Oct 11, 2022
What Is Data Management? A Complete Guide With Examples: Data management is vital for informed decision-making. This blog provides insights into data processing, the importance of data accuracy, and tailoring data management approaches to your technology ecosystem. Integration is highlighted as a means to enhance data quality and improve the customer journey. The growing importance of data literacy is emphasized, given the projected value of the big data market.
3 Ways to Simplify Data Management: Data is crucial for business, but many organizations struggle to embrace a data-first approach. This article suggests identifying obstacles that hinder data-driven transformation and advocates simplifying IT systems to become data-first leaders. It notes that only 13% of respondents have achieved data-first status, making data management simplification essential.
5 Best Practices for Simplifying Data Management: Data management complexity is rising, but strategic thinking can simplify operations. This blog recommends documenting data sources, addressing data silos, centralizing storage and analytics on a cloud platform, implementing a data tagging policy, and enhancing visibility and searchability. These practices ensure effective data management, even as challenges grow.
Oct 4, 2022
“Cleaning data is a required step when building machine learning models. The concept of garbage in, garbage out applies to machine learning. If a model gets dirty data, the model will produce poor results, and vice versa, making data cleaning and preprocessing the first principal step when working with data.”
“Text preprocessing is cleaning data to prepare it for model training. It involves the removal of noise in texts which prevents proper representation of the text in vector form.”
“Techniques of text preprocessing Python includes lower casing, eliminating whitespaces, eliminating punctuation, expanding contractions, spell correction, tokenization, eliminating stopwords, stemming, and lemmatization.”
“Popular tools used for Text Preprocessing include Natural Language Toolkit (NLTK), TextBlob, and spaCy.”
Oct 3, 2022
“In this edition of Executive Data Bytes, we delve into the critical topic of data protection and privacy. Discover 12 ways to safeguard user data and gain insights into Privacy by Design strategies that can enhance your projects.”
“Key takeaways include understanding data protection principles, implementing data loss prevention solutions, and the significance of data encryption. Additionally, learn about Privacy by Design as a legal requirement and its implications for web design.”
“Explore how to start implementing Privacy by Design into your systems and operations. Learn about organizational commitments to data protection, appointing data protection officers, risk management, and privacy training.”
Sep 27, 2022
“Discover the journey of Natural Language Processing (NLP) and its profound influence on human-machine communication. Dive into the history, from early machine translation experiments in the 1950s to the modern era of neural networks.”
“Explore key milestones, including the development of chatbots, speech recognition, grammar checkers, and machine translation. See how NLP has become an integral part of our daily lives, from virtual assistants like Siri to spam detection in emails.”
“The future of NLP holds immense potential, with tech giants like Google and Amazon investing heavily in advanced algorithms. The horizon of possibilities in human-machine language interaction is vast and exciting.”
Sep 26, 2022
“Delve into the world of Explainable AI (XAI) and its evolving definitions. Discover how XAI techniques are being applied in healthcare and finance, with explanations taking various forms, from human-language to heat-map interpretations.”
“Learn how explainability aids developers and machine learning practitioners in clarifying AI system decision-making processes. Understand its role in bridging the gap between technical and non-technical audiences.”
“Despite the demand for explainability, challenges persist in defining key terms and providing guidance on selecting and testing explanations. Explore the debate over whether opaque models should be replaced with interpretable ones.”
Sep 19, 2022
The prevalence of CDO roles has grown globally in many sectors. To be effective, a CDO must be focused on business value, balance enablement and control, and view data and analytics as a value chain instead of a centralized, isolated silo. Read on in this CEOWorld Magazine article to learn more about the future of the CDO role.
Global data creation is expected to reach 175 zettabytes by 2025, and the global big data and analytics market is on pace to reach $135.71 billion. Companies worldwide are scrambling to reengineer their data strategy. The CDO role is one of the newest potential additions to an organization’s senior leadership team, and comprises seven critical job types. The CDO role may differ from one business to another, depending on the market context. To remain competitive, organizations will need to transform their data systems. CDOs are crucial to this process
“Data is the lifeblood of an organization, but many businesses aren’t investing enough in deriving true value from it. This is why the Chief Data Officer (CDO) role has become so popular in recent times. This article from Intelligent CXO lists five reasons why CDOs are the leaders of the future. A CDO is a trained data strategist that can interpret data and make the best use of it. He or she helps to make data less scary and more open and ensures that every employee understands the value of data-driven strategies.
The initial incarnation of the chief data officer (CDO) was more of a governance job than an analytics-focused one. This Forbes article explains how the CDO role is starting to split into several different functions. In the beginning, there was a lot of talk about being data-driven, but as these data stores expanded and evolved, companies recognized that they were not just assets but liabilities, and started to organize and make data actionable. With the shift from focusing on governance to focusing on analytics, it’s important to rethink data architecture.
Sep 12, 2022
For businesses, the race towards accelerating innovation is (or should be) the highest priority. In this article, TechCrunch outlines the benefits and challenges of adopting cloud. Public cloud technology allows businesses to quickly test out new technologies, scale up or down as needed, and retain data in a specific geographical region for privacy and regulatory compliance.
Recently, unexpected events have exposed significant structural weaknesses, which are amplified by global trade disputes, environmental disasters, and civil unrest. This EY article explains how cloud technology can enable organizations to transform quickly and reframe their future. Businesses should prioritize speed and resilience over cost optimization and monitor data in real-time to pre-empt adverse outcomes and gain significant competitive advantages.
The future of cloud appears to demand a new approach that considers business, technical, and financial priorities together to gain greater value from cloud innovation strategies. Read on to explore four potential scenarios for the future of cloud innovation strategy in this article by Deloitte. Cloud technology can help organizations innovate across today’s enabling, tomorrow’s disruptive, and future next-horizon technologies. The CIO should guide organizations in their use of cloud technology to innovate and help them understand the business, technical, and risk sides of the innovation equation.
Aug 29, 2022
A lack of professional development is one of the top reasons for employee turnover. Businesses cannot afford to wait and allow other, more data-forward enterprises to win top talent. This Forbes article explains why data literacy education is crucial for success. The modern workplace is rapidly changing, and data plays a central role in many of those changes. However, despite these changes, only 21% of employees believe their employer is preparing them for a more data-oriented and automated workplace.
Today, companies are amassing more data than ever before, and also undergoing a digital transformation to become more data-driven. Given the changes in the data and analytics landscape, non-data professionals need to become data literate. Read on in this article from Eliiza to learn more about how both business and employees can benefit from improving data literacy across the workforce. Businesses are adopting a data mesh architecture where data and analytics functions are delegated to individual business domains.
A data-driven culture is an organizational mindset that uses data to inform business decisions, predict customer behavior and drive transformative technologies. To create a data-driven culture, follow these 10 steps in this article from Hexacta. The key to making complex data work for a solution-oriented management system is asking the correct questions and knowing how to solve problems. Root Cause Analysis can help identify business problems and priorities early on.
Aug 22, 2022
Data is valuable only if it can be translated into insights, and organizations are still struggling to see insights clearly through their data. In this article, Treehouse Technology Group outlines the steps you can take to derive actionable insights from your data. To turn data into useful information, you need to put context to the data and establish why the data is important. You also need to anticipate the outcome of the project and elaborate on how the results will be used or integrated into the business.
Top-performing companies use data to make decisions about their business, and these companies gain a strategic advantage over their rivals. This blog from Sisense Team aims to help you improve your decision-making process. Data-driven companies are more customer-focused, enjoy deeper insights into the customer and their journey, and are more agile and better able to respond to markets.
Due to the recent advancements in modern technology, data-driven decision making is starting to be employed across various industries. This article from Softjourn explores the ways various companies are benefiting from data-driven decisions. Data-driven decision making (DDDM) uses past information to predict what will happen in the future. KPIs are used to measure the performance of systems and to see how close they are to achieving business goals.
Aug 15, 2022
A data fabric is an architecture and set of data services that provide consistent capabilities across a choice of endpoints spanning hybrid multi-cloud environments. It enables a unified data management architecture so that enterprises can gain from an extensible and converged data capability. This article from Spiceworks outlines what you really need to know about data fabric. While we live in a data-driven age, organizations spend a disproportionate amount of time on routine tasks and not enough on value addition. Data fabric rectifies this imbalance by removing back-end bottlenecks in data management.
Data management is prone to human errors. If data and analytics leaders want to reduce these errors, and overall costs, it’s crucial that they shift toward modern solutions like AI-enabled data integration. In this article, Gartner says the emerging design concept called ‘data fabric’ can be a robust solution to data management challenges. Data fabric works like a car’s autonomous element. It monitors the data pipelines as a passive observer at first, and then starts suggesting alternatives that are far more productive.
Data fabric architecture has emerged as a way to facilitate data exchange among the many disparate systems that support the business. However, like anything, data fabric architecture has pros and cons. This ITPro Today article explores some of these advantages and disadvantages. A data fabric is a technology infrastructure used to access and move data, and is often positioned as a disruptive, zero-sum architecture. However, data fabric is a complement to, not a replacement for, data management tools, practices and concepts.
