In a world where data and analytics (D&A) is more important than ever, managing uncertainty and volatility is set to be a top business priority this year. As regional geo-political tensions compound the ongoing crisis of a global pandemic, leaders in this field will need to navigate persistent uncertainty, and focus on strategies to mitigate risk.
It is high time businesses monitor and experiment first, and then inject the required funds in their D&A strategies to scale value. Businesses must also “keep-up” with the latest trends related to D&A, so they do not find themselves at a competitive disadvantage.
Future of Data and Analytics (D&A) – The Top 10 Trends
Today, a common trait among many forward-thinking and successful enterprises is that they leverage their data to get better business insights, forecast outcomes with high accuracy, and improve their overall customer experiences (CX). In fact, reliable market research backs up the fact that today, a lot of businesses are making a shift from intuition-based decisions, towards data-driven decision-making processes.
Keeping these factors in consideration, we have created a list of top 10 data and analytics trends that will likely shape the year 2023 for businesses.
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Data-Centric Artificial Intelligence (AI)
Data is the key to un-locking the power of AI. However, it is certainly not just about having lots of data; rather it is more about how well you manage it. This is where data-centric AI comes in. By formalizing their approach towards data management, and addressing issues like bias and diversity, businesses are better able to build smarter, more effective AI systems. It is all about putting your data to work for you – and that’s where data-centric AI really shines.
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Contextually Enriched Analytics
It is absolutely vital for enterprises to embrace new and innovative approaches to make the most of the insights generated via D&A. Context-enriched analysis is one such approach that leverages graphical technologies to capture and store information about the various contexts and needs of individual users.
This approach enables deeper analysis of data by creating additional context. This is done by analyzing the relationships between data points, as well as the points themselves. It has been predicted that by the year 2025, 60% of the existing data models that are in use will be replaced by context-driven analytics and AI models.
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Adaptive Artificial Intelligence (AI)
Organizations must embrace adaptive AI systems to enhance their decision-making processes. These systems offer quicker and more flexible decisions by intelligently adapting to changes. To successfully develop and manage an adaptive AI system, enterprises must adopt flexible AI engineering practices. By doing so, organizations can optimize applications to resist or even fully absorb the impact of disruptive events. This will help them effectively manage the various adaptive systems of the organization.
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Meta-data Driven Data Fabric
Metadata-driven data fabric is a game-changer in the world of data management. By listening, learning and acting on metadata, data fabric develops trust in data and decreases various data management tasks by up to 70%.
An example is the city of Turku in Finland, which integrated fragmented data assets to reuse data, and reduced the time to market by two-thirds. This way, they were able to create a monetizable data fabric that fosters innovation.
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The Importance of Data Sharing
Today, data sharing is viewed by many data and analytics leaders as a vital factor for achieving a successful digital transformation. However, many businesses lack the necessary proficiency to effectively share data, especially at a large scale. To overcome this challenge, enterprises are always seeking ways to encourage data sharing and improve access to the right data that better aligns with their business objectives.
One approach is to collaborate across industry and business boundaries. This can accelerate the acceptance of increased budgetary authority and substantive investments in data sharing. Furthermore, adopting a data fabric design can facilitate the creation of a unified architecture for sharing data across diverse internal and external data sources as well as stakeholders.
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Business Composed or Modular D&A
Business-composed D&A is also known as the modular approach to data and analytics. In this approach, more emphasis is laid on the people involved, moving away from IT-centered strategies towards more business-oriented ones.
Here, business users and technologists work together to create data and analytics capabilities that are aligned with mid to long-term business goals. This “composable D&A” model encourages people to collaborate more, and take advantage of the “collective intelligence” of an enterprise.
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Artificial Intelligence (AI) Powered Risk Management
To achieve better results from AI based models, and boost user acceptance, it’s crucial for enterprises to prioritize AI trust, risk and security management (TRiSM). Gartner predicts that by 2026, companies that develop reliable and purpose-driven AI will have a success rate of over 75%. Prioritizing AI TRiSM can lead to stable and controlled implementation of AI models. This could result in fewer failures and less un-intended negative consequences.
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Decision-Centric D&A
Decision intelligence involves a thoughtful analysis of the decision-making process. By carefully considering how decisions should be made, decision intelligence can help enterprises design the best course of action for a given situation. Such models also help enterprises in taking the “optimal decision”, especially in scenarios where multiple viable alternatives exist.
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Connected Governance
At present, enterprises are faced with a multitude of challenges that require quick and efficient solutions. The COVID-19 pandemic has only emphasized the importance of cross-functional collaboration and adaptable structures. For this, connected governance creates a virtual D&A governance layer across various functions and locations. This way, enterprises are able to achieve their desired outcomes in a more effective and seamless manner. The right governance and flexibility can help businesses confidently navigate an ever-evolving business landscape and remain competitive.
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Skills and Literacy Shortfall
In today’s workforce, there is a severe lack of “data literacy”. This is primarily because of the fact that competition for top talent is growing fiercer by the day, and there is an acute shortage of skilled professionals in the field.
Unfortunately, Gartner predicts that by the year 2025, many Chief Data Officers (CDOs) will fail to cultivate the necessary data literacy within their teams to meet strategic goals. One way to combat this issue is by including “claw-back” or “payback” clauses in employment contracts. These will help recover any training costs, in case an employee departs prematurely.
Conclusion
It is justified to assume that in today’s fast-paced world, data is like the “new oil” that drives many meaningful outcomes. However, in order to extract, refine and leverage its true potential, businesses require a powerful engine and must remain abreast of the latest market trends. Businesses that lay the solid foundation of analytics driven culture and competency will for sure win the race for innovation and digital transformation.
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