data, such as blogs like the one you are reading, is written in plaintext. At its core, data encryption employs an encryption algorithm to distort or mask plaintext, resulting in “ciphertext”, which humans interpret as alphanumeric nonsense. An encryption algorithm is incomplete and cannot convert plaintext to ciphertext and vice-versa. Encryption Algorithms to Secure Your Business Data As data security threats have become more sophisticated and aggressive, maintaining online security has become critical. Therefore, modern encryption has grown more complex to protect private data. Different types of encryption algorithms can help you enhance your data encryption strategy. If required, you can create your own algorithm. However, there are a few standard encryption algorithms that you can consider. Data Encryption Standard (DES) The data encryption standard is an older symmetric-key method of encrypting data that was utilized as a standard method by the United States government. But it was withdrawn later as it was not considered secure enough for many modern applications. A DES key has 64 binary digits (also known as bits), 56 of which are randomly generated by the algorithm. The other eight are utilized for the detection of errors. People who use DES know the encryption algorithm, but unauthorized entities do not have the decryption key. Data encryption standard is insecure because the 56-bit key is too small. Triple Data Encryption Standard (Triple DES) The triple data encryption standard (also called Triple DES, or TDES or 3DES) is the newer and safer version of the data encryption standard. There are two kinds of triple DES: two-key and three-key, based on the number of generated keys. Triple DES runs DES three times; the data is encrypted, decrypted, and then again encrypted before it is sent to the receiving party. Rivest-Shamir-Adleman (RSA) Popularly known as RSA, it is named after its creators, Ron Rivest, Adi Shamir, and Len Adelman. RSA is an asymmetric encryption algorithm primarily utilized to share data over insecure networks. RSA is a popular option for secure data transmission. It leverages a robust algorithm for data scrambling. Advanced Encryption Standard (AES) Today the advanced encryption standard (AES) is extensively used and supported in both hardware and software in today's encryption. There have been no realistic cryptanalytic attacks against AES identified so far. Additionally, AES includes built-in key length flexibility, which provides some 'future-proofing' against advancements in the capacity to execute exhaustive key searches. Twofish In terms of encryption techniques, Twofish is regarded as a highly safe solution. Any encryption standard that employs a key length of 128 bits or more is theoretically immune to brute force attacks. This is where Twofish comes into play. Twofish is vulnerable to side channel attacks because it employs "pre-computed key-dependent S-boxes." This is because the tables have already been calculated. Creating these tables key-dependent, on the other hand, helps to limit that danger. Conclusion Cybercrimes constantly evolve, compelling security experts to come up with new strategies and methods. Irrespective of the size or industry, every business can benefit from taking extra steps to protect its data. Whether it is about protecting your email communication or storing data, you should be sure that you include encryption in your lineup of security tools. FAQ What are public and private keys? Both public and private keys are employed in asymmetric encryption. A public key is a key that is known by everyone and is not a secret. Anyone can use it to encrypt data. But, the data can only be decrypted by the user who has access to the private decryption key. Is it possible to break encryption? Yes, in a word. While decrypting encrypted data would require a significant amount of processing power and expertise, it is still possible. It is, however, extremely unusual due to the resources needed. Is it safe to use encryption? Encryption is extremely secure. The majority of encryption standards provide a degree of protection that is unrivaled by other cybersecurity precautions. The U.S. National Security Agency (NSA) has authorized the AES 256 encryption standard due to its fantastic dependability.

Business Intelligence, Big Data Management, Big Data

Article | July 4, 2023

All business functions whether it is finance, marketing, procurement, or others find using data and analytics to drive success an imperative for today. They want to make informed decisions and be able to predict trends that are based on trusted data and insights from the business, operations, and customers. The criticality of delivering these capabilities was emphasised in a recent report, “The Importance of Unified Data and Analytics, Why and How Preintegrated Data and Analytics Solutions Drive Busines Success,” from Forrester Consulting. For approximately two-thirds of the global data warehouse and analytics strategy decision-makers surveyed in the research, their key data and analytics priorities are:

Article | April 20, 2020

Achieving organizational success and making data-driven decisions in 2020 requires embracing tech tools like Data Analytics and collecting, storing and analysing data isn’t.The real data-driven, measurable growth, and development come with the establishment of data-driven company culture.In this type of culture company actively uses data resources as a primary asset to make smart decisions and ensure future growth. Despite the rapid growth of analytic solutions, a recent Gartner survey revealed that almost 75% of organizations thought their analytics maturity had not reached a level that optimized business outcomes. Just like with any endeavor, your organization must have a planned strategy to achieve its analytical goals. Let’s explore ways for overcoming common blockers, and elements used in successful analytics adoption strategies. Table of Contents: - AMM: Analytic Maturity Model - What are the blockers to achieving a strategy-driven analytics? - What are the adoption strategies to achieve an analytics success? - Conclusion AMM: Analytic Maturity Model The Analytic Maturity Model (AMM) evaluates the analytic maturity of an organization.The model identifies the five stages an organization travels through to reach optimization. Organizations must implement the right tools, engage their team in proper training, and provide the management support necessary to generate predictable outcomes with their analytics. Based on the maturity of these processes, the AMM divides organizations into five maturity levels: - Organizations that can build reports. - Organizations that can build and deploy models. - Organizations that have repeatable processes for building and deploying analytics. - Organizations that have consistent enterprise-wide processes for analytics. - Enterprises whose analytics is strategy driven. READ MORE:EFFECTIVE STRATEGIES TO DEMOCRATIZE DATA SCIENCE IN YOUR ORGANIZATION What are the blockers to achieving a strategy-driven analytics? - Missing an Analytics Strategy - Analytics is not for everyone - Data quality presents unique challenges - Siloed Data - Changing the culture What are the adoption strategies to achieve analytic success? • Have you got a plan to achieve analytic success? The strategy begins with business intelligence and moves toward advanced analytics. The approach differs based on the AMM level. The plan may address the strategy for a single year, or it may span 3 or more years. It ideally has milestones for what the team will do. When forming an analytics strategy, it can be expensive and time consuming at the outset. While organizations are encouraged to seek projects that can generate quick wins, the truth is that it may be months before any actionable results are available. During this period, the management team is frantically diverting resources from other high-profile projects. If funds are tight, this situation alone may cause friction. It may not be apparent to everyone how the changes are expected to help. Here are the elements of a successful analytics strategy: • Keep the focus tied to tangible business outcomes The strategy must support business goals first. With as few words as possible, your plan should outline what you intend to achieve, how to complete it, and a target date for completion of the plan. Companies may fail at this step because they mistake implementing a tool for having a strategy. To keep it relevant, tie it to customer-focused goals. The strategy must dig below the surface with the questions that it asks. Instead of asking surface questions such as “How can we save money?”, instead ask, “How can we improve the quality of the outcomes for our customers?” or “What would improve the productivity of each worker?” These questions are more specific and will get the results the business wants. You may need to use actual business cases from your organization to think through the questions. • Select modern, multi-purpose tools The organization should be looking for an enterprise tool that supports integrating data from various databases, spreadsheets, or even external web based sources. Typically, organizations may have their data stored across multiple databases such as Salesforce, Oracle, and even Microsoft Access. The organization can move ahead quicker when access to the relevant data is in a single repository. With the data combined, the analysts have a specific location to find reports and dashboards. The interface needs to be robust enough to show the data from multiple points of view. It should also allow future enhancements, such as when the organization makes the jump into data science. Incorta’s Data Analytics platform simplifies and processes data to provide meaningful information at speed that helps make informed decisions. Incorta is special in that it allows business users to ask the same complex and meaningful questions of their data that typically require many IT people and data scientist to get the answers they need to improve their line of business. At the digital pace of business today, that can mean millions of dollars for business leaders in finance, supply chain or even marketing. Speed is a key differentiator for Incorta in that rarely has anyone been able to query billions of rows of data in seconds for a line of business owner. - Tara Ryan, CMO, Incorta Technology implementations take time. That should not stop you from starting in small areas of the company to look for quick wins. Typically, the customer-facing processes have areas where it is easier to collect data and show opportunities for improvement. • Ensure staff readiness If your current organization is not data literate, then you will need resources who understand how to analyze and use data for process improvement. It is possible that you can make data available and the workers still not realize what they can do with it. The senior leadership may also need training about how to use data and what data analytics makes possible. • Start Small to Control Costs and Show Potential If the leadership team questions the expense, consider doing a proof of concept that focuses on the tools and data being integrated quickly and efficiently to show measurable success. The business may favor specific projects or initiatives to move the company forward over long-term enterprise transformations (Bean & Davenport, 2019). Keeping the project goals precise and directed helps control costs and improve the business. As said earlier, the strategy needs to answer deeper business questions. Consider other ways to introduce analytics into the business. Use initiatives that target smaller areas of the company to build competencies. Provide an analytics sandbox with access to tools and training to encourage other non-analytics workers (or citizen data scientists) to play with the data. One company formed a SWAT team, including individuals from across the organization. The smaller team with various domain experience was better able to drive results. There are also other approaches to use – the key is to show immediate and desirable results that align with organizational goals. • Treating the poor data quality What can you do about poor data quality at your company? Several solutions that can help to improve productivity and reduce the financial impact of poor data quality in your organization include: • Create a team to set the proper objectives Create a team who owns the data quality process. This is important to prove to yourself and to anyone with whom you are conversing about data that you are serious about data quality. The size of the team is not as important as the membership from the parts of the organization that have the right impact and knowledge in the process. When the team is set, make sure that they create a set of goals and objectives for data quality. To gauge performance, you need a set of metrics to measure the performance. After you create the proper team to govern your data quality, ensure that the team focuses on the data you need first. Everyone knows the rules of "good data in, good data out" and "bad data in, bad data out." To put this to work, make sure that your team knows the relevant business questions that are in progress across various data projects to make sure that they focus on the data that supports those business questions. • Focus on the data you need now as the highest priority Once you do that, you can look at the potential data quality issues associated with each of the relevant downstream business questions and put the proper processes and data quality routines in place to ensure that poor data quality has a low probability of Successful Analytics Adoption Strategies, continuing to affect that data. As you decide which data to focus on, remember that the key for innovators across industries is that the size of the data isn’t the most critical factor — having the right data is (Wessel, 2016). • Automate the process of data quality when data volumes grow too large When data volumes become unwieldy and difficult to manage the quality, automate the process. Many data quality tools in the market do a good job of removing the manual effort from the process. Open source options include Talend and DataCleaner. Commercial products include offerings from DataFlux, Informatica, Alteryx and Software AG. As you search for the right tool for you and your team, beware that although the tools help with the organization and automation, the right processes and knowledge of your company's data are paramount to success. • Make the process of data quality repeatable It needs regular care and feeding. Remember that the process is not a one-time activity. It needs regular care and feeding. While good data quality can save you a lot of time, energy, and money downstream, it does take time, investment, and practice to do well. As you improve the quality of your data and the processes around that quality, you will want to look for other opportunities to avoid data quality mishaps. • Beware of data that lives in separate databases When data is stored in different databases, there can be issues with different terms being used for the same subject. The good news is that if you have followed the former solutions, you should have more time to invest in looking for the best cases. As always, look for the opportunities with the biggest bang for the buck first. You don't want to be answering questions from the steering committee about why you are looking for differences between "HR" and "Hr" if you haven't solved bigger issues like knowing the difference between "Human Resources" and "Resources," for example. • De-Siloing Data The solution to removing data silos typically isn’t some neatly packaged, off-the-shelf product. Attempts to quickly create a data lake by simply pouring all the siloed data together can result in an unusable mess, turning more into a data swamp. This is a process that must be done carefully to avoid confusion, liability, and error. Try to identify high-value opportunities and find the various data stores required to execute those projects. Working with various business groups to find business problems that are well-suited to data science solutions and then gathering the necessary data from the various data stores can lead to high-visibility successes. As value is proved from joining disparate data sources together to create new insights, it will be easier to get buy-in from upper levels to invest time and money into consolidating key data stores. In the first efforts, getting data from different areas may be akin to pulling teeth, but as with most things in life, the more you do it, the easier it gets. Once the wheels get moving on a few of these integration projects, make wide-scale integration the new focus. Many organizations at this stage appoint a Chief Analytics Officer (CAO) who helps increase collaboration between the IT and business units ensuring their priorities are aligned. As you work to integrate the data, make sure that you don’t inadvertently create a new “analytics silo.” The final aim here is an integrated platform for your enterprise data. • Education is essential When nearly 45% of workers generally prefer status quo over innovation, how do you encourage an organization to move forward? If the workers are not engaged or see the program as merely just the latest management trend, it may be tricky to convince them. Larger organizations may have a culture that is slow to change due to their size or outside forces. There’s also a culture shift required - moving from experience and knee-jerk reactions to immersion and exploration of rich insights and situational awareness. - Walter Storm, the Chief Data Scientist, Lockheed Martin Companies spend a year talking about an approved analytics tool before moving forward. The employees had time to consider the change and to understand the new skill sets needed. Once the entire team embraced the change, the organization moved forward swiftly to convert existing data and reports into the new tool. In the end, the corporation is more successful, and the employees are still in alignment with the corporate strategy. If using data to support decisions is a foreign concept to the organization, it’s a smart idea to ensure the managers and workers have similar training. This training may involve everything from basic data literacy to selecting the right data for management presentations. However, it cannot stop at the training; the leaders must then ask for the data to move forward with requests that will support conclusions that will be used to make critical decisions across the business. These methods make it easier to sell the idea and keep the organization’s analytic strategy moving forward. Once senior leadership uses data to make decisions, everyone else will follow their lead. It is that simple. Conclusion The analytics maturity model serves as a useful framework for understanding where your organization currently stands regarding strategy, progress, and skill sets. Advancing along the various levels of the model will become increasingly imperative as early adopters of advanced analytics gain a competitive edge in their respective industries. Delay or failure to design and incorporate a clearly defined analytics strategy into an organization’s existing plan will likely result in a significant missed opportunity. READ MORE:BIG DATA ANALYTICS STRATEGIES ARE MATURING QUICKLY IN HEALTHCARE