Future-proof your big data strategy

As big data becomes more pervasive and offers numerous benefits to businesses, technology leaders must steer clear of common misconceptions and embrace new trends to stay at the forefront of innovation, shares Misha Sulpovar, VP of Artificial Intelligence Product at Cherre.

Data is critical to businesses in and outside of technology domains. Enterprise data, especially when it meets the world of second and third-party data, promises to be a mine of insights to catapult all aspects of the business. By now, most organizations have started their data journey and learned that it’s not always the easiest path. Though organizations are more comfortable in the awkward but critical pursuit of data maturity, 2023 is the year that will turn that newfound comfort on its head.

People who have discovered or used generative AI models like ChatGPT have effectively witnessed mature data and AI use cases go from overrated to essential. However, the same obstacles remain that these laggards have encountered in their big data journey, starting with understanding how to manage big data and use it for better decision making.

Big data offers companies numerous advantages, such as: B. increasing business efficiency and predicting future business outcomes. But to stay at the forefront of innovation, technology leaders must avoid common misconceptions and embrace new trends.

Debunking Big Data Myths

Many common misconceptions about Big Data don’t want to die. So what’s the biggest myth that needs debunking? The use of Big Data guarantees better decision-making. While big data projects aim to uncover relationships and patterns from a specific set of data points, successful big data projects are ultimately determined by how stakeholders interpret those relationships and patterns.

It’s also easy to overlook bias or flawed data feeding into decision-making systems or algorithms. At best, these biases can cause decision-making systems to work poorly, and at worst, biases can be completely and dangerously misleading. Along with breakthrough developments, over the last 20 years we have also seen endless projects fall victim to poor planning and misunderstandings of data, problems or domains.

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New data initiatives come with the assumption that these projects will replace data warehouse work. Big data platforms should not be used alone, but as a complement to traditional data management systems. Structured data and predictable workloads always work together. Without data, people, and systems to verify black-box algorithms, these algorithms will continue to wreak havoc when used or abused. These issues will lead to heated dialogue about more responsible AI and, inevitably, regulations.

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Four dominating big data trends

As Big Data becomes ubiquitous, it continues to evolve in four main directions: increased use of metadata-driven data structures and graphs, democratization of machine learning with AutoML, mass adoption and disruption of generative AI, and reduced use of R&D budgets.

1. Metadata driven data structure

A metadata-driven data structure is used to connect a disparate collection of data tools that provide significant flexibility, an infrastructure for modeling, and a much thicker dataset capable of delivering real insights. Increasing agility in data management should be a priority for all organizations, especially those using big data to make informed decisions. When interacting with metadata or “data in context”, Data Fabric enables the integration of disparate lakes and the extraction of knowledge graphs from formally structured data architectures. The data structure listens to, learns from, and responds to metadata, creating a more autonomous and user-friendly data coverage system.

According to GartnerOpens a new window, active metadata-driven automated functions in the data structure will reduce human effort by a third while improving data utilization by four times. The primary goal of deploying this data fabric approach is to add value to big data by improving access to and understanding of contextual information.

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2. Democratization of machine learning
A largely untapped opportunity for those using Big Data is the use of AutoML to democratize machine learning. AutoML is a class of machine learning algorithms that help automate the design and training of a machine learning model. Due to its streamlined methods and processes, AutoML expands the use of big data and machine learning by making it more adaptable for laypeople. The goal of using AutoML is to develop algorithms that can build their own machine learning models, rather than having someone manually input future machine learning models.

In our organization, we have observed that an increasing number of companies are using AutoML to enable people with minimal data science expertise to build robust models. Like generative AI, automated AI is an incredible tool when applied to the right problems, but it can be dangerous when used in the context of citizen data science — that’s ready to go with little process or thought. Auto AI can allow users to create quickly, but it can also create algorithms and analyzes that don’t work as well as they seem, or worse, produce skewed results. These “Gotchyas” are extremely common; There’s no doubt that these tools are powerful and fast, but they require knowledge, nuance, and great data.

3. Generative AI

GPT3 and ChatGPT have demonstrated the power and quality of Large Language Models (LLMs). Although LLMs have been around for a while, ChatGPT has made the masses aware of the potential and maturity of AI and its ability to process and create in sophisticated and versatile ways. The result will be a variety of use cases that will expand the way we apply AI.

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4. Say goodbye to using R&D budgets

The increasing variety of data and the advancement of analytical methods have made commercial outcomes critical in big data initiatives. As big data and the refinement of internal processes become more central to organizations, it is also becoming increasingly rare for big data projects to be funded through R&D budgets. This trend is fueled by the emergence of chief data officers and dedicated data practices and teams within organizations.

Big Data: No set-it-and-forget-it process

When thinking about data strategy, be very purposeful and work diligently to ensure the decision systems you put in place produce good results. It is becoming easier and easier to achieve results with generative AI or citizen AI tools. However, it is imperative that companies are conscious about the way they collect, store, organize and cleanse this data. Otherwise it is easy to get wrong results.

Key factors that define big data success include creating backup decision systems to support the results and providing sufficient funding and strategic considerations for the initiative. Also, always make sure you incorporate as much of the domain as possible into the construction and deployment of decision systems.

How do you improve your big data strategy? What data trends are you paying attention to? Share with us on FacebookOpens a new window, TwitterOpens a new window, and LinkedInOpens a new window.