The unmistakable need for datafication
Since the term big data started this big wave of everyone turning to data as a resource, many companies jumped on board. But it’s easy to utilize data when you can precisely point it out or quantify it. What happens with all the events that you previously couldn’t pinpoint? This is where datafication comes in. Every second of our life is an event. Our behavior, even the behavior and actions of machines and similar, can be described, but it’s often abstract enough that you can’t use numbers or letters to do that. However, just by identifying these events, technological advances (IoT for example) have tried to make these events into useful data points. What is datafication? Datafication is the process of turning events and information into useful, accessible, and actionable data. What does that mean? It means that behavior, actions, and events, that weren’t expressed as data, can now be turned into something utilizable in data-driven projects and analytics. Information that wasn’t measurable before, is now. Even though datafication was a term coined in 2013, it is relevant today more than ever. For example, looking at an average person’s day, we can see how many events and actions occur. And each of them can be described and measured due to technology that tracks our activity. Sensors, smart devices, production processes, business processes, and others, collect and generate large amounts of data that move at a fast pace. Datafication brings those assets to the forefront and can be used to stimulate or generate revenue, minimize risks, and maximize ROI. Datafiction collects that data, stores it, processes it, and analyzes it through various tools, algorithms, and methods to deliver valuable insights and metrics. But it should not be equalized with digitization. Datafication is a broader aspect of converting events into purposeful data that brings value. Datafication defines the quality and potential of any business Quality data creates value. Without good data, you can’t have successful and accurate data-driven projects, especially in machine learning and AI. A larger data set or more relevant data means greater accuracy and better performance of data models. Not everything needs or should be turned into actionable data, but identifying that what could bring value, will strengthen the business and its performance. Ultimately, datafication can be defined as a new business model. By turning focus to data and everything that can be successfully turned into data points, a new strategy emerges. There is no spontaneous decision-making based solely on personal preferences or intuition (not to say this isn’t valuable), but rather on specific data-backed information and correct metrics and insights. Now, certain decisions have a good foundation and can be explained on the “why, how, and what for” front. But, datafication is not only about decision-making. It’s also about creating business processes that can be optimized and run smoothly. It’s about creating a customer experience that will maximize consumption and loyalty. Imagine increasing revenue, ROI, lowering costs, raising efficiency, and more, solely because your business decided to utilize its greatest asset – data that previously wasn’t available, but now is. Although, it should be mentioned that datafication is not easy or quick to implement. It will bring loads of benefits and open up new opportunities if properly executed. However, it should be done carefully and with the help of experts. Sometimes, datafying something is not optimal or even correct. Data without context is useless Datafication doesn’t mean only transforming events into data. It is also about implementing data catalogs, metadata, new forms of data storage, processes, and procedures in data collection and processing. It’s about giving meaning to data. For example, data lineage and data catalogs are important in ensuring that data comes from a trustworthy source, thus trying to avoid data poisoning and similar threats or data manipulation. With data catalogs, you can trace data back to the source. Each step from transforming daily occurrences to data points is vital in ensuring datafication is expertly done. As said previously, not everything should be turned into quantifiable data points. It might seem like every event in your business is of immense value, but if you can’t explain it in a way that benefits your processes or your strategy, they might not be worth the trouble. Each piece of information should bring something to the table. Its context is what makes it valuable. That’s why it’s important to define data sources and important information before starting any datafication process. AI and ML drive the need for better data With the way AI and ML are evolving, data needs to keep up. Each model gets better depending on the data it uses and how much data can it ingest. It could probably be fine with the traditional sorts of data somebody collects or generates, but the point is to find potential and unused data to discover new frontiers and untapped opportunities. Some behaviors or events weren’t described before or they couldn’t be, but now with new technologies it all changes. That’s why AI and ML models can work on data that opens up new doors and on new events that enhance business operations. By datafying almost every event, interaction, or behavior inside and outside of a business, one can predict the behavior of various processes, operations, or customer behavior and purchases. Pushing that information to the forefront unleashes AI and ML to its fullest potential. Towards the necessity of data It is not always easy to balance the need for data and not turning everything into data. Those two, let’s call them forces, will pull businesses in two directions. But, you cannot disregard the fact that if your organization isn’t data-driven yet, it should be. Even though datafication is nothing new, it has gained new traction with the appearance and rising magnitude of AI and machine learning. Companies all around will speed up the process of getting more data-driven just to stay on top of the market. It’s inevitable. But data doesn’t just happen. We turn happening around us
Machine unlearning – a new frontier
Since there is so much talk about machine learning and ways it could be utilized, it is time to mention the exact opposite of it. We have mentioned so many times that the ML model is only as good as the data it was trained on. But what happens when that data is no longer of service to us? The issue arises when the model is already trained on it, and now you have to exclude it or revert the process. From machine learning, we turn to machine unlearning. A bit of a black box still, machine unlearning is gaining track. There are some scientific articles on it, but practical applications are almost still in its wraps. Especially in large language models (LLMs), it could be tricky to make the model, sort of, forget some parts of the data set it was already trained on. A theoretical approach to machine unlearning Machine unlearning is an emerging subfield of machine learning whose sole goal is to remove or forget specific subsets of data that the model was trained on or influenced by. It’s the process of unlearning or forgetting certain data points to retrain the model without that specific data or information. This derives from data privacy laws and “The right to be forgotten”. Private data and data whose security could be in jeopardy are something we definitely don’t want in some ML or AI models. However, once the model has been trained on data sets (training data) it is fairly difficult to revert the process without scraping the whole process which is extremely costly and time-consuming. Machine unlearning could be defined as the process of not only removing data and knowledge but also updating or adapting the trained model to ensure compliance with privacy and security and a higher level of accuracy. Let’s not forget that this approach is not something we can completely be sure about since the outcome of removing data could sometimes lead to model corruption and breakage. If you remove certain data points from the model, how can you be sure it wasn’t something integral that made the model work in high accuracy? Known ways to machine unlearning Despite being such an interesting concept, machine unlearning still requires more advancements. On a smaller scale, ML models can be reverted by retraining. Yes, it will prolong the time to get expected results from machine learning, but you will be sure that you’ve removed unwanted data sets. But, on the other hand, with larger models, retraining is far more expensive and requires more time and effort to do so. Retraining deep learning models or LLMs has those issues with cost and time. Machine unlearning can be grouped into two main categories: exact unlearning and approximate unlearning methods. Exact unlearning is done by removing certain data points (those we want the model to forget) by retraining the model from scratch without the data we want to exclude from the training set. As mentioned above, this is expensive. It’s also not sustainable, since it’s more likely that you will have to remove data points more than once. Forgetting data will be a recurring problem in large datasets so it’s not optimal to retrain the model every time this happens. Approximate unlearning methods try to approximate data points that would be included in the training set as if the model was trained on it without the data it has to unlearn. Often it is done by introducing new data points to replace or overwrite those we want to be forgotten. Or it finds outliers to be excluded from data sets. Various methods are being tested to see which ones will efficiently perform machine unlearning, but it still remains to see which ones are going to be fully efficient and reliable. Machine unlearning is still going through examination and experiments and there is no definite approach to the best practices. Why and where will it appear? Privacy laws, copyright issues, and data poisoning are only some of the factors that influence the development of machine unlearning and the evergrowing need for it. The need to remove unwanted or outdated information is driving advancements in unlearning. Often, data can be also labeled incorrectly or it encounters errors, faces adversarial attacks, or is manipulated somehow. In all these cases you would want to remove such data from the model at any cost. But you have to understand data first, before trying your hand at any machine unlearning method. It has to be known how a certain data point influences the model. Will the data you remove influence the model’s accuracy and its results? What is the reasoning behind forgetting data and will this set back your model? Time moves on and data changes Data and its value change over time. Something actual before, might not be now. For the ML or AI model’s accuracy, data needs to stay fresh and up to date. As much as anything else, data evolves as well. Better control over our private data “The right to be forgotten”, privacy laws, and legislation, all give private individuals to take control over their data. This in turn influences which part of our data can be used in ML and AI models. And it’s not only about our private information but also about content, art, and intellectual property that original creators didn’t allow those models to use. Removing bias Unfortunately, in ML and AI models there is bias. Depending on the data ingested, it can create unfair and biased results. Models can learn that some attributes are better than others and thus create inaccurate results that can lead to discrimination, manipulated results, or prejudice. By unlearning and removing data that produced bias, models can show more leveled results. Data resource optimization Ingesting vast amounts of data will make the models a bit more inflexible. Too much invaluable data can strain memory resources. Not all data points are valuable for models’ efficiency and accuracy. Models should prioritize essential information. With machine unlearning,
Data poisoning – an unexpected foe
Recently, there has been a big buzz around the downsides and negative effects of AI and ML. This has sparked the conversation on adversarial ML and adversarial attacks. Alongside the usual types of attacks, one type has stood out, especially in consideration of copyright infringement. Data poisoning has been making waves, and now people are noticing what it could do to AI and ML models’ precision, correctness, and reliability. Done with either malicious intent or as a way to restrict the growing influence of AI on those who create content and art, data poisoning is not something to take lightly. Considering that AI and ML models are only as good as the data they are trained on, we can see why this poses a serious issue. Introductory call to data poisoning Data poisoning, a term briefly mentioned in some of our previous blog posts on adversarial ML, is an alternation of entries or injection of training data with false or tempered data, to change the outcome of AI and ML models. It influences the data during training time by changing existing data or introducing incorrectly labeled data. The algorithm learns from corrupted data and consequently generates unwanted or unintended conclusions. The model will make incorrect predictions since it was fed wrong or corrupted data to train the model. Beware of the adversaries. By manipulating data, the results of your ML or AI models won’t be what you expect them to be. Data poisoning works in such ways that sometimes it’s hard to see when it happened and how. And when you do realize something is off, it will probably be too late. These attacks are ultimately costly because it’s hard to backtrack to the point of data poisoning. One might argue that machine unlearning is the solution, but it still isn’t advanced enough to be used in such a way. Not many talk about data poisoning in terms of AI, but it’s something to really worry about. Its influence can have drastic effects on the model’s performance, as well as on the users who will try to benefit in some ways from AI. Data poisoning is basically polluting data and models to generate results adversaries try to skew. Attacks from all sides Data poisoning happens in more than one form. It’s not always about inputting corrupted or fake data in the training dataset. There are multiple points of attack, each influencing ML models differently. The most basic types of these attacks are black box and white box attacks, but we can go even more narrower with subtypes. Availability attack or straightforward attack Availability attacks are oriented towards injecting as much bad data into the database as possible. The primary goal is to influence the data the model is trained on, by creating false data or tempering with it. Integrity attack or backdoor attack Integrity attacks are far more complex than availability attacks. They leave the database alone, but they create a backdoor for adversaries to control it. These are harder to detect since they influence the model by tempering with training data by mislabeling or changing the status of a particular piece so it is read like the rest, but it’s changed slightly. One type of such attack is label poisoning described below. Label poisoning (Backdoor poisoning) Here the attackers inject mislabeled data to influence the model in the inference stage. They purposely feed the model with data that is labeled differently than true examples to influence the final results. Here we can distinguish clean label poisoning where the attackers influence the classifier in the model by choosing to attack the target instance. It is done by injecting poison instances into the training data with the intent of fooling the model into labeling the target instance with the base label at test time. Training data poisoning As its name suggests, this attack is targeted towards training data. So it doesn’t influence the model after the fact, but in the early stages of development. The attacker modifies parts of training data so they can manipulate the learning part and the outcome of the AI model. Model inversion attacks These types of attacks are focused on extracting sensitive or private data the model was trained on. So, they don’t necessarily influence the model results, but rather try to collect information on the dataset this model was trained on. Stealth attacks In stealth attacks, adversaries try to leave vulnerabilities on training data so they are undetectable in the development phase, This is done with the purpose of exploiting the model when it’s deployed in the real world. Subpopulation attacks Subpopulation attacks are relevant for large, diverse datasets. The goal is to compromise and affect a particular subpopulation that doesn’t alter the model’s performance on the rest of the population. The adversary induces a model that creates a targeted and incorrect output of the subset and affects a classifier. Where does that leave us? Data poisoning is not a small thing. As inconspicuous as it may seem at first glance, it digs deeper and creates issues that affect the overall result and AI model performance that could have grave consequences. It perhaps requires minimal effort to produce big implications. Recently there have been talks about respecting copyright and AI using copyrighted art as their data source. This is how Nightshade and Glaze came into the spotlight. They are data poisoning tools that artists and creators use to manipulate their artistic products so it, well, ruins AI models’ results, especially in terms of generative AI. The creators whose art and content were “stolen” to train AI are fighting back, and it could prove to be tricky. Such tools that change their art, could ruin models who used them for training. The results will be far from expected or correct, and the reliability of these AI models will remain in shambles. This has started a sort of war between humans and AI. People want to protect their art from being misused or from not being
From continuous intelligence to composite AI – a mix for success
Leveraging business intelligence to make decisions has been the core of any company that somehow wants to utilize data. An approach to the ever-growing data volume has been to integrate BI systems into everyday operations. Which is perfectly fine and might work for some. The issues appear when data velocity becomes greater and data starts moving fast, with continuous changes. That’s the reason why we see more and more companies using data streaming tools, like Kafka, and Spark, or in tandem, to get access to real-time data. And in comes something special, continuous intelligence (CI) as a solution to ever-changing business landscapes. Also, as the world shifts, and IT infrastructures move with it, companies are not tied to only one solution in handling and utilizing data. One source of either AI, ML, or analytics tools is no longer enough. A combination of all will most likely occur. Often it was referred to as multidisciplinary AI. But, now a new term has emerged – composite AI. As one of the most prominent and biggest trends in complete AI integration in business processes, as declared by Gartner, composite AI will make some waves. You may look at it as something that is not new, which might be true because this is rather an approach than a new technology. Both CI and composite AI present positive and must-have new approaches to guiding business decisions in a new era. Continuous intelligence in service of real-time insights Continuous intelligence (CI) is the use of different approaches and technologies integrated into business operations to process current and historical data through real-time data streams to perform real-time analytics. What is different about continuous intelligence is that it uses data in motion but also uses historical and batch data. This specificity requires multiple solutions to coordinate together so they can generate proper insights. Firstly, a solution to ingest data in real-time has to be implemented to handle data streaming. Of course, it needs a platform to collect, organize and analyze data. And we must not forget the analysis of historical data that requires some kind of in-memory technology that will speed up data processing. But, it’s not only about analyzing data streaming and batch data, in real-time or historical. It’s about the implementation of AI and machine learning to make this process as automated as possible to remove human bias, lag time, and possible errors. We already mentioned augmented analytics in previous blog posts, and this is also an integral part of continuous intelligence (CI). CI isn’t just a piece of technology. It’s a cluster of them to form a comprehensive design of tools to analyze data in order to precisely generate metrics and insights at a moment’s notice. It’s about continuously learning and adapting to the system it monitors and uses. It should bring data from being static to something you can mold and turn to your advantage at every single point by everyone. Composite AI – the outlook Composite AI is a combination and application of different AI techniques to reach the best results, improve the efficiency of AI tools, increase the level of ability to solve complex problems, and make significant business decisions. It fuses multiple tools and methods such as deep learning, machine learning, natural language processing, knowledge graphs, contextual analysis, analytics, and more, to generate deeper insights and support more precise data-based business actions. The basic proposition is that it creates a unified approach of multiple AI tools to answer one business problem or question. It consists of layered solutions that form a cohesive outlook on a specific business domain aspect. The point is to line up content with context and deepen the understanding of business data. Composite AI should change and supercharge decision intelligence. It doesn’t sound that revolutionary or does it Probably, it does not sound like something new, but it is. Multiple AI tools that one company uses? It doesn’t sound like anything that will make such an impact if it’s already used in one way. But what companies fail to realize is that one tool is not enough with the wide range of dynamics data and all-around business operations. Usually, businesses use AI as a single tool. They forget that under its umbrella there are so many various tools that on their own don’t reach their full potential. So, in comes composite AI as a platform that uses multiple approaches that complement each other to maximize results. It supports and enhances the quality of AI applications by bringing all data and different methods together to reach the same goal. Its basic intention is to bring AI from ordinary to excellent. CI also isn’t something completely new, but it’s a complete approach to data that is not static. Often we look at data streaming and historical data as separate occurrences, but together in CI, they provide the full picture. By using ML and AI it automates those processes and brings them to another level. It’s about generating value from data As one is oriented towards data streaming and the other towards AI and ML, they might seem contradictory in some aspects and separate answers to data challenges. But it doesn’t have to be so. They can both be complimentary in nature since they both focus on implementing AI and ML in optimizing data and business performance. As said above, composite AI is there to improve decision intelligence, or in this case continuous intelligence. Composite AI can target only one specific business problem, whereas CI can be a comprehensive approach weaved throughout the whole business. One leverages more advanced technologies, while the other is formed more to provide insights based on real-time data. This is where we can see that they could complement each other in new ways. Imagine using both in your business to gain valuable information and recommendations on business operations and decisions. Data holds value, so it makes sense to do everything and use everything to maximize what it has to offer. These collections of tools build
Utilizing multimodal ML in a fast-shifting world
AI and machine learning are both terms that we so freely use. But, what many do not understand is that there are so many sub-areas of these disciplines. And we use them all interchangeably, which would be misleading. ML, in itself, can be divided into supervised, unsupervised, semi-supervised, and reinforcement learning. Each one comes with its own set of characteristics and terms. Because of such vastness of, not only ML techniques but data itself, we recognize the almost fresh term multimodal ML (machine learning). Multimodal machine learning will continue its rise to the top in importance and approach to modern data. As we all know, our surroundings are not just simple text or numerical data. It’s the visual, audio, and other modalities that display the complexity and uniqueness of the world. Have you ever heard of multimodal ML? Have no worries if this term hasn’t come up yet. We seem to encounter so many definitions that describe new or existing technological advances. It almost looks like each day brings some sort of a new concept to describe a breakthrough that changes the world and drives innovation. Multimodal might not be new in terms of what it consists of, but it encompasses multiple areas of ML working in cohesion. We experience the world multimodal. We have senses and feel the world around us through sound, visual aspects, smell, feel or touch. If ML and AI want to understand and mimic the real world, first such data needs to be optimally utilized. And here is where the multimodal ML comes in. Multimodal machine learning is machine learning where the model is trained with data from multiple different modalities, such as text, image, video, and audio. All of these come in different forms and characteristics. Data is not the same, and as such, it needs a different approach in utilization and machine learning methods. What’s important here is that one part, for example, the image, could be a misleading event to the human perception. But when we add sound to it, or text, it will provide a clearer picture. Through multiple modal datasets, multimodal ML tries to connect information and relationships from these sources to train models that can comprehend the intricate observed environment. But, it’s not only about text, image, video, or sound. Modalities can also include heat sensors, depth sensors, 3D visual data, LiDAR, and more. Fast-paced world and ever-shifting business environments Data nowadays isn’t just some spreadsheet. It’s text, videos, music, apps, heat maps, online activity, and so much more. The level of diversity is continuously rising. Ever-shifting environments, especially business ones, are presenting more and more challenges in how to handle them. How do you cover all of the possible modalities and still make sense of the data? Look at your social media accounts and how many modalities you create! Imagine this on a bigger scale now. Imagine it in terms of one business or a whole community. The scope is momentous. And it’s always shifting. Businesses across industries change constantly. The number of media, devices, and software involved in making business decisions is growing. But so does the format in which data is created. Keeping up is challenging. So it’s no wonder new concepts and approaches are created. Simple approaches toward data are no longer sufficient. Unimodal is not enough, and multimodal ML is going to take the helm in a lot of industries. It will almost become unavoidable. Challenges of the heterogeneous nature of multimodal data It is obvious that multimodal ML comes with limitations or challenges. The biggest one is the heterogeneity of data derived from modality diversity. Of course, each modal will generate different forms of data, which is easily seen from modal characteristics. So, it is a whole process to get such data on the same page. Images or videos won’t have the same sort of data or information as text or just audio. So, here lies the challenge of how to extract data for further processing and predictions. Ultimately, it’s about using multiple methods of data analysis and preparation to harmonize features from different data sources and formats. Let’s do an overview of how multimodal learning derives from unimodal components. Image: Multimodal ML architecture Multimodal architecture, by most definitions, consists of three main steps: encoding, fusion, and classification. Because of the heterogeneous nature of inputs and modalities, they need to be put on the same page in terms of data features. Each unimodal input is processed separately, for example, the audiovisual model has audio and visual inputs, so they are processed individually in the encoding process. However, information from each unimodal model needs to be fused in the fusion step. Here the features from each modality are combined. This is one of the most important steps in multimodal learning and it defines the efficiency of the model. In classification, the model accepts encoded fused data and starts training on it. After this step, the data or features are ready for making predictions. It does come with its set of core challenges Even though multimodal Ml seems like a great breakthrough, it does come with challenges that need to be addressed. Capturing and analyzing data from such different modalities is not an easy task due to the diverse format and meaning. Combining, for example, image and audio data is not as simple. Image and audio will have different characteristics and different ways to analyze and draw data from them. Representation Each modality’s data is represented differently. Combining different representations is tasking. It’s hard to get them to a common language or format so they can be analyzed together. Creating a model that can handle and understand all these diverse representations is a challenge. Another issue is how to deal with missing and incomplete data and noise in data. Translation This challenge is in terms of translating one modality into another. It’s about converting one form of modality data to another modality. The differences in the structure, syntax, and semantics present difficulties in translating from
What can we learn about federated learning?
We all know that for machine learning you have to train models on data before actually having fully functional systems that derive results. The same goes for AI. The traditional approach is to collect data, prepare data, train models, and deploy them. But, considering the amount of data and devices we use daily, there was a shift in that approach, and in comes federated learning. If there are a lot of devices, let’s take smartphones for example, how do you efficiently collect data and deploy machine learning models back to them, without lag time and disturbing user experience? But first, theory Federated learning is an old-new concept. It has been around for some time, but it still has major roads ahead to conquer. There are still some questions that need to be answered. Federated learning is a decentralized approach to training machine learning models. Models aren’t centered on one server, but they are rather deployed on each individual edge device, at the source, and on raw data. After the models are trained on each device, results are sent to the central server where a model trains on the aggregated and anonymized data from individual device models. The next step is to send those results in a cycle, back to edge devices and vice versa. So, on one side we have local models, and on the other central server model. They all communicate together and share aggregated local updates to make the model more precise and efficient. The main reason for the rising federated learning popularity is privacy. Data doesn’t leave the local devices but is utilized there. The data that travels to central servers is aggregated and it’s harder to compromise data privacy or access those private user information. Meaning, it’s harder to link data back to the user. How does it actually work? Federated learning is based on decentralization and an ongoing cyclic movement of machine learning models and results. The best description is that it doesn’t move data to the models, it moves models to the data. The main model is stored at the central server. It is then shared across multiple edge devices. This is where local training happens. On each device ML models use and train on local raw data. Note that at this phase they don’t share the data with the central model or other devices yet. After training on local devices, these locally trained models are sent back to the central model where data or results get aggregated for a global model. The central server trains the central model on accumulated data and validates its accuracy. Next, the cycle gets repeated. The central model sends this trained model back to the edge devices for new iterations and processes. This goes on and on, optimizing model performance until it reaches the expected accuracy and better user experience. Why is this good? Because the model works on cyclic iterations making it better and better without breaching user privacy. But, it still provides a personalized experience and approach to device users. Source: https://www.altexsoft.com/blog/federated-learning/ Here we can identify a couple of types of federated learning. We have two main types, centralized and decentralized federated learning. Of, course some may identify more than those two, but we’ll focus on the main ones. Centralized federated learning This type of federated learning is the same as in the process described above. A central server is tasked with aggregating results and re-training the model then sending it back to edge devices or local models. Only the central server communicates with edge devices. Those devices don’t necessarily interact with each other. Decentralized federated learning The decentralized approach implies that there is no central server, but edge devices communicate with each other. This means that they aggregate data among themselves – model updates are shared only among the interconnected edge devices. Other types of federated learning would include horizontal, vertical, and federated transfer learning. The benefits of federated learning Why is federated learning taking the ML and AI world by storm? Well, considering the number of devices used daily, the vast volume of data and the need for real-time analytics or experience have driven this development. The more accurate and safer the model, the better the final user experience. Data privacy is also a big determinant. Increased data privacy and security Considering the fact that user data doesn’t leave the local device, there is a higher level of data privacy. The data sent to the central server is just model results, and with security measures in place, it makes it harder to link results to specific users. In traditional machine learning methods, data is collected and sent to the central location where it is processed and prepared, which can raise concerns about privacy and security. Federated learning allows for more private data to be held at the device location and not central storage. Collaborative nature Because machine learning happens on multiple different devices at the same time, it promotes collaboration between those devices. They all participate in providing model results to reach the higher accuracy of the main model. Real-time predictions Considering that models are trained on local devices, they get updates in real time and provide users with instant results based on their behavior. If the model learns right away from the user’s device usage, they can, at that moment, improve and optimize performance and provide recommendations. That’s the reason why it can give predictions in real time, as well. It can anticipate user behavior. Scalability, adaptability, and cost-efficiency Of course more data means a better and more precise model. However, storing all that data in one centralized depository could lead to inefficient and slow systems. Since with federated learning, all data is stored on local devices, it improves scalability and accelerates ML and AI model deployment and learning cycle. Also, not moving vast amounts of data to the cloud decreases data storage costs. Data accuracy and diversity By exposing models to a wider range of devices and data sources, they can
Data lineage – let’s talk what’s good about it
Every piece of information or data goes through a journey from creation to final utilization. On this journey, you must keep points where something happened to that data. We often disregard all that is related to data by thinking that the information we got is as it is. After its utilization, you either forget about that data or don’t maintain it. But, for proper data management, data lineage is what makes the difference. Data lineage is not anything new, but still something that gets a bit disregarded in some systems. It’s actually crucial in following data, it’s quality and usability. Data lineage as a support system Data lineage follows data and its journey, and it’s the process of recording, understanding, and tracing data origin from start to finish. It shows how data flows from source to its users, and records every change or transformation, usage, and historical touch points with data assets. We can almost equalize data lineage to the data life cycle. It is critical in data management since it provides a clear and transparent view of data movements across systems and technology. Data governance, security, and privacy are widely affected and improved by maintaining proper and adequate data lineage. Basically, data lineage is the context of how data travels and transforms within different tools, systems, software, and others. In a world of massive amounts of data, having a support system like that provides certain structure and reliability. Data teams and other stakeholders have a complete view and insight into what happens with data at any given time. What can you expect from data lineage? Data lineage shouldn’t be considered just as “documentation” on data. It should be observed as a tool that allows trackability and data management. More trusting data migrations By knowing the data origin, its path, and transformations, it is easier to migrate data from one depository to another. There is a higher level of confidence and trust that the right data will be migrated to the right new destination. Migration projects are easier and less risky when data characteristics and lifecycle are easily understandable. Easier errors and mistakes tracking Data lineage provides a clear overview of data movements and their history. This in turn ensures easier errors and mistakes tracking. In case there is an issue, data teams can easily find out at what point did something happen to the data and where the error originated from. Better data governance Data governance, compliance, auditing, privacy enablement, and regulations, can be more optimally managed with data lineage in tow. Complete clearness of data provides support in ensuring that policies and regulations are followed. It also simplifies regulatory compliance implementation. Higher understanding of data meaning and validity Higher data lineage allows for better data trustworthiness. Data users can understand data better when they know how it came to be, from the start to the point of observation or utilization. This improves data validity and veracity. Lower risk process changes If there are upcoming process changes or changes in general within the data management domain, they can be handled with much less pain or stress points since the risk of them is much lower. Data map and comprehensive metadata overview Data lineage is extremely closely connected to data cataloging, data classification, and metadata management. It maps out each data point on its journey or lifecycle. Impact review Data lineage provides insights into how one change can affect other elements in the company. If some aspect of data changes, data lineage can show what else (where this data is used) will be influenced. It’s a must There is no way for you to avoid data lineage and not devote your time to it. Not only because of the benefits it brings but also because it’s an integral part of any data management system and flows. Data integrity and reliability are outcomes of full data visibility or lineage. Trust is a big thing in data. How can you be sure of your final results, metrics, and insights, if you do not trust data and its sources? Transparency in data is vital in knowing if you’re dealing with quality and correct data or not. This in turn makes your analytics more accurate and useful. If you can trace data from its source or moment of generation to the end, you can be sure that when you use it in analytics it’s for the right reasons. This shows that data practices are correctly organized and performed. Data’s value derives from its characteristics and integrity, so we need to be sure it wasn’t manipulated or wrongly transformed at any point. Data lineage also allows users to determine if some of it was streamlined to the wrong depository or endpoint. But, what major advance data lineage provides is easier implementation and management of regulatory compliances. Companies can introduce changes to data systems more easily when they know where to look, process, and introduce the changes. But, when you surveil data, security, and privacy are tighter and stabler. The best use Data lineage is not limited only to data teams. With data democratization on the rise, business executives, sales, production, marketing, and other departments, are all included in the data lineage process. Each data user needs to be familiar with how data is formed and used. These are also the people who can verify the validity of data and its usability. Lineage in itself should always produce benefits and value. It’s about leveraging data to enable better business decisions and strategies. Because in the end, if you do not drive value from data, it’s either redundant or it will become obsolete. But, let’s not forget that we can divide lineage into business and technical lineage. Business lineage refers to the high-level information about data origin, movements, and business context, whereas technical is linked to transformations, pipelines, storage, tables, data format, and such. The value of each depends on the final user. Data lineage has the cause-and-effect principle down to pat. It shows or traces
Data veracity – why should you never forget about it
Data, in itself, has many characteristics and challenges, especially in the world of big data. Those who utilize or collect data know about the hardships it brings. That’s why there are some widely known descriptions and parameters of data where certain characteristics are described for better management. One of the best examples are 5 V’s of data. Those 5 V’s of big data are velocity, volume, value, variety, and veracity. We can all understand why each of them is important to understand and handle, but nowadays the bigger focus and importance is set on data veracity. It is not a newly coined term, but it’s often overlooked by those who do not understand that all data is not good data. And far too many times people forget about establishing systems and rules that will enable data veracity. In data veracity, we must trust Data veracity is the quality, accuracy, consistency, and trustworthiness of data. That’s why we can’t equalize it to just data quality. Data veracity refers also to the level of complete trust in data origin, type, and great data governance. It’s about the processes and systems behind leading data from source to its final utilization. Reliability of data is one of the most prominent traits of veracity, and it means that those who use data in analysis can trust the source and precision of information. Data scientists find this extremely important since all their work depends on it. Being able to trust the data you’re working on, means that your future work, whether it’s data analysis, machine learning, or AI, will be more accurate and significant. The metrics or insights derived from the work will be of greater sustenance and value. Data veracity can be double-natured as high veracity and low veracity. High veracity means that many records or data entries are valuable to analysis and delivering metrics and insights. Low veracity, on the other hand, also contains noisy and meaningless data. Sources that influence data veracity Data veracity can be negatively influenced for multiple reasons. Even the smallest of doubts will cause a lack of trustworthiness in data and data sources. Such things present possible future complications in data analysis, or data science and engineering processes and methods. Statistical and data biases Data bias happens when some data is given more value and weight than the rest. This data is taken into consideration in calculations or analytics and can thus offer wrong or misleading results. Bias is not only operational, it can be the product of human interpretation. When you give more validity to some data, you intentionally or intentionally produce biased results that aren’t always correct. Lack of data lineage To trust data, you have to be certain of the data sources it came from. It takes a lot of time to track down where some data originated, so that’s why data governance and lineage are extremely important in ensuring veracity. Software bugs Bugs can easily create wrong data or miscalculations and data transformations. This leads to the creation of bad data that will skew the results and the reliability of data sources. Noise and abnormalities A lot of time and effort is spent on cleaning data and removing noise, or invaluable data. Noise has to be removed to get better and more accurate insights. Missing or incomplete data, or even outliers all signal that something is wrong with data and should be addressed accordingly. Untrustworthy data sources and falsifications Having untrustworthy data sources means that we cannot rely on that data in analytics. Data veracity relies on trustworthiness and its level, so without complete trust in data, veracity can’t be achieved. Also, veracity is compromised if falsification is at fault. Inputting wrong data or manipulating data and sources alters results and influences security as well. Uncertainty and ambiguity of data Doubt in data that is not in line with expected or correct values undermines veracity. Imprecision, doubt, multiple interpretations, and misleading data can cause low data veracity and slow down the work of data teams. Out-of-date and obsolete data Obsolete data provides no true value in analysis. Metrics and insights lose value and validity if they have taken out-of-date data into account. If analysis is done with old data, subsequent decisions will be wrong and could end up being costly. How to potentiate data veracity? Maintaining and ensuring data veracity is not easy, but it’s integral to keeping your data at the highest quality. You can not fully utilize data and gain value from it if the sources are bad and untrustworthy. Not having that level of reliability in data means that you can’t trust the results and will take wrong actions if you go by it. Proper data management, governance, and infrastructure are vital in providing and maximizing veracity. Establishing good and monitored data flow will provide easier observability and control. Another essential thing is having data knowledge. Companies need to know where data originated, who created it, what’s going on with it, who’s using it, and how. Data users have to be familiar with sources of data and for what they will use it. Validate sources and information before doing anything with it. If you are not sure of data sources and their correctness, you can not guarantee good results in future projects and utilization. Basically, if you don’t know what you’re getting, you can’t expect miracles to happen later on. Plan ahead. Do things preemptively to make sure data systems will support it. Cover every scenario so you can ensure the highest quality and reliability of data. Data veracity dictates the quality of results As seen throughout the whole article, trustworthiness and reliability are characteristics data must follow. It dictates the correctness of all procedures and projects with data, not to mention final results. Data is complex. There’s no doubt about it. So that’s why it needs to be approached with care and diligence. In a world where data grows in volume each second, you must focus on the information that brings
Why should you focus on augmented data management
As time goes on and the volume of data rises, traditional data management processes aren’t enough. It gets more complicated and time-consuming to draw out valuable data, process it, and prepare it for other data-related tasks. But, with the development of AI, we see the possibilities of augmented data management. Traditional approaches are falling somewhat behind and can’t keep up with the increasing data. Managing data has become a challenging task, and automation and AI could speed up things and improve data processing efficiency. Since data management is about collecting, processing, storing, and protecting data, improving those processes will bring immense benefits. Augmented data management – the gist Augmented data management (ADM) is the process of utilizing AI and machine learning to automate data management tasks and procedures. It automates tasks that were previously done manually, so in turn it saves time and ensures more precision and accuracy. It encompasses automating tasks such as ensuring data quality, data preparation, data governance, metadata management, master data management, and data integration. Data management is one of the crucial constituents of data utilization. Without proper management, everything valuable coming from data will fail. The volume, speed, diversity, and format in which data comes are defining how complex it will be for a company to reap the benefits. Why can traditional data management approaches fail? Traditional approaches, mostly manual tasks, were not created to handle larger volumes of data. They are perfectly fine for smaller data but for larger amounts, they’ll have issues keeping up. Traditional approaches can lead to data redundancy, duplications, data silos, or errors in data processing. Controlling data could also get out of hand, and it will become harder and harder to enforce data governance and ensure data protection and privacy. With how data becomes voluminous, it can lead to creating data silos and influencing latency. Data gravity also comes to mind when you think of traditional approaches to data management, since slower data movements mean more prolonged time to convey business information. This can create dispersed teams and data stores which defeat data democratization and collaboration. What can go wrong? There are several downsides to traditional data management processes since it relies so much on manual work which can lead to human error. Also, like with any manual task, it’s a time-consuming process. AI and automation can be so much faster in performance. There’s limited agility and flexibility with data teams and data itself considering it takes more time to store, prepare, organize, or process data. This also begs the question of data quality and its maintenance. It’s harder to ensure that the right data, and quality at that, reach its destination for further usage. Let’s not even mention metadata management or maintenance of data systems and architecture. Of course, some things can help with that, but for better information and data flow, automation of those processes can lead to more stable and scalable systems. But what about data governance, data privacy, and security? If you rely purely on individuals, there could be some oversights, or even manipulation and misconduct. ADM provides a more secure and reliable way to ensure the safety of the company’s data. ADM with data fabric and data mesh Augmented data management is made easier by implementing data fabric or mesh into your organization. We already covered these topics in some of the previous posts, but to summarize, data fabric and mesh allow for data sources and metadata to be optimized for organizational cross-collaboration and easier data management and utilization. Since, both fabric and mesh rely on metadata, augmented data management makes handling and maintaining metadata easier and far more efficient. Data fabric and data mesh are great support systems and layers for organizational changes and they provide the flexibility necessary to manage data. ADM allows data engineers and scientists to focus on higher-value tasks Automating data management saves time. But the biggest value is that it allows data scientists and engineers to focus more on high-value tasks and discover new ways of optimizing data flows and utilization. By minimizing the work that has to be put in for manual tasks, they have more time to focus on those that will bring more benefits in terms of growing businesses and creating opportunities. Some examples are master data management, ensuring data quality, streamlining data from dispersed sources to a central depository, data labeling and governance, and so on. Automating such tasks will improve the efficiency and productivity of data teams while simultaneously improving data management and data accuracy. Key values of ADM Augmented data management, as already mentioned, provides higher value and benefits to any business that deals with data, especially those involved with big data. It’s not about “getting rid of” data engineers or data scientists. On the contrary, it’s about enhancing their skills and allowing them to perform bigger and more complex tasks that bring data utilization to another level. It gives data teams and data itself the foundation to maintain consistency in quality. ADM is not only about speeding up data processes, systems, and infrastructure. It’s also about giving advanced and enhanced insights that drive decision-making. With improved data democratization and data flow, imagine the possibilities and accuracy of actions and decisions from various teams that drive excellence and better business performance. It’s a self-tuning and self-optimizing system that makes managing data better and easier with time. It will configure itself according to changes in the data systems and when new data sources are added. Reduced time from data preparation to insights Doing things manually takes time. Augmented data management processes speed up data preparation with automation and can deliver results faster. This means that metrics and insights get produced quicker and decision-makers can act on them in near-real-time or real-time. More efficient data processing It comes as no surprise that AI and machine learning will optimize data processing and improve accuracy. ADM brings data from multiple sources together and performs ETL processes at a faster pace. Reduced bias ADM, since it
How to manage the pull of data gravity
If you are an enterprise company or an entity that either generates or collects enormous amounts of data, then you must be familiar with the fact that the more data you have, the more you’ll add on. As soon as you recognize the value of data in your everyday operations, you will see that data covers every corner of your business. By recognizing the value and demand for such data and for you to move forward as a company, data will continue to grow. And that calls for an occurrence called data gravity. What actually is data gravity? This term was coined in 2010 by Dave McCrory, a software engineer, to explain the notion that large amounts of data attract more data. Data gravity is an observed tendency that large datasets attract smaller datasets, services, and applications, thus creating an even larger data-heavy or data-rich system. Why gravity? Well, it is explained that like larger bodies of mass attract smaller bodies with gravitational pull, data systems do the same. The most used comparison is with planets. Presume that a data system is a planet. With its gravity, it will pull in, for example, moons and other bodies. In the same way that is hard to move bodies pulled by gravity, data, in these situations, becomes difficult to move as well. When data starts pooling into one place, it becomes inevitable that additional data and applications will almost “attach” to it, which often makes the system inflexible. When gravity is not such a cool concept Even though you might think, oh, great, all my data is in one place, it isn’t always such a great thing. As data and applications move closer to the central data store, they limit its flexibility which makes it harder to scale or even adopt new applications. Data latency also becomes an issue. It gets longer and longer to get vital data from its source to the user. Data slows down and, due to flexibility issues, it doesn’t reach the user as fast as it should. Data should be moved closer to applications to improve latency rather than being stored in remote areas. Most companies don’t consider the fact that their data will grow and consequently outgrow the current system. This will require them to move data to another solution. In the case of data gravity, this becomes an issue since this data has now become harder to move and its portability is low. But one of the biggest issues with data gravity, if data does become harder to move, is vendor lock-in. By not being able to move data to another technology, companies get stuck using the same vendor because it’s too difficult to switch to another service or software provider. Companies become too dependent on current vendors, they let data accumulate and get heavy, so later on the cost and effort of moving to another vendor are too high. Business downsides of data gravity Data gravity doesn’t affect only the data infrastructure and application performance, but it consequently influences the business side as well. Not planning how to centralize data but keeping it close to applications, will in the long run result in low or bad data utilization. Slow response times Due to low data agility and cluttered data, response time to changes is going to be much slower. If the end user doesn’t access data on time or at desired moment, his decisions or actions are going to be delayed. This means that some immediate actions won’t be performed and this can cause costs, loss, or lead to wrong decisions. Inefficient use of resources Data gravity can slow down or limit the digital transformation of companies. Overly centralized data or even data duplications (when multiple teams work on the same data) can lead to inefficient ways of utilizing those resources or applications. There can be missed opportunities or fewer innovations caused by improper or non-maximized use of resources. Information silos One of the biggest problems for data management is data silos. This means that not all stakeholders can access data or it’s not easily accessible. This presents problems in promoting data democratization and in full data utilization. Having one data storage that is unavailable to other groups or departments in the organization defies the purpose of interdepartmental collaboration. So, if someone outside of the group needs access to some information, they will have to go through many hoops to get to it. Slowing down growth As said before, data gravity slows down the performance of applications and presents issues in data flexibility. In these cases, people or users who need that data to perform to perfection, aren’t getting what they require. Certain business operations will face issues that will result in slower response times or delayed decisions. It will cause growth and progress to slow down until issues with data and data gravity are resolved. Data gravity is not necessarily so bad We did mention that data gravity can cause quite a few issues. But having data centralized is not a bad thing, per se. If your data management knows how to handle it and they control it in case they ever need to move data, then centralized data can be beneficial. The key is to weigh benefits with potential challenges and downsides. Until companies reach the point when they have too much data that slows down operations, data gravity won’t pose such an issue, when managed correctly. Having data centralized and accessible, can drive decision-making. Data can be easily analyzed and used to create metrics and insights that help take action and make decisions. The issue occurs when data becomes hard to access or is slow to get to the users. Data centralization also potentiates collaboration across companies and departments, or data democratization. Unless there are data silos, data in one place can allow different teams to work together and share data from a single source of truth. If companies can avoid data duplication, efforts in working with data can be