If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
Did you know that Netflix’s AI-powered recommendation engine is worth $1 billion a year?
AI’s impact on modern businesses has been incredible. Netflix is not the only business to have realized its importance. 84% of the organizations believe AI can give them a competitive advantage.
Covid 19 has upended all traditional business models and made years of carefully curated data and forecasting practically irrelevant. With the world on its head, consumers can’t be expected to behave the same way they did 9 months ago, and we’ve witnessed major shifts in how and where people and businesses are spending their money. This new normal— the “novel economy,” as many have dubbed it—requires business leaders to think on their feet and adjust course quickly while managing the economic impact of lockdowns, consumer fear, and continual uncertainty. The decisions they make today will affect their company’s trajectory for years to come, so it is more important than ever to be empowered to make informed business decisions.
In recent years, organizations across industries have started to implement advanced analytics programs at a record pace, drawn by the allure of increased efficiency and earnings. According to McKinsey, these technologies are expected to offer between $9.5 and $15.4 trillion in annual economic value when properly implemented. However, most organizations struggle to overcome cultural and organizational hurdles, such as adopting agile delivery methods or strong data practices. In other words, adopting advanced analytics programs is happening across the board, but successful implementation takes a long time.
The majority of the growing industrial companies today are betting their biggest bets on tackling diverse technology initiatives in the light of Industry 4.0 transformation.
The IoT universe is manifesting newer forces that are erupting at breakneck speed.
Distributed non-relational database Cassandra combines the benefits of major NoSQL databases to support data management needs not covered by traditional RDBMS vendors and is used at some of the most well-known, global organizations. This Refcard covers data modeling, Cassandra architecture, replication strategies, querying and indexing, libraries across eight languages, and more.
Data Strategy
The Sins of AI Adopters
Artificial intelligence adoption may be tricky. This technology is different than any other you’ve implemented before. There are rules to follow and some of them incomprehensible to someone without extensive AI knowledge. There are certain challenges companies can face while implementing AI: data quality, model errors, lack of data science experts — many of them covered in the article 12 challenges of AI adoption. Some of these issues can be prevented, but others require preparation. However, many organizations are still dreamers when it comes to AI. There’s nothing wrong with having a vision to follow, but the way you follow it matters.
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What You Need to Know About Adopting Big Data, AI, and Machine Learning
What Is a Data Strategy?
Imagine this familiar situation: as an analyst in your company, you've been tasked with the daunting task of assimilating all of your organization's data to collect unique and comprehensive insights. But this is easier said than done. Business development has much of their data siloed into a proprietary CRM solution, finance keeps theirs hidden away in spreadsheets, and application developers have SDK and IoT data streaming in to separate on-prem databases with no fault-tolerance built in. On top of that, compliance and security issues were never even considered. There seems to be no rhyme or reason to how everything works, it's impossible to get a unified view from all of the enterprise data. And "data science" is mostly done around the organization by way of sampling data from different pools and then making a "seat of the pants" guesstimate from arbitrarily sampled data, which is neither productive nor reliable. What a mess!
You need to have a strategy for your data. How will you do this? What data will you collect? Which data will you store — and where? Who is the audience for your data? Who consumes your analyzed data? What kind of access controls and permissions do you want to have on your data?
