Written by: Leon Gordon, Executive Contributor
Executive Contributors at Brainz Magazine are handpicked and invited to contribute because of their knowledge and valuable insight within their area of expertise.
In the near future, the world of data and analytics will be defined by the following trends:
Data Quality—Data quality is paramount to any organization. When organizations have a high-quality data foundation, they can make better decisions because they can trust their analytical insights. Data quality also opens up use cases that would otherwise be unfeasible due to poor or inaccurate data.
Artificial Intelligence (AI) and Machine Learning—The most important trend in the field of AI is the potential for machines to learn, not just execute. Machine learning has shown promise in specific use cases like product recommendations and personalization, where algorithms are continually trained on new data to improve their results. We expect machine learning to become increasingly prevalent as organizations adopt cloud platforms that provide managed services for machine learning running on distributed computing infrastructures. Organizations should use such services with proper governance over model development and deployment so models are monitored for accuracy and audited for compliance over time.
Predictive Analytics—Predictive analytics goes beyond using traditional statistical methods like regression analysis or cluster analysis; it uses methods from machine learning such as decision trees and neural nets. Predictive analytics is used when there is enough historical data available to build a model that can predict values based on patterns in the data rather than a simple calculation of means or median values like other non-predictive analytics methods do.
Predictions
As you can see from these predictions, data quality will remain a top concern. The next big thing is artificial intelligence and machine learning, which will be used in more applications than ever before. Predictive analytics will be used even more than it is today and data literacy may become a key skill for all employees. As trust in AI and data grows, we can expect to see an increase in the number of organizations that turn to predictive analytics tools to solve complex business problems.
Data Quality
Data quality is a top priority for companies and business leaders. Data quality is a key factor in data-driven decision-making, the foundation of Artificial Intelligence (AI) technologies, and many other business capabilities. The importance of data quality has been growing rapidly as organizations continue to move toward digital transformation efforts at scale.
Achieving high-quality data across all aspects of an enterprise is critical to success in any organization's digital transformation journey. Data quality can be defined as “the extent to which a given set of data conforms to its specification” (CCSDS 2009). This definition focuses on both the timeliness and completeness of data within organizations, two important factors that must be considered when evaluating how well your business uses its data assets.
Artificial Intelligence (AI) and Machine Learning
AI is the science that gives computers the ability to behave in ways that are characteristic of human intelligence (e.g., learning, problem-solving, planning, etc.). It is a branch of computer science that deals with creating intelligent machines. Basic AI research aims to create systems able to perform tasks under varying and often complex conditions. As such, it must plan, learn from experience and understand natural language.
AI consists of many different subtopics including machine learning (ML), deep learning (DL), natural language processing (NLP), robotic process automation (RPA), and cognitive computing—to name a few.
Predictive Analytics
Predictive analytics is the use of data and statistical analysis to predict future events, such as customer behavior. It's been used in a wide range of applications, from determining if a patient will get sick again to predict how likely it is that an employee will quit their job. Predictive analytics has grown in popularity over recent years with businesses looking for ways to make better decisions about the future. As more companies implement predictive analytics into their business models, we can expect to see even more growth over the next few years.
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Data Literacy
Data literacy is the ability to use data to make informed decisions. It's a combination of technical skills, business skills, and critical thinking skills. Data literacy is also an evolving skill that can be developed over time by learning how to connect data points with real-world scenarios.
Data literacy is a continuous process because it requires learning new information frequently, applying those lessons in new situations, and adjusting your understanding based on feedback or new information.
Trust in AI and Data
The ability to trust in an AI system is paramount to its success, as the value of a machine learning system lies in its ability to present accurate information. For example, if you were considering buying a home and relied on your estate agent's recommendation, you would expect him or her to have accurately assessed the market value of homes within your price range. If they recommended one house at £1 million when another was worth £500K simply because they had more money than you did, it would probably be frustrating and ultimately not very helpful.
The future is bright for data and analytics.
The future is bright for data and analytics. The growth in data, both structured and unstructured, will continue to increase exponentially. As the number of data increases, so will the demand to analyze it. Data will become a key asset across industries as companies seek to gain a competitive advantage by harnessing information from their business operations, supply chains, and customer interactions.
As enterprises look for ways to improve decision making through more sophisticated analytical techniques and technologies that are more accessible than ever before including artificial intelligence (AI), machine learning (ML), augmented reality/virtual reality (AR/VR), or blockchain the need for trained professionals with these skills has never been greater.
Conclusion
I am truly excited about the future of data and analytics. Every day, new technologies are developed that make it easier to derive value from data and share it with others in meaningful ways. The demand for these skills continues to grow as companies seek to solve business problems and provide services that delight customers. I believe this is just the beginning of a new era for data and analytics, one that will bring unprecedented opportunities for those who are prepared.
Leon Gordon, Executive Contributor Brainz Magazine
Leon Gordon, is a leader in data analytics. A current Microsoft Data Platform MVP based in the UK and a Partner at Pomerol Partners (pomerolpartners.com). During the last decade, he has helped organizations improve their business performance, use data more intelligently, and understand the implications of new technologies such as artificial intelligence and big data.
Leon is a Thought Leader at the Forbes Technical Council, an Executive Contributor to Brainz Magazine, a Thought Leader in Data Science for the Global AI Hub, chair for the Microsoft Power BI – UK community group and the DataDNA data visualization community as well as an international speaker and advisor.
