Future-proofing Your Big Data Strategy
How can you futureproof your big data strategy?
As big data continues to become more widely utilized, providing organizations with numerous advantages, tech leaders must steer clear of prevalent misconceptions and embrace emerging trends to stay at the forefront of innovation, shares Misha Sulpovar, VP of artificial intelligence product at Cherre.
Data is critical for firms in and outside of technology fields. Organizational data, especially when it meets the world of second and third-party data, promises to provide a treasure trove of insights to catapult all aspects of the business. By now, most organizations have embarked on their data journey and have learned it is not always the most straightforward trajectory. Though organizations have become more comfortable in the cumbersome but critical pursuit of data maturity, 2023 is the year that will flip this new-found comfort on its head.
People who have discovered or used generative AI models like ChatGPT have effectively witnessed how mature data and AI use cases transform from overhyped to essential. However, the same barriers these laggards encountered on their big data journey remain, starting with understanding how to manage large volumes of data and harness them for better decision-making.
Big data provides organizations with numerous advantages, such as enhancing business efficiency and predicting future business outcomes. But to stay at the forefront of innovation, tech leaders must steer clear of prevalent misconceptions and embrace emerging trends.
Debunking Big Data Myths
Many common misconceptions about big data refuse to die. So, what’s the biggest myth that needs to be debunked? Utilizing big data guarantees better decision-making. While big data projects aim to uncover relationships and patterns from a given set of data points, ultimately, successful big data projects are determined by how stakeholders interpret these relationships and patterns.
It is also easy to miss bias or erroneous data feeding into decision systems or algorithms. At best, these biases can make decision systems perform poorly, and at worst, biases can completely and dangerously mislead. In parallel to breakthrough developments, throughout the last 20 years, we’ve also seen endless projects fall victim to poor planning and misunderstanding the data, problem or domain.
New data initiatives come with assumptions that these projects will replace data warehouse work. Big data platforms should not be used on their own but rather used to complement traditional data management systems. Structured data and predictable workloads will always work in tandem with one another. Without data, people and systems to verify black box algorithms, these algorithms will continue to wreak havoc when used or misused. These issues will invite a flurry of dialogue around more responsible AI and, inevitably, regulations.
See More: The Good Thing about Big Data Getting Smaller
Four Dominating Big Data Trends
As big data becomes ubiquitous, it continues to evolve in four main ways: the increased use of metadata-driven data fabric and graphs, the democratization of machine learning with AutoML, the mass adoption and disruption from generative AI and the reduced use of R&D budgets.
1. Metadata-driven data fabric
Metadata-driven data fabric is used to connect a disparate collection of data tools that build in significant flexibility, infrastructure for modeling and a much thicker data set that can drive genuine insights. Increasing agility in data management should be a priority for all organizations, especially those using big data to inform decision-making. When interacting with metadata or “data in context,” data fabric allows the integration of disparate lakes and the extraction of knowledge graphs from formally structured data architectures. The data fabric listens to, learns from and acts on metadata, creating a more autonomous and user-friendly data coverage system.
According to Gartner, active metadata-assisted automated functions in the data fabric will reduce human effort by one-third while improving data utilization fourfold. The main goal of deploying this data fabric approach is to provide more value to big data by increasing access to and comprehension of contextualized information.
2. Democratizing machine learning
One widely untapped opportunity for those using big data is utilizing AutoML to democratize machine learning. AutoML is a class of machine learning algorithms that helps automate the designing and training of a machine learning model. Because of its streamlined methods and processes, AutoML widens the use of big data and machine learning by allowing it to be more adaptable for non-experts. The goal when using AutoML is to build algorithms that can create their own machine-learning models instead of requiring someone to manually input future machine-learning models.
At our organization, we have observed an increasing number of firms using AutoML to empower workers with minimal data science expertise to build robust models. Like generative AI, auto AI is an incredible tool when applied to the right problems, but it can be dangerous if used in the context of citizen data science – that is out of the box with little process or thought. Auto AI can let users build quickly, but it can also make algorithms and analyses that don’t work as well as they appear to, or worse, produce biased results. These “gotchyas” are extremely common; there is no doubt 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). Though LLMs have been around for a while, ChatGPT alerted the masses on the potential and state of maturity of AI and its ability to process and create in sophisticated and versatile ways. The result will be a proliferation of use cases that will stretch how we apply AI.
4. Shift away from using R&D budgets
The increasing diversity of data and the advancement of analytical methods have caused commercial outcomes to be crucial in big data initiatives. As big data and the refinement of internal processes become increasingly central to organizations, it is also becoming increasingly uncommon for big data projects to receive funding via R&D budgets. This trend is further accentuated by the emergence of Chief Data Officers and dedicated data practices and teams within companies.
Big Data: Not a Set-it-and-forget-it Process
When thinking about data strategy, be very intentional and work diligently to ensure the decision systems being built will lead to good outcomes. It’s getting easier to achieve results with generative AI or citizen AI tools. However, it’s imperative that organizations consciously approach how they collect, store, organize and clean that data. Otherwise, it’s easy to arrive at the wrong outcomes.
Some core factors that define big data success include creating backup decision systems to corroborate results and allotting sufficient funding and strategic thinking to the initiative. Furthermore, always make sure to inject as much of the domain into the construction and deployment of decision systems as possible.
How are you improving your big data strategy? What data trends are you being mindful of? Share with us on Facebook, Twitter, and LinkedIn.
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